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		<id>https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8359</id>
		<title>Introduction to IFs</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8359"/>
		<updated>2017-09-07T23:09:41Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Purposes&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;International Futures (IFs) is a tool for thinking about long-term global trends and planning more strategically for the&amp;amp;nbsp;future.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
IFs can help you:&lt;br /&gt;
&lt;br /&gt;
*Understand&amp;amp;nbsp;the state of major&amp;amp;nbsp;global systems&lt;br /&gt;
*Explore&amp;amp;nbsp;long-term trends and consider where they might take&amp;amp;nbsp;us&lt;br /&gt;
*Learn about the dynamic&amp;amp;nbsp;interactions between global systems&lt;br /&gt;
*Clarify long-term organizational goals/priorities&lt;br /&gt;
*Develop alternative scenarios (if-then statements) about the future&lt;br /&gt;
*Investigate how different groups (households, firms or governments) can shape the future&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;The IFs platform relies&amp;amp;nbsp;on core, underlying assumptions, including the following.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*Global issues are becoming&amp;amp;nbsp;more significant as the scope of human interaction and human impact on the broader environment grow.&lt;br /&gt;
*Goals and priorities for human systems are becoming clearer and are more frequently and consistently communicated.&lt;br /&gt;
*Understanding of the dynamics of human systems is improving&amp;amp;nbsp;rapidly.&lt;br /&gt;
*The domain of human choice and action is broadening.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Which issues can you&amp;amp;nbsp;investigate with IFs? Some examples include:&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*[[Environment|Environment]]: Atmospheric carbon dioxide levels, world forest area, fossil fuel usage&lt;br /&gt;
*[[Socio-Political|Socio-Political Change]]: Life expectancy, literacy rate, democracy level, status of women, value change&lt;br /&gt;
*[[Population|Demographics]]: Population levels and growth, fertility, mortality, migration&lt;br /&gt;
*[[Agriculture|Food and Agriculture]]: Land use and production levels, calorie availability, malnutrition rates&lt;br /&gt;
*[[Energy|Energy]]: Resource and production levels, demand patterns, renewable energy share&lt;br /&gt;
*[[Economics|Economics]]: Sectoral production, consumption, and trade patterns and structural change&lt;br /&gt;
*[[Interstate_Politics_(IP)|Geopolitics]]: Country and regional power levels&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Visual Representation&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
[[File:IFsOverviewChart.jpg|frame|center|Visual representation of IFs structure]]&lt;br /&gt;
&lt;br /&gt;
Among the philosophical premises of the International Futures (IFs) project is that the model cannot be a &amp;quot;black box&amp;quot; to users and be truly useful. Model users must be able to examine the structures of IFs in order (1) to have confidence in them, and (2) learn from them.&lt;br /&gt;
&lt;br /&gt;
The following topics are useful starting points for better understanding the model.&lt;br /&gt;
&lt;br /&gt;
*[[Understand_IFs#Dominant_Relations|Dominant Relations]] of the model structure&lt;br /&gt;
*[[Understand_IFs#2.2|Structure-Based and Agent-Class Driven Modeling]]&lt;br /&gt;
*[[Understand_IFs#Equation_Notation|Equation Notation]]&lt;br /&gt;
*[[IFs_Bibliography|IFs Bibliography]] of data and data sources&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Models: Quick Survey&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
International Futures is a collection of interconnected models (sometimes referred to as modules). Below is a quick survey of the major models in IFs. For more information on each one, please click on the model headings.&lt;br /&gt;
&lt;br /&gt;
The [[Population|&#039;&#039;&#039;population&#039;&#039;&#039;]] module:&lt;br /&gt;
&lt;br /&gt;
*represents 22 age-sex cohorts to age 100+&lt;br /&gt;
*calculates change in fertility and mortality rates in response to income, income distribution, and analysis multipliers&lt;br /&gt;
*computes average life expectancy at birth, literacy rate, and overall measures of human development (HDI) and physical quality of life&lt;br /&gt;
*represents migration and HIV/AIDS&lt;br /&gt;
*includes a newly developing submodel of formal education across primary, secondary, and tertiary levels&lt;br /&gt;
&lt;br /&gt;
The [[Economics|&#039;&#039;&#039;economic&#039;&#039;&#039;]] module:&lt;br /&gt;
&lt;br /&gt;
*represents the economy in six sectors: agriculture, materials, energy, industry, services, and ICT (other sectors could be configured, using raw data from the GTAP project)&lt;br /&gt;
*computes and uses input-output matrices that change dynamically with development level&lt;br /&gt;
*is a general equilibrium-seeking model that does not assume exact equilibrium will exist in any given year; rather it uses inventories as buffer stocks and to provide price signals so that the model chases equilibrium over time&lt;br /&gt;
*contains an endogenous production function that represents contributions to growth in multifactor productivity from R&amp;amp;D, education, worker health, economic policies (&amp;quot;freedom&amp;quot;), and energy prices (the &amp;quot;quality&amp;quot; of capital)&lt;br /&gt;
*uses a Linear Expenditure System to represent changing consumption patterns&lt;br /&gt;
*utilizes a &amp;quot;pooled&amp;quot; rather than the bilateral trade approach for international trade&lt;br /&gt;
*is being imbedded during 2002 in a social accounting matrix (SAM) envelope that will tie economic production and consumption to intra-actor financial flows&lt;br /&gt;
&lt;br /&gt;
The [[Agriculture|&#039;&#039;&#039;agricultural&#039;&#039;&#039;]] module:&lt;br /&gt;
&lt;br /&gt;
*represents production, consumption and trade of crops and meat; it also carries ocean fish catch and aquaculture in less detail&lt;br /&gt;
*maintains land use in crop, grazing, forest, urban, and &amp;quot;other&amp;quot; categories&lt;br /&gt;
*represents demand for food, for livestock feed, and for industrial use of agricultural products&lt;br /&gt;
*is a partial equilibrium model in which food stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the agricultural sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The [[Energy|&#039;&#039;&#039;energy&#039;&#039;&#039;]] module:&lt;br /&gt;
&lt;br /&gt;
*portrays production of six energy types: oil, gas, coal, nuclear, hydroelectric, and other renewable&lt;br /&gt;
*represents consumption and trade of energy in the aggregate&lt;br /&gt;
*represents known reserves and ultimate resources of the fossil fuels&lt;br /&gt;
*portrays changing capital costs of each energy type with technological change as well as with draw-downs of resources&lt;br /&gt;
*is a partial equilibrium model in which energy stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the energy sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The two [[Socio-Political|&#039;&#039;&#039;socio-political&#039;&#039;&#039;]] sub-modules:&lt;br /&gt;
&lt;br /&gt;
Within countries or geographic groupings&lt;br /&gt;
&lt;br /&gt;
*represents fiscal policy through taxing and spending decisions&lt;br /&gt;
*shows six categories of government spending: military, health, education, R&amp;amp;D, foreign aid, and a residual category&lt;br /&gt;
*represents changes in social conditions of individuals (like fertility rates or literacy levels), attitudes of individuals (such as the level of materialism/postmaterialism of a society from the World Value Survey), and the social organization of people (such as the status of women)&lt;br /&gt;
*represents the evolution of democracy&lt;br /&gt;
*represents the prospects for state instability or failure&lt;br /&gt;
&lt;br /&gt;
Between countries or groupings of countries&lt;br /&gt;
&lt;br /&gt;
*traces changes in power balances across states and regions&lt;br /&gt;
*allows exploration of changes in the level of interstate threat&lt;br /&gt;
*represents possible action-reaction processes and arms races with associated potential for conflict among countries&lt;br /&gt;
&lt;br /&gt;
The implicit [[Environment|&#039;&#039;&#039;environmental&#039;&#039;&#039;]] module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows tracking of remaining resources of fossil fuels, of the area of forested land, of water usage, and of atmospheric carbon dioxide emissions&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;technology&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows changes in assumptions about rates of technological advance in agriculture, energy, and the broader economy&lt;br /&gt;
*explicitly represents the extent of electronic networking of individuals in societies&lt;br /&gt;
*is tied to the governmental spending model with respect to R&amp;amp;D spending&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Background&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
International Futures (IFs) has evolved since 1980 through three &amp;quot;generations,&amp;quot; with a fourth generation now taking form.&lt;br /&gt;
&lt;br /&gt;
The first generation had deep roots in the world models of the 1970s, including those of the Club of Rome. In particular, IFs drew on the Mesarovic-Pestel or World Integrated Model (Mesarovic and Pestel 1974). The author of IFs had contributed to that project, including the construction of the energy submodel. IFs consciously also drew on the Leontief World Model (Leontief et al. 1977), the Bariloche Foundation’s world model (Herrera et al. 1976), and Systems Analysis Research Unit Model (SARU 1977), following comparative analysis of those models by Hughes (1980). That generation was written in FORTRAN and available for use on main-frame computers through CONDUIT, an educational software distribution center at the University of Iowa. Although the primary use of that and subsequent generations was by students, IFs has always had some policy analysis capability that has appealed to specialists. For example, the U.S. Foreign Service Institute used the first generation of IFs in a mid-career training program.&lt;br /&gt;
&lt;br /&gt;
The second generation of International Futures moved to early microcomputers in 1985, using the DOS platform. It was a very simplified version of the original IFs without regional or country differentiation.&lt;br /&gt;
&lt;br /&gt;
The third generation, first available in 1993, became a full-scale microcomputer model. The third generation improved earlier representations of demographic, energy, and food systems, but added new environmental and socio-political content. It built upon the collaboration of the author with the GLOBUS project, and it adopted the economic submodel of GLOBUS (developed by the author). GLOBUS had been created with the inspiration of Karl Deutsch and under the leadership of Stuart Bremer (1987) at the Wissenschaftszentrum in Berlin.&lt;br /&gt;
&lt;br /&gt;
The third generation has produced three editions/major releases of IFs, each accompanied by a book also called International Futures (Hughes 1993, 1996, 1999). The second edition moved to a Visual Basic platform that allowed a much improved menu-driven interface, running under Windows. The third edition incorporated an early global mapping capability and an initial ability to do cross-sectional and longitudinal data analysis.&lt;br /&gt;
&lt;br /&gt;
The fourth generation has been taking shape since early 2000. It has been heavily influenced by the usage of the model in an increasingly policy-analysis mode by several important organizations. First, General Motors commissioned a specialized version of IFs named CoVaTrA (Consumer Values Trends Analysis) with a need for updated and extended demographic modeling and representation of value change. An alliance was established with the World Values Survey, directed by Ronald Inglehart, to create that version. Second, the Strategic Assessments Group of the Central Intelligence Agency commissioned a specialized version named IFs for SAG. The work involved in preparing that greatly extended and enhanced the socio-political representations of the model, both domestic and international. Third, the European Commission sponsored a project named TERRA which has led to a specialized version named IFs for TERRA. IFs for TERRA work led to enhancements across the model, including improved representation of economic sectors, updated IO matrices and a basic Social Accounting Matrix, GINI and Lorenz curves, and preparing for extended environmental impact representation (drawing upon the Advanced Sustainability Analysis framework of the Finland Futures Research Center).&lt;br /&gt;
&lt;br /&gt;
Throughout this emergence of a fourth generation IFs (incorporating all of the above elements for additional users) there has been also a heavy emphasis on enhanced usability. Ideas from Robert Pestel in the TERRA project led to the creation of a new tree-structure for scenario creation and management. Ideas from Ronald Inglehart led to the development of the Guided Use structure and a somewhat more game-like character within that structure. Inglehart also help arrange funding to support the programming of Guided Use through the European Union Center of the University of Michigan.&lt;br /&gt;
&lt;br /&gt;
The fifth version of IFs is currently in use and represents broad strides to improving the model and its usability. It is the first version of this software to be placed online due to the help of the National Intelligence Council ([http://www.ifs.du.edu http://www.ifs.du.edu]). Also, usability has been increased as Packaged Displays and Flex Packaged Displays were introduced that allowed for the creation of very specific lists of countries/regions, groups or Glists. A new education model has also been incorporated into the broader IFs model. New scenarios were created for UNEP (focusing on environmental change) and Pardee (focusing on poverty). Finally, one of the largest changes made was incorporating 182 countries into the Base-Case scenario used by IFs. Previous versions of IFs used broader regions to forecast global trends. This change also did away with the Student and Professional versions.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Geographic Representation of the World&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
186 countries underpin the functioning of IFs and these countries can be displayed separately or as parts of larger groups that users can determine.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;Below is a visual representation of how different entities are organized into Countries/Regions, Groups or Glists:&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
[[File:Geo 186.png|frame|right|Visual representation of IF&#039;s definition of regions/countries/groups/glists]]&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;*Note: In older versions of IFs, Regions were used as intermediaries between Countries and Groups. In the future, they, or some similarly named unit, will be a sub-unit of Countries. Regions, acting as a sub-unit of Countries, are currently not a feature of IFs. See the image located at the bottom of this Help topic.&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
When using IFs, there are many occasions where the user is asked whether or not they would like to display their results as a product of single countries, or larger groups. This is typically a toggle switch that moves between Country/Region and Groups, however, it might be a three-way-toggle that includes Country/Region, Group and Glist.&lt;br /&gt;
&lt;br /&gt;
Countries/Regions are currently the smallest geographical unit that users can represent. The ability to split countries down into smaller regions, or states, is under development. There are 186 different countries/regions that users can display.&lt;br /&gt;
&lt;br /&gt;
Groups are variably organized geographically or by memberships in international institutions/regimes. You can find out who is represented in each group and add or delete members by exploring the [[Extended_Features#Manage_Groups/Regions|Managing Regionalization]] function.&lt;br /&gt;
&lt;br /&gt;
Glists merge both Groups and Countries/Regions. These lists are mostly geographically bound. In the future, the Glist distinction will become more important as some users may want to place, for example, both the Indian state of Kerala in a Glist with Sri Lanka and Nepal.&lt;br /&gt;
&lt;br /&gt;
Users may also want to [[Extended_Features#Change_Grouping/Regionalization|create]] their own groups or [[Extended_Features#Identify_Groups_or_Country/Region_Members|explore]] what countries are members of what groups.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Time Horizon&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Future Forecasts.&#039;&#039;&#039; IFs begins computation with data from 2000 and can dynamically calculate values for all variables annually through 2100.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Historical Analysis and &amp;quot;Forecasts.&amp;quot;&#039;&#039;&#039; IFs also includes an extensive and growing historical data base starting in 1960. The data basis allows analysis of relationships among variables across countries and across time.&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8358</id>
		<title>Introduction to IFs</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8358"/>
		<updated>2017-09-07T23:06:10Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Purposes&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;International Futures (IFs) is a tool for thinking about long-term global trends and planning more strategically for the&amp;amp;nbsp;future.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
IFs can help you:&lt;br /&gt;
&lt;br /&gt;
*Understand&amp;amp;nbsp;the state of major&amp;amp;nbsp;global systems&lt;br /&gt;
*Explore&amp;amp;nbsp;long-term trends and consider where they might take&amp;amp;nbsp;us&lt;br /&gt;
*Learn about the dynamic&amp;amp;nbsp;interactions between global systems&lt;br /&gt;
*Clarify long-term organizational goals/priorities&lt;br /&gt;
*Develop alternative scenarios (if-then statements) about the future&lt;br /&gt;
*Investigate how different groups (households, firms or governments) can shape the future&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;The IFs platform relies&amp;amp;nbsp;on core, underlying assumptions, including the following.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*Global issues are becoming&amp;amp;nbsp;more significant as the scope of human interaction and human impact on the broader environment grow.&lt;br /&gt;
*Goals and priorities for human systems are becoming clearer and are more frequently and consistently communicated.&lt;br /&gt;
*Understanding of the dynamics of human systems is improving&amp;amp;nbsp;rapidly.&lt;br /&gt;
*The domain of human choice and action is broadening.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Which issues can you&amp;amp;nbsp;investigate with IFs? Some examples include:&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*[[Environment|Environment]]: Atmospheric carbon dioxide levels, world forest area, fossil fuel usage&lt;br /&gt;
*[[Socio-Political|Socio-Political Change]]: Life expectancy, literacy rate, democracy level, status of women, value change&lt;br /&gt;
*[[Population|Demographics]]: Population levels and growth, fertility, mortality, migration&lt;br /&gt;
*[[Agriculture|Food and Agriculture]]: Land use and production levels, calorie availability, malnutrition rates&lt;br /&gt;
*[[Energy|Energy]]: Resource and production levels, demand patterns, renewable energy share&lt;br /&gt;
*[[Economics|Economics]]: Sectoral production, consumption, and trade patterns and structural change&lt;br /&gt;
*[[Interstate_Politics_(IP)|Geopolitics]]: Country and regional power levels&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Visual Representation&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
[[File:IFsOverviewChart.jpg|frame|center|Visual representation of IFs structure]]&lt;br /&gt;
&lt;br /&gt;
Among the philosophical premises of the International Futures (IFs) project is that the model cannot be a &amp;quot;black box&amp;quot; to users and be truly useful. Model users must be able to examine the structures of IFs in order (1) to have confidence in them, and (2) learn from them.&lt;br /&gt;
&lt;br /&gt;
The following topics are useful starting points for better understanding the model.&lt;br /&gt;
&lt;br /&gt;
*[[Understand_IFs#Dominant_Relations|Dominant Relations]] of the model structure&lt;br /&gt;
*[[Understand_IFs#2.2|Structure-Based and Agent-Class Driven Modeling]]&lt;br /&gt;
*[[Understand_IFs#Equation_Notation|Equation Notation]]&lt;br /&gt;
*[[IFs_Bibliography|IFs Bibliography]] of data and data sources&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Quick Survey&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;population&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents 22 age-sex cohorts to age 100+&lt;br /&gt;
*calculates change in fertility and mortality rates in response to income, income distribution, and analysis multipliers&lt;br /&gt;
*computes average life expectancy at birth, literacy rate, and overall measures of human development (HDI) and physical quality of life&lt;br /&gt;
*represents migration and HIV/AIDS&lt;br /&gt;
*includes a newly developing submodel of formal education across primary, secondary, and tertiary levels&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;economic&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents the economy in six sectors: agriculture, materials, energy, industry, services, and ICT (other sectors could be configured, using raw data from the GTAP project)&lt;br /&gt;
*computes and uses input-output matrices that change dynamically with development level&lt;br /&gt;
*is a general equilibrium-seeking model that does not assume exact equilibrium will exist in any given year; rather it uses inventories as buffer stocks and to provide price signals so that the model chases equilibrium over time&lt;br /&gt;
*contains an endogenous production function that represents contributions to growth in multifactor productivity from R&amp;amp;D, education, worker health, economic policies (&amp;quot;freedom&amp;quot;), and energy prices (the &amp;quot;quality&amp;quot; of capital)&lt;br /&gt;
*uses a Linear Expenditure System to represent changing consumption patterns&lt;br /&gt;
*utilizes a &amp;quot;pooled&amp;quot; rather than the bilateral trade approach for international trade&lt;br /&gt;
*is being imbedded during 2002 in a social accounting matrix (SAM) envelope that will tie economic production and consumption to intra-actor financial flows&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;agricultural&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents production, consumption and trade of crops and meat; it also carries ocean fish catch and aquaculture in less detail&lt;br /&gt;
*maintains land use in crop, grazing, forest, urban, and &amp;quot;other&amp;quot; categories&lt;br /&gt;
*represents demand for food, for livestock feed, and for industrial use of agricultural products&lt;br /&gt;
*is a partial equilibrium model in which food stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the agricultural sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;energy&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*portrays production of six energy types: oil, gas, coal, nuclear, hydroelectric, and other renewable&lt;br /&gt;
*represents consumption and trade of energy in the aggregate&lt;br /&gt;
*represents known reserves and ultimate resources of the fossil fuels&lt;br /&gt;
*portrays changing capital costs of each energy type with technological change as well as with draw-downs of resources&lt;br /&gt;
*is a partial equilibrium model in which energy stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the energy sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The two &#039;&#039;&#039;socio-political&#039;&#039;&#039; sub-modules:&lt;br /&gt;
&lt;br /&gt;
Within countries or geographic groupings&lt;br /&gt;
&lt;br /&gt;
*represents fiscal policy through taxing and spending decisions&lt;br /&gt;
*shows six categories of government spending: military, health, education, R&amp;amp;D, foreign aid, and a residual category&lt;br /&gt;
*represents changes in social conditions of individuals (like fertility rates or literacy levels), attitudes of individuals (such as the level of materialism/postmaterialism of a society from the World Value Survey), and the social organization of people (such as the status of women)&lt;br /&gt;
*represents the evolution of democracy&lt;br /&gt;
*represents the prospects for state instability or failure&lt;br /&gt;
&lt;br /&gt;
Between countries or groupings of countries&lt;br /&gt;
&lt;br /&gt;
*traces changes in power balances across states and regions&lt;br /&gt;
*allows exploration of changes in the level of interstate threat&lt;br /&gt;
*represents possible action-reaction processes and arms races with associated potential for conflict among countries&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;environmental&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows tracking of remaining resources of fossil fuels, of the area of forested land, of water usage, and of atmospheric carbon dioxide emissions&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;technology&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows changes in assumptions about rates of technological advance in agriculture, energy, and the broader economy&lt;br /&gt;
*explicitly represents the extent of electronic networking of individuals in societies&lt;br /&gt;
*is tied to the governmental spending model with respect to R&amp;amp;D spending&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Background&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
International Futures (IFs) has evolved since 1980 through three &amp;quot;generations,&amp;quot; with a fourth generation now taking form.&lt;br /&gt;
&lt;br /&gt;
The first generation had deep roots in the world models of the 1970s, including those of the Club of Rome. In particular, IFs drew on the Mesarovic-Pestel or World Integrated Model (Mesarovic and Pestel 1974). The author of IFs had contributed to that project, including the construction of the energy submodel. IFs consciously also drew on the Leontief World Model (Leontief et al. 1977), the Bariloche Foundation’s world model (Herrera et al. 1976), and Systems Analysis Research Unit Model (SARU 1977), following comparative analysis of those models by Hughes (1980). That generation was written in FORTRAN and available for use on main-frame computers through CONDUIT, an educational software distribution center at the University of Iowa. Although the primary use of that and subsequent generations was by students, IFs has always had some policy analysis capability that has appealed to specialists. For example, the U.S. Foreign Service Institute used the first generation of IFs in a mid-career training program.&lt;br /&gt;
&lt;br /&gt;
The second generation of International Futures moved to early microcomputers in 1985, using the DOS platform. It was a very simplified version of the original IFs without regional or country differentiation.&lt;br /&gt;
&lt;br /&gt;
The third generation, first available in 1993, became a full-scale microcomputer model. The third generation improved earlier representations of demographic, energy, and food systems, but added new environmental and socio-political content. It built upon the collaboration of the author with the GLOBUS project, and it adopted the economic submodel of GLOBUS (developed by the author). GLOBUS had been created with the inspiration of Karl Deutsch and under the leadership of Stuart Bremer (1987) at the Wissenschaftszentrum in Berlin.&lt;br /&gt;
&lt;br /&gt;
The third generation has produced three editions/major releases of IFs, each accompanied by a book also called International Futures (Hughes 1993, 1996, 1999). The second edition moved to a Visual Basic platform that allowed a much improved menu-driven interface, running under Windows. The third edition incorporated an early global mapping capability and an initial ability to do cross-sectional and longitudinal data analysis.&lt;br /&gt;
&lt;br /&gt;
The fourth generation has been taking shape since early 2000. It has been heavily influenced by the usage of the model in an increasingly policy-analysis mode by several important organizations. First, General Motors commissioned a specialized version of IFs named CoVaTrA (Consumer Values Trends Analysis) with a need for updated and extended demographic modeling and representation of value change. An alliance was established with the World Values Survey, directed by Ronald Inglehart, to create that version. Second, the Strategic Assessments Group of the Central Intelligence Agency commissioned a specialized version named IFs for SAG. The work involved in preparing that greatly extended and enhanced the socio-political representations of the model, both domestic and international. Third, the European Commission sponsored a project named TERRA which has led to a specialized version named IFs for TERRA. IFs for TERRA work led to enhancements across the model, including improved representation of economic sectors, updated IO matrices and a basic Social Accounting Matrix, GINI and Lorenz curves, and preparing for extended environmental impact representation (drawing upon the Advanced Sustainability Analysis framework of the Finland Futures Research Center).&lt;br /&gt;
&lt;br /&gt;
Throughout this emergence of a fourth generation IFs (incorporating all of the above elements for additional users) there has been also a heavy emphasis on enhanced usability. Ideas from Robert Pestel in the TERRA project led to the creation of a new tree-structure for scenario creation and management. Ideas from Ronald Inglehart led to the development of the Guided Use structure and a somewhat more game-like character within that structure. Inglehart also help arrange funding to support the programming of Guided Use through the European Union Center of the University of Michigan.&lt;br /&gt;
&lt;br /&gt;
The fifth version of IFs is currently in use and represents broad strides to improving the model and its usability. It is the first version of this software to be placed online due to the help of the National Intelligence Council ([http://www.ifs.du.edu http://www.ifs.du.edu]). Also, usability has been increased as Packaged Displays and Flex Packaged Displays were introduced that allowed for the creation of very specific lists of countries/regions, groups or Glists. A new education model has also been incorporated into the broader IFs model. New scenarios were created for UNEP (focusing on environmental change) and Pardee (focusing on poverty). Finally, one of the largest changes made was incorporating 182 countries into the Base-Case scenario used by IFs. Previous versions of IFs used broader regions to forecast global trends. This change also did away with the Student and Professional versions.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Geographic Representation of the World&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
186 countries underpin the functioning of IFs and these countries can be displayed separately or as parts of larger groups that users can determine.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;Below is a visual representation of how different entities are organized into Countries/Regions, Groups or Glists:&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
[[File:Geo 186.png|frame|right|Visual representation of IF&#039;s definition of regions/countries/groups/glists]]&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;*Note: In older versions of IFs, Regions were used as intermediaries between Countries and Groups. In the future, they, or some similarly named unit, will be a sub-unit of Countries. Regions, acting as a sub-unit of Countries, are currently not a feature of IFs. See the image located at the bottom of this Help topic.&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
When using IFs, there are many occasions where the user is asked whether or not they would like to display their results as a product of single countries, or larger groups. This is typically a toggle switch that moves between Country/Region and Groups, however, it might be a three-way-toggle that includes Country/Region, Group and Glist.&lt;br /&gt;
&lt;br /&gt;
Countries/Regions are currently the smallest geographical unit that users can represent. The ability to split countries down into smaller regions, or states, is under development. There are 186 different countries/regions that users can display.&lt;br /&gt;
&lt;br /&gt;
Groups are variably organized geographically or by memberships in international institutions/regimes. You can find out who is represented in each group and add or delete members by exploring the [[Extended_Features#Manage_Groups/Regions|Managing Regionalization]] function.&lt;br /&gt;
&lt;br /&gt;
Glists merge both Groups and Countries/Regions. These lists are mostly geographically bound. In the future, the Glist distinction will become more important as some users may want to place, for example, both the Indian state of Kerala in a Glist with Sri Lanka and Nepal.&lt;br /&gt;
&lt;br /&gt;
Users may also want to [[Extended_Features#Change_Grouping/Regionalization|create]] their own groups or [[Extended_Features#Identify_Groups_or_Country/Region_Members|explore]] what countries are members of what groups.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Time Horizon&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Future Forecasts.&#039;&#039;&#039; IFs begins computation with data from 2000 and can dynamically calculate values for all variables annually through 2100.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Historical Analysis and &amp;quot;Forecasts.&amp;quot;&#039;&#039;&#039; IFs also includes an extensive and growing historical data base starting in 1960. The data basis allows analysis of relationships among variables across countries and across time.&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8357</id>
		<title>Introduction to IFs</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8357"/>
		<updated>2017-09-07T23:04:28Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Purposes&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;International Futures (IFs) is a tool for thinking about long-term global trends and planning more strategically for the&amp;amp;nbsp;future.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
IFs can help you:&lt;br /&gt;
&lt;br /&gt;
*Understand&amp;amp;nbsp;the state of major&amp;amp;nbsp;global systems&lt;br /&gt;
*Explore&amp;amp;nbsp;long-term trends and consider where they might take&amp;amp;nbsp;us&lt;br /&gt;
*Learn about the dynamic&amp;amp;nbsp;interactions between global systems&lt;br /&gt;
*Clarify long-term organizational goals/priorities&lt;br /&gt;
*Develop alternative scenarios (if-then statements) about the future&lt;br /&gt;
*Investigate how different groups (households, firms or governments) can shape the future&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;The IFs platform relies&amp;amp;nbsp;on core, underlying assumptions, including the following.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*Global issues are becoming&amp;amp;nbsp;more significant as the scope of human interaction and human impact on the broader environment grow.&lt;br /&gt;
*Goals and priorities for human systems are becoming clearer and are more frequently and consistently communicated.&lt;br /&gt;
*Understanding of the dynamics of human systems is improving&amp;amp;nbsp;rapidly.&lt;br /&gt;
*The domain of human choice and action is broadening.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Which issues can you&amp;amp;nbsp;investigate with IFs? Some examples include:&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*[[Environment|Environment]]: Atmospheric carbon dioxide levels, world forest area, fossil fuel usage&lt;br /&gt;
*[[Socio-Political|Socio-Political Change]]: Life expectancy, literacy rate, democracy level, status of women, value change&lt;br /&gt;
*[[Population|Demographics]]: Population levels and growth, fertility, mortality, migration&lt;br /&gt;
*[[Agriculture|Food and Agriculture]]: Land use and production levels, calorie availability, malnutrition rates&lt;br /&gt;
*[[Energy|Energy]]: Resource and production levels, demand patterns, renewable energy share&lt;br /&gt;
*[[Economics|Economics]]: Sectoral production, consumption, and trade patterns and structural change&lt;br /&gt;
*[[Interstate_Politics_(IP)|Geopolitics]]: Country and regional power levels&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Visual Representation&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
[[File:IFsOverviewChart.jpg|frame|center|Visual representation of IFs structure]]&lt;br /&gt;
&lt;br /&gt;
Among the philosophical premises of the International Futures (IFs) project is that the model cannot be a &amp;quot;black box&amp;quot; to users and be truly useful. Model users must be able to examine the structures of IFs in order (1) to have confidence in them, and (2) learn from them.&lt;br /&gt;
&lt;br /&gt;
There is available (see topics under Understanding the Mode in the contents of this help system):&lt;br /&gt;
&lt;br /&gt;
*[[Understand_IFs#Dominant_Relations|Dominant Relations]] of the model structure&lt;br /&gt;
*[[Understand_IFs#2.2|Structure-Based and Agent-Class Driven Modeling]]&lt;br /&gt;
*[[Understand_IFs#Equation_Notation|Equation Notation]]&lt;br /&gt;
*[[Introduction_to_IFs#IFs_Bibliography|IFs Bibliography]] of data and data sources&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Quick Survey&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;population&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents 22 age-sex cohorts to age 100+&lt;br /&gt;
*calculates change in fertility and mortality rates in response to income, income distribution, and analysis multipliers&lt;br /&gt;
*computes average life expectancy at birth, literacy rate, and overall measures of human development (HDI) and physical quality of life&lt;br /&gt;
*represents migration and HIV/AIDS&lt;br /&gt;
*includes a newly developing submodel of formal education across primary, secondary, and tertiary levels&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;economic&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents the economy in six sectors: agriculture, materials, energy, industry, services, and ICT (other sectors could be configured, using raw data from the GTAP project)&lt;br /&gt;
*computes and uses input-output matrices that change dynamically with development level&lt;br /&gt;
*is a general equilibrium-seeking model that does not assume exact equilibrium will exist in any given year; rather it uses inventories as buffer stocks and to provide price signals so that the model chases equilibrium over time&lt;br /&gt;
*contains an endogenous production function that represents contributions to growth in multifactor productivity from R&amp;amp;D, education, worker health, economic policies (&amp;quot;freedom&amp;quot;), and energy prices (the &amp;quot;quality&amp;quot; of capital)&lt;br /&gt;
*uses a Linear Expenditure System to represent changing consumption patterns&lt;br /&gt;
*utilizes a &amp;quot;pooled&amp;quot; rather than the bilateral trade approach for international trade&lt;br /&gt;
*is being imbedded during 2002 in a social accounting matrix (SAM) envelope that will tie economic production and consumption to intra-actor financial flows&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;agricultural&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents production, consumption and trade of crops and meat; it also carries ocean fish catch and aquaculture in less detail&lt;br /&gt;
*maintains land use in crop, grazing, forest, urban, and &amp;quot;other&amp;quot; categories&lt;br /&gt;
*represents demand for food, for livestock feed, and for industrial use of agricultural products&lt;br /&gt;
*is a partial equilibrium model in which food stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the agricultural sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;energy&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*portrays production of six energy types: oil, gas, coal, nuclear, hydroelectric, and other renewable&lt;br /&gt;
*represents consumption and trade of energy in the aggregate&lt;br /&gt;
*represents known reserves and ultimate resources of the fossil fuels&lt;br /&gt;
*portrays changing capital costs of each energy type with technological change as well as with draw-downs of resources&lt;br /&gt;
*is a partial equilibrium model in which energy stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the energy sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The two &#039;&#039;&#039;socio-political&#039;&#039;&#039; sub-modules:&lt;br /&gt;
&lt;br /&gt;
Within countries or geographic groupings&lt;br /&gt;
&lt;br /&gt;
*represents fiscal policy through taxing and spending decisions&lt;br /&gt;
*shows six categories of government spending: military, health, education, R&amp;amp;D, foreign aid, and a residual category&lt;br /&gt;
*represents changes in social conditions of individuals (like fertility rates or literacy levels), attitudes of individuals (such as the level of materialism/postmaterialism of a society from the World Value Survey), and the social organization of people (such as the status of women)&lt;br /&gt;
*represents the evolution of democracy&lt;br /&gt;
*represents the prospects for state instability or failure&lt;br /&gt;
&lt;br /&gt;
Between countries or groupings of countries&lt;br /&gt;
&lt;br /&gt;
*traces changes in power balances across states and regions&lt;br /&gt;
*allows exploration of changes in the level of interstate threat&lt;br /&gt;
*represents possible action-reaction processes and arms races with associated potential for conflict among countries&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;environmental&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows tracking of remaining resources of fossil fuels, of the area of forested land, of water usage, and of atmospheric carbon dioxide emissions&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;technology&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows changes in assumptions about rates of technological advance in agriculture, energy, and the broader economy&lt;br /&gt;
*explicitly represents the extent of electronic networking of individuals in societies&lt;br /&gt;
*is tied to the governmental spending model with respect to R&amp;amp;D spending&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Background&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
International Futures (IFs) has evolved since 1980 through three &amp;quot;generations,&amp;quot; with a fourth generation now taking form.&lt;br /&gt;
&lt;br /&gt;
The first generation had deep roots in the world models of the 1970s, including those of the Club of Rome. In particular, IFs drew on the Mesarovic-Pestel or World Integrated Model (Mesarovic and Pestel 1974). The author of IFs had contributed to that project, including the construction of the energy submodel. IFs consciously also drew on the Leontief World Model (Leontief et al. 1977), the Bariloche Foundation’s world model (Herrera et al. 1976), and Systems Analysis Research Unit Model (SARU 1977), following comparative analysis of those models by Hughes (1980). That generation was written in FORTRAN and available for use on main-frame computers through CONDUIT, an educational software distribution center at the University of Iowa. Although the primary use of that and subsequent generations was by students, IFs has always had some policy analysis capability that has appealed to specialists. For example, the U.S. Foreign Service Institute used the first generation of IFs in a mid-career training program.&lt;br /&gt;
&lt;br /&gt;
The second generation of International Futures moved to early microcomputers in 1985, using the DOS platform. It was a very simplified version of the original IFs without regional or country differentiation.&lt;br /&gt;
&lt;br /&gt;
The third generation, first available in 1993, became a full-scale microcomputer model. The third generation improved earlier representations of demographic, energy, and food systems, but added new environmental and socio-political content. It built upon the collaboration of the author with the GLOBUS project, and it adopted the economic submodel of GLOBUS (developed by the author). GLOBUS had been created with the inspiration of Karl Deutsch and under the leadership of Stuart Bremer (1987) at the Wissenschaftszentrum in Berlin.&lt;br /&gt;
&lt;br /&gt;
The third generation has produced three editions/major releases of IFs, each accompanied by a book also called International Futures (Hughes 1993, 1996, 1999). The second edition moved to a Visual Basic platform that allowed a much improved menu-driven interface, running under Windows. The third edition incorporated an early global mapping capability and an initial ability to do cross-sectional and longitudinal data analysis.&lt;br /&gt;
&lt;br /&gt;
The fourth generation has been taking shape since early 2000. It has been heavily influenced by the usage of the model in an increasingly policy-analysis mode by several important organizations. First, General Motors commissioned a specialized version of IFs named CoVaTrA (Consumer Values Trends Analysis) with a need for updated and extended demographic modeling and representation of value change. An alliance was established with the World Values Survey, directed by Ronald Inglehart, to create that version. Second, the Strategic Assessments Group of the Central Intelligence Agency commissioned a specialized version named IFs for SAG. The work involved in preparing that greatly extended and enhanced the socio-political representations of the model, both domestic and international. Third, the European Commission sponsored a project named TERRA which has led to a specialized version named IFs for TERRA. IFs for TERRA work led to enhancements across the model, including improved representation of economic sectors, updated IO matrices and a basic Social Accounting Matrix, GINI and Lorenz curves, and preparing for extended environmental impact representation (drawing upon the Advanced Sustainability Analysis framework of the Finland Futures Research Center).&lt;br /&gt;
&lt;br /&gt;
Throughout this emergence of a fourth generation IFs (incorporating all of the above elements for additional users) there has been also a heavy emphasis on enhanced usability. Ideas from Robert Pestel in the TERRA project led to the creation of a new tree-structure for scenario creation and management. Ideas from Ronald Inglehart led to the development of the Guided Use structure and a somewhat more game-like character within that structure. Inglehart also help arrange funding to support the programming of Guided Use through the European Union Center of the University of Michigan.&lt;br /&gt;
&lt;br /&gt;
The fifth version of IFs is currently in use and represents broad strides to improving the model and its usability. It is the first version of this software to be placed online due to the help of the National Intelligence Council ([http://www.ifs.du.edu http://www.ifs.du.edu]). Also, usability has been increased as Packaged Displays and Flex Packaged Displays were introduced that allowed for the creation of very specific lists of countries/regions, groups or Glists. A new education model has also been incorporated into the broader IFs model. New scenarios were created for UNEP (focusing on environmental change) and Pardee (focusing on poverty). Finally, one of the largest changes made was incorporating 182 countries into the Base-Case scenario used by IFs. Previous versions of IFs used broader regions to forecast global trends. This change also did away with the Student and Professional versions.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Geographic Representation of the World&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
186 countries underpin the functioning of IFs and these countries can be displayed separately or as parts of larger groups that users can determine.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;Below is a visual representation of how different entities are organized into Countries/Regions, Groups or Glists:&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
[[File:Geo 186.png|frame|right|Visual representation of IF&#039;s definition of regions/countries/groups/glists]]&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;*Note: In older versions of IFs, Regions were used as intermediaries between Countries and Groups. In the future, they, or some similarly named unit, will be a sub-unit of Countries. Regions, acting as a sub-unit of Countries, are currently not a feature of IFs. See the image located at the bottom of this Help topic.&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
When using IFs, there are many occasions where the user is asked whether or not they would like to display their results as a product of single countries, or larger groups. This is typically a toggle switch that moves between Country/Region and Groups, however, it might be a three-way-toggle that includes Country/Region, Group and Glist.&lt;br /&gt;
&lt;br /&gt;
Countries/Regions are currently the smallest geographical unit that users can represent. The ability to split countries down into smaller regions, or states, is under development. There are 186 different countries/regions that users can display.&lt;br /&gt;
&lt;br /&gt;
Groups are variably organized geographically or by memberships in international institutions/regimes. You can find out who is represented in each group and add or delete members by exploring the [[Extended_Features#Manage_Groups/Regions|Managing Regionalization]] function.&lt;br /&gt;
&lt;br /&gt;
Glists merge both Groups and Countries/Regions. These lists are mostly geographically bound. In the future, the Glist distinction will become more important as some users may want to place, for example, both the Indian state of Kerala in a Glist with Sri Lanka and Nepal.&lt;br /&gt;
&lt;br /&gt;
Users may also want to [[Extended_Features#Change_Grouping/Regionalization|create]] their own groups or [[Extended_Features#Identify_Groups_or_Country/Region_Members|explore]] what countries are members of what groups.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Time Horizon&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Future Forecasts.&#039;&#039;&#039; IFs begins computation with data from 2000 and can dynamically calculate values for all variables annually through 2100.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Historical Analysis and &amp;quot;Forecasts.&amp;quot;&#039;&#039;&#039; IFs also includes an extensive and growing historical data base starting in 1960. The data basis allows analysis of relationships among variables across countries and across time.&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=International_Futures_(IFs)&amp;diff=8356</id>
		<title>International Futures (IFs)</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=International_Futures_(IFs)&amp;diff=8356"/>
		<updated>2017-09-07T22:55:31Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&#039;&#039;&#039;Welcome to the International Futures (IFs) wiki.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
Consult the [//meta.wikimedia.org/wiki/Help:Contents User&#039;s Guide] for information on using the wiki software.&lt;br /&gt;
&lt;br /&gt;
[[Introduction_to_IFs|Introduction to IFs]]&amp;amp;nbsp;- Click here for an overview of the IFs system, including a description of the tool&#039;s&amp;amp;nbsp;purpose and major assumptions in its Base Case forecasts.&lt;br /&gt;
&lt;br /&gt;
[[Use_IFs_(Download_Version)|Use IFs (Download Version)]]&amp;amp;nbsp;- Click here for instructions on using the standalone desktop version of IFs.&lt;br /&gt;
&lt;br /&gt;
[[Use_IFs_(Online_Version)|Use IFs (Online Version)]]&amp;amp;nbsp;- Click here for instructions on using the web (cloud) version of IFs.&lt;br /&gt;
&lt;br /&gt;
[[Understand_the_Model|Understand IFs]]&amp;amp;nbsp;- Click here to access documentation&amp;amp;nbsp;on each of the major system models in IFs.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
[[Guide_to_Scenario_Analysis_in_International_Futures_(IFs)|Guide to Scenario Analysis in IFs]] - Click here for instructions on creating and comparing alternative scenarios in IFs. This guide also includes an updated list of IFs parameters.&lt;br /&gt;
&lt;br /&gt;
[[Additional_resources|Additional Resources]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Feedback ==&lt;br /&gt;
&lt;br /&gt;
Feedback on how to improve IFs is always appreciated, especially if you find something that is not working. Compliments are also accepted.&amp;amp;nbsp;To send feedback or if you have any questions about using IFs, please email us&amp;amp;nbsp;at pardee.center [at] du.edu. You can also find additional contact information on our center&#039;s website.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== Getting started ==&lt;br /&gt;
&lt;br /&gt;
*[//www.mediawiki.org/wiki/Manual:Configuration_settings Configuration settings list]&lt;br /&gt;
*[//www.mediawiki.org/wiki/Manual:FAQ MediaWiki FAQ]&lt;br /&gt;
*[https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]&lt;br /&gt;
*[//www.mediawiki.org/wiki/Localisation#Translation_resources Localise MediaWiki for your language]&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=International_Futures_(IFs)&amp;diff=8355</id>
		<title>International Futures (IFs)</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=International_Futures_(IFs)&amp;diff=8355"/>
		<updated>2017-09-07T22:55:09Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&#039;&#039;&#039;Welcome to the International Futures (IFs) wiki.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
Consult the [//meta.wikimedia.org/wiki/Help:Contents User&#039;s Guide] for information on using the wiki software.&lt;br /&gt;
&lt;br /&gt;
[[Introduction_to_IFs|Introduction to IFs]]&amp;amp;nbsp;- Click here for an overview of the IFs system, including a description of the tool&#039;s&amp;amp;nbsp;purpose and major assumptions in its Base Case forecasts.&lt;br /&gt;
&lt;br /&gt;
[[Use_IFs_(Download_Version)|Use IFs (Download Version)]]&amp;amp;nbsp;- Click here for instructions on using the standalone desktop version of IFs.&lt;br /&gt;
&lt;br /&gt;
[[Use_IFs_(Online_Version)|Use IFs (Online Version)]]&amp;amp;nbsp;- Click here for instructions on using the web (cloud) version of IFs.&lt;br /&gt;
&lt;br /&gt;
[[Understand_the_Model|Understand IFs]]&amp;amp;nbsp;- Click here to access documentation&amp;amp;nbsp;on each of the major system models in IFs.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
[[Guide_to_Scenario_Analysis_in_International_Futures_(IFs)|Guide to Scenario Analysis in IFs]] - Click here for instructions on creating and comparing alternative scenarios in IFs. This guide also includes an updated list of IFs parameters.&lt;br /&gt;
&lt;br /&gt;
[[Additional_resources|Additional Resources]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Feedback ==&lt;br /&gt;
&lt;br /&gt;
Feedback on how to improve IFs is always appreciated, especially if you find something that is not working. Compliments are also accepted.&amp;amp;nbsp;To send feedback or if you have any questions about using IFs, please email us&amp;amp;nbsp;at pardee.center [at] du.edu. You can also find additional contact information on our website: [[pardee.du.edu/contact|pardee.du.edu/contact]]&lt;br /&gt;
&lt;br /&gt;
== Getting started ==&lt;br /&gt;
&lt;br /&gt;
*[//www.mediawiki.org/wiki/Manual:Configuration_settings Configuration settings list]&lt;br /&gt;
*[//www.mediawiki.org/wiki/Manual:FAQ MediaWiki FAQ]&lt;br /&gt;
*[https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]&lt;br /&gt;
*[//www.mediawiki.org/wiki/Localisation#Translation_resources Localise MediaWiki for your language]&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=International_Futures_(IFs)&amp;diff=8354</id>
		<title>International Futures (IFs)</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=International_Futures_(IFs)&amp;diff=8354"/>
		<updated>2017-09-07T22:53:54Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&#039;&#039;&#039;Welcome to the International Futures (IFs) wiki.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
Consult the [//meta.wikimedia.org/wiki/Help:Contents User&#039;s Guide] for information on using the wiki software.&lt;br /&gt;
&lt;br /&gt;
[[Introduction_to_IFs|Introduction to IFs]]&amp;amp;nbsp;- Click here for an overview of the IFs system, including a description of the tool&#039;s&amp;amp;nbsp;purpose and major assumptions in its Base Case forecasts.&lt;br /&gt;
&lt;br /&gt;
[[Use_IFs_(Download_Version)|Use IFs (Download Version)]]&amp;amp;nbsp;- Click here for instructions on using the standalone desktop version of IFs.&lt;br /&gt;
&lt;br /&gt;
[[Use_IFs_(Online_Version)|Use IFs (Online Version)]]&amp;amp;nbsp;- Click here for instructions on using the web (cloud) version of IFs.&lt;br /&gt;
&lt;br /&gt;
[[Understand_the_Model|Understand IFs]]&amp;amp;nbsp;- Click here to access documentation&amp;amp;nbsp;on each of the major system models in IFs.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
[[Guide_to_Scenario_Analysis_in_International_Futures_(IFs)|Guide to Scenario Analysis in IFs]] - Click here for instructions on creating and comparing alternative scenarios in IFs. This guide also includes an updated list of IFs parameters.&lt;br /&gt;
&lt;br /&gt;
[[Additional_resources|Additional Resources]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Feedback ==&lt;br /&gt;
&lt;br /&gt;
Feedback on how to improve IFs is always appreciated, especially if you find something that is not working. Compliments are also accepted.&amp;amp;nbsp;To send feedback or if you have any questions about using IFs, please email us&amp;amp;nbsp;at pardee.center [at] du.edu. You can also find additional contact information on our website: pardee.du.edu/contact&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Getting started ==&lt;br /&gt;
&lt;br /&gt;
*[//www.mediawiki.org/wiki/Manual:Configuration_settings Configuration settings list]&lt;br /&gt;
*[//www.mediawiki.org/wiki/Manual:FAQ MediaWiki FAQ]&lt;br /&gt;
*[https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]&lt;br /&gt;
*[//www.mediawiki.org/wiki/Localisation#Translation_resources Localise MediaWiki for your language]&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8353</id>
		<title>Introduction to IFs</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8353"/>
		<updated>2017-09-07T22:48:51Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Purposes&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;International Futures (IFs) is a tool for thinking about long-term global trends and planning more strategically for the&amp;amp;nbsp;future.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
IFs can help you:&lt;br /&gt;
&lt;br /&gt;
*Understand&amp;amp;nbsp;the state of major&amp;amp;nbsp;global systems&lt;br /&gt;
*Explore&amp;amp;nbsp;long-term trends and consider where they might take&amp;amp;nbsp;us&lt;br /&gt;
*Learn about the dynamic&amp;amp;nbsp;interactions between global systems&lt;br /&gt;
*Clarify long-term organizational goals/priorities&lt;br /&gt;
*Develop alternative scenarios (if-then statements) about the future&lt;br /&gt;
*Investigate how different groups (households, firms or governments) can shape the future&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;IFs development and analysis depend&amp;amp;nbsp;on core, underlying assumptions, including the following.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*Global issues are becoming&amp;amp;nbsp;more significant as the scope of human interaction and human impact on the broader environment grow.&lt;br /&gt;
*Goals and priorities for human systems are becoming clearer and are more frequently and consistently communicated.&lt;br /&gt;
*Understanding of the dynamics of human systems is improving&amp;amp;nbsp;rapidly.&lt;br /&gt;
*The domain of human choice and action is broadening.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;What issues can you&amp;amp;nbsp;investigate with IFs?&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*[[Environment|Environment]]: Atmospheric carbon dioxide levels, world forest area, fossil fuel usage&lt;br /&gt;
*[[Socio-Political|Socio-Political Change]]: Life expectancy, literacy rate, democracy level, status of women, value change&lt;br /&gt;
*[[Population|Demographics]]: Population levels and growth, fertility, mortality, migration&lt;br /&gt;
*[[Agriculture|Food and Agriculture]]: Land use and production levels, calorie availability, malnutrition rates&lt;br /&gt;
*[[Energy|Energy]]: Resource and production levels, demand patterns, renewable energy share&lt;br /&gt;
*[[Economics|Economics]]: Sectoral production, consumption, and trade patterns and structural change&lt;br /&gt;
*[[Interstate_Politics_(IP)|Geopolitics]]: Country and regional power levels&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Visual Representation&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
[[File:IFsOverviewChart.jpg|frame|center|Visual representation of IFs structure]]&lt;br /&gt;
&lt;br /&gt;
Among the philosophical premises of the International Futures (IFs) project is that the model cannot be a &amp;quot;black box&amp;quot; to users and be truly useful. Model users must be able to examine the structures of IFs in order (1) to have confidence in them, and (2) learn from them.&lt;br /&gt;
&lt;br /&gt;
There is available (see topics under Understanding the Mode in the contents of this help system):&lt;br /&gt;
&lt;br /&gt;
*[[Understand_IFs#Dominant_Relations|Dominant Relations]] of the model structure&lt;br /&gt;
*[[Understand_IFs#2.2|Structure-Based and Agent-Class Driven Modeling]]&lt;br /&gt;
*[[Understand_IFs#Equation_Notation|Equation Notation]]&lt;br /&gt;
*[[Introduction_to_IFs#IFs_Bibliography|IFs Bibliography]] of data and data sources&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Quick Survey&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;population&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents 22 age-sex cohorts to age 100+&lt;br /&gt;
*calculates change in fertility and mortality rates in response to income, income distribution, and analysis multipliers&lt;br /&gt;
*computes average life expectancy at birth, literacy rate, and overall measures of human development (HDI) and physical quality of life&lt;br /&gt;
*represents migration and HIV/AIDS&lt;br /&gt;
*includes a newly developing submodel of formal education across primary, secondary, and tertiary levels&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;economic&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents the economy in six sectors: agriculture, materials, energy, industry, services, and ICT (other sectors could be configured, using raw data from the GTAP project)&lt;br /&gt;
*computes and uses input-output matrices that change dynamically with development level&lt;br /&gt;
*is a general equilibrium-seeking model that does not assume exact equilibrium will exist in any given year; rather it uses inventories as buffer stocks and to provide price signals so that the model chases equilibrium over time&lt;br /&gt;
*contains an endogenous production function that represents contributions to growth in multifactor productivity from R&amp;amp;D, education, worker health, economic policies (&amp;quot;freedom&amp;quot;), and energy prices (the &amp;quot;quality&amp;quot; of capital)&lt;br /&gt;
*uses a Linear Expenditure System to represent changing consumption patterns&lt;br /&gt;
*utilizes a &amp;quot;pooled&amp;quot; rather than the bilateral trade approach for international trade&lt;br /&gt;
*is being imbedded during 2002 in a social accounting matrix (SAM) envelope that will tie economic production and consumption to intra-actor financial flows&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;agricultural&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents production, consumption and trade of crops and meat; it also carries ocean fish catch and aquaculture in less detail&lt;br /&gt;
*maintains land use in crop, grazing, forest, urban, and &amp;quot;other&amp;quot; categories&lt;br /&gt;
*represents demand for food, for livestock feed, and for industrial use of agricultural products&lt;br /&gt;
*is a partial equilibrium model in which food stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the agricultural sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;energy&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*portrays production of six energy types: oil, gas, coal, nuclear, hydroelectric, and other renewable&lt;br /&gt;
*represents consumption and trade of energy in the aggregate&lt;br /&gt;
*represents known reserves and ultimate resources of the fossil fuels&lt;br /&gt;
*portrays changing capital costs of each energy type with technological change as well as with draw-downs of resources&lt;br /&gt;
*is a partial equilibrium model in which energy stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the energy sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The two &#039;&#039;&#039;socio-political&#039;&#039;&#039; sub-modules:&lt;br /&gt;
&lt;br /&gt;
Within countries or geographic groupings&lt;br /&gt;
&lt;br /&gt;
*represents fiscal policy through taxing and spending decisions&lt;br /&gt;
*shows six categories of government spending: military, health, education, R&amp;amp;D, foreign aid, and a residual category&lt;br /&gt;
*represents changes in social conditions of individuals (like fertility rates or literacy levels), attitudes of individuals (such as the level of materialism/postmaterialism of a society from the World Value Survey), and the social organization of people (such as the status of women)&lt;br /&gt;
*represents the evolution of democracy&lt;br /&gt;
*represents the prospects for state instability or failure&lt;br /&gt;
&lt;br /&gt;
Between countries or groupings of countries&lt;br /&gt;
&lt;br /&gt;
*traces changes in power balances across states and regions&lt;br /&gt;
*allows exploration of changes in the level of interstate threat&lt;br /&gt;
*represents possible action-reaction processes and arms races with associated potential for conflict among countries&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;environmental&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows tracking of remaining resources of fossil fuels, of the area of forested land, of water usage, and of atmospheric carbon dioxide emissions&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;technology&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows changes in assumptions about rates of technological advance in agriculture, energy, and the broader economy&lt;br /&gt;
*explicitly represents the extent of electronic networking of individuals in societies&lt;br /&gt;
*is tied to the governmental spending model with respect to R&amp;amp;D spending&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Background&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
International Futures (IFs) has evolved since 1980 through three &amp;quot;generations,&amp;quot; with a fourth generation now taking form.&lt;br /&gt;
&lt;br /&gt;
The first generation had deep roots in the world models of the 1970s, including those of the Club of Rome. In particular, IFs drew on the Mesarovic-Pestel or World Integrated Model (Mesarovic and Pestel 1974). The author of IFs had contributed to that project, including the construction of the energy submodel. IFs consciously also drew on the Leontief World Model (Leontief et al. 1977), the Bariloche Foundation’s world model (Herrera et al. 1976), and Systems Analysis Research Unit Model (SARU 1977), following comparative analysis of those models by Hughes (1980). That generation was written in FORTRAN and available for use on main-frame computers through CONDUIT, an educational software distribution center at the University of Iowa. Although the primary use of that and subsequent generations was by students, IFs has always had some policy analysis capability that has appealed to specialists. For example, the U.S. Foreign Service Institute used the first generation of IFs in a mid-career training program.&lt;br /&gt;
&lt;br /&gt;
The second generation of International Futures moved to early microcomputers in 1985, using the DOS platform. It was a very simplified version of the original IFs without regional or country differentiation.&lt;br /&gt;
&lt;br /&gt;
The third generation, first available in 1993, became a full-scale microcomputer model. The third generation improved earlier representations of demographic, energy, and food systems, but added new environmental and socio-political content. It built upon the collaboration of the author with the GLOBUS project, and it adopted the economic submodel of GLOBUS (developed by the author). GLOBUS had been created with the inspiration of Karl Deutsch and under the leadership of Stuart Bremer (1987) at the Wissenschaftszentrum in Berlin.&lt;br /&gt;
&lt;br /&gt;
The third generation has produced three editions/major releases of IFs, each accompanied by a book also called International Futures (Hughes 1993, 1996, 1999). The second edition moved to a Visual Basic platform that allowed a much improved menu-driven interface, running under Windows. The third edition incorporated an early global mapping capability and an initial ability to do cross-sectional and longitudinal data analysis.&lt;br /&gt;
&lt;br /&gt;
The fourth generation has been taking shape since early 2000. It has been heavily influenced by the usage of the model in an increasingly policy-analysis mode by several important organizations. First, General Motors commissioned a specialized version of IFs named CoVaTrA (Consumer Values Trends Analysis) with a need for updated and extended demographic modeling and representation of value change. An alliance was established with the World Values Survey, directed by Ronald Inglehart, to create that version. Second, the Strategic Assessments Group of the Central Intelligence Agency commissioned a specialized version named IFs for SAG. The work involved in preparing that greatly extended and enhanced the socio-political representations of the model, both domestic and international. Third, the European Commission sponsored a project named TERRA which has led to a specialized version named IFs for TERRA. IFs for TERRA work led to enhancements across the model, including improved representation of economic sectors, updated IO matrices and a basic Social Accounting Matrix, GINI and Lorenz curves, and preparing for extended environmental impact representation (drawing upon the Advanced Sustainability Analysis framework of the Finland Futures Research Center).&lt;br /&gt;
&lt;br /&gt;
Throughout this emergence of a fourth generation IFs (incorporating all of the above elements for additional users) there has been also a heavy emphasis on enhanced usability. Ideas from Robert Pestel in the TERRA project led to the creation of a new tree-structure for scenario creation and management. Ideas from Ronald Inglehart led to the development of the Guided Use structure and a somewhat more game-like character within that structure. Inglehart also help arrange funding to support the programming of Guided Use through the European Union Center of the University of Michigan.&lt;br /&gt;
&lt;br /&gt;
The fifth version of IFs is currently in use and represents broad strides to improving the model and its usability. It is the first version of this software to be placed online due to the help of the National Intelligence Council ([http://www.ifs.du.edu http://www.ifs.du.edu]). Also, usability has been increased as Packaged Displays and Flex Packaged Displays were introduced that allowed for the creation of very specific lists of countries/regions, groups or Glists. A new education model has also been incorporated into the broader IFs model. New scenarios were created for UNEP (focusing on environmental change) and Pardee (focusing on poverty). Finally, one of the largest changes made was incorporating 182 countries into the Base-Case scenario used by IFs. Previous versions of IFs used broader regions to forecast global trends. This change also did away with the Student and Professional versions.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Geographic Representation of the World&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
186 countries underpin the functioning of IFs and these countries can be displayed separately or as parts of larger groups that users can determine.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;Below is a visual representation of how different entities are organized into Countries/Regions, Groups or Glists:&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
[[File:Geo 186.png|frame|right|Visual representation of IF&#039;s definition of regions/countries/groups/glists]]&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;*Note: In older versions of IFs, Regions were used as intermediaries between Countries and Groups. In the future, they, or some similarly named unit, will be a sub-unit of Countries. Regions, acting as a sub-unit of Countries, are currently not a feature of IFs. See the image located at the bottom of this Help topic.&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
When using IFs, there are many occasions where the user is asked whether or not they would like to display their results as a product of single countries, or larger groups. This is typically a toggle switch that moves between Country/Region and Groups, however, it might be a three-way-toggle that includes Country/Region, Group and Glist.&lt;br /&gt;
&lt;br /&gt;
Countries/Regions are currently the smallest geographical unit that users can represent. The ability to split countries down into smaller regions, or states, is under development. There are 186 different countries/regions that users can display.&lt;br /&gt;
&lt;br /&gt;
Groups are variably organized geographically or by memberships in international institutions/regimes. You can find out who is represented in each group and add or delete members by exploring the [[Extended_Features#Manage_Groups/Regions|Managing Regionalization]] function.&lt;br /&gt;
&lt;br /&gt;
Glists merge both Groups and Countries/Regions. These lists are mostly geographically bound. In the future, the Glist distinction will become more important as some users may want to place, for example, both the Indian state of Kerala in a Glist with Sri Lanka and Nepal.&lt;br /&gt;
&lt;br /&gt;
Users may also want to [[Extended_Features#Change_Grouping/Regionalization|create]] their own groups or [[Extended_Features#Identify_Groups_or_Country/Region_Members|explore]] what countries are members of what groups.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Time Horizon&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Future Forecasts.&#039;&#039;&#039; IFs begins computation with data from 2000 and can dynamically calculate values for all variables annually through 2100.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Historical Analysis and &amp;quot;Forecasts.&amp;quot;&#039;&#039;&#039; IFs also includes an extensive and growing historical data base starting in 1960. The data basis allows analysis of relationships among variables across countries and across time.&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Acknowledgements&amp;diff=8352</id>
		<title>Acknowledgements</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Acknowledgements&amp;diff=8352"/>
		<updated>2017-09-07T22:47:23Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: Created page with &amp;quot;Our team gratefully recognizes critical contributions in the forms of:  1. Testing and suggestions for development of IFs in one or more of multiple generations. By Donald Bor...&amp;quot;&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;Our team gratefully recognizes critical contributions in the forms of:&lt;br /&gt;
&lt;br /&gt;
1. Testing and suggestions for development of IFs in one or more of multiple generations. By Donald Borock, Richard Chadwick, William Dixon, Dale Rothman, Phil Schrodt, Douglas Stuart, Donald Sylvan, Jonathan Wilkenfeld, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
2. Computer assistance across many releases. By Michael Niemann, Terrance Peet-Lukes, Douglas McClure, Mohammod Irfan, and Jose Solorzano. &lt;br /&gt;
&lt;br /&gt;
3. Data gathering and general assistance. By James Chung, Padma Padula, Shannon Brady, David Horan, Michael Ferrier, Kay Drucker, Warren Christopher, and Anwar Hossain. &lt;br /&gt;
&lt;br /&gt;
4. Long-term encouragement and support. By Harold Guetzkow, Karl Deutsch, Richard Chadwick, Gerald Barney, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
5. Association in related world modeling projects and projects building upon IFs. By Mihajlo Mesarovic, Aldo Barsotti, Juan Huerta, John Richardson, Thomas Shook, Patricia Strauch, and other members of the World Integrated Model (WIM) team. By Stuart Bremer, Peter Brecke, Thomas Cusack, Wolf Dieter-Eberwein, Brian Pollins, and Dale Smith of the GLOBUS modeling project. By Evan Hillebrand, Paul Herman, and others of the IFs for SAG project. By Rob Lempert and Steve Bankes at RAND, Santa Monica. By Robert Pestel, Jonathan Cave, Ronald Inglehart, Sergei Parinov, Pentti Malaska, and many others in the IFs for TERRA project.&lt;br /&gt;
&lt;br /&gt;
6. Financial assistance (without responsibility for the form of the evolving product). By the National Science Foundation, the Cleveland Foundation, the Exxon Education Foundation, the Kettering Family Foundation, the Pacific Cultural Foundation, the United States Institute of Peace, General Motors, the Strategic Assessments Group of the Central Intelligence Agency, the European Commission (Information Society Technology) Programme, the European Union Center of the University of Michigan, the National Intelligence Council (for web conversion), and Frederick S. Pardee. &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp;&amp;amp;nbsp;&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Additional_resources&amp;diff=8351</id>
		<title>Additional resources</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Additional_resources&amp;diff=8351"/>
		<updated>2017-09-07T22:46:13Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;[[Data|Data]]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Model_validation_and_verification Model&amp;amp;nbsp;validation&amp;amp;nbsp;and&amp;amp;nbsp;verification]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Consolidation Consolidation]&lt;br /&gt;
&lt;br /&gt;
[[Preprocessor|Preprocessor]]&lt;br /&gt;
&lt;br /&gt;
[[Sub-modules|Sub-modules]]&lt;br /&gt;
&lt;br /&gt;
[[SubRegionalization_Handbook|Sub-Regionalization Handbook]]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Version_notes Version notes]&lt;br /&gt;
&lt;br /&gt;
[[IFs_Bibliography|IFs Bibliography]]&lt;br /&gt;
&lt;br /&gt;
[[Development_Mode_Features|Development Mode Features]]&lt;br /&gt;
&lt;br /&gt;
[[Publications_on_IFs|Publications on IFs]]&lt;br /&gt;
&lt;br /&gt;
[[Instructional_Use_of_IFs_(For_Classrooms)|IFs for the Classroom]]&lt;br /&gt;
&lt;br /&gt;
[[Acknowledgements]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== In progress[[http://pardee.du.edu/wiki/index.php?title=International_Futures_(IFs)&amp;amp;action=edit&amp;amp;section=1 edit]] ===&lt;br /&gt;
&lt;br /&gt;
[[Sandbox|Sandbox]]&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8350</id>
		<title>Introduction to IFs</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8350"/>
		<updated>2017-09-07T22:45:14Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Purposes&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;International Futures (IFs) is a tool for thinking about long-term global trends and planning more strategically for the&amp;amp;nbsp;future.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
IFs can help you:&lt;br /&gt;
&lt;br /&gt;
*Understand&amp;amp;nbsp;the state of major&amp;amp;nbsp;global systems&lt;br /&gt;
*Explore&amp;amp;nbsp;long-term trends and consider where they might take&amp;amp;nbsp;us&lt;br /&gt;
*Learn about the dynamic&amp;amp;nbsp;interactions between global systems&lt;br /&gt;
*Clarify long-term organizational goals/priorities&lt;br /&gt;
*Develop alternative scenarios (if-then statements) about the future&lt;br /&gt;
*Investigate how different groups (households, firms or governments) can shape the future&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;IFs development and analysis depend&amp;amp;nbsp;on core, underlying assumptions, including the following.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*Global issues are becoming&amp;amp;nbsp;more significant as the scope of human interaction and human impact on the broader environment grow.&lt;br /&gt;
*Goals and priorities for human systems are becoming clearer and are more frequently and consistently communicated.&lt;br /&gt;
*Understanding of the dynamics of human systems is improving&amp;amp;nbsp;rapidly.&lt;br /&gt;
*The domain of human choice and action is broadening.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;What issues can you&amp;amp;nbsp;investigate with IFs?&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*[[Environment]]: Atmospheric carbon dioxide levels, world forest area, fossil fuel usage&lt;br /&gt;
*[[Socio-Political|Socio-Political Change]]: Life expectancy, literacy rate, democracy level, status of women, value change&lt;br /&gt;
*[[Population|Demographics]]: Population levels and growth, fertility, mortality, migration&lt;br /&gt;
*[[Agriculture|Food and Agriculture]]: Land use and production levels, calorie availability, malnutrition rates&lt;br /&gt;
*[[Energy]]: Resource and production levels, demand patterns, renewable energy share&lt;br /&gt;
*[[Economics]]: Sectoral production, consumption, and trade patterns and structural change&lt;br /&gt;
*[[Interstate_Politics_(IP)|Geopolitics]]: Country and regional power levels&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Visual Representation&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
[[File:IFsOverviewChart.jpg|frame|center|Visual representation of IFs structure]]&lt;br /&gt;
&lt;br /&gt;
Among the philosophical premises of the International Futures (IFs) project is that the model cannot be a &amp;quot;black box&amp;quot; to users and be truly useful. Model users must be able to examine the structures of IFs in order (1) to have confidence in them, and (2) learn from them.&lt;br /&gt;
&lt;br /&gt;
There is available (see topics under Understanding the Mode in the contents of this help system):&lt;br /&gt;
&lt;br /&gt;
*[[Understand_IFs#Dominant_Relations|Dominant Relations]] of the model structure&lt;br /&gt;
*[[Understand_IFs#2.2|Structure-Based and Agent-Class Driven Modeling]]&lt;br /&gt;
*[[Understand_IFs#Equation_Notation|Equation Notation]]&lt;br /&gt;
*[[Introduction_to_IFs#IFs_Bibliography|IFs Bibliography]] of data and data sources&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Quick Survey&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;population&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents 22 age-sex cohorts to age 100+&lt;br /&gt;
*calculates change in fertility and mortality rates in response to income, income distribution, and analysis multipliers&lt;br /&gt;
*computes average life expectancy at birth, literacy rate, and overall measures of human development (HDI) and physical quality of life&lt;br /&gt;
*represents migration and HIV/AIDS&lt;br /&gt;
*includes a newly developing submodel of formal education across primary, secondary, and tertiary levels&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;economic&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents the economy in six sectors: agriculture, materials, energy, industry, services, and ICT (other sectors could be configured, using raw data from the GTAP project)&lt;br /&gt;
*computes and uses input-output matrices that change dynamically with development level&lt;br /&gt;
*is a general equilibrium-seeking model that does not assume exact equilibrium will exist in any given year; rather it uses inventories as buffer stocks and to provide price signals so that the model chases equilibrium over time&lt;br /&gt;
*contains an endogenous production function that represents contributions to growth in multifactor productivity from R&amp;amp;D, education, worker health, economic policies (&amp;quot;freedom&amp;quot;), and energy prices (the &amp;quot;quality&amp;quot; of capital)&lt;br /&gt;
*uses a Linear Expenditure System to represent changing consumption patterns&lt;br /&gt;
*utilizes a &amp;quot;pooled&amp;quot; rather than the bilateral trade approach for international trade&lt;br /&gt;
*is being imbedded during 2002 in a social accounting matrix (SAM) envelope that will tie economic production and consumption to intra-actor financial flows&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;agricultural&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents production, consumption and trade of crops and meat; it also carries ocean fish catch and aquaculture in less detail&lt;br /&gt;
*maintains land use in crop, grazing, forest, urban, and &amp;quot;other&amp;quot; categories&lt;br /&gt;
*represents demand for food, for livestock feed, and for industrial use of agricultural products&lt;br /&gt;
*is a partial equilibrium model in which food stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the agricultural sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;energy&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*portrays production of six energy types: oil, gas, coal, nuclear, hydroelectric, and other renewable&lt;br /&gt;
*represents consumption and trade of energy in the aggregate&lt;br /&gt;
*represents known reserves and ultimate resources of the fossil fuels&lt;br /&gt;
*portrays changing capital costs of each energy type with technological change as well as with draw-downs of resources&lt;br /&gt;
*is a partial equilibrium model in which energy stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the energy sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The two &#039;&#039;&#039;socio-political&#039;&#039;&#039; sub-modules:&lt;br /&gt;
&lt;br /&gt;
Within countries or geographic groupings&lt;br /&gt;
&lt;br /&gt;
*represents fiscal policy through taxing and spending decisions&lt;br /&gt;
*shows six categories of government spending: military, health, education, R&amp;amp;D, foreign aid, and a residual category&lt;br /&gt;
*represents changes in social conditions of individuals (like fertility rates or literacy levels), attitudes of individuals (such as the level of materialism/postmaterialism of a society from the World Value Survey), and the social organization of people (such as the status of women)&lt;br /&gt;
*represents the evolution of democracy&lt;br /&gt;
*represents the prospects for state instability or failure&lt;br /&gt;
&lt;br /&gt;
Between countries or groupings of countries&lt;br /&gt;
&lt;br /&gt;
*traces changes in power balances across states and regions&lt;br /&gt;
*allows exploration of changes in the level of interstate threat&lt;br /&gt;
*represents possible action-reaction processes and arms races with associated potential for conflict among countries&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;environmental&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows tracking of remaining resources of fossil fuels, of the area of forested land, of water usage, and of atmospheric carbon dioxide emissions&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;technology&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows changes in assumptions about rates of technological advance in agriculture, energy, and the broader economy&lt;br /&gt;
*explicitly represents the extent of electronic networking of individuals in societies&lt;br /&gt;
*is tied to the governmental spending model with respect to R&amp;amp;D spending&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Background&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
International Futures (IFs) has evolved since 1980 through three &amp;quot;generations,&amp;quot; with a fourth generation now taking form.&lt;br /&gt;
&lt;br /&gt;
The first generation had deep roots in the world models of the 1970s, including those of the Club of Rome. In particular, IFs drew on the Mesarovic-Pestel or World Integrated Model (Mesarovic and Pestel 1974). The author of IFs had contributed to that project, including the construction of the energy submodel. IFs consciously also drew on the Leontief World Model (Leontief et al. 1977), the Bariloche Foundation’s world model (Herrera et al. 1976), and Systems Analysis Research Unit Model (SARU 1977), following comparative analysis of those models by Hughes (1980). That generation was written in FORTRAN and available for use on main-frame computers through CONDUIT, an educational software distribution center at the University of Iowa. Although the primary use of that and subsequent generations was by students, IFs has always had some policy analysis capability that has appealed to specialists. For example, the U.S. Foreign Service Institute used the first generation of IFs in a mid-career training program.&lt;br /&gt;
&lt;br /&gt;
The second generation of International Futures moved to early microcomputers in 1985, using the DOS platform. It was a very simplified version of the original IFs without regional or country differentiation.&lt;br /&gt;
&lt;br /&gt;
The third generation, first available in 1993, became a full-scale microcomputer model. The third generation improved earlier representations of demographic, energy, and food systems, but added new environmental and socio-political content. It built upon the collaboration of the author with the GLOBUS project, and it adopted the economic submodel of GLOBUS (developed by the author). GLOBUS had been created with the inspiration of Karl Deutsch and under the leadership of Stuart Bremer (1987) at the Wissenschaftszentrum in Berlin.&lt;br /&gt;
&lt;br /&gt;
The third generation has produced three editions/major releases of IFs, each accompanied by a book also called International Futures (Hughes 1993, 1996, 1999). The second edition moved to a Visual Basic platform that allowed a much improved menu-driven interface, running under Windows. The third edition incorporated an early global mapping capability and an initial ability to do cross-sectional and longitudinal data analysis.&lt;br /&gt;
&lt;br /&gt;
The fourth generation has been taking shape since early 2000. It has been heavily influenced by the usage of the model in an increasingly policy-analysis mode by several important organizations. First, General Motors commissioned a specialized version of IFs named CoVaTrA (Consumer Values Trends Analysis) with a need for updated and extended demographic modeling and representation of value change. An alliance was established with the World Values Survey, directed by Ronald Inglehart, to create that version. Second, the Strategic Assessments Group of the Central Intelligence Agency commissioned a specialized version named IFs for SAG. The work involved in preparing that greatly extended and enhanced the socio-political representations of the model, both domestic and international. Third, the European Commission sponsored a project named TERRA which has led to a specialized version named IFs for TERRA. IFs for TERRA work led to enhancements across the model, including improved representation of economic sectors, updated IO matrices and a basic Social Accounting Matrix, GINI and Lorenz curves, and preparing for extended environmental impact representation (drawing upon the Advanced Sustainability Analysis framework of the Finland Futures Research Center).&lt;br /&gt;
&lt;br /&gt;
Throughout this emergence of a fourth generation IFs (incorporating all of the above elements for additional users) there has been also a heavy emphasis on enhanced usability. Ideas from Robert Pestel in the TERRA project led to the creation of a new tree-structure for scenario creation and management. Ideas from Ronald Inglehart led to the development of the Guided Use structure and a somewhat more game-like character within that structure. Inglehart also help arrange funding to support the programming of Guided Use through the European Union Center of the University of Michigan.&lt;br /&gt;
&lt;br /&gt;
The fifth version of IFs is currently in use and represents broad strides to improving the model and its usability. It is the first version of this software to be placed online due to the help of the National Intelligence Council ([http://www.ifs.du.edu http://www.ifs.du.edu]). Also, usability has been increased as Packaged Displays and Flex Packaged Displays were introduced that allowed for the creation of very specific lists of countries/regions, groups or Glists. A new education model has also been incorporated into the broader IFs model. New scenarios were created for UNEP (focusing on environmental change) and Pardee (focusing on poverty). Finally, one of the largest changes made was incorporating 182 countries into the Base-Case scenario used by IFs. Previous versions of IFs used broader regions to forecast global trends. This change also did away with the Student and Professional versions.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Geographic Representation of the World&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
186 countries underpin the functioning of IFs and these countries can be displayed separately or as parts of larger groups that users can determine.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;Below is a visual representation of how different entities are organized into Countries/Regions, Groups or Glists:&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
[[File:Geo 186.png|frame|right|Visual representation of IF&#039;s definition of regions/countries/groups/glists]]&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;*Note: In older versions of IFs, Regions were used as intermediaries between Countries and Groups. In the future, they, or some similarly named unit, will be a sub-unit of Countries. Regions, acting as a sub-unit of Countries, are currently not a feature of IFs. See the image located at the bottom of this Help topic.&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
When using IFs, there are many occasions where the user is asked whether or not they would like to display their results as a product of single countries, or larger groups. This is typically a toggle switch that moves between Country/Region and Groups, however, it might be a three-way-toggle that includes Country/Region, Group and Glist.&lt;br /&gt;
&lt;br /&gt;
Countries/Regions are currently the smallest geographical unit that users can represent. The ability to split countries down into smaller regions, or states, is under development. There are 186 different countries/regions that users can display.&lt;br /&gt;
&lt;br /&gt;
Groups are variably organized geographically or by memberships in international institutions/regimes. You can find out who is represented in each group and add or delete members by exploring the [[Extended_Features#Manage_Groups/Regions|Managing Regionalization]] function.&lt;br /&gt;
&lt;br /&gt;
Glists merge both Groups and Countries/Regions. These lists are mostly geographically bound. In the future, the Glist distinction will become more important as some users may want to place, for example, both the Indian state of Kerala in a Glist with Sri Lanka and Nepal.&lt;br /&gt;
&lt;br /&gt;
Users may also want to [[Extended_Features#Change_Grouping/Regionalization|create]] their own groups or [[Extended_Features#Identify_Groups_or_Country/Region_Members|explore]] what countries are members of what groups.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Time Horizon&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Future Forecasts.&#039;&#039;&#039; IFs begins computation with data from 2000 and can dynamically calculate values for all variables annually through 2100.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Historical Analysis and &amp;quot;Forecasts.&amp;quot;&#039;&#039;&#039; IFs also includes an extensive and growing historical data base starting in 1960. The data basis allows analysis of relationships among variables across countries and across time.&lt;br /&gt;
&lt;br /&gt;
=  =&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Feedback&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
Feedback on how to improve IFs is always appreciated, especially if you find something that is not working. Compliments are also accepted. Please contact. To send the IFs team an e-mail, click on&amp;amp;nbsp;[mailto:pardee.center@du.edu Pardee Center]&amp;amp;nbsp;in stand-alone versions or on the web.&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8349</id>
		<title>Introduction to IFs</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8349"/>
		<updated>2017-09-07T22:44:43Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Purposes&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;International Futures (IFs) is a tool for thinking about long-term global trends and planning more strategically for the&amp;amp;nbsp;future.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
IFs can help you:&lt;br /&gt;
&lt;br /&gt;
*Understand&amp;amp;nbsp;the state of major&amp;amp;nbsp;global systems&lt;br /&gt;
*Explore&amp;amp;nbsp;long-term trends and consider where they might take&amp;amp;nbsp;us&lt;br /&gt;
*Learn about the dynamic&amp;amp;nbsp;interactions between global systems&lt;br /&gt;
*Clarify long-term organizational goals/priorities&lt;br /&gt;
*Develop alternative scenarios (if-then statements) about the future&lt;br /&gt;
*Investigate how different groups (households, firms or governments) can shape the future&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;IFs development and analysis depend&amp;amp;nbsp;on core, underlying assumptions, including the following.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*Global issues are becoming&amp;amp;nbsp;more significant as the scope of human interaction and human impact on the broader environment grow.&lt;br /&gt;
*Goals and priorities for human systems are becoming clearer and are more frequently and consistently communicated.&lt;br /&gt;
*Understanding of the dynamics of human systems is improving&amp;amp;nbsp;rapidly.&lt;br /&gt;
*The domain of human choice and action is broadening.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;What issues can you&amp;amp;nbsp;investigate with IFs?&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*[[Environment]]: Atmospheric carbon dioxide levels, world forest area, fossil fuel usage&lt;br /&gt;
*[[Socio-Political|Socio-Political Change]]: Life expectancy, literacy rate, democracy level, status of women, value change&lt;br /&gt;
*[[Population|Demographics]]: Population levels and growth, fertility, mortality, migration&lt;br /&gt;
*[[Agriculture|Food and Agriculture]]: Land use and production levels, calorie availability, malnutrition rates&lt;br /&gt;
*[[Energy]]: Resource and production levels, demand patterns, renewable energy share&lt;br /&gt;
*[[Economics]]: Sectoral production, consumption, and trade patterns and structural change&lt;br /&gt;
*[[Interstate_Politics_(IP)|Geopolitics]]: Country and regional power levels&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Visual Representation&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
[[File:IFsOverviewChart.jpg|frame|center|Visual representation of IFs structure]]&lt;br /&gt;
&lt;br /&gt;
Among the philosophical premises of the International Futures (IFs) project is that the model cannot be a &amp;quot;black box&amp;quot; to users and be truly useful. Model users must be able to examine the structures of IFs in order (1) to have confidence in them, and (2) learn from them.&lt;br /&gt;
&lt;br /&gt;
There is available (see topics under Understanding the Mode in the contents of this help system):&lt;br /&gt;
&lt;br /&gt;
*[[Understand_IFs#Dominant_Relations|Dominant Relations]] of the model structure&lt;br /&gt;
*[[Understand_IFs#2.2|Structure-Based and Agent-Class Driven Modeling]]&lt;br /&gt;
*[[Understand_IFs#Equation_Notation|Equation Notation]]&lt;br /&gt;
*[[Introduction_to_IFs#IFs_Bibliography|IFs Bibliography]] of data and data sources&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Quick Survey&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;population&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents 22 age-sex cohorts to age 100+&lt;br /&gt;
*calculates change in fertility and mortality rates in response to income, income distribution, and analysis multipliers&lt;br /&gt;
*computes average life expectancy at birth, literacy rate, and overall measures of human development (HDI) and physical quality of life&lt;br /&gt;
*represents migration and HIV/AIDS&lt;br /&gt;
*includes a newly developing submodel of formal education across primary, secondary, and tertiary levels&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;economic&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents the economy in six sectors: agriculture, materials, energy, industry, services, and ICT (other sectors could be configured, using raw data from the GTAP project)&lt;br /&gt;
*computes and uses input-output matrices that change dynamically with development level&lt;br /&gt;
*is a general equilibrium-seeking model that does not assume exact equilibrium will exist in any given year; rather it uses inventories as buffer stocks and to provide price signals so that the model chases equilibrium over time&lt;br /&gt;
*contains an endogenous production function that represents contributions to growth in multifactor productivity from R&amp;amp;D, education, worker health, economic policies (&amp;quot;freedom&amp;quot;), and energy prices (the &amp;quot;quality&amp;quot; of capital)&lt;br /&gt;
*uses a Linear Expenditure System to represent changing consumption patterns&lt;br /&gt;
*utilizes a &amp;quot;pooled&amp;quot; rather than the bilateral trade approach for international trade&lt;br /&gt;
*is being imbedded during 2002 in a social accounting matrix (SAM) envelope that will tie economic production and consumption to intra-actor financial flows&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;agricultural&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents production, consumption and trade of crops and meat; it also carries ocean fish catch and aquaculture in less detail&lt;br /&gt;
*maintains land use in crop, grazing, forest, urban, and &amp;quot;other&amp;quot; categories&lt;br /&gt;
*represents demand for food, for livestock feed, and for industrial use of agricultural products&lt;br /&gt;
*is a partial equilibrium model in which food stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the agricultural sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;energy&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*portrays production of six energy types: oil, gas, coal, nuclear, hydroelectric, and other renewable&lt;br /&gt;
*represents consumption and trade of energy in the aggregate&lt;br /&gt;
*represents known reserves and ultimate resources of the fossil fuels&lt;br /&gt;
*portrays changing capital costs of each energy type with technological change as well as with draw-downs of resources&lt;br /&gt;
*is a partial equilibrium model in which energy stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the energy sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The two &#039;&#039;&#039;socio-political&#039;&#039;&#039; sub-modules:&lt;br /&gt;
&lt;br /&gt;
Within countries or geographic groupings&lt;br /&gt;
&lt;br /&gt;
*represents fiscal policy through taxing and spending decisions&lt;br /&gt;
*shows six categories of government spending: military, health, education, R&amp;amp;D, foreign aid, and a residual category&lt;br /&gt;
*represents changes in social conditions of individuals (like fertility rates or literacy levels), attitudes of individuals (such as the level of materialism/postmaterialism of a society from the World Value Survey), and the social organization of people (such as the status of women)&lt;br /&gt;
*represents the evolution of democracy&lt;br /&gt;
*represents the prospects for state instability or failure&lt;br /&gt;
&lt;br /&gt;
Between countries or groupings of countries&lt;br /&gt;
&lt;br /&gt;
*traces changes in power balances across states and regions&lt;br /&gt;
*allows exploration of changes in the level of interstate threat&lt;br /&gt;
*represents possible action-reaction processes and arms races with associated potential for conflict among countries&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;environmental&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows tracking of remaining resources of fossil fuels, of the area of forested land, of water usage, and of atmospheric carbon dioxide emissions&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;technology&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows changes in assumptions about rates of technological advance in agriculture, energy, and the broader economy&lt;br /&gt;
*explicitly represents the extent of electronic networking of individuals in societies&lt;br /&gt;
*is tied to the governmental spending model with respect to R&amp;amp;D spending&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Background&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
International Futures (IFs) has evolved since 1980 through three &amp;quot;generations,&amp;quot; with a fourth generation now taking form.&lt;br /&gt;
&lt;br /&gt;
The first generation had deep roots in the world models of the 1970s, including those of the Club of Rome. In particular, IFs drew on the Mesarovic-Pestel or World Integrated Model (Mesarovic and Pestel 1974). The author of IFs had contributed to that project, including the construction of the energy submodel. IFs consciously also drew on the Leontief World Model (Leontief et al. 1977), the Bariloche Foundation’s world model (Herrera et al. 1976), and Systems Analysis Research Unit Model (SARU 1977), following comparative analysis of those models by Hughes (1980). That generation was written in FORTRAN and available for use on main-frame computers through CONDUIT, an educational software distribution center at the University of Iowa. Although the primary use of that and subsequent generations was by students, IFs has always had some policy analysis capability that has appealed to specialists. For example, the U.S. Foreign Service Institute used the first generation of IFs in a mid-career training program.&lt;br /&gt;
&lt;br /&gt;
The second generation of International Futures moved to early microcomputers in 1985, using the DOS platform. It was a very simplified version of the original IFs without regional or country differentiation.&lt;br /&gt;
&lt;br /&gt;
The third generation, first available in 1993, became a full-scale microcomputer model. The third generation improved earlier representations of demographic, energy, and food systems, but added new environmental and socio-political content. It built upon the collaboration of the author with the GLOBUS project, and it adopted the economic submodel of GLOBUS (developed by the author). GLOBUS had been created with the inspiration of Karl Deutsch and under the leadership of Stuart Bremer (1987) at the Wissenschaftszentrum in Berlin.&lt;br /&gt;
&lt;br /&gt;
The third generation has produced three editions/major releases of IFs, each accompanied by a book also called International Futures (Hughes 1993, 1996, 1999). The second edition moved to a Visual Basic platform that allowed a much improved menu-driven interface, running under Windows. The third edition incorporated an early global mapping capability and an initial ability to do cross-sectional and longitudinal data analysis.&lt;br /&gt;
&lt;br /&gt;
The fourth generation has been taking shape since early 2000. It has been heavily influenced by the usage of the model in an increasingly policy-analysis mode by several important organizations. First, General Motors commissioned a specialized version of IFs named CoVaTrA (Consumer Values Trends Analysis) with a need for updated and extended demographic modeling and representation of value change. An alliance was established with the World Values Survey, directed by Ronald Inglehart, to create that version. Second, the Strategic Assessments Group of the Central Intelligence Agency commissioned a specialized version named IFs for SAG. The work involved in preparing that greatly extended and enhanced the socio-political representations of the model, both domestic and international. Third, the European Commission sponsored a project named TERRA which has led to a specialized version named IFs for TERRA. IFs for TERRA work led to enhancements across the model, including improved representation of economic sectors, updated IO matrices and a basic Social Accounting Matrix, GINI and Lorenz curves, and preparing for extended environmental impact representation (drawing upon the Advanced Sustainability Analysis framework of the Finland Futures Research Center).&lt;br /&gt;
&lt;br /&gt;
Throughout this emergence of a fourth generation IFs (incorporating all of the above elements for additional users) there has been also a heavy emphasis on enhanced usability. Ideas from Robert Pestel in the TERRA project led to the creation of a new tree-structure for scenario creation and management. Ideas from Ronald Inglehart led to the development of the Guided Use structure and a somewhat more game-like character within that structure. Inglehart also help arrange funding to support the programming of Guided Use through the European Union Center of the University of Michigan.&lt;br /&gt;
&lt;br /&gt;
The fifth version of IFs is currently in use and represents broad strides to improving the model and its usability. It is the first version of this software to be placed online due to the help of the National Intelligence Council ([http://www.ifs.du.edu http://www.ifs.du.edu]). Also, usability has been increased as Packaged Displays and Flex Packaged Displays were introduced that allowed for the creation of very specific lists of countries/regions, groups or Glists. A new education model has also been incorporated into the broader IFs model. New scenarios were created for UNEP (focusing on environmental change) and Pardee (focusing on poverty). Finally, one of the largest changes made was incorporating 182 countries into the Base-Case scenario used by IFs. Previous versions of IFs used broader regions to forecast global trends. This change also did away with the Student and Professional versions.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Geographic Representation of the World&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
186 countries underpin the functioning of IFs and these countries can be displayed separately or as parts of larger groups that users can determine.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;Below is a visual representation of how different entities are organized into Countries/Regions, Groups or Glists:&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
[[File:Geo 186.png|frame|right|Visual representation of IF&#039;s definition of regions/countries/groups/glists]]&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;*Note: In older versions of IFs, Regions were used as intermediaries between Countries and Groups. In the future, they, or some similarly named unit, will be a sub-unit of Countries. Regions, acting as a sub-unit of Countries, are currently not a feature of IFs. See the image located at the bottom of this Help topic.&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
When using IFs, there are many occasions where the user is asked whether or not they would like to display their results as a product of single countries, or larger groups. This is typically a toggle switch that moves between Country/Region and Groups, however, it might be a three-way-toggle that includes Country/Region, Group and Glist.&lt;br /&gt;
&lt;br /&gt;
Countries/Regions are currently the smallest geographical unit that users can represent. The ability to split countries down into smaller regions, or states, is under development. There are 186 different countries/regions that users can display.&lt;br /&gt;
&lt;br /&gt;
Groups are variably organized geographically or by memberships in international institutions/regimes. You can find out who is represented in each group and add or delete members by exploring the [[Extended_Features#Manage_Groups/Regions|Managing Regionalization]] function.&lt;br /&gt;
&lt;br /&gt;
Glists merge both Groups and Countries/Regions. These lists are mostly geographically bound. In the future, the Glist distinction will become more important as some users may want to place, for example, both the Indian state of Kerala in a Glist with Sri Lanka and Nepal.&lt;br /&gt;
&lt;br /&gt;
Users may also want to [[Extended_Features#Change_Grouping/Regionalization|create]] their own groups or [[Extended_Features#Identify_Groups_or_Country/Region_Members|explore]] what countries are members of what groups.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Time Horizon&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Future Forecasts.&#039;&#039;&#039; IFs begins computation with data from 2000 and can dynamically calculate values for all variables annually through 2100.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Historical Analysis and &amp;quot;Forecasts.&amp;quot;&#039;&#039;&#039; IFs also includes an extensive and growing historical data base starting in 1960. The data basis allows analysis of relationships among variables across countries and across time.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Acknowledgements&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The author gratefully recognizes critical contributions in the forms of:&lt;br /&gt;
&lt;br /&gt;
:1. Testing and suggestions for development of IFs in one or more of multiple generations. By Donald Borock, Richard Chadwick, William Dixon, Dale Rothman, Phil Schrodt, Douglas Stuart, Donald Sylvan, Jonathan Wilkenfeld, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
:2. Computer assistance across many releases. By Michael Niemann, Terrance Peet-Lukes, Douglas McClure, Mohammod Irfan, and Jose Solorzano.&lt;br /&gt;
&lt;br /&gt;
:3. Data gathering and general assistance. By James Chung, Padma Padula, Shannon Brady, David Horan, Michael Ferrier, Kay Drucker, Warren Christopher, and Anwar Hossain.&lt;br /&gt;
&lt;br /&gt;
:4. Long-term encouragement and support. By Harold Guetzkow, Karl Deutsch, Richard Chadwick, Gerald Barney, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
:5. Association in related world modeling projects and projects building upon IFs. By Mihajlo Mesarovic, Aldo Barsotti, Juan Huerta, John Richardson, Thomas Shook, Patricia Strauch, and other members of the World Integrated Model (WIM) team. By Stuart Bremer, Peter Brecke, Thomas Cusack, Wolf Dieter-Eberwein, Brian Pollins, and Dale Smith of the GLOBUS modeling project. By Evan Hillebrand, Paul Herman, and others of the IFs for SAG project. By Rob Lempert and Steve Bankes at RAND, Santa Monica. By Robert Pestel, Jonathan Cave, Ronald Inglehart, Sergei Parinov, Pentti Malaska, and many others in the IFs for TERRA project.&lt;br /&gt;
&lt;br /&gt;
:6. Financial assistance (without responsibility for the form of the evolving product). By the National Science Foundation, the Cleveland Foundation, the Exxon Education Foundation, the Kettering Family Foundation, the Pacific Cultural Foundation, the United States Institute of Peace, General Motors, the Strategic Assessments Group of the Central Intelligence Agency, the European Commission (Information Society Technology) Programme, the European Union Center of the University of Michigan, the National Intelligence Council (for web conversion), and Frederick S. Pardee. &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Feedback&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
Feedback on how to improve IFs is always appreciated, especially if you find something that is not working. Compliments are also accepted. Please contact. To send the IFs team an e-mail, click on&amp;amp;nbsp;[mailto:pardee.center@du.edu Pardee Center]&amp;amp;nbsp;in stand-alone versions or on the web.&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8348</id>
		<title>Introduction to IFs</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8348"/>
		<updated>2017-09-07T22:44:22Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Purposes&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;International Futures (IFs) is a tool for thinking about long-term global trends and planning more strategically for the&amp;amp;nbsp;future.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
IFs can help you:&lt;br /&gt;
&lt;br /&gt;
*Understand&amp;amp;nbsp;the state of major&amp;amp;nbsp;global systems&lt;br /&gt;
*Explore&amp;amp;nbsp;long-term trends and consider where they might take&amp;amp;nbsp;us&lt;br /&gt;
*Learn about the dynamic&amp;amp;nbsp;interactions between global systems&lt;br /&gt;
*Clarify long-term organizational goals/priorities&lt;br /&gt;
*Develop alternative scenarios (if-then statements) about the future&lt;br /&gt;
*Investigate how different groups (households, firms or governments) can shape the future&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;IFs development and analysis depend&amp;amp;nbsp;on core, underlying assumptions, including the following.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*Global issues are becoming&amp;amp;nbsp;more significant as the scope of human interaction and human impact on the broader environment grow.&lt;br /&gt;
*Goals and priorities for human systems are becoming clearer and are more frequently and consistently communicated.&lt;br /&gt;
*Understanding of the dynamics of human systems is improving&amp;amp;nbsp;rapidly.&lt;br /&gt;
*The domain of human choice and action is broadening.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;What issues can you&amp;amp;nbsp;investigate with IFs?&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*[[Environment]]: Atmospheric carbon dioxide levels, world forest area, fossil fuel usage&lt;br /&gt;
*[[Socio-Political|Socio-Political Change]]: Life expectancy, literacy rate, democracy level, status of women, value change&lt;br /&gt;
*[[Population|Demographics]]: Population levels and growth, fertility, mortality, migration&lt;br /&gt;
*[[Agriculture|Food and Agriculture]]: Land use and production levels, calorie availability, malnutrition rates&lt;br /&gt;
*[[Energy]]: Resource and production levels, demand patterns, renewable energy share&lt;br /&gt;
*[[Economics]]: Sectoral production, consumption, and trade patterns and structural change&lt;br /&gt;
*[[Interstate_Politics_(IP)|Geopolitics]]: Country and regional power levels&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Visual Representation&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
[[File:IFsOverviewChart.jpg|frame|center|Visual representation of IFs structure]]&lt;br /&gt;
&lt;br /&gt;
Among the philosophical premises of the International Futures (IFs) project is that the model cannot be a &amp;quot;black box&amp;quot; to users and be truly useful. Model users must be able to examine the structures of IFs in order (1) to have confidence in them, and (2) learn from them.&lt;br /&gt;
&lt;br /&gt;
There is available (see topics under Understanding the Mode in the contents of this help system):&lt;br /&gt;
&lt;br /&gt;
*[[Understand_IFs#Dominant_Relations|Dominant Relations]] of the model structure&lt;br /&gt;
*[[Understand_IFs#2.2|Structure-Based and Agent-Class Driven Modeling]]&lt;br /&gt;
*[[Understand_IFs#Equation_Notation|Equation Notation]]&lt;br /&gt;
*[[Introduction_to_IFs#IFs_Bibliography|IFs Bibliography]] of data and data sources&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Quick Survey&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;population&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents 22 age-sex cohorts to age 100+&lt;br /&gt;
*calculates change in fertility and mortality rates in response to income, income distribution, and analysis multipliers&lt;br /&gt;
*computes average life expectancy at birth, literacy rate, and overall measures of human development (HDI) and physical quality of life&lt;br /&gt;
*represents migration and HIV/AIDS&lt;br /&gt;
*includes a newly developing submodel of formal education across primary, secondary, and tertiary levels&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;economic&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents the economy in six sectors: agriculture, materials, energy, industry, services, and ICT (other sectors could be configured, using raw data from the GTAP project)&lt;br /&gt;
*computes and uses input-output matrices that change dynamically with development level&lt;br /&gt;
*is a general equilibrium-seeking model that does not assume exact equilibrium will exist in any given year; rather it uses inventories as buffer stocks and to provide price signals so that the model chases equilibrium over time&lt;br /&gt;
*contains an endogenous production function that represents contributions to growth in multifactor productivity from R&amp;amp;D, education, worker health, economic policies (&amp;quot;freedom&amp;quot;), and energy prices (the &amp;quot;quality&amp;quot; of capital)&lt;br /&gt;
*uses a Linear Expenditure System to represent changing consumption patterns&lt;br /&gt;
*utilizes a &amp;quot;pooled&amp;quot; rather than the bilateral trade approach for international trade&lt;br /&gt;
*is being imbedded during 2002 in a social accounting matrix (SAM) envelope that will tie economic production and consumption to intra-actor financial flows&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;agricultural&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents production, consumption and trade of crops and meat; it also carries ocean fish catch and aquaculture in less detail&lt;br /&gt;
*maintains land use in crop, grazing, forest, urban, and &amp;quot;other&amp;quot; categories&lt;br /&gt;
*represents demand for food, for livestock feed, and for industrial use of agricultural products&lt;br /&gt;
*is a partial equilibrium model in which food stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the agricultural sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;energy&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*portrays production of six energy types: oil, gas, coal, nuclear, hydroelectric, and other renewable&lt;br /&gt;
*represents consumption and trade of energy in the aggregate&lt;br /&gt;
*represents known reserves and ultimate resources of the fossil fuels&lt;br /&gt;
*portrays changing capital costs of each energy type with technological change as well as with draw-downs of resources&lt;br /&gt;
*is a partial equilibrium model in which energy stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the energy sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The two &#039;&#039;&#039;socio-political&#039;&#039;&#039; sub-modules:&lt;br /&gt;
&lt;br /&gt;
Within countries or geographic groupings&lt;br /&gt;
&lt;br /&gt;
*represents fiscal policy through taxing and spending decisions&lt;br /&gt;
*shows six categories of government spending: military, health, education, R&amp;amp;D, foreign aid, and a residual category&lt;br /&gt;
*represents changes in social conditions of individuals (like fertility rates or literacy levels), attitudes of individuals (such as the level of materialism/postmaterialism of a society from the World Value Survey), and the social organization of people (such as the status of women)&lt;br /&gt;
*represents the evolution of democracy&lt;br /&gt;
*represents the prospects for state instability or failure&lt;br /&gt;
&lt;br /&gt;
Between countries or groupings of countries&lt;br /&gt;
&lt;br /&gt;
*traces changes in power balances across states and regions&lt;br /&gt;
*allows exploration of changes in the level of interstate threat&lt;br /&gt;
*represents possible action-reaction processes and arms races with associated potential for conflict among countries&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;environmental&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows tracking of remaining resources of fossil fuels, of the area of forested land, of water usage, and of atmospheric carbon dioxide emissions&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;technology&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows changes in assumptions about rates of technological advance in agriculture, energy, and the broader economy&lt;br /&gt;
*explicitly represents the extent of electronic networking of individuals in societies&lt;br /&gt;
*is tied to the governmental spending model with respect to R&amp;amp;D spending&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Background&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
International Futures (IFs) has evolved since 1980 through three &amp;quot;generations,&amp;quot; with a fourth generation now taking form.&lt;br /&gt;
&lt;br /&gt;
The first generation had deep roots in the world models of the 1970s, including those of the Club of Rome. In particular, IFs drew on the Mesarovic-Pestel or World Integrated Model (Mesarovic and Pestel 1974). The author of IFs had contributed to that project, including the construction of the energy submodel. IFs consciously also drew on the Leontief World Model (Leontief et al. 1977), the Bariloche Foundation’s world model (Herrera et al. 1976), and Systems Analysis Research Unit Model (SARU 1977), following comparative analysis of those models by Hughes (1980). That generation was written in FORTRAN and available for use on main-frame computers through CONDUIT, an educational software distribution center at the University of Iowa. Although the primary use of that and subsequent generations was by students, IFs has always had some policy analysis capability that has appealed to specialists. For example, the U.S. Foreign Service Institute used the first generation of IFs in a mid-career training program.&lt;br /&gt;
&lt;br /&gt;
The second generation of International Futures moved to early microcomputers in 1985, using the DOS platform. It was a very simplified version of the original IFs without regional or country differentiation.&lt;br /&gt;
&lt;br /&gt;
The third generation, first available in 1993, became a full-scale microcomputer model. The third generation improved earlier representations of demographic, energy, and food systems, but added new environmental and socio-political content. It built upon the collaboration of the author with the GLOBUS project, and it adopted the economic submodel of GLOBUS (developed by the author). GLOBUS had been created with the inspiration of Karl Deutsch and under the leadership of Stuart Bremer (1987) at the Wissenschaftszentrum in Berlin.&lt;br /&gt;
&lt;br /&gt;
The third generation has produced three editions/major releases of IFs, each accompanied by a book also called International Futures (Hughes 1993, 1996, 1999). The second edition moved to a Visual Basic platform that allowed a much improved menu-driven interface, running under Windows. The third edition incorporated an early global mapping capability and an initial ability to do cross-sectional and longitudinal data analysis.&lt;br /&gt;
&lt;br /&gt;
The fourth generation has been taking shape since early 2000. It has been heavily influenced by the usage of the model in an increasingly policy-analysis mode by several important organizations. First, General Motors commissioned a specialized version of IFs named CoVaTrA (Consumer Values Trends Analysis) with a need for updated and extended demographic modeling and representation of value change. An alliance was established with the World Values Survey, directed by Ronald Inglehart, to create that version. Second, the Strategic Assessments Group of the Central Intelligence Agency commissioned a specialized version named IFs for SAG. The work involved in preparing that greatly extended and enhanced the socio-political representations of the model, both domestic and international. Third, the European Commission sponsored a project named TERRA which has led to a specialized version named IFs for TERRA. IFs for TERRA work led to enhancements across the model, including improved representation of economic sectors, updated IO matrices and a basic Social Accounting Matrix, GINI and Lorenz curves, and preparing for extended environmental impact representation (drawing upon the Advanced Sustainability Analysis framework of the Finland Futures Research Center).&lt;br /&gt;
&lt;br /&gt;
Throughout this emergence of a fourth generation IFs (incorporating all of the above elements for additional users) there has been also a heavy emphasis on enhanced usability. Ideas from Robert Pestel in the TERRA project led to the creation of a new tree-structure for scenario creation and management. Ideas from Ronald Inglehart led to the development of the Guided Use structure and a somewhat more game-like character within that structure. Inglehart also help arrange funding to support the programming of Guided Use through the European Union Center of the University of Michigan.&lt;br /&gt;
&lt;br /&gt;
The fifth version of IFs is currently in use and represents broad strides to improving the model and its usability. It is the first version of this software to be placed online due to the help of the National Intelligence Council ([http://www.ifs.du.edu http://www.ifs.du.edu]). Also, usability has been increased as Packaged Displays and Flex Packaged Displays were introduced that allowed for the creation of very specific lists of countries/regions, groups or Glists. A new education model has also been incorporated into the broader IFs model. New scenarios were created for UNEP (focusing on environmental change) and Pardee (focusing on poverty). Finally, one of the largest changes made was incorporating 182 countries into the Base-Case scenario used by IFs. Previous versions of IFs used broader regions to forecast global trends. This change also did away with the Student and Professional versions.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Geographic Representation of the World&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
186 countries underpin the functioning of IFs and these countries can be displayed separately or as parts of larger groups that users can determine.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;Below is a visual representation of how different entities are organized into Countries/Regions, Groups or Glists:&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
[[File:Geo 186.png|frame|right|Visual representation of IF&#039;s definition of regions/countries/groups/glists]]&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;*Note: In older versions of IFs, Regions were used as intermediaries between Countries and Groups. In the future, they, or some similarly named unit, will be a sub-unit of Countries. Regions, acting as a sub-unit of Countries, are currently not a feature of IFs. See the image located at the bottom of this Help topic.&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
When using IFs, there are many occasions where the user is asked whether or not they would like to display their results as a product of single countries, or larger groups. This is typically a toggle switch that moves between Country/Region and Groups, however, it might be a three-way-toggle that includes Country/Region, Group and Glist.&lt;br /&gt;
&lt;br /&gt;
Countries/Regions are currently the smallest geographical unit that users can represent. The ability to split countries down into smaller regions, or states, is under development. There are 186 different countries/regions that users can display.&lt;br /&gt;
&lt;br /&gt;
Groups are variably organized geographically or by memberships in international institutions/regimes. You can find out who is represented in each group and add or delete members by exploring the [[Extended_Features#Manage_Groups/Regions|Managing Regionalization]] function.&lt;br /&gt;
&lt;br /&gt;
Glists merge both Groups and Countries/Regions. These lists are mostly geographically bound. In the future, the Glist distinction will become more important as some users may want to place, for example, both the Indian state of Kerala in a Glist with Sri Lanka and Nepal.&lt;br /&gt;
&lt;br /&gt;
Users may also want to [[Extended_Features#Change_Grouping/Regionalization|create]] their own groups or [[Extended_Features#Identify_Groups_or_Country/Region_Members|explore]] what countries are members of what groups.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Time Horizon&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Future Forecasts.&#039;&#039;&#039; IFs begins computation with data from 2000 and can dynamically calculate values for all variables annually through 2100.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Historical Analysis and &amp;quot;Forecasts.&amp;quot;&#039;&#039;&#039; IFs also includes an extensive and growing historical data base starting in 1960. The data basis allows analysis of relationships among variables across countries and across time.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
==  ==&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Acknowledgements&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The author gratefully recognizes critical contributions in the forms of:&lt;br /&gt;
&lt;br /&gt;
:1. Testing and suggestions for development of IFs in one or more of multiple generations. By Donald Borock, Richard Chadwick, William Dixon, Dale Rothman, Phil Schrodt, Douglas Stuart, Donald Sylvan, Jonathan Wilkenfeld, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
:2. Computer assistance across many releases. By Michael Niemann, Terrance Peet-Lukes, Douglas McClure, Mohammod Irfan, and Jose Solorzano.&lt;br /&gt;
&lt;br /&gt;
:3. Data gathering and general assistance. By James Chung, Padma Padula, Shannon Brady, David Horan, Michael Ferrier, Kay Drucker, Warren Christopher, and Anwar Hossain.&lt;br /&gt;
&lt;br /&gt;
:4. Long-term encouragement and support. By Harold Guetzkow, Karl Deutsch, Richard Chadwick, Gerald Barney, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
:5. Association in related world modeling projects and projects building upon IFs. By Mihajlo Mesarovic, Aldo Barsotti, Juan Huerta, John Richardson, Thomas Shook, Patricia Strauch, and other members of the World Integrated Model (WIM) team. By Stuart Bremer, Peter Brecke, Thomas Cusack, Wolf Dieter-Eberwein, Brian Pollins, and Dale Smith of the GLOBUS modeling project. By Evan Hillebrand, Paul Herman, and others of the IFs for SAG project. By Rob Lempert and Steve Bankes at RAND, Santa Monica. By Robert Pestel, Jonathan Cave, Ronald Inglehart, Sergei Parinov, Pentti Malaska, and many others in the IFs for TERRA project.&lt;br /&gt;
&lt;br /&gt;
:6. Financial assistance (without responsibility for the form of the evolving product). By the National Science Foundation, the Cleveland Foundation, the Exxon Education Foundation, the Kettering Family Foundation, the Pacific Cultural Foundation, the United States Institute of Peace, General Motors, the Strategic Assessments Group of the Central Intelligence Agency, the European Commission (Information Society Technology) Programme, the European Union Center of the University of Michigan, the National Intelligence Council (for web conversion), and Frederick S. Pardee. &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Feedback&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
Feedback on how to improve IFs is always appreciated, especially if you find something that is not working. Compliments are also accepted. Please contact. To send the IFs team an e-mail, click on&amp;amp;nbsp;[mailto:pardee.center@du.edu Pardee Center]&amp;amp;nbsp;in stand-alone versions or on the web.&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Additional_resources&amp;diff=8347</id>
		<title>Additional resources</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Additional_resources&amp;diff=8347"/>
		<updated>2017-09-07T22:43:55Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;[[Data|Data]]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Model_validation_and_verification Model&amp;amp;nbsp;validation&amp;amp;nbsp;and&amp;amp;nbsp;verification]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Consolidation Consolidation]&lt;br /&gt;
&lt;br /&gt;
[[Preprocessor|Preprocessor]]&lt;br /&gt;
&lt;br /&gt;
[[Sub-modules|Sub-modules]]&lt;br /&gt;
&lt;br /&gt;
[[SubRegionalization_Handbook|Sub-Regionalization Handbook]]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Version_notes Version notes]&lt;br /&gt;
&lt;br /&gt;
[[IFs_Bibliography|IFs Bibliography]]&lt;br /&gt;
&lt;br /&gt;
[[Development_Mode_Features|Development Mode Features]]&lt;br /&gt;
&lt;br /&gt;
[[Publications_on_IFs|Publications on IFs]]&lt;br /&gt;
&lt;br /&gt;
[[Instructional_Use_of_IFs_(For_Classrooms)|IFs for the Classroom]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== In progress[[http://pardee.du.edu/wiki/index.php?title=International_Futures_(IFs)&amp;amp;action=edit&amp;amp;section=1 edit]] ===&lt;br /&gt;
&lt;br /&gt;
[[Sandbox|Sandbox]]&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Instructional_Use_of_IFs_(For_Classrooms)&amp;diff=8345</id>
		<title>Instructional Use of IFs (For Classrooms)</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Instructional_Use_of_IFs_(For_Classrooms)&amp;diff=8345"/>
		<updated>2017-09-07T22:41:38Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: Created page with &amp;quot;The standard modes for using IFs in a classroom are:  1. Assigning class members to an issue area or topic. Consider identifying specific questions for them to address.  2. As...&amp;quot;&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;The standard modes for using IFs in a classroom are:&lt;br /&gt;
&lt;br /&gt;
1. Assigning class members to an issue area or topic. Consider identifying specific questions for them to address.&lt;br /&gt;
&lt;br /&gt;
2. Assigning class members to a country/geographic region. Again, specificity helps.&lt;br /&gt;
&lt;br /&gt;
Most often, students will work independently or in groups on projects and share information after completing them. It is possible, however, to have students work interactively, by assigning them topics or regions, letting them begin work, and then have the interacting groups (or individuals) create a collective model run with the changes that each group proposes by topic or region. That process, although more difficult to organize, allows the class as whole to investigate the interaction of their topics or regions (and to share learning about model use).&lt;br /&gt;
&lt;br /&gt;
There is a&amp;amp;nbsp;[http://portfolio.du.edu/bhughes web site]&amp;amp;nbsp;available in support of the educational use of IFs. You will find syllabi at that site. There are several [[Introduction_to_IFs#Publications_on_IFs|publications]] on IFs, including a book structured specifically for educational use.&lt;br /&gt;
&lt;br /&gt;
Donald Borock has described his classroom use of IFs in print. Borock, Donald. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot; Advances in Social Science and Computers 4, 103-127.&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Additional_resources&amp;diff=8344</id>
		<title>Additional resources</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Additional_resources&amp;diff=8344"/>
		<updated>2017-09-07T22:41:19Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;[[Data|Data]]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Model_validation_and_verification Model&amp;amp;nbsp;validation&amp;amp;nbsp;and&amp;amp;nbsp;verification]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Consolidation Consolidation]&lt;br /&gt;
&lt;br /&gt;
[[Preprocessor|Preprocessor]]&lt;br /&gt;
&lt;br /&gt;
[[Sub-modules|Sub-modules]]&lt;br /&gt;
&lt;br /&gt;
[[SubRegionalization_Handbook|Sub-Regionalization Handbook]]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Version_notes Version notes]&lt;br /&gt;
&lt;br /&gt;
[[IFs_Bibliography|IFs Bibliography]]&lt;br /&gt;
&lt;br /&gt;
[[Development_Mode_Features|Development Mode Features]]&lt;br /&gt;
&lt;br /&gt;
[[Publications_on_IFs|Publications on IFs]]&lt;br /&gt;
&lt;br /&gt;
[[Instructional_Use_of_IFs_(For_Classrooms)]]&lt;br /&gt;
&lt;br /&gt;
=== In progress[[http://pardee.du.edu/wiki/index.php?title=International_Futures_(IFs)&amp;amp;action=edit&amp;amp;section=1 edit]] ===&lt;br /&gt;
&lt;br /&gt;
[[Sandbox|Sandbox]]&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8343</id>
		<title>Introduction to IFs</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8343"/>
		<updated>2017-09-07T22:40:16Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Purposes&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;International Futures (IFs) is a tool for thinking about long-term global trends and planning more strategically for the&amp;amp;nbsp;future.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
IFs can help you:&lt;br /&gt;
&lt;br /&gt;
*Understand&amp;amp;nbsp;the state of major&amp;amp;nbsp;global systems&lt;br /&gt;
*Explore&amp;amp;nbsp;long-term trends and consider where they might take&amp;amp;nbsp;us&lt;br /&gt;
*Learn about the dynamic&amp;amp;nbsp;interactions between global systems&lt;br /&gt;
*Clarify long-term organizational goals/priorities&lt;br /&gt;
*Develop alternative scenarios (if-then statements) about the future&lt;br /&gt;
*Investigate how different groups (households, firms or governments) can shape the future&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;IFs development and analysis depend&amp;amp;nbsp;on core, underlying assumptions, including the following.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*Global issues are becoming&amp;amp;nbsp;more significant as the scope of human interaction and human impact on the broader environment grow.&lt;br /&gt;
*Goals and priorities for human systems are becoming clearer and are more frequently and consistently communicated.&lt;br /&gt;
*Understanding of the dynamics of human systems is improving&amp;amp;nbsp;rapidly.&lt;br /&gt;
*The domain of human choice and action is broadening.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;What issues can you&amp;amp;nbsp;investigate with IFs?&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*[[Environment]]: Atmospheric carbon dioxide levels, world forest area, fossil fuel usage&lt;br /&gt;
*[[Socio-Political|Socio-Political Change]]: Life expectancy, literacy rate, democracy level, status of women, value change&lt;br /&gt;
*[[Population|Demographics]]: Population levels and growth, fertility, mortality, migration&lt;br /&gt;
*[[Agriculture|Food and Agriculture]]: Land use and production levels, calorie availability, malnutrition rates&lt;br /&gt;
*[[Energy]]: Resource and production levels, demand patterns, renewable energy share&lt;br /&gt;
*[[Economics]]: Sectoral production, consumption, and trade patterns and structural change&lt;br /&gt;
*[[Interstate_Politics_(IP)|Geopolitics]]: Country and regional power levels&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Visual Representation&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
[[File:IFsOverviewChart.jpg|frame|center|Visual representation of IFs structure]]&lt;br /&gt;
&lt;br /&gt;
Among the philosophical premises of the International Futures (IFs) project is that the model cannot be a &amp;quot;black box&amp;quot; to users and be truly useful. Model users must be able to examine the structures of IFs in order (1) to have confidence in them, and (2) learn from them.&lt;br /&gt;
&lt;br /&gt;
There is available (see topics under Understanding the Mode in the contents of this help system):&lt;br /&gt;
&lt;br /&gt;
*[[Understand_IFs#Dominant_Relations|Dominant Relations]] of the model structure&lt;br /&gt;
*[[Understand_IFs#2.2|Structure-Based and Agent-Class Driven Modeling]]&lt;br /&gt;
*[[Understand_IFs#Equation_Notation|Equation Notation]]&lt;br /&gt;
*[[Introduction_to_IFs#IFs_Bibliography|IFs Bibliography]] of data and data sources&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Quick Survey&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;population&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents 22 age-sex cohorts to age 100+&lt;br /&gt;
*calculates change in fertility and mortality rates in response to income, income distribution, and analysis multipliers&lt;br /&gt;
*computes average life expectancy at birth, literacy rate, and overall measures of human development (HDI) and physical quality of life&lt;br /&gt;
*represents migration and HIV/AIDS&lt;br /&gt;
*includes a newly developing submodel of formal education across primary, secondary, and tertiary levels&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;economic&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents the economy in six sectors: agriculture, materials, energy, industry, services, and ICT (other sectors could be configured, using raw data from the GTAP project)&lt;br /&gt;
*computes and uses input-output matrices that change dynamically with development level&lt;br /&gt;
*is a general equilibrium-seeking model that does not assume exact equilibrium will exist in any given year; rather it uses inventories as buffer stocks and to provide price signals so that the model chases equilibrium over time&lt;br /&gt;
*contains an endogenous production function that represents contributions to growth in multifactor productivity from R&amp;amp;D, education, worker health, economic policies (&amp;quot;freedom&amp;quot;), and energy prices (the &amp;quot;quality&amp;quot; of capital)&lt;br /&gt;
*uses a Linear Expenditure System to represent changing consumption patterns&lt;br /&gt;
*utilizes a &amp;quot;pooled&amp;quot; rather than the bilateral trade approach for international trade&lt;br /&gt;
*is being imbedded during 2002 in a social accounting matrix (SAM) envelope that will tie economic production and consumption to intra-actor financial flows&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;agricultural&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents production, consumption and trade of crops and meat; it also carries ocean fish catch and aquaculture in less detail&lt;br /&gt;
*maintains land use in crop, grazing, forest, urban, and &amp;quot;other&amp;quot; categories&lt;br /&gt;
*represents demand for food, for livestock feed, and for industrial use of agricultural products&lt;br /&gt;
*is a partial equilibrium model in which food stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the agricultural sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;energy&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*portrays production of six energy types: oil, gas, coal, nuclear, hydroelectric, and other renewable&lt;br /&gt;
*represents consumption and trade of energy in the aggregate&lt;br /&gt;
*represents known reserves and ultimate resources of the fossil fuels&lt;br /&gt;
*portrays changing capital costs of each energy type with technological change as well as with draw-downs of resources&lt;br /&gt;
*is a partial equilibrium model in which energy stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the energy sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The two &#039;&#039;&#039;socio-political&#039;&#039;&#039; sub-modules:&lt;br /&gt;
&lt;br /&gt;
Within countries or geographic groupings&lt;br /&gt;
&lt;br /&gt;
*represents fiscal policy through taxing and spending decisions&lt;br /&gt;
*shows six categories of government spending: military, health, education, R&amp;amp;D, foreign aid, and a residual category&lt;br /&gt;
*represents changes in social conditions of individuals (like fertility rates or literacy levels), attitudes of individuals (such as the level of materialism/postmaterialism of a society from the World Value Survey), and the social organization of people (such as the status of women)&lt;br /&gt;
*represents the evolution of democracy&lt;br /&gt;
*represents the prospects for state instability or failure&lt;br /&gt;
&lt;br /&gt;
Between countries or groupings of countries&lt;br /&gt;
&lt;br /&gt;
*traces changes in power balances across states and regions&lt;br /&gt;
*allows exploration of changes in the level of interstate threat&lt;br /&gt;
*represents possible action-reaction processes and arms races with associated potential for conflict among countries&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;environmental&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows tracking of remaining resources of fossil fuels, of the area of forested land, of water usage, and of atmospheric carbon dioxide emissions&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;technology&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows changes in assumptions about rates of technological advance in agriculture, energy, and the broader economy&lt;br /&gt;
*explicitly represents the extent of electronic networking of individuals in societies&lt;br /&gt;
*is tied to the governmental spending model with respect to R&amp;amp;D spending&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Background&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
International Futures (IFs) has evolved since 1980 through three &amp;quot;generations,&amp;quot; with a fourth generation now taking form.&lt;br /&gt;
&lt;br /&gt;
The first generation had deep roots in the world models of the 1970s, including those of the Club of Rome. In particular, IFs drew on the Mesarovic-Pestel or World Integrated Model (Mesarovic and Pestel 1974). The author of IFs had contributed to that project, including the construction of the energy submodel. IFs consciously also drew on the Leontief World Model (Leontief et al. 1977), the Bariloche Foundation’s world model (Herrera et al. 1976), and Systems Analysis Research Unit Model (SARU 1977), following comparative analysis of those models by Hughes (1980). That generation was written in FORTRAN and available for use on main-frame computers through CONDUIT, an educational software distribution center at the University of Iowa. Although the primary use of that and subsequent generations was by students, IFs has always had some policy analysis capability that has appealed to specialists. For example, the U.S. Foreign Service Institute used the first generation of IFs in a mid-career training program.&lt;br /&gt;
&lt;br /&gt;
The second generation of International Futures moved to early microcomputers in 1985, using the DOS platform. It was a very simplified version of the original IFs without regional or country differentiation.&lt;br /&gt;
&lt;br /&gt;
The third generation, first available in 1993, became a full-scale microcomputer model. The third generation improved earlier representations of demographic, energy, and food systems, but added new environmental and socio-political content. It built upon the collaboration of the author with the GLOBUS project, and it adopted the economic submodel of GLOBUS (developed by the author). GLOBUS had been created with the inspiration of Karl Deutsch and under the leadership of Stuart Bremer (1987) at the Wissenschaftszentrum in Berlin.&lt;br /&gt;
&lt;br /&gt;
The third generation has produced three editions/major releases of IFs, each accompanied by a book also called International Futures (Hughes 1993, 1996, 1999). The second edition moved to a Visual Basic platform that allowed a much improved menu-driven interface, running under Windows. The third edition incorporated an early global mapping capability and an initial ability to do cross-sectional and longitudinal data analysis.&lt;br /&gt;
&lt;br /&gt;
The fourth generation has been taking shape since early 2000. It has been heavily influenced by the usage of the model in an increasingly policy-analysis mode by several important organizations. First, General Motors commissioned a specialized version of IFs named CoVaTrA (Consumer Values Trends Analysis) with a need for updated and extended demographic modeling and representation of value change. An alliance was established with the World Values Survey, directed by Ronald Inglehart, to create that version. Second, the Strategic Assessments Group of the Central Intelligence Agency commissioned a specialized version named IFs for SAG. The work involved in preparing that greatly extended and enhanced the socio-political representations of the model, both domestic and international. Third, the European Commission sponsored a project named TERRA which has led to a specialized version named IFs for TERRA. IFs for TERRA work led to enhancements across the model, including improved representation of economic sectors, updated IO matrices and a basic Social Accounting Matrix, GINI and Lorenz curves, and preparing for extended environmental impact representation (drawing upon the Advanced Sustainability Analysis framework of the Finland Futures Research Center).&lt;br /&gt;
&lt;br /&gt;
Throughout this emergence of a fourth generation IFs (incorporating all of the above elements for additional users) there has been also a heavy emphasis on enhanced usability. Ideas from Robert Pestel in the TERRA project led to the creation of a new tree-structure for scenario creation and management. Ideas from Ronald Inglehart led to the development of the Guided Use structure and a somewhat more game-like character within that structure. Inglehart also help arrange funding to support the programming of Guided Use through the European Union Center of the University of Michigan.&lt;br /&gt;
&lt;br /&gt;
The fifth version of IFs is currently in use and represents broad strides to improving the model and its usability. It is the first version of this software to be placed online due to the help of the National Intelligence Council ([http://www.ifs.du.edu http://www.ifs.du.edu]). Also, usability has been increased as Packaged Displays and Flex Packaged Displays were introduced that allowed for the creation of very specific lists of countries/regions, groups or Glists. A new education model has also been incorporated into the broader IFs model. New scenarios were created for UNEP (focusing on environmental change) and Pardee (focusing on poverty). Finally, one of the largest changes made was incorporating 182 countries into the Base-Case scenario used by IFs. Previous versions of IFs used broader regions to forecast global trends. This change also did away with the Student and Professional versions.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Geographic Representation of the World&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
186 countries underpin the functioning of IFs and these countries can be displayed separately or as parts of larger groups that users can determine.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;Below is a visual representation of how different entities are organized into Countries/Regions, Groups or Glists:&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
[[File:Geo 186.png|frame|right|Visual representation of IF&#039;s definition of regions/countries/groups/glists]]&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;*Note: In older versions of IFs, Regions were used as intermediaries between Countries and Groups. In the future, they, or some similarly named unit, will be a sub-unit of Countries. Regions, acting as a sub-unit of Countries, are currently not a feature of IFs. See the image located at the bottom of this Help topic.&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
When using IFs, there are many occasions where the user is asked whether or not they would like to display their results as a product of single countries, or larger groups. This is typically a toggle switch that moves between Country/Region and Groups, however, it might be a three-way-toggle that includes Country/Region, Group and Glist.&lt;br /&gt;
&lt;br /&gt;
Countries/Regions are currently the smallest geographical unit that users can represent. The ability to split countries down into smaller regions, or states, is under development. There are 186 different countries/regions that users can display.&lt;br /&gt;
&lt;br /&gt;
Groups are variably organized geographically or by memberships in international institutions/regimes. You can find out who is represented in each group and add or delete members by exploring the [[Extended_Features#Manage_Groups/Regions|Managing Regionalization]] function.&lt;br /&gt;
&lt;br /&gt;
Glists merge both Groups and Countries/Regions. These lists are mostly geographically bound. In the future, the Glist distinction will become more important as some users may want to place, for example, both the Indian state of Kerala in a Glist with Sri Lanka and Nepal.&lt;br /&gt;
&lt;br /&gt;
Users may also want to [[Extended_Features#Change_Grouping/Regionalization|create]] their own groups or [[Extended_Features#Identify_Groups_or_Country/Region_Members|explore]] what countries are members of what groups.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Time Horizon&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Future Forecasts.&#039;&#039;&#039; IFs begins computation with data from 2000 and can dynamically calculate values for all variables annually through 2100.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Historical Analysis and &amp;quot;Forecasts.&amp;quot;&#039;&#039;&#039; IFs also includes an extensive and growing historical data base starting in 1960. The data basis allows analysis of relationships among variables across countries and across time.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Instructional Use&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The standard modes for using IFs in a classroom are:&lt;br /&gt;
&lt;br /&gt;
1. Assigning class members to an issue area or topic. Consider identifying specific questions for them to address.&lt;br /&gt;
&lt;br /&gt;
2. Assigning class members to a country/geographic region. Again, specificity helps.&lt;br /&gt;
&lt;br /&gt;
Most often, students will work independently or in groups on projects and share information after completing them. It is possible, however, to have students work interactively, by assigning them topics or regions, letting them begin work, and then have the interacting groups (or individuals) create a collective model run with the changes that each group proposes by topic or region. That process, although more difficult to organize, allows the class as whole to investigate the interaction of their topics or regions (and to share learning about model use).&lt;br /&gt;
&lt;br /&gt;
There is a&amp;amp;nbsp;[http://portfolio.du.edu/bhughes web site]&amp;amp;nbsp;available in support of the educational use of IFs. You will find syllabi at that site. There are several [[Introduction_to_IFs#Publications_on_IFs|publications]] on IFs, including a book structured specifically for educational use.&lt;br /&gt;
&lt;br /&gt;
Donald Borock has described his classroom use of IFs in print. Borock, Donald. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot; Advances in Social Science and Computers 4, 103-127.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Acknowledgements&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The author gratefully recognizes critical contributions in the forms of:&lt;br /&gt;
&lt;br /&gt;
:1. Testing and suggestions for development of IFs in one or more of multiple generations. By Donald Borock, Richard Chadwick, William Dixon, Dale Rothman, Phil Schrodt, Douglas Stuart, Donald Sylvan, Jonathan Wilkenfeld, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
:2. Computer assistance across many releases. By Michael Niemann, Terrance Peet-Lukes, Douglas McClure, Mohammod Irfan, and Jose Solorzano.&lt;br /&gt;
&lt;br /&gt;
:3. Data gathering and general assistance. By James Chung, Padma Padula, Shannon Brady, David Horan, Michael Ferrier, Kay Drucker, Warren Christopher, and Anwar Hossain.&lt;br /&gt;
&lt;br /&gt;
:4. Long-term encouragement and support. By Harold Guetzkow, Karl Deutsch, Richard Chadwick, Gerald Barney, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
:5. Association in related world modeling projects and projects building upon IFs. By Mihajlo Mesarovic, Aldo Barsotti, Juan Huerta, John Richardson, Thomas Shook, Patricia Strauch, and other members of the World Integrated Model (WIM) team. By Stuart Bremer, Peter Brecke, Thomas Cusack, Wolf Dieter-Eberwein, Brian Pollins, and Dale Smith of the GLOBUS modeling project. By Evan Hillebrand, Paul Herman, and others of the IFs for SAG project. By Rob Lempert and Steve Bankes at RAND, Santa Monica. By Robert Pestel, Jonathan Cave, Ronald Inglehart, Sergei Parinov, Pentti Malaska, and many others in the IFs for TERRA project.&lt;br /&gt;
&lt;br /&gt;
:6. Financial assistance (without responsibility for the form of the evolving product). By the National Science Foundation, the Cleveland Foundation, the Exxon Education Foundation, the Kettering Family Foundation, the Pacific Cultural Foundation, the United States Institute of Peace, General Motors, the Strategic Assessments Group of the Central Intelligence Agency, the European Commission (Information Society Technology) Programme, the European Union Center of the University of Michigan, the National Intelligence Council (for web conversion), and Frederick S. Pardee. &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Feedback&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
Feedback on how to improve IFs is always appreciated, especially if you find something that is not working. Compliments are also accepted. Please contact. To send the IFs team an e-mail, click on&amp;amp;nbsp;[mailto:pardee.center@du.edu Pardee Center]&amp;amp;nbsp;in stand-alone versions or on the web.&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Support_for_IFs_Use&amp;diff=8342</id>
		<title>Support for IFs Use</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Support_for_IFs_Use&amp;diff=8342"/>
		<updated>2017-09-07T22:39:49Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: Blanked the page&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Additional_resources&amp;diff=8341</id>
		<title>Additional resources</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Additional_resources&amp;diff=8341"/>
		<updated>2017-09-07T22:39:39Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;[[Data|Data]]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Model_validation_and_verification Model&amp;amp;nbsp;validation&amp;amp;nbsp;and&amp;amp;nbsp;verification]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Consolidation Consolidation]&lt;br /&gt;
&lt;br /&gt;
[[Preprocessor|Preprocessor]]&lt;br /&gt;
&lt;br /&gt;
[[Sub-modules|Sub-modules]]&lt;br /&gt;
&lt;br /&gt;
[[SubRegionalization_Handbook|Sub-Regionalization Handbook]]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Version_notes Version notes]&lt;br /&gt;
&lt;br /&gt;
[[IFs_Bibliography|IFs Bibliography]]&lt;br /&gt;
&lt;br /&gt;
[[Development_Mode_Features|Development Mode Features]]&lt;br /&gt;
&lt;br /&gt;
[[Publications_on_IFs|Publications on IFs]]&lt;br /&gt;
&lt;br /&gt;
=== In progress[[http://pardee.du.edu/wiki/index.php?title=International_Futures_(IFs)&amp;amp;action=edit&amp;amp;section=1 edit]] ===&lt;br /&gt;
&lt;br /&gt;
[[Sandbox|Sandbox]]&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Publications_on_IFs&amp;diff=8340</id>
		<title>Publications on IFs</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Publications_on_IFs&amp;diff=8340"/>
		<updated>2017-09-07T22:39:20Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: Created page with &amp;quot;To obtain additional information about IFs and its use, consult:  Barry B. Hughes and Evan E. Hillebrand, &amp;#039;&amp;#039;&amp;#039;Exploring and Shaping International Futures.&amp;#039;&amp;#039;&amp;#039; Boulder, CO: Parad...&amp;quot;&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;To obtain additional information about IFs and its use, consult:&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes and Evan E. Hillebrand, &#039;&#039;&#039;Exploring and Shaping International Futures.&#039;&#039;&#039; Boulder, CO: Paradigm Publishers, 2006. Specifically, see chapter 4.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &#039;&#039;&#039;International Futures: Choices in the Face of Uncertainty,&#039;&#039;&#039; 3rd ed. Boulder, CO: Westview Press, 1999. This volume is built around IFs and contains detailed suggestions for its use. Version 3.17 of IFs, which runs under Windows 95, is distributed with the third edition of the book. The second edition contained a version for Windows 3.1, and the first edition ran under DOS. Chapter 4 of the 2nd edition of IFs included Flow Charts of Worldviews , reproduced now in this Help system.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &#039;&#039;&#039;Continuity and Change in World Politics,&#039;&#039;&#039; 4th ed. Englewood Cliffs, N.J.: Prentice Hall, 2000. IFs can also usefully supplement this textbook on global politics.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &amp;quot;The International Futures (IFs) Modeling Project. 1999. &#039;&#039;&#039;Simulation and Gaming&#039;&#039;&#039; 30, No. 3 (September): 304-326.&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Additional_resources&amp;diff=8339</id>
		<title>Additional resources</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Additional_resources&amp;diff=8339"/>
		<updated>2017-09-07T22:38:53Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;[[Data|Data]]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Model_validation_and_verification Model&amp;amp;nbsp;validation&amp;amp;nbsp;and&amp;amp;nbsp;verification]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Consolidation Consolidation]&lt;br /&gt;
&lt;br /&gt;
[[Preprocessor|Preprocessor]]&lt;br /&gt;
&lt;br /&gt;
[[Sub-modules|Sub-modules]]&lt;br /&gt;
&lt;br /&gt;
[[SubRegionalization_Handbook|Sub-Regionalization Handbook]]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Version_notes Version notes]&lt;br /&gt;
&lt;br /&gt;
[[IFs_Bibliography|IFs Bibliography]]&lt;br /&gt;
&lt;br /&gt;
[[Development_Mode_Features|Development Mode Features]]&lt;br /&gt;
&lt;br /&gt;
[[Publications_on_IFs]]&lt;br /&gt;
&lt;br /&gt;
=== In progress[[http://pardee.du.edu/wiki/index.php?title=International_Futures_(IFs)&amp;amp;action=edit&amp;amp;section=1 edit]] ===&lt;br /&gt;
&lt;br /&gt;
[[Sandbox|Sandbox]]&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=International_Futures_(IFs)&amp;diff=8338</id>
		<title>International Futures (IFs)</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=International_Futures_(IFs)&amp;diff=8338"/>
		<updated>2017-09-07T22:38:22Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&#039;&#039;&#039;Welcome to the International Futures (IFs) wiki.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
Consult the [//meta.wikimedia.org/wiki/Help:Contents User&#039;s Guide] for information on using the wiki software.&lt;br /&gt;
&lt;br /&gt;
[[Introduction_to_IFs|Introduction to IFs]]&amp;amp;nbsp;- Click here for an overview of the IFs system, including a description of the tool&#039;s&amp;amp;nbsp;purpose and major assumptions in its Base Case forecasts.&lt;br /&gt;
&lt;br /&gt;
[[Use_IFs_(Download_Version)|Use IFs (Download Version)]]&amp;amp;nbsp;- Click here for instructions on using the standalone desktop version of IFs.&lt;br /&gt;
&lt;br /&gt;
[[Use_IFs_(Online_Version)|Use IFs (Online Version)]]&amp;amp;nbsp;- Click here for instructions on using the web (cloud) version of IFs.&lt;br /&gt;
&lt;br /&gt;
[[Understand_the_Model|Understand IFs]]&amp;amp;nbsp;- Click here to access documentation&amp;amp;nbsp;on each of the major system models in IFs.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
[[Guide_to_Scenario_Analysis_in_International_Futures_(IFs)|Guide to Scenario Analysis in IFs]] - Click here for instructions on creating and comparing alternative scenarios in IFs. This guide also includes an updated list of IFs parameters.&lt;br /&gt;
&lt;br /&gt;
[[Additional_resources|Additional Resources]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Getting started ==&lt;br /&gt;
&lt;br /&gt;
*[//www.mediawiki.org/wiki/Manual:Configuration_settings Configuration settings list]&lt;br /&gt;
*[//www.mediawiki.org/wiki/Manual:FAQ MediaWiki FAQ]&lt;br /&gt;
*[https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]&lt;br /&gt;
*[//www.mediawiki.org/wiki/Localisation#Translation_resources Localise MediaWiki for your language]&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=International_Futures_(IFs)&amp;diff=8337</id>
		<title>International Futures (IFs)</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=International_Futures_(IFs)&amp;diff=8337"/>
		<updated>2017-09-07T22:37:35Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&#039;&#039;&#039;Welcome to the International Futures (IFs) wiki.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
Consult the [//meta.wikimedia.org/wiki/Help:Contents User&#039;s Guide] for information on using the wiki software.&lt;br /&gt;
&lt;br /&gt;
[[Introduction_to_IFs|Introduction to IFs]]&amp;amp;nbsp;- Click here for an overview of the IFs system, including a description of the tool&#039;s&amp;amp;nbsp;purpose and major assumptions in its Base Case forecasts.&lt;br /&gt;
&lt;br /&gt;
[[Use_IFs_(Download_Version)|Use IFs (Download Version)]]&amp;amp;nbsp;- Click here for instructions on using the standalone desktop version of IFs.&lt;br /&gt;
&lt;br /&gt;
[[Use_IFs_(Online_Version)|Use IFs (Online Version)]]&amp;amp;nbsp;- Click here for instructions on using the web (cloud) version of IFs.&lt;br /&gt;
&lt;br /&gt;
[[Understand_the_Model|Understand IFs]]&amp;amp;nbsp;- Click here to access documentation&amp;amp;nbsp;on each of the major system models in IFs.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
[[Guide_to_Scenario_Analysis_in_International_Futures_(IFs)|Guide to Scenario Analysis in IFs]] - Click here for instructions on creating and comparing alternative scenarios in IFs. This guide also includes an updated list of IFs parameters.&lt;br /&gt;
&lt;br /&gt;
[[Additional_Resources|Additional Resources]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Getting started ==&lt;br /&gt;
&lt;br /&gt;
*[//www.mediawiki.org/wiki/Manual:Configuration_settings Configuration settings list]&lt;br /&gt;
*[//www.mediawiki.org/wiki/Manual:FAQ MediaWiki FAQ]&lt;br /&gt;
*[https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]&lt;br /&gt;
*[//www.mediawiki.org/wiki/Localisation#Translation_resources Localise MediaWiki for your language]&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Additional_resources&amp;diff=8336</id>
		<title>Additional resources</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Additional_resources&amp;diff=8336"/>
		<updated>2017-09-07T22:36:43Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;[[Data|Data]]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Model_validation_and_verification Model&amp;amp;nbsp;validation&amp;amp;nbsp;and&amp;amp;nbsp;verification]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Consolidation Consolidation]&lt;br /&gt;
&lt;br /&gt;
[[Preprocessor|Preprocessor]]&lt;br /&gt;
&lt;br /&gt;
[[Sub-modules|Sub-modules]]&lt;br /&gt;
&lt;br /&gt;
[[SubRegionalization_Handbook|Sub-Regionalization Handbook]]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Version_notes Version notes]&lt;br /&gt;
&lt;br /&gt;
[[IFs_Bibliography|IFs Bibliography]]&lt;br /&gt;
&lt;br /&gt;
[[Development_Mode_Features|Development Mode Features]]&lt;br /&gt;
&lt;br /&gt;
=== In progress[[http://pardee.du.edu/wiki/index.php?title=International_Futures_(IFs)&amp;amp;action=edit&amp;amp;section=1 edit]] ===&lt;br /&gt;
&lt;br /&gt;
[[Sandbox|Sandbox]]&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Additional_resources&amp;diff=8335</id>
		<title>Additional resources</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Additional_resources&amp;diff=8335"/>
		<updated>2017-09-07T22:36:25Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;[[Data|Data]]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Model_validation_and_verification Model&amp;amp;nbsp;validation&amp;amp;nbsp;and&amp;amp;nbsp;verification]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Consolidation Consolidation]&lt;br /&gt;
&lt;br /&gt;
[[Preprocessor|Preprocessor]]&lt;br /&gt;
&lt;br /&gt;
[[Sub-modules|Sub-modules]]&lt;br /&gt;
&lt;br /&gt;
[[SubRegionalization_Handbook|Sub-Regionalization Handbook]]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Version_notes Version notes]&lt;br /&gt;
&lt;br /&gt;
[[IFs_Bibliography|IFs Bibliography]]&lt;br /&gt;
&lt;br /&gt;
[[Development_Mode_Features|Development Mode Features]]&lt;br /&gt;
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[[SubRegionalization_Handbook|Sub-Regionalization Handbook]]&lt;br /&gt;
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[http://pardee.du.edu/wiki/index.php?title=Version_notes Version notes]&lt;br /&gt;
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[[IFs_Bibliography|IFs Bibliography]]&lt;br /&gt;
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[[Development_Mode_Features|Development Mode Features]]&lt;br /&gt;
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		<title>Additional resources</title>
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&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Consolidation Consolidation]&lt;br /&gt;
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[[Preprocessor|Preprocessor]]&lt;br /&gt;
&lt;br /&gt;
[[Sub-modules|Sub-modules]]&lt;br /&gt;
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[[SubRegionalization_Handbook|Sub-Regionalization Handbook]]&lt;br /&gt;
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[http://pardee.du.edu/wiki/index.php?title=Version_notes Version notes]&lt;br /&gt;
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[[IFs_Bibliography|IFs Bibliography]]&lt;br /&gt;
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=== In progress[[http://pardee.du.edu/wiki/index.php?title=International_Futures_(IFs)&amp;amp;action=edit&amp;amp;section=1 edit]] ===&lt;br /&gt;
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		<id>https://pardeewiki.du.edu//index.php?title=IFs_Bibliography&amp;diff=8332</id>
		<title>IFs Bibliography</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=IFs_Bibliography&amp;diff=8332"/>
		<updated>2017-09-07T22:34:44Z</updated>

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&lt;div&gt;Alcamo, Joseph, Rik Leemans and Eric Kreileman, eds. 1998.&amp;amp;nbsp;&#039;&#039;Global Change Scenarios of the 21st Century: Results from the IMAGE 2.1 Model&#039;&#039;. The Netherlands: Pergamon.&lt;br /&gt;
&lt;br /&gt;
Alcamo, Joseph. 1994.&amp;amp;nbsp;&#039;&#039;IMAGE 2.0: Integrated Modeling of Global Climate Change&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
&lt;br /&gt;
Alexandratos, Nikos, ed. 1995.&amp;amp;nbsp;&#039;&#039;World Agriculture: Towards 2010&#039;&#039;&amp;amp;nbsp;(An FAO Study). New York: FAO and John Wiley and Sons.&lt;br /&gt;
&lt;br /&gt;
Allen, R. G. D. 1968.&amp;amp;nbsp;&#039;&#039;Macro-Economic Theory: A Mathematical Treatment&#039;&#039;. New York: St. Martin&#039;s Press.&lt;br /&gt;
&lt;br /&gt;
Avery, Dennis. 1995. &amp;quot;Saving the Planet with Pesticides,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 50-82.&lt;br /&gt;
&lt;br /&gt;
Bailey, Ronald, ed. 1995.&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;. New York: The Free Press.&lt;br /&gt;
&lt;br /&gt;
Barbieri, Kathleen. 1996. &amp;quot;Economic Interdependence: A Path to Peace or a Source of Interstate Conflict?&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Peace Research&#039;&#039;&amp;amp;nbsp;33: 29-50.&lt;br /&gt;
&lt;br /&gt;
Barker, T.S. and A.W.A. Peterson, eds. 1987.&amp;amp;nbsp;&#039;&#039;The Cambridge Multisectoral Dynamic Model of the British Economy&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Barney, Gerald O., W. Brian Kreutzer, and Martha J. Garrett, eds. 1991.&amp;amp;nbsp;&#039;&#039;Managing a Nation&#039;&#039;, 2nd ed. Boulder: Westview Press.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. 1997.&amp;amp;nbsp;&#039;&#039;Determinants of Economic Growth: A Cross-Country Empirical Study&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. and Xavier Sala-i-Martin. 1999.&amp;amp;nbsp;&#039;&#039;Economic Growth&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
&lt;br /&gt;
Bennett, D. Scott, and Allan Stam. 2003.&amp;amp;nbsp;&#039;&#039;The Behavioral Origins of War: Cumulation and Limits to Knowledge in Understanding International Conflict&#039;&#039;. Ann Arbor: University of Michigan Press&lt;br /&gt;
&lt;br /&gt;
Birg, Herwig. 1995.&amp;amp;nbsp;&#039;&#039;World Population Projections for the 21st Century&#039;&#039;. Frankfurt: Campus Verlag.&lt;br /&gt;
&lt;br /&gt;
Borock, Donald M. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot;&amp;amp;nbsp;&#039;&#039;Advances in Social Science and Computers&#039;&#039;&amp;amp;nbsp;4, 103-127.&lt;br /&gt;
&lt;br /&gt;
Bos, Eduard, My T. Vu, Ernest Massiah, and Rodolfo A. Bulatao. 1994.&amp;amp;nbsp;&#039;&#039;World Population Projections 1994-95 Edition&#039;&#039;&amp;amp;nbsp;[editions are biannual] Baltimore: Johns Hopkins Press.&lt;br /&gt;
&lt;br /&gt;
Boulding, Elise and Kenneth E. Boulding. 1995.&amp;amp;nbsp;&#039;&#039;The Future: Images and Processes&#039;&#039;. Thousand Oaks, CA: Sage Publications.&lt;br /&gt;
&lt;br /&gt;
Brecke, Peter. 1993. &amp;quot;Integrated Global Models that Run on Personal Computers,&amp;quot;&amp;amp;nbsp;&#039;&#039;Simulation&#039;&#039;60 (2).&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. 1977.&amp;amp;nbsp;&#039;&#039;Simulated Worlds: A Computer Model of National Decision-Making&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A., ed. 1987.&amp;amp;nbsp;&#039;&#039;The GLOBUS Model: Computer Simulation of World-wide Political and Economic Developments&#039;&#039;. Boulder, CO: Westview.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. and Walter Gruhn. 1988.&amp;amp;nbsp;&#039;&#039;Micro GLOBUS: A Computer Model of Long-Term Global Political and Economic Processes&#039;&#039;. Berlin: edition sigma.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. and Barry B. Hughes. 1990.&amp;amp;nbsp;&#039;&#039;Disarmament and Development: A Design for the Future?&#039;&#039;&amp;amp;nbsp;Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Brockmeier, Martina and Channing Arndt (presentor). 2002. Social Accounting Matrices. Powerpoint presentation on GTAP and SAMs (June 21). Found on the web.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1981.&amp;amp;nbsp;&#039;&#039;Building a Sustainable Society&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1988. &amp;quot;Analyzing the Demographic Trap,&amp;quot; in&amp;amp;nbsp;&#039;&#039;State of the World 1987&#039;&#039;, eds. Lester R. Brown and others. New York: W.W. Norton, pp. 20-37.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1995.&amp;amp;nbsp;&#039;&#039;Who Will Feed China?&#039;&#039;&amp;amp;nbsp;New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1996.&amp;amp;nbsp;&#039;&#039;Tough Choices: Facing the Challenge of Food Scarcity&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., et al. 1996&amp;amp;nbsp;&#039;&#039;State of the World 1996&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., Nicholas Lenssen, and Hal Kane. 1995.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1995. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., Christopher Flavin, and Hal Kane. 1996.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1996. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Burkhardt, Helmut. 1995. &amp;quot;Priorities for a Sustainable Civilization,&amp;quot; unpublished conference paper. Department of Physics, Ryerson Polytechnic University, Toronto, Canada.&lt;br /&gt;
&lt;br /&gt;
Bussolo, Maurizio, Mohamed Chemingui and David O’Connor. 2002. A Multi-Region Social Accounting Matrix (1995) and Regional Environmental General Equilibrium Model for India (REGEMI). Paris: OECD Development Centre (February). Available at&amp;amp;nbsp;[http://www.oecd.org/dev/technics www.oecd.org/dev/technics].&lt;br /&gt;
&lt;br /&gt;
British Petroleum Company. 1995.&amp;amp;nbsp;&#039;&#039;BP Statistical Review of World Energy&#039;&#039;. London: British Petroleum Company.&lt;br /&gt;
&lt;br /&gt;
Central Intelligence Agency (CIA). 1991.&amp;amp;nbsp;&#039;&#039;Handbook of Economic Statistics, 1991&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
&lt;br /&gt;
Central Intelligence Agency (CIA). 1994.&#039;&#039;&amp;amp;nbsp;The World Factbook 1994&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
&lt;br /&gt;
Chang, Sheldon S. L. 1961.&amp;amp;nbsp;&#039;&#039;Synthesis of Optimum Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Chenery, Hollis and Moises Syrquin. 1975.&amp;amp;nbsp;&#039;&#039;Patterns of Development 1950-1970&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Cipolla, Carlo M. 1962.&amp;amp;nbsp;&#039;&#039;The Economic History of World Population&#039;&#039;. Baltimore: Penguin.&lt;br /&gt;
&lt;br /&gt;
Cook, Earl. 1976.&amp;amp;nbsp;&#039;&#039;Man, Energy, Society&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
&lt;br /&gt;
Committee on the Strategic Assessment of the U.S. Department of Energy’s Coal Program. 1995.&amp;amp;nbsp;&#039;&#039;Coal: Energy for the Future&#039;&#039;. Washington, D.C.: National Academy Press.&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1981.&amp;amp;nbsp;&#039;&#039;The Global 2000 Report to the President&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1981b.&amp;amp;nbsp;&#039;&#039;Environmental Trends&#039;&#039;. Washington, D.C. (July).&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1991.&amp;amp;nbsp;&#039;&#039;21st Annual Report&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Crescenzi, Mark J.C. and Andrew J. Enterline. 2001. &amp;quot;Time Remembered: A Dynamic Model of Interstate Interaction,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;45, no. 3 (September): 409-431.&lt;br /&gt;
&lt;br /&gt;
Crosson, Pierre, and Jock R. Anderson. 1992.&amp;amp;nbsp;&#039;&#039;Resources and Global Food Prospects&#039;&#039;. Washington, D.C.: The World Bank. World Bank Technical Paper Number 184.&lt;br /&gt;
&lt;br /&gt;
Cusack, Thomas R. and Richard J. Stoll. 1990.&amp;amp;nbsp;&#039;&#039;Exploring Realpolitik: Probing International Relations with Computer Simulatio&#039;&#039;n. Boulder: Lynne Rienner Publishers.&lt;br /&gt;
&lt;br /&gt;
Dargay, Joyce and Dermot Gately. 1999. &amp;quot;Income’s Effect on Car and Vehicle Ownership, Worldwide: 1960-2015,&amp;quot;&amp;amp;nbsp;&#039;&#039;Transportation Research Part A&#039;&#039;&amp;amp;nbsp;33: 101-138.&lt;br /&gt;
&lt;br /&gt;
Dall, P., Kaspar, F. and Alcamo, J. 1998. &amp;quot;Modeling World-wide Water Availability and Water Use Under the Influence of Climate Change,&amp;quot;&amp;amp;nbsp;&#039;&#039;Proceedings of the Second International Conference on Climate and Water&#039;&#039;, July 17-20, Espoo, Finland.&lt;br /&gt;
&lt;br /&gt;
Dimaranan, Betina V. and Robert A. McDougall, eds. 2002.&amp;amp;nbsp;&#039;&#039;Global Trade, Assistance, and Production: The GTAP 5 Data Base&#039;&#039;. Center for Global Trade Analysis, Purdue University. Available at [http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp].&lt;br /&gt;
&lt;br /&gt;
Dowlatabadi, H., and Morgan, M.G. 1993. &amp;quot;A Model Framework for Integrated Studies of the Climate Problem,&amp;quot;&amp;amp;nbsp;&#039;&#039;Energy Policy&#039;&#039;&amp;amp;nbsp;(March): 209-221.&lt;br /&gt;
&lt;br /&gt;
Duchin, Faye. 1998.&amp;amp;nbsp;&#039;&#039;Structural Economics: Measuring Change in Technology, Lifestyles, and the Environment&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
&lt;br /&gt;
Edwards, Stephen R. 1995. &amp;quot;Conserving Biodiversity,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 212-265.&lt;br /&gt;
&lt;br /&gt;
Edmonds, J., and Reilly, J.M. 1985.&amp;amp;nbsp;&#039;&#039;Global Energy: Assessing the Future&#039;&#039;. Oxford, UK: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Edmonds, J., Pitcher, H. Rosenberg, N., and Wigley, T. &amp;quot;Design for the Global Change Assessment Model.&amp;quot;&amp;amp;nbsp;&#039;&#039;Integrative Assessment of Mitigation, Impacts and Adaptation to Climate Change&#039;&#039;. Laxenburg, Austria.&lt;br /&gt;
&lt;br /&gt;
Ehrlich, Paul R. and Anne H. Ehrlich. 1972.&amp;amp;nbsp;&#039;&#039;Population, Resources, Environment&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
&lt;br /&gt;
Eicher, Carl. 1982. &amp;quot;Facing up to Africa&#039;s Food Crisis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Foreign Affairs&#039;&#039;&amp;amp;nbsp;61, no. 1 (Fall): 151-74.&lt;br /&gt;
&lt;br /&gt;
Eberstadt, Nicholas. 1995. &amp;quot;Population, Food, and Income,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 8-47.&lt;br /&gt;
&lt;br /&gt;
Esty, Daniel C., Jack A. Goldstone, Ted Robert Gurr, Barbara Harff, Marc Levy, Geoffrey D. Dabelko, Pamela T. Surko, and Alan N. Unger. 1998. State Failure Task Force Report: Phase II Findings. Volume provided courtesy of Ted Robert Gurr.&lt;br /&gt;
&lt;br /&gt;
Flavin, Christopher. 1996. &amp;quot;Facing Up to the Risks of Climate Change,&amp;quot; in Lester R. Brown and others, eds., State of the World 1996 (New York: W.W. Norton), pp. 21-39.&lt;br /&gt;
&lt;br /&gt;
Forrester, Jay W. 1968.&amp;amp;nbsp;&#039;&#039;Principles of Systems&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Gilpin, Robert. 1981.&amp;amp;nbsp;&#039;&#039;War and Change in World Politics&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Globerman, Steven. 2000 (May). Linkages Between Technological Change and Productivity Growth. Industry Canada Research Publications Program: Occasional Paper 23.&lt;br /&gt;
&lt;br /&gt;
Grant, Lindsey. 1982.&amp;amp;nbsp;&#039;&#039;The Cornucopian Fallacies&#039;&#039;. Washington, D.C.: Environmental Fund.&lt;br /&gt;
&lt;br /&gt;
Griffith, Rachel, Stephen Redding, and John Van Reenen. 2000.&amp;amp;nbsp;&#039;&#039;Mapping the Two Faces of R&amp;amp;D: Productivity Growth in a Panel of OECD Industries&#039;&#039;. Institute for Fiscal Studies (January)&lt;br /&gt;
&lt;br /&gt;
Gwartney, James and Robert Lawson with Dexter Samida. 2000.&amp;amp;nbsp;&#039;&#039;Economic Freedom of the World: 2000 Annual Report&#039;&#039;. Vancouver, B.C.: the Fraser Institute.&lt;br /&gt;
&lt;br /&gt;
Hammond, Allen. 1998.&amp;amp;nbsp;&#039;&#039;Which World? Scenarios for the 21st Century&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
&lt;br /&gt;
Harff, Barbara, with Ted Robert Gurr and Alan Unger. 1999. Preconditions of Genocide and Politicide: 1955-1998. Paper prepared for the State Failure Task Force and provided courtesy of Barbara Harff and Ted Gurr.&lt;br /&gt;
&lt;br /&gt;
Henderson, Hazel. 1996. &amp;quot;Changing Paradigms and Indicators: Implementing Equitable, Sustainable and Participatory Development,&amp;quot; in Jo Marie Griesgraber and Bernhard G. Gunter,&amp;amp;nbsp;&#039;&#039;Development: New Paradigms and Principles for the 21st Century&#039;&#039;. East Haven, CT: Pluto Press, pp. 103-136.&lt;br /&gt;
&lt;br /&gt;
Herrera, Amilcar O., et al. 1976.&#039;&#039;&amp;amp;nbsp;Catastrophe or New Society? A Latin American World Model&#039;&#039;. Ottawa: International Development Research Centre.&lt;br /&gt;
&lt;br /&gt;
Hoekstra, A.Y. 1998.&amp;amp;nbsp;&#039;&#039;Perspectives on Water: An Integrated Model-Based Exploration of the Future&#039;&#039;. Utrecht, the Netherlands: International Books.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1980.&amp;amp;nbsp;&#039;&#039;World Modeling&#039;&#039;. Lexington, Mass: Lexington Books.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1982.&amp;amp;nbsp;&#039;&#039;International Futures Simulation: User&#039;s Manual&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985a.&amp;amp;nbsp;&#039;&#039;International Futures Simulation&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985b. &amp;quot;World Models: The Bases of Difference,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;29, 77-101.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985c.&amp;amp;nbsp;&#039;&#039;World Futures: A Critical Analysis of Alternatives&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1987. &amp;quot;Domestic Economic Processes,&amp;quot; in Stuart A. Bremer, ed.,&amp;amp;nbsp;&#039;&#039;The Globus Model: Computer Simulation of Worldwide Political Economic Development&#039;&#039;&amp;amp;nbsp;(Frankfurt and Boulder: Campus and Westview), pp. 39-158.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1988. &amp;quot;International Futures: History and Status,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Science Microcomputer Review&#039;&#039;&amp;amp;nbsp;6, 43-48.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1999. &amp;quot;The International Futures (IFs) Modeling Project.&#039;&#039;&amp;amp;nbsp;Simulation and Gaming&#039;&#039;&amp;amp;nbsp;Vol 30, No. 3 (September): 304-326.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1999.&amp;amp;nbsp;&#039;&#039;International Futures&#039;&#039;, 3rd edition Boulder: Westview Press, 1999.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2000.&amp;amp;nbsp;&#039;&#039;Continuity and Change in World Politics&#039;&#039;. Englewood Cliffs, N.J.: Prentice-Hall, fourth edition.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2001. &amp;quot;Global Social Transformation: The Sweet Spot, the Steady Slog, and the Systemic Shift,&amp;quot;&amp;amp;nbsp;&#039;&#039;Economic Development and Cultural Change&#039;&#039;&amp;amp;nbsp;49, No. 2 (January): 423-458.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2002.&amp;amp;nbsp;&#039;&#039;Theats and Opportunities Analysis&#039;&#039;. Living document prepared for the Strategic Assessments Group, Office of Transnational Issues, Central Intelligence Agency, August 2002.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. and Anwar Hossain. 2003. Long-Term Socio-Economic Modeling: With Universal, Globally-Integrated Social Accounting Matrices (SAMs) in a General Equilibrium Model Structure. IFs Project Living Document, University of Denver.&lt;br /&gt;
&lt;br /&gt;
Huth, Paul. 1996.&amp;amp;nbsp;&#039;&#039;Standing Your Ground: Territorial Disputes and International Conflict&#039;&#039;. Ann Arbor, MI: University of Michigan Press.&lt;br /&gt;
&lt;br /&gt;
Inglehart, Ronald. 1997.&amp;amp;nbsp;&#039;&#039;Modernization and Postmodernization: Cultural, Economic, and Political Change in 43 Societies&#039;&#039;. Ewing, NJ: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1995.&amp;amp;nbsp;&#039;&#039;Oil, Gas, and Coal Supply Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996.&amp;amp;nbsp;&#039;&#039;World Energy Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996b.&amp;amp;nbsp;&#039;&#039;The Strategic Value of Fossil Fuels: Challenges and Responses&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;International Financial Statistics&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;World Economic Outlook&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
Intergovernmental Panel on Climate Change (IPCC). 1995. Several volumes by various working groups. Published by Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Jansen, Karel and Rob Vos, eds. 1997.&amp;amp;nbsp;&#039;&#039;External Finance and Adjustment: Failure and Success in the Developing World&#039;&#039;. London: Macmillan Press Ltd.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1998.&amp;amp;nbsp;&#039;&#039;Modeling Global Change: The Art of Integrated Assessment Modelling&#039;&#039;. Cheltenham, UK: Edward Elgar.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1996.&amp;amp;nbsp;&#039;&#039;Meeting Targets: Tools to Support Integrated Modelling of Global Change&#039;&#039;. Den Haag: CIP-Gegevens Koninklijke Bibliotheek.&lt;br /&gt;
&lt;br /&gt;
Jansson, Kurt, Michael Harris, Angela Penrose. 1987.&amp;amp;nbsp;&#039;&#039;The Ethiopian Famine&#039;&#039;. London: Zed Books Ltd.&lt;br /&gt;
&lt;br /&gt;
Jeffreys, Kent. 1995. &amp;quot;Rescuing the Oceans,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 296-338.&lt;br /&gt;
&lt;br /&gt;
Jones, Daniel M., Stuart A. Bremer, and J. David Singer. 1996. &amp;quot;Militarized Interstate Disputes, 1816-1992: Rationale, Coding Rules, and Empirical Patterns,&amp;quot;&amp;amp;nbsp;&#039;&#039;Conflict Management and Peace Science&#039;&#039;&amp;amp;nbsp;XV, No. 2: 163-215.&lt;br /&gt;
&lt;br /&gt;
Khan, Haider A. 1998.&amp;amp;nbsp;&#039;&#039;Technology, Development and Democracy&#039;&#039;. Northhampton, Mass: Edward Elgar Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Kahn, Herman, William Brown, and Leon Martel. 1976.&amp;amp;nbsp;&#039;&#039;The Next 200 Years&#039;&#039;. New York: William Morrow.&lt;br /&gt;
&lt;br /&gt;
Kalymon, Basil A. 1975. &amp;quot;Economic Incentives in OPEC Oil Pricing Policy.&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Development Economics&#039;&#039;&amp;amp;nbsp;2: 337-362.&lt;br /&gt;
&lt;br /&gt;
Kaplan, Robert. 1994. &amp;quot;The Coming Anarchy,&amp;quot;&amp;amp;nbsp;&#039;&#039;The Atlantic Monthly&#039;&#039;&amp;amp;nbsp;273 (February): .&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999a. &amp;quot;Aggregating Governance Indicators&amp;quot;. World Bank Policy Research Department Working Paper No. 2195.&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999b. &amp;quot;Governance Matters&amp;quot;. World Bank Policy Research Department Working Paper No. 2196.&lt;br /&gt;
&lt;br /&gt;
Keepin, B. and B. Wynne. 1984. &amp;quot;Technical Analysis of the IIASA Energy Scenarios,&amp;quot;&amp;amp;nbsp;&#039;&#039;Nature&#039;&#039;312: 691-695.&lt;br /&gt;
&lt;br /&gt;
Kehoe, Timothy J. 1996. Social Accounting Matrices and Applied General Equilibrium Models. Federal Reserve Bank of Minneapolis, Working Paper 563.&lt;br /&gt;
&lt;br /&gt;
Kennedy, Paul. 1993.&amp;amp;nbsp;&#039;&#039;Preparing for the Twenty-First Century&#039;&#039;. New York: Random House.&lt;br /&gt;
&lt;br /&gt;
Klein, Lawrence R. and Fu-chen Lo, eds. 1995.&amp;amp;nbsp;&#039;&#039;Modeling Global Change&#039;&#039;. Tokyo: United Nations University Press.&lt;br /&gt;
&lt;br /&gt;
Kornai, J. 1971.&amp;amp;nbsp;&#039;&#039;Anti-Equilibrium&#039;&#039;. Amsterdam: North Holland.&lt;br /&gt;
&lt;br /&gt;
Kwasnicki, Witold and Halina Kwasnicka. 1996. &amp;quot;Long-Term Diffusion Factors of Technological Development: An Evolutionary Model and Case Study,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 31-57.&lt;br /&gt;
&lt;br /&gt;
Leontief, Wassily, Anne Carter and Peter Petri. 1977.&amp;amp;nbsp;&#039;&#039;The Future of the World Economy&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander H., and Elizabeth R. Ducot. 1976. &amp;quot;AGRIMOD: A Simulation Model for the Analysis of U.S. Food Policies.&amp;quot; Paper delivered at Conference on Systems Analysis of Grain Reserves, Joint Annual Meeting of GRSA and TIMS, Philadelphia, Pa., March 31-April 2.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander, H., et al. 1977. Energy in Agriculture: On Modeling Inputs in AGRIMOD. Final Report to U.S. Department of Energy. Palo Alto: Systems Control, Inc., August, available through NTIS.&lt;br /&gt;
&lt;br /&gt;
Lichbach, Mark Irving. 1989. &amp;quot;An Evaluation of ‘Does Economic Inequality Breed Political Conflict?,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;, Vol 41 , No. 4 (July 1989): 431-470.&lt;br /&gt;
&lt;br /&gt;
Liverman, Dianne. 1983.&amp;amp;nbsp;&#039;&#039;The Use of Global Simulation Models in Assessing Climate Impacts on the World Food System&#039;&#039;. Dissertation, University of California, Los Angeles.&lt;br /&gt;
&lt;br /&gt;
Londregan, John B. and Keith T. Poole. 1996. &amp;quot;Does High Income Promote Democrary?&amp;quot;,&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 1 (October): 1-30.&lt;br /&gt;
&lt;br /&gt;
MacKenzie, James J. 1996. &amp;quot;Oil as a Finite Resource: When is Global Production Likely to Peak?&amp;quot; Paper of the World Resources Institute. Washington, D.C.: WRI.&lt;br /&gt;
&lt;br /&gt;
Maddison, Angus. 1995.&amp;amp;nbsp;&#039;&#039;Monitoring the World Economy 1820-1992&#039;&#039;. Paris: OECD.&lt;br /&gt;
&lt;br /&gt;
Malthus, Thomas. 1798.&amp;amp;nbsp;&#039;&#039;An Essay on the Principle of Population as It Affects the Future Improvement of Society&#039;&#039;. London (reprinted many times).&lt;br /&gt;
&lt;br /&gt;
Mansfield, Edward D. 1994.&amp;amp;nbsp;&#039;&#039;Power, Trade, and War&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Marchetti, Cesare, Perrin S. Meyer, and Jesse H. Ausubel. 1996. &amp;quot;Human Population Dynamics Revisited with the Logistic Model: How Much Can be Modeled and Predicted?,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 1-30.&lt;br /&gt;
&lt;br /&gt;
Martens, Pim and Jan Rotmans, eds. 1999.&amp;amp;nbsp;&#039;&#039;Climate Change: An Integrated Perspective&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
&lt;br /&gt;
Martens, W.J.M. 1997. &amp;quot;Health Impacts of Climate Change and Ozone Depletion: An Eco-Epidemiological Approach,&amp;quot; Maastricht, the Netherlands: Maastricht University.&lt;br /&gt;
&lt;br /&gt;
Mason, Andrew. 1997. &amp;quot;The Role of Population Change in the Asian Economic Miracle,&amp;quot; Honolulu, Hawaii: East-West Center, AsiaPacific Issues, No. 33 (October), 8 pages.&lt;br /&gt;
&lt;br /&gt;
McMahon, Walter W. 1997.&amp;amp;nbsp;&#039;&#039;Education and Development: Measuring the Social Benefits&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, Jorgen Randers, and William K. Behrens, III. 1972.&amp;amp;nbsp;&#039;&#039;Limits to Growth&#039;&#039;. New York: Universe Books.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, and Jorgen Randers. 1992.&amp;amp;nbsp;&#039;&#039;Beyond the Limits&#039;&#039;. Post Mills, Vermont: Chelsea Green Publishing Company.&lt;br /&gt;
&lt;br /&gt;
Meadows, Dennis L. et al. 1974.&amp;amp;nbsp;&#039;&#039;Dynamics of Growth in a Finite World&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Mesarovic, Mihajlo D. and Eduard Pestel. 1974.&amp;amp;nbsp;&#039;&#039;Mankind at the Turning Point&#039;&#039;. New York: E.P. Dutton &amp;amp; Co.&lt;br /&gt;
&lt;br /&gt;
Mishkin, Eli. And Ludwig Braun, ed. 1961.&amp;amp;nbsp;&#039;&#039;Adaptive Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Moore, Will H., Ronny Lindstrom, and Valerie O’Regan. 1996. &amp;quot;Land Reform, Political Violence and the Economic Inequality-Political Conflict Nexus: A Longitudinal Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Interactions&#039;&#039;&amp;amp;nbsp;21, No. 4: 335-363.&lt;br /&gt;
&lt;br /&gt;
Mori, Shunsuke and Masato Takahaashi, 1997. An Integrated Assessment Model for the Evaluation of New Energy Technologies and Food Production, accepted by&amp;amp;nbsp;&#039;&#039;International Journal of Global Energy Issues&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
Naill, Roger F. 1977.&amp;amp;nbsp;&#039;&#039;Managing the Energy Transition&#039;&#039;. Vols. 1 and 2. Cambridge, Mass: Ballinger Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1992. &amp;quot;The DICE Model: Background and Structure of a Dynamic Integrated Climate Economy,&amp;quot; New Haven: Yale University.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1979.&amp;amp;nbsp;&#039;&#039;The Efficient Use of Energy Resources&#039;&#039;. New Haven, CT: Yale University Press.&lt;br /&gt;
&lt;br /&gt;
Oneal, John R. and Bruce M. Russett. 1997. The Classical Liberals were Right: Democracy, Interdependence, and Conflict, 1950-1985.&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;41, no. 2 (June): 267-294.&lt;br /&gt;
&lt;br /&gt;
Pan, Xiaoming. 2000 (January). &amp;quot;Social and Ecological Accounting Matrix: an Empirical Study for China,&amp;quot; paper submitted for the Thirteenth International Conference on Input-Output Techniques, Macerata, Italy, August 21-25, 2000.&lt;br /&gt;
&lt;br /&gt;
Pesaran, M. Hashem and G. C. Harcourt. 1999. Life and Work of John Richard Nicholas Stone.&lt;br /&gt;
&lt;br /&gt;
Pirages, Dennis. 1989.&amp;amp;nbsp;&#039;&#039;Global Technopolitics&#039;&#039;. Pacific Grove, Calif: Brooks/Cole Publishing.&lt;br /&gt;
&lt;br /&gt;
Prinn, R. H.J., A. Sokolov, C. Wand, X. Xiao, Z. Yang, R. Eckhaus, P. Stone, D. Ellerman, J Melilo, J. Fitzmaurice, D. Kicklighter, and Y. Liu. 1996. &amp;quot;Integrated Global System Model for Climate Policy Analysis: Model Framework and Sensitivity Analysis.&amp;quot; Cambridge, Mass: Global Change Center, Massachusetts Institute of Technology.&lt;br /&gt;
&lt;br /&gt;
Przeworski, Adam and Fernando Limongi. 1997. &amp;quot;Modernization: Theories and Facts,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 2 (January): 155-183.&lt;br /&gt;
&lt;br /&gt;
Population Reference Bureau. 1996. World Population Data Sheet 1996. Washington, D.C.: Population Reference Bureau.&lt;br /&gt;
&lt;br /&gt;
Postel, Sandra. 1996.&amp;amp;nbsp;&#039;&#039;Dividing the Waters: Food Security, Ecosystem Health, and the New Politics of Scarcity&#039;&#039;. Worldwatch Paper 132. Washington, D.C.: Worldwatch Institute, September.&lt;br /&gt;
&lt;br /&gt;
Pyatt, G. and J.I. Round, eds. 1985.&amp;amp;nbsp;&#039;&#039;Social Accounting Matrices: A Basis for Planning&#039;&#039;. Washington, D.C.: The World Bank.&lt;br /&gt;
&lt;br /&gt;
Raskin, P., T. Banuri, G. Gallopín, P. Gutman, A. Hammond, R. Kates, and R. Swart. 2001. Great Transition:&amp;amp;nbsp;&#039;&#039;The Promise and Lure of the Times Ahead&#039;&#039;. Forthcoming.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1990.&amp;amp;nbsp;&#039;&#039;Global Politics&#039;&#039;, 4th edition. Boston: Houghton Mifflin.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1995.&amp;amp;nbsp;&#039;&#039;Democracy and International Conflict&#039;&#039;. Columbia: University of South Carolina Press.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee and J. David Singer. 1973. &amp;quot; Measuring the Concentration of Power in the International System,&amp;quot;&#039;&#039;&amp;amp;nbsp;Sociological Methods and Research&#039;&#039;&amp;amp;nbsp;1, no. 4: 403-436. Reprinted in&amp;amp;nbsp;&#039;&#039;Measuring the Correlates of War&#039;&#039;, edited by J. David Singer and Paul Diehl. Ann Arbor: University of Michigan Press, 1990.&lt;br /&gt;
&lt;br /&gt;
Rayner. S. 1992. &amp;quot;Cultural Theory and Risk Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Theory of Risk&#039;&#039;, ed. G. D. Preagor. Westport, USA.&lt;br /&gt;
&lt;br /&gt;
Repetto, Robert and Duncan Austin. 1997.&amp;amp;nbsp;&#039;&#039;The Costs of Climate Protection&#039;&#039;. Washington, D.C.: World Resources Institute.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis Fry. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Chicago: Quadrangle Books.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis F. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Pittsburgh: Boxwood Press.&lt;br /&gt;
&lt;br /&gt;
Romer, Paul M. 1994. &amp;quot;The Origins of Endogenous Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Economic Perspectives&#039;&#039;Vol 8, No. 1 (Winter): 3-22.&lt;br /&gt;
&lt;br /&gt;
Root T. and Stephen Schneider. 1995. &amp;quot;Ecology and Climate: Research Strategies and Implications,&amp;quot; Science 269 (52): 334-341.&lt;br /&gt;
&lt;br /&gt;
Rosegrant, Mark W., Mercedita Agcaoili-Sombilla, and Nicostrato D. Perez. 1995. &amp;quot;Global Food Projections to 2020: Implications for Investment.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 5.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1999. Integrated Assessment Models: Uncertainty, Quality and Use. Maastricht, the Netherlands: Maastricht University, International Centre for Integrative Studies (ICIS), Working Paper 199-E005.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and Burt de Vries, eds. 1997.&amp;amp;nbsp;&#039;&#039;Perspectives on Global Change: The Targets Approach&#039;&#039;. Cambridge, UK: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and M.B.A. van Asselt. 1996. &amp;quot;Integrated Assessment: A Growing Child on its Way to Maturity,&amp;quot;&amp;amp;nbsp;&#039;&#039;Climatic Change&#039;&#039;&amp;amp;nbsp;34 (3-4): 327-336.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1990.&amp;amp;nbsp;&#039;&#039;IMAGE: An Integrated Model to Assess the Greenhouse Effect&#039;&#039;. Dordrecht, the Netherlands: Kluwer Academics.&lt;br /&gt;
&lt;br /&gt;
Saaty, Thomas L. 1996. The Analytic Network Process: Decision Making with Dependence and Feedback. Pittsburgh: RWS Publications.&lt;br /&gt;
&lt;br /&gt;
Schafer, Andreas and David G. Victor. 1997. The Future Mobility of the World Population. Massachusetts Institute of Technology and International Institute for Applied Systems Analysis, Discussion Paper 97-6-4 (revision 2, September).&lt;br /&gt;
&lt;br /&gt;
Scheer, Sara J. and Satya Yadav. 1996. &amp;quot;Land Degradation in the Developing World: Implications for Food, Agriculture, and the Environment to 2020.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 14.&lt;br /&gt;
&lt;br /&gt;
Schneider, Stephen. 1997. &amp;quot;Integrated Assessment Modeling of Climate Change: Transparent Rational Tool for Policy Making or Opaque Screen Hiding Value-Laden Assumptions?&amp;quot;&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(4): 229-250.&lt;br /&gt;
&lt;br /&gt;
Schwartz, Peter. 1996.&#039;&#039;&amp;amp;nbsp;The Art of the Long View.&#039;&#039;&amp;amp;nbsp;New York: Doubleday.&lt;br /&gt;
&lt;br /&gt;
Sedjo, Roger A. 1995. &amp;quot;Forests: Conflicting Signals,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 178-209.&lt;br /&gt;
&lt;br /&gt;
Shane, Harold G. and Gary A. Sojka. 1990. &amp;quot;John Elfreth Watkins, Jr.: Forgotten Genius of Forecasting,&amp;quot; in Edward Cornish, ed.,&#039;&#039;&amp;amp;nbsp;The 1990s and Beyond&#039;&#039;. Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Shaw, Timothy W. and Clement E. Adibe. 1995-96. &amp;quot;Africa and Global Developments in the Twenty-First Century,&amp;quot; International Journal 51 (Winter): 1-26.&lt;br /&gt;
&lt;br /&gt;
Siegmann, Heinrich. 1985.&amp;amp;nbsp;&#039;&#039;Recent Developments in World Modeling&#039;&#039;. Berlin: Science Center.&lt;br /&gt;
&lt;br /&gt;
Simon, Julian. 1981.&amp;amp;nbsp;&#039;&#039;The Ultimate Resource&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Singer, J. David, Stuart Bremer, and John Stuckey. 1972. &amp;quot;Capability Distribution, Uncertainty, and Major Power Wars, 1820-1965.&amp;quot; In Bruce Russett, ed.,&amp;amp;nbsp;&#039;&#039;Peace, War, and Numbers.&#039;&#039;&amp;amp;nbsp;Beverly Hills: Sage.&lt;br /&gt;
&lt;br /&gt;
Sivard, Ruth Leger. 1993.&amp;amp;nbsp;&#039;&#039;World Military and Social Expenditures 1993.&#039;&#039;&amp;amp;nbsp;Washington, D.C. 20007: World Priorities, Box 25140.&lt;br /&gt;
&lt;br /&gt;
Solow, Robert M. 1956. &amp;quot;A Contribution to the Theory of Economic Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Quarterly Journal of Economics&#039;&#039;&amp;amp;nbsp;70, 1 (February): 65-94.&lt;br /&gt;
&lt;br /&gt;
Stanford University. 1978.&amp;amp;nbsp;&#039;&#039;Stanford Pilot Energy/Economic Model&#039;&#039;. Stanford: Department of Research, Interim Report, Vol. 1.&lt;br /&gt;
&lt;br /&gt;
Stockholm International Peace Research Institute (SIPRI). 1994.&amp;amp;nbsp;&#039;&#039;SIPRI Yearbook&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Stone, Richard. 1986. &amp;quot;The Accounts of Society,&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Applied Econometrics&#039;&#039;&amp;amp;nbsp;1, no. 1 (January): 5-28.&lt;br /&gt;
&lt;br /&gt;
Strategic Assessments Group (SAG), Office of Transnational Issues, Directorate of Intelligence. 2001 (February). The Global Economy in the Long Term. OTI IR 2001-013.&lt;br /&gt;
&lt;br /&gt;
Systems Analysis Research Unit (SARU). 1977.&amp;amp;nbsp;&#039;&#039;SARUM 76 Global Modeling Project&#039;&#039;. Departments of the Environment and Transport, 2 Marsham Street, London, 3WIP 3EB.&lt;br /&gt;
&lt;br /&gt;
Tammen, Ronald L, Jacek Kugler, Douglas Lemke, Allan C. Stam III, Carole Alsharabati, Mark Andrew Abdollahian, Brian Efird, and A.F.K. Organski. 2000. Power Transitions: Strategies for the 21st Century. New York: Chatham House Publishers.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1975. &amp;quot;Theoretical Foundations and Technical Implications.&amp;quot; in Charles Blitzer, Peter Clark and Lance Taylor, eds.,&amp;amp;nbsp;&#039;&#039;Economy-Wide Models and Development Planning.&#039;&#039;&amp;amp;nbsp;Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1979.&amp;amp;nbsp;&#039;&#039;Macro Models for Developing Countries&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Thirlwall, A. P. 1977.&amp;amp;nbsp;&#039;&#039;Growth and Development&#039;&#039;. New York: John Wiley and Sons.&lt;br /&gt;
&lt;br /&gt;
Thompson, M. 1997. Cultural Theory and Integrated Assessment.&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(3): 139-150.&lt;br /&gt;
&lt;br /&gt;
Thompson, M., R. Ellis and A. Wildavsky. 1990.&amp;amp;nbsp;&#039;&#039;Cultural Theory&#039;&#039;. Boulder, Co: Westview Press.&lt;br /&gt;
&lt;br /&gt;
Thorbecke, Erik. 2001. &amp;quot;The Social Accounting Matrix: Deterministic or Stochastic Concept?&amp;quot;, paper prepared for a conference in honor of Graham Pyatt&#039;s retirement, at the Institute of Social Studies, The Hague, Netherlands (November 29 and 30). Available at [http://people.cornell.edu/pages/et17/etpapers.html http://people.cornell.edu/pages/et17/etpapers.html].&lt;br /&gt;
&lt;br /&gt;
United Nations, Department of Economic and Social Affairs. 1956.&amp;amp;nbsp;&#039;&#039;Methods of Population Projections by Sex and Age&#039;&#039;. New York: United Nations, ST/SOA Series A.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1992.&amp;amp;nbsp;&#039;&#039;Long-Range World Population Projections. Two Centuries of Population Growth: 1950-2150&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1993.&amp;amp;nbsp;&#039;&#039;World Population Prospects - the 1992 Revision&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations Development Program (UNDP). 1995.&amp;amp;nbsp;&#039;&#039;Human Development Report&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1992.&amp;amp;nbsp;&#039;&#039;Production Yearbook.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1995.&#039;&#039;&amp;amp;nbsp;World Agriculture: Towards 2010.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 1999. The World at Six Billion New York: UN.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2000. Replacement Migration: Is it a Solution to Declining and Ageing Populations? New York: UN.&lt;br /&gt;
&lt;br /&gt;
United States Arms Control and Disarmament Agency (ACDA). 1995.&amp;amp;nbsp;&#039;&#039;World Military Expenditures and Arms Transfers 1995&#039;&#039;. Washington, D.C.: Arms Control and Disarmament Agency.&lt;br /&gt;
&lt;br /&gt;
United States Bureau of the Census. 1991.&amp;amp;nbsp;&#039;&#039;World Population Profile: 1991&#039;&#039;. Report WP/91 Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Walters, Robert S. and David H. Blake. 1992.&amp;amp;nbsp;&#039;&#039;The Politics of Global Economic Relations&#039;&#039;, 4th edition. Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Waltz, Kenneth N. 1959. Man, the State, and War: A Theoretical Analysis. New York: Columbia University Press.&lt;br /&gt;
&lt;br /&gt;
Watkins, John Elfreth, Jr. 1990. &amp;quot;What May Happen in the Next Hundred Years,&amp;quot; in Edward Cornish, ed.,&amp;amp;nbsp;&#039;&#039;The 1990s and Beyond.&#039;&#039;&amp;amp;nbsp;Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Wildavsky, Aaron, and Ellen Tenenbaum. 1981.&amp;amp;nbsp;&#039;&#039;The Politics of Mistrust&#039;&#039;. Beverly Hills: Sage Publications.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1991b.&amp;amp;nbsp;&#039;&#039;World Tables 1991&#039;&#039;. New York: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1995&amp;amp;nbsp;&#039;&#039;World Development Report 1995&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
World Energy Council (WEC) Commission. 1993.&amp;amp;nbsp;&#039;&#039;Energy for Tomorrow’s World.&#039;&#039;&amp;amp;nbsp;New York: St. Martin’s Press.&lt;br /&gt;
&lt;br /&gt;
World Resources Institute (WRI). 1994.&amp;amp;nbsp;&#039;&#039;World Resources 1994-95.&#039;&#039;&amp;amp;nbsp;New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Wortman, Sterling and Ralph W. Cummings, Jr. 1978.&#039;&#039;&amp;amp;nbsp;To Feed This World&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Zinnes, Dina A. and John W. Gillespie, eds. 1976.&amp;amp;nbsp;&#039;&#039;Mathematical Models in International Relations&#039;&#039;&amp;amp;nbsp;(New York: Preaeger).&lt;/div&gt;</summary>
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		<summary type="html">&lt;p&gt;JessRettig: Created page with &amp;quot;== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;IFs Bibliography&amp;lt;/span&amp;gt; ==  Alcamo, Joseph, Rik Leemans and Eric Kreileman, eds. 1998.&amp;amp;nbsp;&amp;#039;&amp;#039;Global Change Scenarios of the 21st Century:...&amp;quot;&lt;/p&gt;
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&lt;div&gt;== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;IFs Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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Alcamo, Joseph, Rik Leemans and Eric Kreileman, eds. 1998.&amp;amp;nbsp;&#039;&#039;Global Change Scenarios of the 21st Century: Results from the IMAGE 2.1 Model&#039;&#039;. The Netherlands: Pergamon.&lt;br /&gt;
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Alcamo, Joseph. 1994.&amp;amp;nbsp;&#039;&#039;IMAGE 2.0: Integrated Modeling of Global Climate Change&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
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Alexandratos, Nikos, ed. 1995.&amp;amp;nbsp;&#039;&#039;World Agriculture: Towards 2010&#039;&#039;&amp;amp;nbsp;(An FAO Study). New York: FAO and John Wiley and Sons.&lt;br /&gt;
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Allen, R. G. D. 1968.&amp;amp;nbsp;&#039;&#039;Macro-Economic Theory: A Mathematical Treatment&#039;&#039;. New York: St. Martin&#039;s Press.&lt;br /&gt;
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Avery, Dennis. 1995. &amp;quot;Saving the Planet with Pesticides,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 50-82.&lt;br /&gt;
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Bailey, Ronald, ed. 1995.&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;. New York: The Free Press.&lt;br /&gt;
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Barbieri, Kathleen. 1996. &amp;quot;Economic Interdependence: A Path to Peace or a Source of Interstate Conflict?&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Peace Research&#039;&#039;&amp;amp;nbsp;33: 29-50.&lt;br /&gt;
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Barker, T.S. and A.W.A. Peterson, eds. 1987.&amp;amp;nbsp;&#039;&#039;The Cambridge Multisectoral Dynamic Model of the British Economy&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
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Barney, Gerald O., W. Brian Kreutzer, and Martha J. Garrett, eds. 1991.&amp;amp;nbsp;&#039;&#039;Managing a Nation&#039;&#039;, 2nd ed. Boulder: Westview Press.&lt;br /&gt;
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Barro, Robert J. 1997.&amp;amp;nbsp;&#039;&#039;Determinants of Economic Growth: A Cross-Country Empirical Study&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
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Barro, Robert J. and Xavier Sala-i-Martin. 1999.&amp;amp;nbsp;&#039;&#039;Economic Growth&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
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Bennett, D. Scott, and Allan Stam. 2003.&amp;amp;nbsp;&#039;&#039;The Behavioral Origins of War: Cumulation and Limits to Knowledge in Understanding International Conflict&#039;&#039;. Ann Arbor: University of Michigan Press&lt;br /&gt;
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Birg, Herwig. 1995.&amp;amp;nbsp;&#039;&#039;World Population Projections for the 21st Century&#039;&#039;. Frankfurt: Campus Verlag.&lt;br /&gt;
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Borock, Donald M. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot;&amp;amp;nbsp;&#039;&#039;Advances in Social Science and Computers&#039;&#039;&amp;amp;nbsp;4, 103-127.&lt;br /&gt;
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Bos, Eduard, My T. Vu, Ernest Massiah, and Rodolfo A. Bulatao. 1994.&amp;amp;nbsp;&#039;&#039;World Population Projections 1994-95 Edition&#039;&#039;&amp;amp;nbsp;[editions are biannual] Baltimore: Johns Hopkins Press.&lt;br /&gt;
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Boulding, Elise and Kenneth E. Boulding. 1995.&amp;amp;nbsp;&#039;&#039;The Future: Images and Processes&#039;&#039;. Thousand Oaks, CA: Sage Publications.&lt;br /&gt;
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Brecke, Peter. 1993. &amp;quot;Integrated Global Models that Run on Personal Computers,&amp;quot;&amp;amp;nbsp;&#039;&#039;Simulation&#039;&#039;60 (2).&lt;br /&gt;
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Bremer, Stuart A. 1977.&amp;amp;nbsp;&#039;&#039;Simulated Worlds: A Computer Model of National Decision-Making&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
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Bremer, Stuart A., ed. 1987.&amp;amp;nbsp;&#039;&#039;The GLOBUS Model: Computer Simulation of World-wide Political and Economic Developments&#039;&#039;. Boulder, CO: Westview.&lt;br /&gt;
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Bremer, Stuart A. and Walter Gruhn. 1988.&amp;amp;nbsp;&#039;&#039;Micro GLOBUS: A Computer Model of Long-Term Global Political and Economic Processes&#039;&#039;. Berlin: edition sigma.&lt;br /&gt;
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Bremer, Stuart A. and Barry B. Hughes. 1990.&amp;amp;nbsp;&#039;&#039;Disarmament and Development: A Design for the Future?&#039;&#039;&amp;amp;nbsp;Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
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Brockmeier, Martina and Channing Arndt (presentor). 2002. Social Accounting Matrices. Powerpoint presentation on GTAP and SAMs (June 21). Found on the web.&lt;br /&gt;
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Brown, Lester R. 1981.&amp;amp;nbsp;&#039;&#039;Building a Sustainable Society&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
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Brown, Lester R. 1988. &amp;quot;Analyzing the Demographic Trap,&amp;quot; in&amp;amp;nbsp;&#039;&#039;State of the World 1987&#039;&#039;, eds. Lester R. Brown and others. New York: W.W. Norton, pp. 20-37.&lt;br /&gt;
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Brown, Lester R. 1995.&amp;amp;nbsp;&#039;&#039;Who Will Feed China?&#039;&#039;&amp;amp;nbsp;New York: W.W. Norton.&lt;br /&gt;
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Brown, Lester R. 1996.&amp;amp;nbsp;&#039;&#039;Tough Choices: Facing the Challenge of Food Scarcity&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
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Brown, Lester R., et al. 1996&amp;amp;nbsp;&#039;&#039;State of the World 1996&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
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Brown, Lester R., Nicholas Lenssen, and Hal Kane. 1995.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1995. New York: W.W. Norton.&lt;br /&gt;
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Brown, Lester R., Christopher Flavin, and Hal Kane. 1996.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1996. New York: W.W. Norton.&lt;br /&gt;
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Burkhardt, Helmut. 1995. &amp;quot;Priorities for a Sustainable Civilization,&amp;quot; unpublished conference paper. Department of Physics, Ryerson Polytechnic University, Toronto, Canada.&lt;br /&gt;
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Bussolo, Maurizio, Mohamed Chemingui and David O’Connor. 2002. A Multi-Region Social Accounting Matrix (1995) and Regional Environmental General Equilibrium Model for India (REGEMI). Paris: OECD Development Centre (February). Available at&amp;amp;nbsp;[http://www.oecd.org/dev/technics www.oecd.org/dev/technics].&lt;br /&gt;
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British Petroleum Company. 1995.&amp;amp;nbsp;&#039;&#039;BP Statistical Review of World Energy&#039;&#039;. London: British Petroleum Company.&lt;br /&gt;
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Central Intelligence Agency (CIA). 1991.&amp;amp;nbsp;&#039;&#039;Handbook of Economic Statistics, 1991&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
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Central Intelligence Agency (CIA). 1994.&#039;&#039;&amp;amp;nbsp;The World Factbook 1994&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
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Chang, Sheldon S. L. 1961.&amp;amp;nbsp;&#039;&#039;Synthesis of Optimum Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
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Chenery, Hollis and Moises Syrquin. 1975.&amp;amp;nbsp;&#039;&#039;Patterns of Development 1950-1970&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
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Cipolla, Carlo M. 1962.&amp;amp;nbsp;&#039;&#039;The Economic History of World Population&#039;&#039;. Baltimore: Penguin.&lt;br /&gt;
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Cook, Earl. 1976.&amp;amp;nbsp;&#039;&#039;Man, Energy, Society&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
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Committee on the Strategic Assessment of the U.S. Department of Energy’s Coal Program. 1995.&amp;amp;nbsp;&#039;&#039;Coal: Energy for the Future&#039;&#039;. Washington, D.C.: National Academy Press.&lt;br /&gt;
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Council on Environmental Quality (CEQ). 1981.&amp;amp;nbsp;&#039;&#039;The Global 2000 Report to the President&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
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Council on Environmental Quality (CEQ). 1981b.&amp;amp;nbsp;&#039;&#039;Environmental Trends&#039;&#039;. Washington, D.C. (July).&lt;br /&gt;
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Council on Environmental Quality (CEQ). 1991.&amp;amp;nbsp;&#039;&#039;21st Annual Report&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
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Crescenzi, Mark J.C. and Andrew J. Enterline. 2001. &amp;quot;Time Remembered: A Dynamic Model of Interstate Interaction,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;45, no. 3 (September): 409-431.&lt;br /&gt;
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Crosson, Pierre, and Jock R. Anderson. 1992.&amp;amp;nbsp;&#039;&#039;Resources and Global Food Prospects&#039;&#039;. Washington, D.C.: The World Bank. World Bank Technical Paper Number 184.&lt;br /&gt;
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Cusack, Thomas R. and Richard J. Stoll. 1990.&amp;amp;nbsp;&#039;&#039;Exploring Realpolitik: Probing International Relations with Computer Simulatio&#039;&#039;n. Boulder: Lynne Rienner Publishers.&lt;br /&gt;
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Dargay, Joyce and Dermot Gately. 1999. &amp;quot;Income’s Effect on Car and Vehicle Ownership, Worldwide: 1960-2015,&amp;quot;&amp;amp;nbsp;&#039;&#039;Transportation Research Part A&#039;&#039;&amp;amp;nbsp;33: 101-138.&lt;br /&gt;
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Dall, P., Kaspar, F. and Alcamo, J. 1998. &amp;quot;Modeling World-wide Water Availability and Water Use Under the Influence of Climate Change,&amp;quot;&amp;amp;nbsp;&#039;&#039;Proceedings of the Second International Conference on Climate and Water&#039;&#039;, July 17-20, Espoo, Finland.&lt;br /&gt;
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Dimaranan, Betina V. and Robert A. McDougall, eds. 2002.&amp;amp;nbsp;&#039;&#039;Global Trade, Assistance, and Production: The GTAP 5 Data Base&#039;&#039;. Center for Global Trade Analysis, Purdue University. Available at [http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp].&lt;br /&gt;
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Dowlatabadi, H., and Morgan, M.G. 1993. &amp;quot;A Model Framework for Integrated Studies of the Climate Problem,&amp;quot;&amp;amp;nbsp;&#039;&#039;Energy Policy&#039;&#039;&amp;amp;nbsp;(March): 209-221.&lt;br /&gt;
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Duchin, Faye. 1998.&amp;amp;nbsp;&#039;&#039;Structural Economics: Measuring Change in Technology, Lifestyles, and the Environment&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
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Edwards, Stephen R. 1995. &amp;quot;Conserving Biodiversity,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 212-265.&lt;br /&gt;
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Edmonds, J., and Reilly, J.M. 1985.&amp;amp;nbsp;&#039;&#039;Global Energy: Assessing the Future&#039;&#039;. Oxford, UK: Oxford University Press.&lt;br /&gt;
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Edmonds, J., Pitcher, H. Rosenberg, N., and Wigley, T. &amp;quot;Design for the Global Change Assessment Model.&amp;quot;&amp;amp;nbsp;&#039;&#039;Integrative Assessment of Mitigation, Impacts and Adaptation to Climate Change&#039;&#039;. Laxenburg, Austria.&lt;br /&gt;
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Ehrlich, Paul R. and Anne H. Ehrlich. 1972.&amp;amp;nbsp;&#039;&#039;Population, Resources, Environment&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
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Eicher, Carl. 1982. &amp;quot;Facing up to Africa&#039;s Food Crisis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Foreign Affairs&#039;&#039;&amp;amp;nbsp;61, no. 1 (Fall): 151-74.&lt;br /&gt;
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Eberstadt, Nicholas. 1995. &amp;quot;Population, Food, and Income,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 8-47.&lt;br /&gt;
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Esty, Daniel C., Jack A. Goldstone, Ted Robert Gurr, Barbara Harff, Marc Levy, Geoffrey D. Dabelko, Pamela T. Surko, and Alan N. Unger. 1998. State Failure Task Force Report: Phase II Findings. Volume provided courtesy of Ted Robert Gurr.&lt;br /&gt;
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Flavin, Christopher. 1996. &amp;quot;Facing Up to the Risks of Climate Change,&amp;quot; in Lester R. Brown and others, eds., State of the World 1996 (New York: W.W. Norton), pp. 21-39.&lt;br /&gt;
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Forrester, Jay W. 1968.&amp;amp;nbsp;&#039;&#039;Principles of Systems&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
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Gilpin, Robert. 1981.&amp;amp;nbsp;&#039;&#039;War and Change in World Politics&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
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Globerman, Steven. 2000 (May). Linkages Between Technological Change and Productivity Growth. Industry Canada Research Publications Program: Occasional Paper 23.&lt;br /&gt;
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Grant, Lindsey. 1982.&amp;amp;nbsp;&#039;&#039;The Cornucopian Fallacies&#039;&#039;. Washington, D.C.: Environmental Fund.&lt;br /&gt;
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Griffith, Rachel, Stephen Redding, and John Van Reenen. 2000.&amp;amp;nbsp;&#039;&#039;Mapping the Two Faces of R&amp;amp;D: Productivity Growth in a Panel of OECD Industries&#039;&#039;. Institute for Fiscal Studies (January)&lt;br /&gt;
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Gwartney, James and Robert Lawson with Dexter Samida. 2000.&amp;amp;nbsp;&#039;&#039;Economic Freedom of the World: 2000 Annual Report&#039;&#039;. Vancouver, B.C.: the Fraser Institute.&lt;br /&gt;
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Hammond, Allen. 1998.&amp;amp;nbsp;&#039;&#039;Which World? Scenarios for the 21st Century&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
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Harff, Barbara, with Ted Robert Gurr and Alan Unger. 1999. Preconditions of Genocide and Politicide: 1955-1998. Paper prepared for the State Failure Task Force and provided courtesy of Barbara Harff and Ted Gurr.&lt;br /&gt;
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Henderson, Hazel. 1996. &amp;quot;Changing Paradigms and Indicators: Implementing Equitable, Sustainable and Participatory Development,&amp;quot; in Jo Marie Griesgraber and Bernhard G. Gunter,&amp;amp;nbsp;&#039;&#039;Development: New Paradigms and Principles for the 21st Century&#039;&#039;. East Haven, CT: Pluto Press, pp. 103-136.&lt;br /&gt;
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Herrera, Amilcar O., et al. 1976.&#039;&#039;&amp;amp;nbsp;Catastrophe or New Society? A Latin American World Model&#039;&#039;. Ottawa: International Development Research Centre.&lt;br /&gt;
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Hoekstra, A.Y. 1998.&amp;amp;nbsp;&#039;&#039;Perspectives on Water: An Integrated Model-Based Exploration of the Future&#039;&#039;. Utrecht, the Netherlands: International Books.&lt;br /&gt;
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Hughes, Barry B. 1980.&amp;amp;nbsp;&#039;&#039;World Modeling&#039;&#039;. Lexington, Mass: Lexington Books.&lt;br /&gt;
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Hughes, Barry B. 1982.&amp;amp;nbsp;&#039;&#039;International Futures Simulation: User&#039;s Manual&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
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Hughes, Barry B. 1985a.&amp;amp;nbsp;&#039;&#039;International Futures Simulation&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
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Hughes, Barry B. 1985b. &amp;quot;World Models: The Bases of Difference,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;29, 77-101.&lt;br /&gt;
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Hughes, Barry B. 1985c.&amp;amp;nbsp;&#039;&#039;World Futures: A Critical Analysis of Alternatives&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
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Hughes, Barry B. 1987. &amp;quot;Domestic Economic Processes,&amp;quot; in Stuart A. Bremer, ed.,&amp;amp;nbsp;&#039;&#039;The Globus Model: Computer Simulation of Worldwide Political Economic Development&#039;&#039;&amp;amp;nbsp;(Frankfurt and Boulder: Campus and Westview), pp. 39-158.&lt;br /&gt;
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Hughes, Barry B. 1988. &amp;quot;International Futures: History and Status,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Science Microcomputer Review&#039;&#039;&amp;amp;nbsp;6, 43-48.&lt;br /&gt;
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Hughes, Barry B. 1999. &amp;quot;The International Futures (IFs) Modeling Project.&#039;&#039;&amp;amp;nbsp;Simulation and Gaming&#039;&#039;&amp;amp;nbsp;Vol 30, No. 3 (September): 304-326.&lt;br /&gt;
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Hughes, Barry B. 1999.&amp;amp;nbsp;&#039;&#039;International Futures&#039;&#039;, 3rd edition Boulder: Westview Press, 1999.&lt;br /&gt;
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Hughes, Barry B. 2000.&amp;amp;nbsp;&#039;&#039;Continuity and Change in World Politics&#039;&#039;. Englewood Cliffs, N.J.: Prentice-Hall, fourth edition.&lt;br /&gt;
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Hughes, Barry B. 2001. &amp;quot;Global Social Transformation: The Sweet Spot, the Steady Slog, and the Systemic Shift,&amp;quot;&amp;amp;nbsp;&#039;&#039;Economic Development and Cultural Change&#039;&#039;&amp;amp;nbsp;49, No. 2 (January): 423-458.&lt;br /&gt;
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Hughes, Barry B. 2002.&amp;amp;nbsp;&#039;&#039;Theats and Opportunities Analysis&#039;&#039;. Living document prepared for the Strategic Assessments Group, Office of Transnational Issues, Central Intelligence Agency, August 2002.&lt;br /&gt;
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Hughes, Barry B. and Anwar Hossain. 2003. Long-Term Socio-Economic Modeling: With Universal, Globally-Integrated Social Accounting Matrices (SAMs) in a General Equilibrium Model Structure. IFs Project Living Document, University of Denver.&lt;br /&gt;
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Huth, Paul. 1996.&amp;amp;nbsp;&#039;&#039;Standing Your Ground: Territorial Disputes and International Conflict&#039;&#039;. Ann Arbor, MI: University of Michigan Press.&lt;br /&gt;
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Inglehart, Ronald. 1997.&amp;amp;nbsp;&#039;&#039;Modernization and Postmodernization: Cultural, Economic, and Political Change in 43 Societies&#039;&#039;. Ewing, NJ: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1995.&amp;amp;nbsp;&#039;&#039;Oil, Gas, and Coal Supply Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996.&amp;amp;nbsp;&#039;&#039;World Energy Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996b.&amp;amp;nbsp;&#039;&#039;The Strategic Value of Fossil Fuels: Challenges and Responses&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;International Financial Statistics&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;World Economic Outlook&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
Intergovernmental Panel on Climate Change (IPCC). 1995. Several volumes by various working groups. Published by Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Jansen, Karel and Rob Vos, eds. 1997.&amp;amp;nbsp;&#039;&#039;External Finance and Adjustment: Failure and Success in the Developing World&#039;&#039;. London: Macmillan Press Ltd.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1998.&amp;amp;nbsp;&#039;&#039;Modeling Global Change: The Art of Integrated Assessment Modelling&#039;&#039;. Cheltenham, UK: Edward Elgar.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1996.&amp;amp;nbsp;&#039;&#039;Meeting Targets: Tools to Support Integrated Modelling of Global Change&#039;&#039;. Den Haag: CIP-Gegevens Koninklijke Bibliotheek.&lt;br /&gt;
&lt;br /&gt;
Jansson, Kurt, Michael Harris, Angela Penrose. 1987.&amp;amp;nbsp;&#039;&#039;The Ethiopian Famine&#039;&#039;. London: Zed Books Ltd.&lt;br /&gt;
&lt;br /&gt;
Jeffreys, Kent. 1995. &amp;quot;Rescuing the Oceans,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 296-338.&lt;br /&gt;
&lt;br /&gt;
Jones, Daniel M., Stuart A. Bremer, and J. David Singer. 1996. &amp;quot;Militarized Interstate Disputes, 1816-1992: Rationale, Coding Rules, and Empirical Patterns,&amp;quot;&amp;amp;nbsp;&#039;&#039;Conflict Management and Peace Science&#039;&#039;&amp;amp;nbsp;XV, No. 2: 163-215.&lt;br /&gt;
&lt;br /&gt;
Khan, Haider A. 1998.&amp;amp;nbsp;&#039;&#039;Technology, Development and Democracy&#039;&#039;. Northhampton, Mass: Edward Elgar Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Kahn, Herman, William Brown, and Leon Martel. 1976.&amp;amp;nbsp;&#039;&#039;The Next 200 Years&#039;&#039;. New York: William Morrow.&lt;br /&gt;
&lt;br /&gt;
Kalymon, Basil A. 1975. &amp;quot;Economic Incentives in OPEC Oil Pricing Policy.&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Development Economics&#039;&#039;&amp;amp;nbsp;2: 337-362.&lt;br /&gt;
&lt;br /&gt;
Kaplan, Robert. 1994. &amp;quot;The Coming Anarchy,&amp;quot;&amp;amp;nbsp;&#039;&#039;The Atlantic Monthly&#039;&#039;&amp;amp;nbsp;273 (February): .&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999a. &amp;quot;Aggregating Governance Indicators&amp;quot;. World Bank Policy Research Department Working Paper No. 2195.&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999b. &amp;quot;Governance Matters&amp;quot;. World Bank Policy Research Department Working Paper No. 2196.&lt;br /&gt;
&lt;br /&gt;
Keepin, B. and B. Wynne. 1984. &amp;quot;Technical Analysis of the IIASA Energy Scenarios,&amp;quot;&amp;amp;nbsp;&#039;&#039;Nature&#039;&#039;312: 691-695.&lt;br /&gt;
&lt;br /&gt;
Kehoe, Timothy J. 1996. Social Accounting Matrices and Applied General Equilibrium Models. Federal Reserve Bank of Minneapolis, Working Paper 563.&lt;br /&gt;
&lt;br /&gt;
Kennedy, Paul. 1993.&amp;amp;nbsp;&#039;&#039;Preparing for the Twenty-First Century&#039;&#039;. New York: Random House.&lt;br /&gt;
&lt;br /&gt;
Klein, Lawrence R. and Fu-chen Lo, eds. 1995.&amp;amp;nbsp;&#039;&#039;Modeling Global Change&#039;&#039;. Tokyo: United Nations University Press.&lt;br /&gt;
&lt;br /&gt;
Kornai, J. 1971.&amp;amp;nbsp;&#039;&#039;Anti-Equilibrium&#039;&#039;. Amsterdam: North Holland.&lt;br /&gt;
&lt;br /&gt;
Kwasnicki, Witold and Halina Kwasnicka. 1996. &amp;quot;Long-Term Diffusion Factors of Technological Development: An Evolutionary Model and Case Study,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 31-57.&lt;br /&gt;
&lt;br /&gt;
Leontief, Wassily, Anne Carter and Peter Petri. 1977.&amp;amp;nbsp;&#039;&#039;The Future of the World Economy&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander H., and Elizabeth R. Ducot. 1976. &amp;quot;AGRIMOD: A Simulation Model for the Analysis of U.S. Food Policies.&amp;quot; Paper delivered at Conference on Systems Analysis of Grain Reserves, Joint Annual Meeting of GRSA and TIMS, Philadelphia, Pa., March 31-April 2.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander, H., et al. 1977. Energy in Agriculture: On Modeling Inputs in AGRIMOD. Final Report to U.S. Department of Energy. Palo Alto: Systems Control, Inc., August, available through NTIS.&lt;br /&gt;
&lt;br /&gt;
Lichbach, Mark Irving. 1989. &amp;quot;An Evaluation of ‘Does Economic Inequality Breed Political Conflict?,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;, Vol 41 , No. 4 (July 1989): 431-470.&lt;br /&gt;
&lt;br /&gt;
Liverman, Dianne. 1983.&amp;amp;nbsp;&#039;&#039;The Use of Global Simulation Models in Assessing Climate Impacts on the World Food System&#039;&#039;. Dissertation, University of California, Los Angeles.&lt;br /&gt;
&lt;br /&gt;
Londregan, John B. and Keith T. Poole. 1996. &amp;quot;Does High Income Promote Democrary?&amp;quot;,&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 1 (October): 1-30.&lt;br /&gt;
&lt;br /&gt;
MacKenzie, James J. 1996. &amp;quot;Oil as a Finite Resource: When is Global Production Likely to Peak?&amp;quot; Paper of the World Resources Institute. Washington, D.C.: WRI.&lt;br /&gt;
&lt;br /&gt;
Maddison, Angus. 1995.&amp;amp;nbsp;&#039;&#039;Monitoring the World Economy 1820-1992&#039;&#039;. Paris: OECD.&lt;br /&gt;
&lt;br /&gt;
Malthus, Thomas. 1798.&amp;amp;nbsp;&#039;&#039;An Essay on the Principle of Population as It Affects the Future Improvement of Society&#039;&#039;. London (reprinted many times).&lt;br /&gt;
&lt;br /&gt;
Mansfield, Edward D. 1994.&amp;amp;nbsp;&#039;&#039;Power, Trade, and War&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Marchetti, Cesare, Perrin S. Meyer, and Jesse H. Ausubel. 1996. &amp;quot;Human Population Dynamics Revisited with the Logistic Model: How Much Can be Modeled and Predicted?,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 1-30.&lt;br /&gt;
&lt;br /&gt;
Martens, Pim and Jan Rotmans, eds. 1999.&amp;amp;nbsp;&#039;&#039;Climate Change: An Integrated Perspective&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
&lt;br /&gt;
Martens, W.J.M. 1997. &amp;quot;Health Impacts of Climate Change and Ozone Depletion: An Eco-Epidemiological Approach,&amp;quot; Maastricht, the Netherlands: Maastricht University.&lt;br /&gt;
&lt;br /&gt;
Mason, Andrew. 1997. &amp;quot;The Role of Population Change in the Asian Economic Miracle,&amp;quot; Honolulu, Hawaii: East-West Center, AsiaPacific Issues, No. 33 (October), 8 pages.&lt;br /&gt;
&lt;br /&gt;
McMahon, Walter W. 1997.&amp;amp;nbsp;&#039;&#039;Education and Development: Measuring the Social Benefits&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, Jorgen Randers, and William K. Behrens, III. 1972.&amp;amp;nbsp;&#039;&#039;Limits to Growth&#039;&#039;. New York: Universe Books.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, and Jorgen Randers. 1992.&amp;amp;nbsp;&#039;&#039;Beyond the Limits&#039;&#039;. Post Mills, Vermont: Chelsea Green Publishing Company.&lt;br /&gt;
&lt;br /&gt;
Meadows, Dennis L. et al. 1974.&amp;amp;nbsp;&#039;&#039;Dynamics of Growth in a Finite World&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Mesarovic, Mihajlo D. and Eduard Pestel. 1974.&amp;amp;nbsp;&#039;&#039;Mankind at the Turning Point&#039;&#039;. New York: E.P. Dutton &amp;amp; Co.&lt;br /&gt;
&lt;br /&gt;
Mishkin, Eli. And Ludwig Braun, ed. 1961.&amp;amp;nbsp;&#039;&#039;Adaptive Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Moore, Will H., Ronny Lindstrom, and Valerie O’Regan. 1996. &amp;quot;Land Reform, Political Violence and the Economic Inequality-Political Conflict Nexus: A Longitudinal Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Interactions&#039;&#039;&amp;amp;nbsp;21, No. 4: 335-363.&lt;br /&gt;
&lt;br /&gt;
Mori, Shunsuke and Masato Takahaashi, 1997. An Integrated Assessment Model for the Evaluation of New Energy Technologies and Food Production, accepted by&amp;amp;nbsp;&#039;&#039;International Journal of Global Energy Issues&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
Naill, Roger F. 1977.&amp;amp;nbsp;&#039;&#039;Managing the Energy Transition&#039;&#039;. Vols. 1 and 2. Cambridge, Mass: Ballinger Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1992. &amp;quot;The DICE Model: Background and Structure of a Dynamic Integrated Climate Economy,&amp;quot; New Haven: Yale University.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1979.&amp;amp;nbsp;&#039;&#039;The Efficient Use of Energy Resources&#039;&#039;. New Haven, CT: Yale University Press.&lt;br /&gt;
&lt;br /&gt;
Oneal, John R. and Bruce M. Russett. 1997. The Classical Liberals were Right: Democracy, Interdependence, and Conflict, 1950-1985.&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;41, no. 2 (June): 267-294.&lt;br /&gt;
&lt;br /&gt;
Pan, Xiaoming. 2000 (January). &amp;quot;Social and Ecological Accounting Matrix: an Empirical Study for China,&amp;quot; paper submitted for the Thirteenth International Conference on Input-Output Techniques, Macerata, Italy, August 21-25, 2000.&lt;br /&gt;
&lt;br /&gt;
Pesaran, M. Hashem and G. C. Harcourt. 1999. Life and Work of John Richard Nicholas Stone.&lt;br /&gt;
&lt;br /&gt;
Pirages, Dennis. 1989.&amp;amp;nbsp;&#039;&#039;Global Technopolitics&#039;&#039;. Pacific Grove, Calif: Brooks/Cole Publishing.&lt;br /&gt;
&lt;br /&gt;
Prinn, R. H.J., A. Sokolov, C. Wand, X. Xiao, Z. Yang, R. Eckhaus, P. Stone, D. Ellerman, J Melilo, J. Fitzmaurice, D. Kicklighter, and Y. Liu. 1996. &amp;quot;Integrated Global System Model for Climate Policy Analysis: Model Framework and Sensitivity Analysis.&amp;quot; Cambridge, Mass: Global Change Center, Massachusetts Institute of Technology.&lt;br /&gt;
&lt;br /&gt;
Przeworski, Adam and Fernando Limongi. 1997. &amp;quot;Modernization: Theories and Facts,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 2 (January): 155-183.&lt;br /&gt;
&lt;br /&gt;
Population Reference Bureau. 1996. World Population Data Sheet 1996. Washington, D.C.: Population Reference Bureau.&lt;br /&gt;
&lt;br /&gt;
Postel, Sandra. 1996.&amp;amp;nbsp;&#039;&#039;Dividing the Waters: Food Security, Ecosystem Health, and the New Politics of Scarcity&#039;&#039;. Worldwatch Paper 132. Washington, D.C.: Worldwatch Institute, September.&lt;br /&gt;
&lt;br /&gt;
Pyatt, G. and J.I. Round, eds. 1985.&amp;amp;nbsp;&#039;&#039;Social Accounting Matrices: A Basis for Planning&#039;&#039;. Washington, D.C.: The World Bank.&lt;br /&gt;
&lt;br /&gt;
Raskin, P., T. Banuri, G. Gallopín, P. Gutman, A. Hammond, R. Kates, and R. Swart. 2001. Great Transition:&amp;amp;nbsp;&#039;&#039;The Promise and Lure of the Times Ahead&#039;&#039;. Forthcoming.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1990.&amp;amp;nbsp;&#039;&#039;Global Politics&#039;&#039;, 4th edition. Boston: Houghton Mifflin.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1995.&amp;amp;nbsp;&#039;&#039;Democracy and International Conflict&#039;&#039;. Columbia: University of South Carolina Press.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee and J. David Singer. 1973. &amp;quot; Measuring the Concentration of Power in the International System,&amp;quot;&#039;&#039;&amp;amp;nbsp;Sociological Methods and Research&#039;&#039;&amp;amp;nbsp;1, no. 4: 403-436. Reprinted in&amp;amp;nbsp;&#039;&#039;Measuring the Correlates of War&#039;&#039;, edited by J. David Singer and Paul Diehl. Ann Arbor: University of Michigan Press, 1990.&lt;br /&gt;
&lt;br /&gt;
Rayner. S. 1992. &amp;quot;Cultural Theory and Risk Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Theory of Risk&#039;&#039;, ed. G. D. Preagor. Westport, USA.&lt;br /&gt;
&lt;br /&gt;
Repetto, Robert and Duncan Austin. 1997.&amp;amp;nbsp;&#039;&#039;The Costs of Climate Protection&#039;&#039;. Washington, D.C.: World Resources Institute.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis Fry. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Chicago: Quadrangle Books.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis F. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Pittsburgh: Boxwood Press.&lt;br /&gt;
&lt;br /&gt;
Romer, Paul M. 1994. &amp;quot;The Origins of Endogenous Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Economic Perspectives&#039;&#039;Vol 8, No. 1 (Winter): 3-22.&lt;br /&gt;
&lt;br /&gt;
Root T. and Stephen Schneider. 1995. &amp;quot;Ecology and Climate: Research Strategies and Implications,&amp;quot; Science 269 (52): 334-341.&lt;br /&gt;
&lt;br /&gt;
Rosegrant, Mark W., Mercedita Agcaoili-Sombilla, and Nicostrato D. Perez. 1995. &amp;quot;Global Food Projections to 2020: Implications for Investment.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 5.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1999. Integrated Assessment Models: Uncertainty, Quality and Use. Maastricht, the Netherlands: Maastricht University, International Centre for Integrative Studies (ICIS), Working Paper 199-E005.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and Burt de Vries, eds. 1997.&amp;amp;nbsp;&#039;&#039;Perspectives on Global Change: The Targets Approach&#039;&#039;. Cambridge, UK: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and M.B.A. van Asselt. 1996. &amp;quot;Integrated Assessment: A Growing Child on its Way to Maturity,&amp;quot;&amp;amp;nbsp;&#039;&#039;Climatic Change&#039;&#039;&amp;amp;nbsp;34 (3-4): 327-336.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1990.&amp;amp;nbsp;&#039;&#039;IMAGE: An Integrated Model to Assess the Greenhouse Effect&#039;&#039;. Dordrecht, the Netherlands: Kluwer Academics.&lt;br /&gt;
&lt;br /&gt;
Saaty, Thomas L. 1996. The Analytic Network Process: Decision Making with Dependence and Feedback. Pittsburgh: RWS Publications.&lt;br /&gt;
&lt;br /&gt;
Schafer, Andreas and David G. Victor. 1997. The Future Mobility of the World Population. Massachusetts Institute of Technology and International Institute for Applied Systems Analysis, Discussion Paper 97-6-4 (revision 2, September).&lt;br /&gt;
&lt;br /&gt;
Scheer, Sara J. and Satya Yadav. 1996. &amp;quot;Land Degradation in the Developing World: Implications for Food, Agriculture, and the Environment to 2020.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 14.&lt;br /&gt;
&lt;br /&gt;
Schneider, Stephen. 1997. &amp;quot;Integrated Assessment Modeling of Climate Change: Transparent Rational Tool for Policy Making or Opaque Screen Hiding Value-Laden Assumptions?&amp;quot;&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(4): 229-250.&lt;br /&gt;
&lt;br /&gt;
Schwartz, Peter. 1996.&#039;&#039;&amp;amp;nbsp;The Art of the Long View.&#039;&#039;&amp;amp;nbsp;New York: Doubleday.&lt;br /&gt;
&lt;br /&gt;
Sedjo, Roger A. 1995. &amp;quot;Forests: Conflicting Signals,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 178-209.&lt;br /&gt;
&lt;br /&gt;
Shane, Harold G. and Gary A. Sojka. 1990. &amp;quot;John Elfreth Watkins, Jr.: Forgotten Genius of Forecasting,&amp;quot; in Edward Cornish, ed.,&#039;&#039;&amp;amp;nbsp;The 1990s and Beyond&#039;&#039;. Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Shaw, Timothy W. and Clement E. Adibe. 1995-96. &amp;quot;Africa and Global Developments in the Twenty-First Century,&amp;quot; International Journal 51 (Winter): 1-26.&lt;br /&gt;
&lt;br /&gt;
Siegmann, Heinrich. 1985.&amp;amp;nbsp;&#039;&#039;Recent Developments in World Modeling&#039;&#039;. Berlin: Science Center.&lt;br /&gt;
&lt;br /&gt;
Simon, Julian. 1981.&amp;amp;nbsp;&#039;&#039;The Ultimate Resource&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Singer, J. David, Stuart Bremer, and John Stuckey. 1972. &amp;quot;Capability Distribution, Uncertainty, and Major Power Wars, 1820-1965.&amp;quot; In Bruce Russett, ed.,&amp;amp;nbsp;&#039;&#039;Peace, War, and Numbers.&#039;&#039;&amp;amp;nbsp;Beverly Hills: Sage.&lt;br /&gt;
&lt;br /&gt;
Sivard, Ruth Leger. 1993.&amp;amp;nbsp;&#039;&#039;World Military and Social Expenditures 1993.&#039;&#039;&amp;amp;nbsp;Washington, D.C. 20007: World Priorities, Box 25140.&lt;br /&gt;
&lt;br /&gt;
Solow, Robert M. 1956. &amp;quot;A Contribution to the Theory of Economic Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Quarterly Journal of Economics&#039;&#039;&amp;amp;nbsp;70, 1 (February): 65-94.&lt;br /&gt;
&lt;br /&gt;
Stanford University. 1978.&amp;amp;nbsp;&#039;&#039;Stanford Pilot Energy/Economic Model&#039;&#039;. Stanford: Department of Research, Interim Report, Vol. 1.&lt;br /&gt;
&lt;br /&gt;
Stockholm International Peace Research Institute (SIPRI). 1994.&amp;amp;nbsp;&#039;&#039;SIPRI Yearbook&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Stone, Richard. 1986. &amp;quot;The Accounts of Society,&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Applied Econometrics&#039;&#039;&amp;amp;nbsp;1, no. 1 (January): 5-28.&lt;br /&gt;
&lt;br /&gt;
Strategic Assessments Group (SAG), Office of Transnational Issues, Directorate of Intelligence. 2001 (February). The Global Economy in the Long Term. OTI IR 2001-013.&lt;br /&gt;
&lt;br /&gt;
Systems Analysis Research Unit (SARU). 1977.&amp;amp;nbsp;&#039;&#039;SARUM 76 Global Modeling Project&#039;&#039;. Departments of the Environment and Transport, 2 Marsham Street, London, 3WIP 3EB.&lt;br /&gt;
&lt;br /&gt;
Tammen, Ronald L, Jacek Kugler, Douglas Lemke, Allan C. Stam III, Carole Alsharabati, Mark Andrew Abdollahian, Brian Efird, and A.F.K. Organski. 2000. Power Transitions: Strategies for the 21st Century. New York: Chatham House Publishers.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1975. &amp;quot;Theoretical Foundations and Technical Implications.&amp;quot; in Charles Blitzer, Peter Clark and Lance Taylor, eds.,&amp;amp;nbsp;&#039;&#039;Economy-Wide Models and Development Planning.&#039;&#039;&amp;amp;nbsp;Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1979.&amp;amp;nbsp;&#039;&#039;Macro Models for Developing Countries&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Thirlwall, A. P. 1977.&amp;amp;nbsp;&#039;&#039;Growth and Development&#039;&#039;. New York: John Wiley and Sons.&lt;br /&gt;
&lt;br /&gt;
Thompson, M. 1997. Cultural Theory and Integrated Assessment.&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(3): 139-150.&lt;br /&gt;
&lt;br /&gt;
Thompson, M., R. Ellis and A. Wildavsky. 1990.&amp;amp;nbsp;&#039;&#039;Cultural Theory&#039;&#039;. Boulder, Co: Westview Press.&lt;br /&gt;
&lt;br /&gt;
Thorbecke, Erik. 2001. &amp;quot;The Social Accounting Matrix: Deterministic or Stochastic Concept?&amp;quot;, paper prepared for a conference in honor of Graham Pyatt&#039;s retirement, at the Institute of Social Studies, The Hague, Netherlands (November 29 and 30). Available at [http://people.cornell.edu/pages/et17/etpapers.html http://people.cornell.edu/pages/et17/etpapers.html].&lt;br /&gt;
&lt;br /&gt;
United Nations, Department of Economic and Social Affairs. 1956.&amp;amp;nbsp;&#039;&#039;Methods of Population Projections by Sex and Age&#039;&#039;. New York: United Nations, ST/SOA Series A.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1992.&amp;amp;nbsp;&#039;&#039;Long-Range World Population Projections. Two Centuries of Population Growth: 1950-2150&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1993.&amp;amp;nbsp;&#039;&#039;World Population Prospects - the 1992 Revision&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations Development Program (UNDP). 1995.&amp;amp;nbsp;&#039;&#039;Human Development Report&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1992.&amp;amp;nbsp;&#039;&#039;Production Yearbook.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1995.&#039;&#039;&amp;amp;nbsp;World Agriculture: Towards 2010.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 1999. The World at Six Billion New York: UN.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2000. Replacement Migration: Is it a Solution to Declining and Ageing Populations? New York: UN.&lt;br /&gt;
&lt;br /&gt;
United States Arms Control and Disarmament Agency (ACDA). 1995.&amp;amp;nbsp;&#039;&#039;World Military Expenditures and Arms Transfers 1995&#039;&#039;. Washington, D.C.: Arms Control and Disarmament Agency.&lt;br /&gt;
&lt;br /&gt;
United States Bureau of the Census. 1991.&amp;amp;nbsp;&#039;&#039;World Population Profile: 1991&#039;&#039;. Report WP/91 Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Walters, Robert S. and David H. Blake. 1992.&amp;amp;nbsp;&#039;&#039;The Politics of Global Economic Relations&#039;&#039;, 4th edition. Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Waltz, Kenneth N. 1959. Man, the State, and War: A Theoretical Analysis. New York: Columbia University Press.&lt;br /&gt;
&lt;br /&gt;
Watkins, John Elfreth, Jr. 1990. &amp;quot;What May Happen in the Next Hundred Years,&amp;quot; in Edward Cornish, ed.,&amp;amp;nbsp;&#039;&#039;The 1990s and Beyond.&#039;&#039;&amp;amp;nbsp;Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Wildavsky, Aaron, and Ellen Tenenbaum. 1981.&amp;amp;nbsp;&#039;&#039;The Politics of Mistrust&#039;&#039;. Beverly Hills: Sage Publications.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1991b.&amp;amp;nbsp;&#039;&#039;World Tables 1991&#039;&#039;. New York: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1995&amp;amp;nbsp;&#039;&#039;World Development Report 1995&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
World Energy Council (WEC) Commission. 1993.&amp;amp;nbsp;&#039;&#039;Energy for Tomorrow’s World.&#039;&#039;&amp;amp;nbsp;New York: St. Martin’s Press.&lt;br /&gt;
&lt;br /&gt;
World Resources Institute (WRI). 1994.&amp;amp;nbsp;&#039;&#039;World Resources 1994-95.&#039;&#039;&amp;amp;nbsp;New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Wortman, Sterling and Ralph W. Cummings, Jr. 1978.&#039;&#039;&amp;amp;nbsp;To Feed This World&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Zinnes, Dina A. and John W. Gillespie, eds. 1976.&amp;amp;nbsp;&#039;&#039;Mathematical Models in International Relations&#039;&#039;&amp;amp;nbsp;(New York: Preaeger).&lt;/div&gt;</summary>
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	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Additional_resources&amp;diff=8330</id>
		<title>Additional resources</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Additional_resources&amp;diff=8330"/>
		<updated>2017-09-07T22:34:07Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
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&lt;div&gt;[[Data|Data]]&lt;br /&gt;
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[http://pardee.du.edu/wiki/index.php?title=Model_validation_and_verification Model&amp;amp;nbsp;validation&amp;amp;nbsp;and&amp;amp;nbsp;verification]&lt;br /&gt;
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[http://pardee.du.edu/wiki/index.php?title=Consolidation Consolidation]&lt;br /&gt;
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[[Preprocessor|Preprocessor]]&lt;br /&gt;
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[[Sub-modules|Sub-modules]]&lt;br /&gt;
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[[SubRegionalization_Handbook|Sub-Regionalization Handbook]]&lt;br /&gt;
&lt;br /&gt;
[http://pardee.du.edu/wiki/index.php?title=Version_notes Version notes]&lt;br /&gt;
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[[IFs_Bibliography]]&lt;br /&gt;
&lt;br /&gt;
=== In progress[[http://pardee.du.edu/wiki/index.php?title=International_Futures_(IFs)&amp;amp;action=edit&amp;amp;section=1 edit]] ===&lt;br /&gt;
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[[Sandbox|Sandbox]]&lt;/div&gt;</summary>
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	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Support_for_IFs_Use&amp;diff=8328</id>
		<title>Support for IFs Use</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Support_for_IFs_Use&amp;diff=8328"/>
		<updated>2017-09-07T22:32:17Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: Created page with &amp;quot;= &amp;#039;&amp;#039;&amp;#039;Support_for_IFs_Use&amp;#039;&amp;#039;&amp;#039; =  == &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Publications on IFs&amp;lt;/span&amp;gt; ==  To obtain additional information about IFs and its use, consult:  Barry B. Hu...&amp;quot;&lt;/p&gt;
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&lt;div&gt;= &#039;&#039;&#039;Support_for_IFs_Use&#039;&#039;&#039; =&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Publications on IFs&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
To obtain additional information about IFs and its use, consult:&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes and Evan E. Hillebrand, &#039;&#039;&#039;Exploring and Shaping International Futures.&#039;&#039;&#039; Boulder, CO: Paradigm Publishers, 2006. Specifically, see chapter 4.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &#039;&#039;&#039;International Futures: Choices in the Face of Uncertainty,&#039;&#039;&#039; 3rd ed. Boulder, CO: Westview Press, 1999. This volume is built around IFs and contains detailed suggestions for its use. Version 3.17 of IFs, which runs under Windows 95, is distributed with the third edition of the book. The second edition contained a version for Windows 3.1, and the first edition ran under DOS. Chapter 4 of the 2nd edition of IFs included Flow Charts of Worldviews , reproduced now in this Help system.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &#039;&#039;&#039;Continuity and Change in World Politics,&#039;&#039;&#039; 4th ed. Englewood Cliffs, N.J.: Prentice Hall, 2000. IFs can also usefully supplement this textbook on global politics.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &amp;quot;The International Futures (IFs) Modeling Project. 1999. &#039;&#039;&#039;Simulation and Gaming&#039;&#039;&#039; 30, No. 3 (September): 304-326.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;IFs Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Alcamo, Joseph, Rik Leemans and Eric Kreileman, eds. 1998.&amp;amp;nbsp;&#039;&#039;Global Change Scenarios of the 21st Century: Results from the IMAGE 2.1 Model&#039;&#039;. The Netherlands: Pergamon.&lt;br /&gt;
&lt;br /&gt;
Alcamo, Joseph. 1994.&amp;amp;nbsp;&#039;&#039;IMAGE 2.0: Integrated Modeling of Global Climate Change&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
&lt;br /&gt;
Alexandratos, Nikos, ed. 1995.&amp;amp;nbsp;&#039;&#039;World Agriculture: Towards 2010&#039;&#039;&amp;amp;nbsp;(An FAO Study). New York: FAO and John Wiley and Sons.&lt;br /&gt;
&lt;br /&gt;
Allen, R. G. D. 1968.&amp;amp;nbsp;&#039;&#039;Macro-Economic Theory: A Mathematical Treatment&#039;&#039;. New York: St. Martin&#039;s Press.&lt;br /&gt;
&lt;br /&gt;
Avery, Dennis. 1995. &amp;quot;Saving the Planet with Pesticides,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 50-82.&lt;br /&gt;
&lt;br /&gt;
Bailey, Ronald, ed. 1995.&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;. New York: The Free Press.&lt;br /&gt;
&lt;br /&gt;
Barbieri, Kathleen. 1996. &amp;quot;Economic Interdependence: A Path to Peace or a Source of Interstate Conflict?&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Peace Research&#039;&#039;&amp;amp;nbsp;33: 29-50.&lt;br /&gt;
&lt;br /&gt;
Barker, T.S. and A.W.A. Peterson, eds. 1987.&amp;amp;nbsp;&#039;&#039;The Cambridge Multisectoral Dynamic Model of the British Economy&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Barney, Gerald O., W. Brian Kreutzer, and Martha J. Garrett, eds. 1991.&amp;amp;nbsp;&#039;&#039;Managing a Nation&#039;&#039;, 2nd ed. Boulder: Westview Press.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. 1997.&amp;amp;nbsp;&#039;&#039;Determinants of Economic Growth: A Cross-Country Empirical Study&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. and Xavier Sala-i-Martin. 1999.&amp;amp;nbsp;&#039;&#039;Economic Growth&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
&lt;br /&gt;
Bennett, D. Scott, and Allan Stam. 2003.&amp;amp;nbsp;&#039;&#039;The Behavioral Origins of War: Cumulation and Limits to Knowledge in Understanding International Conflict&#039;&#039;. Ann Arbor: University of Michigan Press&lt;br /&gt;
&lt;br /&gt;
Birg, Herwig. 1995.&amp;amp;nbsp;&#039;&#039;World Population Projections for the 21st Century&#039;&#039;. Frankfurt: Campus Verlag.&lt;br /&gt;
&lt;br /&gt;
Borock, Donald M. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot;&amp;amp;nbsp;&#039;&#039;Advances in Social Science and Computers&#039;&#039;&amp;amp;nbsp;4, 103-127.&lt;br /&gt;
&lt;br /&gt;
Bos, Eduard, My T. Vu, Ernest Massiah, and Rodolfo A. Bulatao. 1994.&amp;amp;nbsp;&#039;&#039;World Population Projections 1994-95 Edition&#039;&#039;&amp;amp;nbsp;[editions are biannual] Baltimore: Johns Hopkins Press.&lt;br /&gt;
&lt;br /&gt;
Boulding, Elise and Kenneth E. Boulding. 1995.&amp;amp;nbsp;&#039;&#039;The Future: Images and Processes&#039;&#039;. Thousand Oaks, CA: Sage Publications.&lt;br /&gt;
&lt;br /&gt;
Brecke, Peter. 1993. &amp;quot;Integrated Global Models that Run on Personal Computers,&amp;quot;&amp;amp;nbsp;&#039;&#039;Simulation&#039;&#039;60 (2).&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. 1977.&amp;amp;nbsp;&#039;&#039;Simulated Worlds: A Computer Model of National Decision-Making&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A., ed. 1987.&amp;amp;nbsp;&#039;&#039;The GLOBUS Model: Computer Simulation of World-wide Political and Economic Developments&#039;&#039;. Boulder, CO: Westview.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. and Walter Gruhn. 1988.&amp;amp;nbsp;&#039;&#039;Micro GLOBUS: A Computer Model of Long-Term Global Political and Economic Processes&#039;&#039;. Berlin: edition sigma.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. and Barry B. Hughes. 1990.&amp;amp;nbsp;&#039;&#039;Disarmament and Development: A Design for the Future?&#039;&#039;&amp;amp;nbsp;Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Brockmeier, Martina and Channing Arndt (presentor). 2002. Social Accounting Matrices. Powerpoint presentation on GTAP and SAMs (June 21). Found on the web.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1981.&amp;amp;nbsp;&#039;&#039;Building a Sustainable Society&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1988. &amp;quot;Analyzing the Demographic Trap,&amp;quot; in&amp;amp;nbsp;&#039;&#039;State of the World 1987&#039;&#039;, eds. Lester R. Brown and others. New York: W.W. Norton, pp. 20-37.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1995.&amp;amp;nbsp;&#039;&#039;Who Will Feed China?&#039;&#039;&amp;amp;nbsp;New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1996.&amp;amp;nbsp;&#039;&#039;Tough Choices: Facing the Challenge of Food Scarcity&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., et al. 1996&amp;amp;nbsp;&#039;&#039;State of the World 1996&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., Nicholas Lenssen, and Hal Kane. 1995.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1995. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., Christopher Flavin, and Hal Kane. 1996.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1996. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Burkhardt, Helmut. 1995. &amp;quot;Priorities for a Sustainable Civilization,&amp;quot; unpublished conference paper. Department of Physics, Ryerson Polytechnic University, Toronto, Canada.&lt;br /&gt;
&lt;br /&gt;
Bussolo, Maurizio, Mohamed Chemingui and David O’Connor. 2002. A Multi-Region Social Accounting Matrix (1995) and Regional Environmental General Equilibrium Model for India (REGEMI). Paris: OECD Development Centre (February). Available at&amp;amp;nbsp;[http://www.oecd.org/dev/technics www.oecd.org/dev/technics].&lt;br /&gt;
&lt;br /&gt;
British Petroleum Company. 1995.&amp;amp;nbsp;&#039;&#039;BP Statistical Review of World Energy&#039;&#039;. London: British Petroleum Company.&lt;br /&gt;
&lt;br /&gt;
Central Intelligence Agency (CIA). 1991.&amp;amp;nbsp;&#039;&#039;Handbook of Economic Statistics, 1991&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
&lt;br /&gt;
Central Intelligence Agency (CIA). 1994.&#039;&#039;&amp;amp;nbsp;The World Factbook 1994&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
&lt;br /&gt;
Chang, Sheldon S. L. 1961.&amp;amp;nbsp;&#039;&#039;Synthesis of Optimum Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Chenery, Hollis and Moises Syrquin. 1975.&amp;amp;nbsp;&#039;&#039;Patterns of Development 1950-1970&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Cipolla, Carlo M. 1962.&amp;amp;nbsp;&#039;&#039;The Economic History of World Population&#039;&#039;. Baltimore: Penguin.&lt;br /&gt;
&lt;br /&gt;
Cook, Earl. 1976.&amp;amp;nbsp;&#039;&#039;Man, Energy, Society&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
&lt;br /&gt;
Committee on the Strategic Assessment of the U.S. Department of Energy’s Coal Program. 1995.&amp;amp;nbsp;&#039;&#039;Coal: Energy for the Future&#039;&#039;. Washington, D.C.: National Academy Press.&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1981.&amp;amp;nbsp;&#039;&#039;The Global 2000 Report to the President&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1981b.&amp;amp;nbsp;&#039;&#039;Environmental Trends&#039;&#039;. Washington, D.C. (July).&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1991.&amp;amp;nbsp;&#039;&#039;21st Annual Report&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Crescenzi, Mark J.C. and Andrew J. Enterline. 2001. &amp;quot;Time Remembered: A Dynamic Model of Interstate Interaction,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;45, no. 3 (September): 409-431.&lt;br /&gt;
&lt;br /&gt;
Crosson, Pierre, and Jock R. Anderson. 1992.&amp;amp;nbsp;&#039;&#039;Resources and Global Food Prospects&#039;&#039;. Washington, D.C.: The World Bank. World Bank Technical Paper Number 184.&lt;br /&gt;
&lt;br /&gt;
Cusack, Thomas R. and Richard J. Stoll. 1990.&amp;amp;nbsp;&#039;&#039;Exploring Realpolitik: Probing International Relations with Computer Simulatio&#039;&#039;n. Boulder: Lynne Rienner Publishers.&lt;br /&gt;
&lt;br /&gt;
Dargay, Joyce and Dermot Gately. 1999. &amp;quot;Income’s Effect on Car and Vehicle Ownership, Worldwide: 1960-2015,&amp;quot;&amp;amp;nbsp;&#039;&#039;Transportation Research Part A&#039;&#039;&amp;amp;nbsp;33: 101-138.&lt;br /&gt;
&lt;br /&gt;
Dall, P., Kaspar, F. and Alcamo, J. 1998. &amp;quot;Modeling World-wide Water Availability and Water Use Under the Influence of Climate Change,&amp;quot;&amp;amp;nbsp;&#039;&#039;Proceedings of the Second International Conference on Climate and Water&#039;&#039;, July 17-20, Espoo, Finland.&lt;br /&gt;
&lt;br /&gt;
Dimaranan, Betina V. and Robert A. McDougall, eds. 2002.&amp;amp;nbsp;&#039;&#039;Global Trade, Assistance, and Production: The GTAP 5 Data Base&#039;&#039;. Center for Global Trade Analysis, Purdue University. Available at [http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp].&lt;br /&gt;
&lt;br /&gt;
Dowlatabadi, H., and Morgan, M.G. 1993. &amp;quot;A Model Framework for Integrated Studies of the Climate Problem,&amp;quot;&amp;amp;nbsp;&#039;&#039;Energy Policy&#039;&#039;&amp;amp;nbsp;(March): 209-221.&lt;br /&gt;
&lt;br /&gt;
Duchin, Faye. 1998.&amp;amp;nbsp;&#039;&#039;Structural Economics: Measuring Change in Technology, Lifestyles, and the Environment&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
&lt;br /&gt;
Edwards, Stephen R. 1995. &amp;quot;Conserving Biodiversity,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 212-265.&lt;br /&gt;
&lt;br /&gt;
Edmonds, J., and Reilly, J.M. 1985.&amp;amp;nbsp;&#039;&#039;Global Energy: Assessing the Future&#039;&#039;. Oxford, UK: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Edmonds, J., Pitcher, H. Rosenberg, N., and Wigley, T. &amp;quot;Design for the Global Change Assessment Model.&amp;quot;&amp;amp;nbsp;&#039;&#039;Integrative Assessment of Mitigation, Impacts and Adaptation to Climate Change&#039;&#039;. Laxenburg, Austria.&lt;br /&gt;
&lt;br /&gt;
Ehrlich, Paul R. and Anne H. Ehrlich. 1972.&amp;amp;nbsp;&#039;&#039;Population, Resources, Environment&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
&lt;br /&gt;
Eicher, Carl. 1982. &amp;quot;Facing up to Africa&#039;s Food Crisis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Foreign Affairs&#039;&#039;&amp;amp;nbsp;61, no. 1 (Fall): 151-74.&lt;br /&gt;
&lt;br /&gt;
Eberstadt, Nicholas. 1995. &amp;quot;Population, Food, and Income,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 8-47.&lt;br /&gt;
&lt;br /&gt;
Esty, Daniel C., Jack A. Goldstone, Ted Robert Gurr, Barbara Harff, Marc Levy, Geoffrey D. Dabelko, Pamela T. Surko, and Alan N. Unger. 1998. State Failure Task Force Report: Phase II Findings. Volume provided courtesy of Ted Robert Gurr.&lt;br /&gt;
&lt;br /&gt;
Flavin, Christopher. 1996. &amp;quot;Facing Up to the Risks of Climate Change,&amp;quot; in Lester R. Brown and others, eds., State of the World 1996 (New York: W.W. Norton), pp. 21-39.&lt;br /&gt;
&lt;br /&gt;
Forrester, Jay W. 1968.&amp;amp;nbsp;&#039;&#039;Principles of Systems&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Gilpin, Robert. 1981.&amp;amp;nbsp;&#039;&#039;War and Change in World Politics&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Globerman, Steven. 2000 (May). Linkages Between Technological Change and Productivity Growth. Industry Canada Research Publications Program: Occasional Paper 23.&lt;br /&gt;
&lt;br /&gt;
Grant, Lindsey. 1982.&amp;amp;nbsp;&#039;&#039;The Cornucopian Fallacies&#039;&#039;. Washington, D.C.: Environmental Fund.&lt;br /&gt;
&lt;br /&gt;
Griffith, Rachel, Stephen Redding, and John Van Reenen. 2000.&amp;amp;nbsp;&#039;&#039;Mapping the Two Faces of R&amp;amp;D: Productivity Growth in a Panel of OECD Industries&#039;&#039;. Institute for Fiscal Studies (January)&lt;br /&gt;
&lt;br /&gt;
Gwartney, James and Robert Lawson with Dexter Samida. 2000.&amp;amp;nbsp;&#039;&#039;Economic Freedom of the World: 2000 Annual Report&#039;&#039;. Vancouver, B.C.: the Fraser Institute.&lt;br /&gt;
&lt;br /&gt;
Hammond, Allen. 1998.&amp;amp;nbsp;&#039;&#039;Which World? Scenarios for the 21st Century&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
&lt;br /&gt;
Harff, Barbara, with Ted Robert Gurr and Alan Unger. 1999. Preconditions of Genocide and Politicide: 1955-1998. Paper prepared for the State Failure Task Force and provided courtesy of Barbara Harff and Ted Gurr.&lt;br /&gt;
&lt;br /&gt;
Henderson, Hazel. 1996. &amp;quot;Changing Paradigms and Indicators: Implementing Equitable, Sustainable and Participatory Development,&amp;quot; in Jo Marie Griesgraber and Bernhard G. Gunter,&amp;amp;nbsp;&#039;&#039;Development: New Paradigms and Principles for the 21st Century&#039;&#039;. East Haven, CT: Pluto Press, pp. 103-136.&lt;br /&gt;
&lt;br /&gt;
Herrera, Amilcar O., et al. 1976.&#039;&#039;&amp;amp;nbsp;Catastrophe or New Society? A Latin American World Model&#039;&#039;. Ottawa: International Development Research Centre.&lt;br /&gt;
&lt;br /&gt;
Hoekstra, A.Y. 1998.&amp;amp;nbsp;&#039;&#039;Perspectives on Water: An Integrated Model-Based Exploration of the Future&#039;&#039;. Utrecht, the Netherlands: International Books.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1980.&amp;amp;nbsp;&#039;&#039;World Modeling&#039;&#039;. Lexington, Mass: Lexington Books.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1982.&amp;amp;nbsp;&#039;&#039;International Futures Simulation: User&#039;s Manual&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985a.&amp;amp;nbsp;&#039;&#039;International Futures Simulation&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985b. &amp;quot;World Models: The Bases of Difference,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;29, 77-101.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985c.&amp;amp;nbsp;&#039;&#039;World Futures: A Critical Analysis of Alternatives&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1987. &amp;quot;Domestic Economic Processes,&amp;quot; in Stuart A. Bremer, ed.,&amp;amp;nbsp;&#039;&#039;The Globus Model: Computer Simulation of Worldwide Political Economic Development&#039;&#039;&amp;amp;nbsp;(Frankfurt and Boulder: Campus and Westview), pp. 39-158.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1988. &amp;quot;International Futures: History and Status,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Science Microcomputer Review&#039;&#039;&amp;amp;nbsp;6, 43-48.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1999. &amp;quot;The International Futures (IFs) Modeling Project.&#039;&#039;&amp;amp;nbsp;Simulation and Gaming&#039;&#039;&amp;amp;nbsp;Vol 30, No. 3 (September): 304-326.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1999.&amp;amp;nbsp;&#039;&#039;International Futures&#039;&#039;, 3rd edition Boulder: Westview Press, 1999.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2000.&amp;amp;nbsp;&#039;&#039;Continuity and Change in World Politics&#039;&#039;. Englewood Cliffs, N.J.: Prentice-Hall, fourth edition.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2001. &amp;quot;Global Social Transformation: The Sweet Spot, the Steady Slog, and the Systemic Shift,&amp;quot;&amp;amp;nbsp;&#039;&#039;Economic Development and Cultural Change&#039;&#039;&amp;amp;nbsp;49, No. 2 (January): 423-458.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2002.&amp;amp;nbsp;&#039;&#039;Theats and Opportunities Analysis&#039;&#039;. Living document prepared for the Strategic Assessments Group, Office of Transnational Issues, Central Intelligence Agency, August 2002.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. and Anwar Hossain. 2003. Long-Term Socio-Economic Modeling: With Universal, Globally-Integrated Social Accounting Matrices (SAMs) in a General Equilibrium Model Structure. IFs Project Living Document, University of Denver.&lt;br /&gt;
&lt;br /&gt;
Huth, Paul. 1996.&amp;amp;nbsp;&#039;&#039;Standing Your Ground: Territorial Disputes and International Conflict&#039;&#039;. Ann Arbor, MI: University of Michigan Press.&lt;br /&gt;
&lt;br /&gt;
Inglehart, Ronald. 1997.&amp;amp;nbsp;&#039;&#039;Modernization and Postmodernization: Cultural, Economic, and Political Change in 43 Societies&#039;&#039;. Ewing, NJ: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1995.&amp;amp;nbsp;&#039;&#039;Oil, Gas, and Coal Supply Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996.&amp;amp;nbsp;&#039;&#039;World Energy Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996b.&amp;amp;nbsp;&#039;&#039;The Strategic Value of Fossil Fuels: Challenges and Responses&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;International Financial Statistics&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;World Economic Outlook&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
Intergovernmental Panel on Climate Change (IPCC). 1995. Several volumes by various working groups. Published by Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Jansen, Karel and Rob Vos, eds. 1997.&amp;amp;nbsp;&#039;&#039;External Finance and Adjustment: Failure and Success in the Developing World&#039;&#039;. London: Macmillan Press Ltd.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1998.&amp;amp;nbsp;&#039;&#039;Modeling Global Change: The Art of Integrated Assessment Modelling&#039;&#039;. Cheltenham, UK: Edward Elgar.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1996.&amp;amp;nbsp;&#039;&#039;Meeting Targets: Tools to Support Integrated Modelling of Global Change&#039;&#039;. Den Haag: CIP-Gegevens Koninklijke Bibliotheek.&lt;br /&gt;
&lt;br /&gt;
Jansson, Kurt, Michael Harris, Angela Penrose. 1987.&amp;amp;nbsp;&#039;&#039;The Ethiopian Famine&#039;&#039;. London: Zed Books Ltd.&lt;br /&gt;
&lt;br /&gt;
Jeffreys, Kent. 1995. &amp;quot;Rescuing the Oceans,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 296-338.&lt;br /&gt;
&lt;br /&gt;
Jones, Daniel M., Stuart A. Bremer, and J. David Singer. 1996. &amp;quot;Militarized Interstate Disputes, 1816-1992: Rationale, Coding Rules, and Empirical Patterns,&amp;quot;&amp;amp;nbsp;&#039;&#039;Conflict Management and Peace Science&#039;&#039;&amp;amp;nbsp;XV, No. 2: 163-215.&lt;br /&gt;
&lt;br /&gt;
Khan, Haider A. 1998.&amp;amp;nbsp;&#039;&#039;Technology, Development and Democracy&#039;&#039;. Northhampton, Mass: Edward Elgar Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Kahn, Herman, William Brown, and Leon Martel. 1976.&amp;amp;nbsp;&#039;&#039;The Next 200 Years&#039;&#039;. New York: William Morrow.&lt;br /&gt;
&lt;br /&gt;
Kalymon, Basil A. 1975. &amp;quot;Economic Incentives in OPEC Oil Pricing Policy.&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Development Economics&#039;&#039;&amp;amp;nbsp;2: 337-362.&lt;br /&gt;
&lt;br /&gt;
Kaplan, Robert. 1994. &amp;quot;The Coming Anarchy,&amp;quot;&amp;amp;nbsp;&#039;&#039;The Atlantic Monthly&#039;&#039;&amp;amp;nbsp;273 (February): .&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999a. &amp;quot;Aggregating Governance Indicators&amp;quot;. World Bank Policy Research Department Working Paper No. 2195.&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999b. &amp;quot;Governance Matters&amp;quot;. World Bank Policy Research Department Working Paper No. 2196.&lt;br /&gt;
&lt;br /&gt;
Keepin, B. and B. Wynne. 1984. &amp;quot;Technical Analysis of the IIASA Energy Scenarios,&amp;quot;&amp;amp;nbsp;&#039;&#039;Nature&#039;&#039;312: 691-695.&lt;br /&gt;
&lt;br /&gt;
Kehoe, Timothy J. 1996. Social Accounting Matrices and Applied General Equilibrium Models. Federal Reserve Bank of Minneapolis, Working Paper 563.&lt;br /&gt;
&lt;br /&gt;
Kennedy, Paul. 1993.&amp;amp;nbsp;&#039;&#039;Preparing for the Twenty-First Century&#039;&#039;. New York: Random House.&lt;br /&gt;
&lt;br /&gt;
Klein, Lawrence R. and Fu-chen Lo, eds. 1995.&amp;amp;nbsp;&#039;&#039;Modeling Global Change&#039;&#039;. Tokyo: United Nations University Press.&lt;br /&gt;
&lt;br /&gt;
Kornai, J. 1971.&amp;amp;nbsp;&#039;&#039;Anti-Equilibrium&#039;&#039;. Amsterdam: North Holland.&lt;br /&gt;
&lt;br /&gt;
Kwasnicki, Witold and Halina Kwasnicka. 1996. &amp;quot;Long-Term Diffusion Factors of Technological Development: An Evolutionary Model and Case Study,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 31-57.&lt;br /&gt;
&lt;br /&gt;
Leontief, Wassily, Anne Carter and Peter Petri. 1977.&amp;amp;nbsp;&#039;&#039;The Future of the World Economy&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander H., and Elizabeth R. Ducot. 1976. &amp;quot;AGRIMOD: A Simulation Model for the Analysis of U.S. Food Policies.&amp;quot; Paper delivered at Conference on Systems Analysis of Grain Reserves, Joint Annual Meeting of GRSA and TIMS, Philadelphia, Pa., March 31-April 2.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander, H., et al. 1977. Energy in Agriculture: On Modeling Inputs in AGRIMOD. Final Report to U.S. Department of Energy. Palo Alto: Systems Control, Inc., August, available through NTIS.&lt;br /&gt;
&lt;br /&gt;
Lichbach, Mark Irving. 1989. &amp;quot;An Evaluation of ‘Does Economic Inequality Breed Political Conflict?,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;, Vol 41 , No. 4 (July 1989): 431-470.&lt;br /&gt;
&lt;br /&gt;
Liverman, Dianne. 1983.&amp;amp;nbsp;&#039;&#039;The Use of Global Simulation Models in Assessing Climate Impacts on the World Food System&#039;&#039;. Dissertation, University of California, Los Angeles.&lt;br /&gt;
&lt;br /&gt;
Londregan, John B. and Keith T. Poole. 1996. &amp;quot;Does High Income Promote Democrary?&amp;quot;,&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 1 (October): 1-30.&lt;br /&gt;
&lt;br /&gt;
MacKenzie, James J. 1996. &amp;quot;Oil as a Finite Resource: When is Global Production Likely to Peak?&amp;quot; Paper of the World Resources Institute. Washington, D.C.: WRI.&lt;br /&gt;
&lt;br /&gt;
Maddison, Angus. 1995.&amp;amp;nbsp;&#039;&#039;Monitoring the World Economy 1820-1992&#039;&#039;. Paris: OECD.&lt;br /&gt;
&lt;br /&gt;
Malthus, Thomas. 1798.&amp;amp;nbsp;&#039;&#039;An Essay on the Principle of Population as It Affects the Future Improvement of Society&#039;&#039;. London (reprinted many times).&lt;br /&gt;
&lt;br /&gt;
Mansfield, Edward D. 1994.&amp;amp;nbsp;&#039;&#039;Power, Trade, and War&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Marchetti, Cesare, Perrin S. Meyer, and Jesse H. Ausubel. 1996. &amp;quot;Human Population Dynamics Revisited with the Logistic Model: How Much Can be Modeled and Predicted?,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 1-30.&lt;br /&gt;
&lt;br /&gt;
Martens, Pim and Jan Rotmans, eds. 1999.&amp;amp;nbsp;&#039;&#039;Climate Change: An Integrated Perspective&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
&lt;br /&gt;
Martens, W.J.M. 1997. &amp;quot;Health Impacts of Climate Change and Ozone Depletion: An Eco-Epidemiological Approach,&amp;quot; Maastricht, the Netherlands: Maastricht University.&lt;br /&gt;
&lt;br /&gt;
Mason, Andrew. 1997. &amp;quot;The Role of Population Change in the Asian Economic Miracle,&amp;quot; Honolulu, Hawaii: East-West Center, AsiaPacific Issues, No. 33 (October), 8 pages.&lt;br /&gt;
&lt;br /&gt;
McMahon, Walter W. 1997.&amp;amp;nbsp;&#039;&#039;Education and Development: Measuring the Social Benefits&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, Jorgen Randers, and William K. Behrens, III. 1972.&amp;amp;nbsp;&#039;&#039;Limits to Growth&#039;&#039;. New York: Universe Books.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, and Jorgen Randers. 1992.&amp;amp;nbsp;&#039;&#039;Beyond the Limits&#039;&#039;. Post Mills, Vermont: Chelsea Green Publishing Company.&lt;br /&gt;
&lt;br /&gt;
Meadows, Dennis L. et al. 1974.&amp;amp;nbsp;&#039;&#039;Dynamics of Growth in a Finite World&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Mesarovic, Mihajlo D. and Eduard Pestel. 1974.&amp;amp;nbsp;&#039;&#039;Mankind at the Turning Point&#039;&#039;. New York: E.P. Dutton &amp;amp; Co.&lt;br /&gt;
&lt;br /&gt;
Mishkin, Eli. And Ludwig Braun, ed. 1961.&amp;amp;nbsp;&#039;&#039;Adaptive Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Moore, Will H., Ronny Lindstrom, and Valerie O’Regan. 1996. &amp;quot;Land Reform, Political Violence and the Economic Inequality-Political Conflict Nexus: A Longitudinal Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Interactions&#039;&#039;&amp;amp;nbsp;21, No. 4: 335-363.&lt;br /&gt;
&lt;br /&gt;
Mori, Shunsuke and Masato Takahaashi, 1997. An Integrated Assessment Model for the Evaluation of New Energy Technologies and Food Production, accepted by&amp;amp;nbsp;&#039;&#039;International Journal of Global Energy Issues&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
Naill, Roger F. 1977.&amp;amp;nbsp;&#039;&#039;Managing the Energy Transition&#039;&#039;. Vols. 1 and 2. Cambridge, Mass: Ballinger Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1992. &amp;quot;The DICE Model: Background and Structure of a Dynamic Integrated Climate Economy,&amp;quot; New Haven: Yale University.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1979.&amp;amp;nbsp;&#039;&#039;The Efficient Use of Energy Resources&#039;&#039;. New Haven, CT: Yale University Press.&lt;br /&gt;
&lt;br /&gt;
Oneal, John R. and Bruce M. Russett. 1997. The Classical Liberals were Right: Democracy, Interdependence, and Conflict, 1950-1985.&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;41, no. 2 (June): 267-294.&lt;br /&gt;
&lt;br /&gt;
Pan, Xiaoming. 2000 (January). &amp;quot;Social and Ecological Accounting Matrix: an Empirical Study for China,&amp;quot; paper submitted for the Thirteenth International Conference on Input-Output Techniques, Macerata, Italy, August 21-25, 2000.&lt;br /&gt;
&lt;br /&gt;
Pesaran, M. Hashem and G. C. Harcourt. 1999. Life and Work of John Richard Nicholas Stone.&lt;br /&gt;
&lt;br /&gt;
Pirages, Dennis. 1989.&amp;amp;nbsp;&#039;&#039;Global Technopolitics&#039;&#039;. Pacific Grove, Calif: Brooks/Cole Publishing.&lt;br /&gt;
&lt;br /&gt;
Prinn, R. H.J., A. Sokolov, C. Wand, X. Xiao, Z. Yang, R. Eckhaus, P. Stone, D. Ellerman, J Melilo, J. Fitzmaurice, D. Kicklighter, and Y. Liu. 1996. &amp;quot;Integrated Global System Model for Climate Policy Analysis: Model Framework and Sensitivity Analysis.&amp;quot; Cambridge, Mass: Global Change Center, Massachusetts Institute of Technology.&lt;br /&gt;
&lt;br /&gt;
Przeworski, Adam and Fernando Limongi. 1997. &amp;quot;Modernization: Theories and Facts,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 2 (January): 155-183.&lt;br /&gt;
&lt;br /&gt;
Population Reference Bureau. 1996. World Population Data Sheet 1996. Washington, D.C.: Population Reference Bureau.&lt;br /&gt;
&lt;br /&gt;
Postel, Sandra. 1996.&amp;amp;nbsp;&#039;&#039;Dividing the Waters: Food Security, Ecosystem Health, and the New Politics of Scarcity&#039;&#039;. Worldwatch Paper 132. Washington, D.C.: Worldwatch Institute, September.&lt;br /&gt;
&lt;br /&gt;
Pyatt, G. and J.I. Round, eds. 1985.&amp;amp;nbsp;&#039;&#039;Social Accounting Matrices: A Basis for Planning&#039;&#039;. Washington, D.C.: The World Bank.&lt;br /&gt;
&lt;br /&gt;
Raskin, P., T. Banuri, G. Gallopín, P. Gutman, A. Hammond, R. Kates, and R. Swart. 2001. Great Transition:&amp;amp;nbsp;&#039;&#039;The Promise and Lure of the Times Ahead&#039;&#039;. Forthcoming.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1990.&amp;amp;nbsp;&#039;&#039;Global Politics&#039;&#039;, 4th edition. Boston: Houghton Mifflin.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1995.&amp;amp;nbsp;&#039;&#039;Democracy and International Conflict&#039;&#039;. Columbia: University of South Carolina Press.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee and J. David Singer. 1973. &amp;quot; Measuring the Concentration of Power in the International System,&amp;quot;&#039;&#039;&amp;amp;nbsp;Sociological Methods and Research&#039;&#039;&amp;amp;nbsp;1, no. 4: 403-436. Reprinted in&amp;amp;nbsp;&#039;&#039;Measuring the Correlates of War&#039;&#039;, edited by J. David Singer and Paul Diehl. Ann Arbor: University of Michigan Press, 1990.&lt;br /&gt;
&lt;br /&gt;
Rayner. S. 1992. &amp;quot;Cultural Theory and Risk Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Theory of Risk&#039;&#039;, ed. G. D. Preagor. Westport, USA.&lt;br /&gt;
&lt;br /&gt;
Repetto, Robert and Duncan Austin. 1997.&amp;amp;nbsp;&#039;&#039;The Costs of Climate Protection&#039;&#039;. Washington, D.C.: World Resources Institute.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis Fry. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Chicago: Quadrangle Books.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis F. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Pittsburgh: Boxwood Press.&lt;br /&gt;
&lt;br /&gt;
Romer, Paul M. 1994. &amp;quot;The Origins of Endogenous Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Economic Perspectives&#039;&#039;Vol 8, No. 1 (Winter): 3-22.&lt;br /&gt;
&lt;br /&gt;
Root T. and Stephen Schneider. 1995. &amp;quot;Ecology and Climate: Research Strategies and Implications,&amp;quot; Science 269 (52): 334-341.&lt;br /&gt;
&lt;br /&gt;
Rosegrant, Mark W., Mercedita Agcaoili-Sombilla, and Nicostrato D. Perez. 1995. &amp;quot;Global Food Projections to 2020: Implications for Investment.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 5.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1999. Integrated Assessment Models: Uncertainty, Quality and Use. Maastricht, the Netherlands: Maastricht University, International Centre for Integrative Studies (ICIS), Working Paper 199-E005.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and Burt de Vries, eds. 1997.&amp;amp;nbsp;&#039;&#039;Perspectives on Global Change: The Targets Approach&#039;&#039;. Cambridge, UK: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and M.B.A. van Asselt. 1996. &amp;quot;Integrated Assessment: A Growing Child on its Way to Maturity,&amp;quot;&amp;amp;nbsp;&#039;&#039;Climatic Change&#039;&#039;&amp;amp;nbsp;34 (3-4): 327-336.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1990.&amp;amp;nbsp;&#039;&#039;IMAGE: An Integrated Model to Assess the Greenhouse Effect&#039;&#039;. Dordrecht, the Netherlands: Kluwer Academics.&lt;br /&gt;
&lt;br /&gt;
Saaty, Thomas L. 1996. The Analytic Network Process: Decision Making with Dependence and Feedback. Pittsburgh: RWS Publications.&lt;br /&gt;
&lt;br /&gt;
Schafer, Andreas and David G. Victor. 1997. The Future Mobility of the World Population. Massachusetts Institute of Technology and International Institute for Applied Systems Analysis, Discussion Paper 97-6-4 (revision 2, September).&lt;br /&gt;
&lt;br /&gt;
Scheer, Sara J. and Satya Yadav. 1996. &amp;quot;Land Degradation in the Developing World: Implications for Food, Agriculture, and the Environment to 2020.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 14.&lt;br /&gt;
&lt;br /&gt;
Schneider, Stephen. 1997. &amp;quot;Integrated Assessment Modeling of Climate Change: Transparent Rational Tool for Policy Making or Opaque Screen Hiding Value-Laden Assumptions?&amp;quot;&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(4): 229-250.&lt;br /&gt;
&lt;br /&gt;
Schwartz, Peter. 1996.&#039;&#039;&amp;amp;nbsp;The Art of the Long View.&#039;&#039;&amp;amp;nbsp;New York: Doubleday.&lt;br /&gt;
&lt;br /&gt;
Sedjo, Roger A. 1995. &amp;quot;Forests: Conflicting Signals,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 178-209.&lt;br /&gt;
&lt;br /&gt;
Shane, Harold G. and Gary A. Sojka. 1990. &amp;quot;John Elfreth Watkins, Jr.: Forgotten Genius of Forecasting,&amp;quot; in Edward Cornish, ed.,&#039;&#039;&amp;amp;nbsp;The 1990s and Beyond&#039;&#039;. Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Shaw, Timothy W. and Clement E. Adibe. 1995-96. &amp;quot;Africa and Global Developments in the Twenty-First Century,&amp;quot; International Journal 51 (Winter): 1-26.&lt;br /&gt;
&lt;br /&gt;
Siegmann, Heinrich. 1985.&amp;amp;nbsp;&#039;&#039;Recent Developments in World Modeling&#039;&#039;. Berlin: Science Center.&lt;br /&gt;
&lt;br /&gt;
Simon, Julian. 1981.&amp;amp;nbsp;&#039;&#039;The Ultimate Resource&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Singer, J. David, Stuart Bremer, and John Stuckey. 1972. &amp;quot;Capability Distribution, Uncertainty, and Major Power Wars, 1820-1965.&amp;quot; In Bruce Russett, ed.,&amp;amp;nbsp;&#039;&#039;Peace, War, and Numbers.&#039;&#039;&amp;amp;nbsp;Beverly Hills: Sage.&lt;br /&gt;
&lt;br /&gt;
Sivard, Ruth Leger. 1993.&amp;amp;nbsp;&#039;&#039;World Military and Social Expenditures 1993.&#039;&#039;&amp;amp;nbsp;Washington, D.C. 20007: World Priorities, Box 25140.&lt;br /&gt;
&lt;br /&gt;
Solow, Robert M. 1956. &amp;quot;A Contribution to the Theory of Economic Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Quarterly Journal of Economics&#039;&#039;&amp;amp;nbsp;70, 1 (February): 65-94.&lt;br /&gt;
&lt;br /&gt;
Stanford University. 1978.&amp;amp;nbsp;&#039;&#039;Stanford Pilot Energy/Economic Model&#039;&#039;. Stanford: Department of Research, Interim Report, Vol. 1.&lt;br /&gt;
&lt;br /&gt;
Stockholm International Peace Research Institute (SIPRI). 1994.&amp;amp;nbsp;&#039;&#039;SIPRI Yearbook&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Stone, Richard. 1986. &amp;quot;The Accounts of Society,&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Applied Econometrics&#039;&#039;&amp;amp;nbsp;1, no. 1 (January): 5-28.&lt;br /&gt;
&lt;br /&gt;
Strategic Assessments Group (SAG), Office of Transnational Issues, Directorate of Intelligence. 2001 (February). The Global Economy in the Long Term. OTI IR 2001-013.&lt;br /&gt;
&lt;br /&gt;
Systems Analysis Research Unit (SARU). 1977.&amp;amp;nbsp;&#039;&#039;SARUM 76 Global Modeling Project&#039;&#039;. Departments of the Environment and Transport, 2 Marsham Street, London, 3WIP 3EB.&lt;br /&gt;
&lt;br /&gt;
Tammen, Ronald L, Jacek Kugler, Douglas Lemke, Allan C. Stam III, Carole Alsharabati, Mark Andrew Abdollahian, Brian Efird, and A.F.K. Organski. 2000. Power Transitions: Strategies for the 21st Century. New York: Chatham House Publishers.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1975. &amp;quot;Theoretical Foundations and Technical Implications.&amp;quot; in Charles Blitzer, Peter Clark and Lance Taylor, eds.,&amp;amp;nbsp;&#039;&#039;Economy-Wide Models and Development Planning.&#039;&#039;&amp;amp;nbsp;Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1979.&amp;amp;nbsp;&#039;&#039;Macro Models for Developing Countries&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Thirlwall, A. P. 1977.&amp;amp;nbsp;&#039;&#039;Growth and Development&#039;&#039;. New York: John Wiley and Sons.&lt;br /&gt;
&lt;br /&gt;
Thompson, M. 1997. Cultural Theory and Integrated Assessment.&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(3): 139-150.&lt;br /&gt;
&lt;br /&gt;
Thompson, M., R. Ellis and A. Wildavsky. 1990.&amp;amp;nbsp;&#039;&#039;Cultural Theory&#039;&#039;. Boulder, Co: Westview Press.&lt;br /&gt;
&lt;br /&gt;
Thorbecke, Erik. 2001. &amp;quot;The Social Accounting Matrix: Deterministic or Stochastic Concept?&amp;quot;, paper prepared for a conference in honor of Graham Pyatt&#039;s retirement, at the Institute of Social Studies, The Hague, Netherlands (November 29 and 30). Available at [http://people.cornell.edu/pages/et17/etpapers.html http://people.cornell.edu/pages/et17/etpapers.html].&lt;br /&gt;
&lt;br /&gt;
United Nations, Department of Economic and Social Affairs. 1956.&amp;amp;nbsp;&#039;&#039;Methods of Population Projections by Sex and Age&#039;&#039;. New York: United Nations, ST/SOA Series A.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1992.&amp;amp;nbsp;&#039;&#039;Long-Range World Population Projections. Two Centuries of Population Growth: 1950-2150&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1993.&amp;amp;nbsp;&#039;&#039;World Population Prospects - the 1992 Revision&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations Development Program (UNDP). 1995.&amp;amp;nbsp;&#039;&#039;Human Development Report&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1992.&amp;amp;nbsp;&#039;&#039;Production Yearbook.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1995.&#039;&#039;&amp;amp;nbsp;World Agriculture: Towards 2010.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 1999. The World at Six Billion New York: UN.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2000. Replacement Migration: Is it a Solution to Declining and Ageing Populations? New York: UN.&lt;br /&gt;
&lt;br /&gt;
United States Arms Control and Disarmament Agency (ACDA). 1995.&amp;amp;nbsp;&#039;&#039;World Military Expenditures and Arms Transfers 1995&#039;&#039;. Washington, D.C.: Arms Control and Disarmament Agency.&lt;br /&gt;
&lt;br /&gt;
United States Bureau of the Census. 1991.&amp;amp;nbsp;&#039;&#039;World Population Profile: 1991&#039;&#039;. Report WP/91 Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Walters, Robert S. and David H. Blake. 1992.&amp;amp;nbsp;&#039;&#039;The Politics of Global Economic Relations&#039;&#039;, 4th edition. Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Waltz, Kenneth N. 1959. Man, the State, and War: A Theoretical Analysis. New York: Columbia University Press.&lt;br /&gt;
&lt;br /&gt;
Watkins, John Elfreth, Jr. 1990. &amp;quot;What May Happen in the Next Hundred Years,&amp;quot; in Edward Cornish, ed.,&amp;amp;nbsp;&#039;&#039;The 1990s and Beyond.&#039;&#039;&amp;amp;nbsp;Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Wildavsky, Aaron, and Ellen Tenenbaum. 1981.&amp;amp;nbsp;&#039;&#039;The Politics of Mistrust&#039;&#039;. Beverly Hills: Sage Publications.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1991b.&amp;amp;nbsp;&#039;&#039;World Tables 1991&#039;&#039;. New York: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1995&amp;amp;nbsp;&#039;&#039;World Development Report 1995&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
World Energy Council (WEC) Commission. 1993.&amp;amp;nbsp;&#039;&#039;Energy for Tomorrow’s World.&#039;&#039;&amp;amp;nbsp;New York: St. Martin’s Press.&lt;br /&gt;
&lt;br /&gt;
World Resources Institute (WRI). 1994.&amp;amp;nbsp;&#039;&#039;World Resources 1994-95.&#039;&#039;&amp;amp;nbsp;New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Wortman, Sterling and Ralph W. Cummings, Jr. 1978.&#039;&#039;&amp;amp;nbsp;To Feed This World&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Zinnes, Dina A. and John W. Gillespie, eds. 1976.&amp;amp;nbsp;&#039;&#039;Mathematical Models in International Relations&#039;&#039;&amp;amp;nbsp;(New York: Preaeger).&lt;br /&gt;
&lt;br /&gt;
== [[Development_Mode_Features|Development Mode Features]] ==&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8327</id>
		<title>Introduction to IFs</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8327"/>
		<updated>2017-09-07T22:31:56Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Purposes&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;International Futures (IFs) is a tool for thinking about long-term global trends and planning more strategically for the&amp;amp;nbsp;future.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
IFs can help you:&lt;br /&gt;
&lt;br /&gt;
*Understand&amp;amp;nbsp;the state of major&amp;amp;nbsp;global systems&lt;br /&gt;
*Explore&amp;amp;nbsp;long-term trends and consider where they might take&amp;amp;nbsp;us&lt;br /&gt;
*Learn about the dynamic&amp;amp;nbsp;interactions between global systems&lt;br /&gt;
*Clarify long-term organizational goals/priorities&lt;br /&gt;
*Develop alternative scenarios (if-then statements) about the future&lt;br /&gt;
*Investigate how different groups (households, firms or governments) can shape the future&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;IFs development and analysis depend&amp;amp;nbsp;on core, underlying assumptions, including the following.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*Global issues are becoming&amp;amp;nbsp;more significant as the scope of human interaction and human impact on the broader environment grow.&lt;br /&gt;
*Goals and priorities for human systems are becoming clearer and are more frequently and consistently communicated.&lt;br /&gt;
*Understanding of the dynamics of human systems is improving&amp;amp;nbsp;rapidly.&lt;br /&gt;
*The domain of human choice and action is broadening.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;What issues can you&amp;amp;nbsp;investigate with IFs?&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*[[Environment]]: Atmospheric carbon dioxide levels, world forest area, fossil fuel usage&lt;br /&gt;
*[[Socio-Political|Socio-Political Change]]: Life expectancy, literacy rate, democracy level, status of women, value change&lt;br /&gt;
*[[Population|Demographics]]: Population levels and growth, fertility, mortality, migration&lt;br /&gt;
*[[Agriculture|Food and Agriculture]]: Land use and production levels, calorie availability, malnutrition rates&lt;br /&gt;
*[[Energy]]: Resource and production levels, demand patterns, renewable energy share&lt;br /&gt;
*[[Economics]]: Sectoral production, consumption, and trade patterns and structural change&lt;br /&gt;
*[[Interstate_Politics_(IP)|Geopolitics]]: Country and regional power levels&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Visual Representation&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
[[File:IFsOverviewChart.jpg|frame|center|Visual representation of IFs structure]]&lt;br /&gt;
&lt;br /&gt;
Among the philosophical premises of the International Futures (IFs) project is that the model cannot be a &amp;quot;black box&amp;quot; to users and be truly useful. Model users must be able to examine the structures of IFs in order (1) to have confidence in them, and (2) learn from them.&lt;br /&gt;
&lt;br /&gt;
There is available (see topics under Understanding the Mode in the contents of this help system):&lt;br /&gt;
&lt;br /&gt;
*[[Understand_IFs#Dominant_Relations|Dominant Relations]] of the model structure&lt;br /&gt;
*[[Understand_IFs#2.2|Structure-Based and Agent-Class Driven Modeling]]&lt;br /&gt;
*[[Understand_IFs#Equation_Notation|Equation Notation]]&lt;br /&gt;
*[[Introduction_to_IFs#IFs_Bibliography|IFs Bibliography]] of data and data sources&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Quick Survey&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;population&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents 22 age-sex cohorts to age 100+&lt;br /&gt;
*calculates change in fertility and mortality rates in response to income, income distribution, and analysis multipliers&lt;br /&gt;
*computes average life expectancy at birth, literacy rate, and overall measures of human development (HDI) and physical quality of life&lt;br /&gt;
*represents migration and HIV/AIDS&lt;br /&gt;
*includes a newly developing submodel of formal education across primary, secondary, and tertiary levels&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;economic&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents the economy in six sectors: agriculture, materials, energy, industry, services, and ICT (other sectors could be configured, using raw data from the GTAP project)&lt;br /&gt;
*computes and uses input-output matrices that change dynamically with development level&lt;br /&gt;
*is a general equilibrium-seeking model that does not assume exact equilibrium will exist in any given year; rather it uses inventories as buffer stocks and to provide price signals so that the model chases equilibrium over time&lt;br /&gt;
*contains an endogenous production function that represents contributions to growth in multifactor productivity from R&amp;amp;D, education, worker health, economic policies (&amp;quot;freedom&amp;quot;), and energy prices (the &amp;quot;quality&amp;quot; of capital)&lt;br /&gt;
*uses a Linear Expenditure System to represent changing consumption patterns&lt;br /&gt;
*utilizes a &amp;quot;pooled&amp;quot; rather than the bilateral trade approach for international trade&lt;br /&gt;
*is being imbedded during 2002 in a social accounting matrix (SAM) envelope that will tie economic production and consumption to intra-actor financial flows&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;agricultural&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents production, consumption and trade of crops and meat; it also carries ocean fish catch and aquaculture in less detail&lt;br /&gt;
*maintains land use in crop, grazing, forest, urban, and &amp;quot;other&amp;quot; categories&lt;br /&gt;
*represents demand for food, for livestock feed, and for industrial use of agricultural products&lt;br /&gt;
*is a partial equilibrium model in which food stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the agricultural sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;energy&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*portrays production of six energy types: oil, gas, coal, nuclear, hydroelectric, and other renewable&lt;br /&gt;
*represents consumption and trade of energy in the aggregate&lt;br /&gt;
*represents known reserves and ultimate resources of the fossil fuels&lt;br /&gt;
*portrays changing capital costs of each energy type with technological change as well as with draw-downs of resources&lt;br /&gt;
*is a partial equilibrium model in which energy stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the energy sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The two &#039;&#039;&#039;socio-political&#039;&#039;&#039; sub-modules:&lt;br /&gt;
&lt;br /&gt;
Within countries or geographic groupings&lt;br /&gt;
&lt;br /&gt;
*represents fiscal policy through taxing and spending decisions&lt;br /&gt;
*shows six categories of government spending: military, health, education, R&amp;amp;D, foreign aid, and a residual category&lt;br /&gt;
*represents changes in social conditions of individuals (like fertility rates or literacy levels), attitudes of individuals (such as the level of materialism/postmaterialism of a society from the World Value Survey), and the social organization of people (such as the status of women)&lt;br /&gt;
*represents the evolution of democracy&lt;br /&gt;
*represents the prospects for state instability or failure&lt;br /&gt;
&lt;br /&gt;
Between countries or groupings of countries&lt;br /&gt;
&lt;br /&gt;
*traces changes in power balances across states and regions&lt;br /&gt;
*allows exploration of changes in the level of interstate threat&lt;br /&gt;
*represents possible action-reaction processes and arms races with associated potential for conflict among countries&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;environmental&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows tracking of remaining resources of fossil fuels, of the area of forested land, of water usage, and of atmospheric carbon dioxide emissions&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;technology&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows changes in assumptions about rates of technological advance in agriculture, energy, and the broader economy&lt;br /&gt;
*explicitly represents the extent of electronic networking of individuals in societies&lt;br /&gt;
*is tied to the governmental spending model with respect to R&amp;amp;D spending&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Background&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
International Futures (IFs) has evolved since 1980 through three &amp;quot;generations,&amp;quot; with a fourth generation now taking form.&lt;br /&gt;
&lt;br /&gt;
The first generation had deep roots in the world models of the 1970s, including those of the Club of Rome. In particular, IFs drew on the Mesarovic-Pestel or World Integrated Model (Mesarovic and Pestel 1974). The author of IFs had contributed to that project, including the construction of the energy submodel. IFs consciously also drew on the Leontief World Model (Leontief et al. 1977), the Bariloche Foundation’s world model (Herrera et al. 1976), and Systems Analysis Research Unit Model (SARU 1977), following comparative analysis of those models by Hughes (1980). That generation was written in FORTRAN and available for use on main-frame computers through CONDUIT, an educational software distribution center at the University of Iowa. Although the primary use of that and subsequent generations was by students, IFs has always had some policy analysis capability that has appealed to specialists. For example, the U.S. Foreign Service Institute used the first generation of IFs in a mid-career training program.&lt;br /&gt;
&lt;br /&gt;
The second generation of International Futures moved to early microcomputers in 1985, using the DOS platform. It was a very simplified version of the original IFs without regional or country differentiation.&lt;br /&gt;
&lt;br /&gt;
The third generation, first available in 1993, became a full-scale microcomputer model. The third generation improved earlier representations of demographic, energy, and food systems, but added new environmental and socio-political content. It built upon the collaboration of the author with the GLOBUS project, and it adopted the economic submodel of GLOBUS (developed by the author). GLOBUS had been created with the inspiration of Karl Deutsch and under the leadership of Stuart Bremer (1987) at the Wissenschaftszentrum in Berlin.&lt;br /&gt;
&lt;br /&gt;
The third generation has produced three editions/major releases of IFs, each accompanied by a book also called International Futures (Hughes 1993, 1996, 1999). The second edition moved to a Visual Basic platform that allowed a much improved menu-driven interface, running under Windows. The third edition incorporated an early global mapping capability and an initial ability to do cross-sectional and longitudinal data analysis.&lt;br /&gt;
&lt;br /&gt;
The fourth generation has been taking shape since early 2000. It has been heavily influenced by the usage of the model in an increasingly policy-analysis mode by several important organizations. First, General Motors commissioned a specialized version of IFs named CoVaTrA (Consumer Values Trends Analysis) with a need for updated and extended demographic modeling and representation of value change. An alliance was established with the World Values Survey, directed by Ronald Inglehart, to create that version. Second, the Strategic Assessments Group of the Central Intelligence Agency commissioned a specialized version named IFs for SAG. The work involved in preparing that greatly extended and enhanced the socio-political representations of the model, both domestic and international. Third, the European Commission sponsored a project named TERRA which has led to a specialized version named IFs for TERRA. IFs for TERRA work led to enhancements across the model, including improved representation of economic sectors, updated IO matrices and a basic Social Accounting Matrix, GINI and Lorenz curves, and preparing for extended environmental impact representation (drawing upon the Advanced Sustainability Analysis framework of the Finland Futures Research Center).&lt;br /&gt;
&lt;br /&gt;
Throughout this emergence of a fourth generation IFs (incorporating all of the above elements for additional users) there has been also a heavy emphasis on enhanced usability. Ideas from Robert Pestel in the TERRA project led to the creation of a new tree-structure for scenario creation and management. Ideas from Ronald Inglehart led to the development of the Guided Use structure and a somewhat more game-like character within that structure. Inglehart also help arrange funding to support the programming of Guided Use through the European Union Center of the University of Michigan.&lt;br /&gt;
&lt;br /&gt;
The fifth version of IFs is currently in use and represents broad strides to improving the model and its usability. It is the first version of this software to be placed online due to the help of the National Intelligence Council ([http://www.ifs.du.edu http://www.ifs.du.edu]). Also, usability has been increased as Packaged Displays and Flex Packaged Displays were introduced that allowed for the creation of very specific lists of countries/regions, groups or Glists. A new education model has also been incorporated into the broader IFs model. New scenarios were created for UNEP (focusing on environmental change) and Pardee (focusing on poverty). Finally, one of the largest changes made was incorporating 182 countries into the Base-Case scenario used by IFs. Previous versions of IFs used broader regions to forecast global trends. This change also did away with the Student and Professional versions.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Geographic Representation of the World&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
186 countries underpin the functioning of IFs and these countries can be displayed separately or as parts of larger groups that users can determine.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;Below is a visual representation of how different entities are organized into Countries/Regions, Groups or Glists:&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
[[File:Geo 186.png|frame|right|Visual representation of IF&#039;s definition of regions/countries/groups/glists]]&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;*Note: In older versions of IFs, Regions were used as intermediaries between Countries and Groups. In the future, they, or some similarly named unit, will be a sub-unit of Countries. Regions, acting as a sub-unit of Countries, are currently not a feature of IFs. See the image located at the bottom of this Help topic.&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
When using IFs, there are many occasions where the user is asked whether or not they would like to display their results as a product of single countries, or larger groups. This is typically a toggle switch that moves between Country/Region and Groups, however, it might be a three-way-toggle that includes Country/Region, Group and Glist.&lt;br /&gt;
&lt;br /&gt;
Countries/Regions are currently the smallest geographical unit that users can represent. The ability to split countries down into smaller regions, or states, is under development. There are 186 different countries/regions that users can display.&lt;br /&gt;
&lt;br /&gt;
Groups are variably organized geographically or by memberships in international institutions/regimes. You can find out who is represented in each group and add or delete members by exploring the [[Extended_Features#Manage_Groups/Regions|Managing Regionalization]] function.&lt;br /&gt;
&lt;br /&gt;
Glists merge both Groups and Countries/Regions. These lists are mostly geographically bound. In the future, the Glist distinction will become more important as some users may want to place, for example, both the Indian state of Kerala in a Glist with Sri Lanka and Nepal.&lt;br /&gt;
&lt;br /&gt;
Users may also want to [[Extended_Features#Change_Grouping/Regionalization|create]] their own groups or [[Extended_Features#Identify_Groups_or_Country/Region_Members|explore]] what countries are members of what groups.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Time Horizon&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Future Forecasts.&#039;&#039;&#039; IFs begins computation with data from 2000 and can dynamically calculate values for all variables annually through 2100.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Historical Analysis and &amp;quot;Forecasts.&amp;quot;&#039;&#039;&#039; IFs also includes an extensive and growing historical data base starting in 1960. The data basis allows analysis of relationships among variables across countries and across time.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Instructional Use&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The standard modes for using IFs in a classroom are:&lt;br /&gt;
&lt;br /&gt;
1. Assigning class members to an issue area or topic. Consider identifying specific questions for them to address.&lt;br /&gt;
&lt;br /&gt;
2. Assigning class members to a country/geographic region. Again, specificity helps.&lt;br /&gt;
&lt;br /&gt;
Most often, students will work independently or in groups on projects and share information after completing them. It is possible, however, to have students work interactively, by assigning them topics or regions, letting them begin work, and then have the interacting groups (or individuals) create a collective model run with the changes that each group proposes by topic or region. That process, although more difficult to organize, allows the class as whole to investigate the interaction of their topics or regions (and to share learning about model use).&lt;br /&gt;
&lt;br /&gt;
There is a&amp;amp;nbsp;[http://portfolio.du.edu/bhughes web site]&amp;amp;nbsp;available in support of the educational use of IFs. You will find syllabi at that site. There are several [[Introduction_to_IFs#Publications_on_IFs|publications]] on IFs, including a book structured specifically for educational use.&lt;br /&gt;
&lt;br /&gt;
Donald Borock has described his classroom use of IFs in print. Borock, Donald. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot; Advances in Social Science and Computers 4, 103-127.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Acknowledgements&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The author gratefully recognizes critical contributions in the forms of:&lt;br /&gt;
&lt;br /&gt;
:1. Testing and suggestions for development of IFs in one or more of multiple generations. By Donald Borock, Richard Chadwick, William Dixon, Dale Rothman, Phil Schrodt, Douglas Stuart, Donald Sylvan, Jonathan Wilkenfeld, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
:2. Computer assistance across many releases. By Michael Niemann, Terrance Peet-Lukes, Douglas McClure, Mohammod Irfan, and Jose Solorzano.&lt;br /&gt;
&lt;br /&gt;
:3. Data gathering and general assistance. By James Chung, Padma Padula, Shannon Brady, David Horan, Michael Ferrier, Kay Drucker, Warren Christopher, and Anwar Hossain.&lt;br /&gt;
&lt;br /&gt;
:4. Long-term encouragement and support. By Harold Guetzkow, Karl Deutsch, Richard Chadwick, Gerald Barney, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
:5. Association in related world modeling projects and projects building upon IFs. By Mihajlo Mesarovic, Aldo Barsotti, Juan Huerta, John Richardson, Thomas Shook, Patricia Strauch, and other members of the World Integrated Model (WIM) team. By Stuart Bremer, Peter Brecke, Thomas Cusack, Wolf Dieter-Eberwein, Brian Pollins, and Dale Smith of the GLOBUS modeling project. By Evan Hillebrand, Paul Herman, and others of the IFs for SAG project. By Rob Lempert and Steve Bankes at RAND, Santa Monica. By Robert Pestel, Jonathan Cave, Ronald Inglehart, Sergei Parinov, Pentti Malaska, and many others in the IFs for TERRA project.&lt;br /&gt;
&lt;br /&gt;
:6. Financial assistance (without responsibility for the form of the evolving product). By the National Science Foundation, the Cleveland Foundation, the Exxon Education Foundation, the Kettering Family Foundation, the Pacific Cultural Foundation, the United States Institute of Peace, General Motors, the Strategic Assessments Group of the Central Intelligence Agency, the European Commission (Information Society Technology) Programme, the European Union Center of the University of Michigan, the National Intelligence Council (for web conversion), and Frederick S. Pardee. &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Feedback&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
Feedback on how to improve IFs is always appreciated, especially if you find something that is not working. Compliments are also accepted. Please contact. To send the IFs team an e-mail, click on&amp;amp;nbsp;[mailto:pardee.center@du.edu Pardee Center]&amp;amp;nbsp;in stand-alone versions or on the web.&lt;br /&gt;
&lt;br /&gt;
= [[Support_for_IFs_Use|Support_for_IFs_Use]] =&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=International_Futures_(IFs)&amp;diff=8326</id>
		<title>International Futures (IFs)</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=International_Futures_(IFs)&amp;diff=8326"/>
		<updated>2017-09-07T22:30:58Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&#039;&#039;&#039;Welcome to the International Futures (IFs) wiki.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
Consult the [//meta.wikimedia.org/wiki/Help:Contents User&#039;s Guide] for information on using the wiki software.&lt;br /&gt;
&lt;br /&gt;
[[Introduction_to_IFs|Introduction to IFs]]&amp;amp;nbsp;- Click here for an overview of the IFs system, including a description of the tool&#039;s&amp;amp;nbsp;purpose and major assumptions in its Base Case forecasts.&lt;br /&gt;
&lt;br /&gt;
[[Use_IFs_(Download_Version)|Use IFs (Download Version)]]&amp;amp;nbsp;- Click here for instructions on using the standalone desktop version of IFs.&lt;br /&gt;
&lt;br /&gt;
[[Use_IFs_(Online_Version)|Use IFs (Online Version)]]&amp;amp;nbsp;- Click here for instructions on using the web (cloud) version of IFs.&lt;br /&gt;
&lt;br /&gt;
[[Understand_the_Model|Understand IFs]]&amp;amp;nbsp;- Click here to access documentation&amp;amp;nbsp;on each of the major system models in IFs.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
[[Guide_to_Scenario_Analysis_in_International_Futures_(IFs)|Guide to Scenario Analysis in IFs]] - Click here for instructions on creating and comparing alternative scenarios in IFs. This guide also includes an updated list of IFs parameters.&lt;br /&gt;
&lt;br /&gt;
[[Additional_resources|Additional resources]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Getting started ==&lt;br /&gt;
&lt;br /&gt;
*[//www.mediawiki.org/wiki/Manual:Configuration_settings Configuration settings list]&lt;br /&gt;
*[//www.mediawiki.org/wiki/Manual:FAQ MediaWiki FAQ]&lt;br /&gt;
*[https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]&lt;br /&gt;
*[//www.mediawiki.org/wiki/Localisation#Translation_resources Localise MediaWiki for your language]&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=International_Futures_(IFs)&amp;diff=8325</id>
		<title>International Futures (IFs)</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=International_Futures_(IFs)&amp;diff=8325"/>
		<updated>2017-09-07T22:29:20Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&#039;&#039;&#039;Welcome to the International Futures (IFs) wiki.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
Consult the [//meta.wikimedia.org/wiki/Help:Contents User&#039;s Guide] for information on using the wiki software.&lt;br /&gt;
&lt;br /&gt;
[[Introduction_to_IFs|Introduction to IFs]]&amp;amp;nbsp;- Click here for an overview of the IFs system, including a description of the tool&#039;s&amp;amp;nbsp;purpose and major assumptions in its Base Case forecasts.&lt;br /&gt;
&lt;br /&gt;
[[Use_IFs_(Download_Version)|Use IFs (Download Version)]]&amp;amp;nbsp;- Click here for instructions on using the standalone desktop version of IFs.&lt;br /&gt;
&lt;br /&gt;
[[Use_IFs_(Online_Version)|Use IFs (Online Version)]]&amp;amp;nbsp;- Click here for instructions on using the web (cloud) version of IFs.&lt;br /&gt;
&lt;br /&gt;
[[Understand_the_Model|Understand IFs]]&amp;amp;nbsp;- Click here to access documentation&amp;amp;nbsp;on each of the major systems in IFs.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
[[Guide_to_Scenario_Analysis_in_International_Futures_(IFs)|Guide to Scenario Analysis in IFs]] - Click here for instructions on creating and comparing alternative scenarios in IFs.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
[[Additional_resources|Additional resources]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Getting started ==&lt;br /&gt;
&lt;br /&gt;
*[//www.mediawiki.org/wiki/Manual:Configuration_settings Configuration settings list]&lt;br /&gt;
*[//www.mediawiki.org/wiki/Manual:FAQ MediaWiki FAQ]&lt;br /&gt;
*[https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]&lt;br /&gt;
*[//www.mediawiki.org/wiki/Localisation#Translation_resources Localise MediaWiki for your language]&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8324</id>
		<title>Introduction to IFs</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8324"/>
		<updated>2017-09-07T22:27:19Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Purposes&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;International Futures (IFs) is a tool for thinking about long-term global trends and planning more strategically for the&amp;amp;nbsp;future.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
IFs can help you:&lt;br /&gt;
&lt;br /&gt;
*Understand&amp;amp;nbsp;the state of major&amp;amp;nbsp;global systems&lt;br /&gt;
*Explore&amp;amp;nbsp;long-term trends and consider where they might take&amp;amp;nbsp;us&lt;br /&gt;
*Learn about the dynamic&amp;amp;nbsp;interactions between global systems&lt;br /&gt;
*Clarify long-term organizational goals/priorities&lt;br /&gt;
*Develop alternative scenarios (if-then statements) about the future&lt;br /&gt;
*Investigate how different groups (households, firms or governments) can shape the future&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;IFs development and analysis depend&amp;amp;nbsp;on core, underlying assumptions, including the following.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*Global issues are becoming&amp;amp;nbsp;more significant as the scope of human interaction and human impact on the broader environment grow.&lt;br /&gt;
*Goals and priorities for human systems are becoming clearer and are more frequently and consistently communicated.&lt;br /&gt;
*Understanding of the dynamics of human systems is improving&amp;amp;nbsp;rapidly.&lt;br /&gt;
*The domain of human choice and action is broadening.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;What issues can you&amp;amp;nbsp;investigate with IFs?&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*[[Environment]]: Atmospheric carbon dioxide levels, world forest area, fossil fuel usage&lt;br /&gt;
*[[Socio-Political|Socio-Political Change]]: Life expectancy, literacy rate, democracy level, status of women, value change&lt;br /&gt;
*[[Population|Demographics]]: Population levels and growth, fertility, mortality, migration&lt;br /&gt;
*[[Agriculture|Food and Agriculture]]: Land use and production levels, calorie availability, malnutrition rates&lt;br /&gt;
*[[Energy]]: Resource and production levels, demand patterns, renewable energy share&lt;br /&gt;
*[[Economics]]: Sectoral production, consumption, and trade patterns and structural change&lt;br /&gt;
*[[Interstate_Politics_(IP)|Geopolitics]]: Country and regional power levels&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Visual Representation&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
[[File:IFsOverviewChart.jpg|frame|center|Visual representation of IFs structure]]&lt;br /&gt;
&lt;br /&gt;
Among the philosophical premises of the International Futures (IFs) project is that the model cannot be a &amp;quot;black box&amp;quot; to users and be truly useful. Model users must be able to examine the structures of IFs in order (1) to have confidence in them, and (2) learn from them.&lt;br /&gt;
&lt;br /&gt;
There is available (see topics under Understanding the Mode in the contents of this help system):&lt;br /&gt;
&lt;br /&gt;
*[[Understand_IFs#Dominant_Relations|Dominant Relations]] of the model structure&lt;br /&gt;
*[[Understand_IFs#2.2|Structure-Based and Agent-Class Driven Modeling]]&lt;br /&gt;
*[[Understand_IFs#Equation_Notation|Equation Notation]]&lt;br /&gt;
*[[Introduction_to_IFs#IFs_Bibliography|IFs Bibliography]] of data and data sources&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Quick Survey&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;population&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents 22 age-sex cohorts to age 100+&lt;br /&gt;
*calculates change in fertility and mortality rates in response to income, income distribution, and analysis multipliers&lt;br /&gt;
*computes average life expectancy at birth, literacy rate, and overall measures of human development (HDI) and physical quality of life&lt;br /&gt;
*represents migration and HIV/AIDS&lt;br /&gt;
*includes a newly developing submodel of formal education across primary, secondary, and tertiary levels&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;economic&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents the economy in six sectors: agriculture, materials, energy, industry, services, and ICT (other sectors could be configured, using raw data from the GTAP project)&lt;br /&gt;
*computes and uses input-output matrices that change dynamically with development level&lt;br /&gt;
*is a general equilibrium-seeking model that does not assume exact equilibrium will exist in any given year; rather it uses inventories as buffer stocks and to provide price signals so that the model chases equilibrium over time&lt;br /&gt;
*contains an endogenous production function that represents contributions to growth in multifactor productivity from R&amp;amp;D, education, worker health, economic policies (&amp;quot;freedom&amp;quot;), and energy prices (the &amp;quot;quality&amp;quot; of capital)&lt;br /&gt;
*uses a Linear Expenditure System to represent changing consumption patterns&lt;br /&gt;
*utilizes a &amp;quot;pooled&amp;quot; rather than the bilateral trade approach for international trade&lt;br /&gt;
*is being imbedded during 2002 in a social accounting matrix (SAM) envelope that will tie economic production and consumption to intra-actor financial flows&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;agricultural&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents production, consumption and trade of crops and meat; it also carries ocean fish catch and aquaculture in less detail&lt;br /&gt;
*maintains land use in crop, grazing, forest, urban, and &amp;quot;other&amp;quot; categories&lt;br /&gt;
*represents demand for food, for livestock feed, and for industrial use of agricultural products&lt;br /&gt;
*is a partial equilibrium model in which food stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the agricultural sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;energy&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*portrays production of six energy types: oil, gas, coal, nuclear, hydroelectric, and other renewable&lt;br /&gt;
*represents consumption and trade of energy in the aggregate&lt;br /&gt;
*represents known reserves and ultimate resources of the fossil fuels&lt;br /&gt;
*portrays changing capital costs of each energy type with technological change as well as with draw-downs of resources&lt;br /&gt;
*is a partial equilibrium model in which energy stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the energy sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The two &#039;&#039;&#039;socio-political&#039;&#039;&#039; sub-modules:&lt;br /&gt;
&lt;br /&gt;
Within countries or geographic groupings&lt;br /&gt;
&lt;br /&gt;
*represents fiscal policy through taxing and spending decisions&lt;br /&gt;
*shows six categories of government spending: military, health, education, R&amp;amp;D, foreign aid, and a residual category&lt;br /&gt;
*represents changes in social conditions of individuals (like fertility rates or literacy levels), attitudes of individuals (such as the level of materialism/postmaterialism of a society from the World Value Survey), and the social organization of people (such as the status of women)&lt;br /&gt;
*represents the evolution of democracy&lt;br /&gt;
*represents the prospects for state instability or failure&lt;br /&gt;
&lt;br /&gt;
Between countries or groupings of countries&lt;br /&gt;
&lt;br /&gt;
*traces changes in power balances across states and regions&lt;br /&gt;
*allows exploration of changes in the level of interstate threat&lt;br /&gt;
*represents possible action-reaction processes and arms races with associated potential for conflict among countries&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;environmental&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows tracking of remaining resources of fossil fuels, of the area of forested land, of water usage, and of atmospheric carbon dioxide emissions&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;technology&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows changes in assumptions about rates of technological advance in agriculture, energy, and the broader economy&lt;br /&gt;
*explicitly represents the extent of electronic networking of individuals in societies&lt;br /&gt;
*is tied to the governmental spending model with respect to R&amp;amp;D spending&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Background&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
International Futures (IFs) has evolved since 1980 through three &amp;quot;generations,&amp;quot; with a fourth generation now taking form.&lt;br /&gt;
&lt;br /&gt;
The first generation had deep roots in the world models of the 1970s, including those of the Club of Rome. In particular, IFs drew on the Mesarovic-Pestel or World Integrated Model (Mesarovic and Pestel 1974). The author of IFs had contributed to that project, including the construction of the energy submodel. IFs consciously also drew on the Leontief World Model (Leontief et al. 1977), the Bariloche Foundation’s world model (Herrera et al. 1976), and Systems Analysis Research Unit Model (SARU 1977), following comparative analysis of those models by Hughes (1980). That generation was written in FORTRAN and available for use on main-frame computers through CONDUIT, an educational software distribution center at the University of Iowa. Although the primary use of that and subsequent generations was by students, IFs has always had some policy analysis capability that has appealed to specialists. For example, the U.S. Foreign Service Institute used the first generation of IFs in a mid-career training program.&lt;br /&gt;
&lt;br /&gt;
The second generation of International Futures moved to early microcomputers in 1985, using the DOS platform. It was a very simplified version of the original IFs without regional or country differentiation.&lt;br /&gt;
&lt;br /&gt;
The third generation, first available in 1993, became a full-scale microcomputer model. The third generation improved earlier representations of demographic, energy, and food systems, but added new environmental and socio-political content. It built upon the collaboration of the author with the GLOBUS project, and it adopted the economic submodel of GLOBUS (developed by the author). GLOBUS had been created with the inspiration of Karl Deutsch and under the leadership of Stuart Bremer (1987) at the Wissenschaftszentrum in Berlin.&lt;br /&gt;
&lt;br /&gt;
The third generation has produced three editions/major releases of IFs, each accompanied by a book also called International Futures (Hughes 1993, 1996, 1999). The second edition moved to a Visual Basic platform that allowed a much improved menu-driven interface, running under Windows. The third edition incorporated an early global mapping capability and an initial ability to do cross-sectional and longitudinal data analysis.&lt;br /&gt;
&lt;br /&gt;
The fourth generation has been taking shape since early 2000. It has been heavily influenced by the usage of the model in an increasingly policy-analysis mode by several important organizations. First, General Motors commissioned a specialized version of IFs named CoVaTrA (Consumer Values Trends Analysis) with a need for updated and extended demographic modeling and representation of value change. An alliance was established with the World Values Survey, directed by Ronald Inglehart, to create that version. Second, the Strategic Assessments Group of the Central Intelligence Agency commissioned a specialized version named IFs for SAG. The work involved in preparing that greatly extended and enhanced the socio-political representations of the model, both domestic and international. Third, the European Commission sponsored a project named TERRA which has led to a specialized version named IFs for TERRA. IFs for TERRA work led to enhancements across the model, including improved representation of economic sectors, updated IO matrices and a basic Social Accounting Matrix, GINI and Lorenz curves, and preparing for extended environmental impact representation (drawing upon the Advanced Sustainability Analysis framework of the Finland Futures Research Center).&lt;br /&gt;
&lt;br /&gt;
Throughout this emergence of a fourth generation IFs (incorporating all of the above elements for additional users) there has been also a heavy emphasis on enhanced usability. Ideas from Robert Pestel in the TERRA project led to the creation of a new tree-structure for scenario creation and management. Ideas from Ronald Inglehart led to the development of the Guided Use structure and a somewhat more game-like character within that structure. Inglehart also help arrange funding to support the programming of Guided Use through the European Union Center of the University of Michigan.&lt;br /&gt;
&lt;br /&gt;
The fifth version of IFs is currently in use and represents broad strides to improving the model and its usability. It is the first version of this software to be placed online due to the help of the National Intelligence Council ([http://www.ifs.du.edu http://www.ifs.du.edu]). Also, usability has been increased as Packaged Displays and Flex Packaged Displays were introduced that allowed for the creation of very specific lists of countries/regions, groups or Glists. A new education model has also been incorporated into the broader IFs model. New scenarios were created for UNEP (focusing on environmental change) and Pardee (focusing on poverty). Finally, one of the largest changes made was incorporating 182 countries into the Base-Case scenario used by IFs. Previous versions of IFs used broader regions to forecast global trends. This change also did away with the Student and Professional versions.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Geographic Representation of the World&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
186 countries underpin the functioning of IFs and these countries can be displayed separately or as parts of larger groups that users can determine.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;Below is a visual representation of how different entities are organized into Countries/Regions, Groups or Glists:&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
[[File:Geo 186.png|frame|right|Visual representation of IF&#039;s definition of regions/countries/groups/glists]]&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;*Note: In older versions of IFs, Regions were used as intermediaries between Countries and Groups. In the future, they, or some similarly named unit, will be a sub-unit of Countries. Regions, acting as a sub-unit of Countries, are currently not a feature of IFs. See the image located at the bottom of this Help topic.&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
When using IFs, there are many occasions where the user is asked whether or not they would like to display their results as a product of single countries, or larger groups. This is typically a toggle switch that moves between Country/Region and Groups, however, it might be a three-way-toggle that includes Country/Region, Group and Glist.&lt;br /&gt;
&lt;br /&gt;
Countries/Regions are currently the smallest geographical unit that users can represent. The ability to split countries down into smaller regions, or states, is under development. There are 186 different countries/regions that users can display.&lt;br /&gt;
&lt;br /&gt;
Groups are variably organized geographically or by memberships in international institutions/regimes. You can find out who is represented in each group and add or delete members by exploring the [[Extended_Features#Manage_Groups/Regions|Managing Regionalization]] function.&lt;br /&gt;
&lt;br /&gt;
Glists merge both Groups and Countries/Regions. These lists are mostly geographically bound. In the future, the Glist distinction will become more important as some users may want to place, for example, both the Indian state of Kerala in a Glist with Sri Lanka and Nepal.&lt;br /&gt;
&lt;br /&gt;
Users may also want to [[Extended_Features#Change_Grouping/Regionalization|create]] their own groups or [[Extended_Features#Identify_Groups_or_Country/Region_Members|explore]] what countries are members of what groups.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Time Horizon&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Future Forecasts.&#039;&#039;&#039; IFs begins computation with data from 2000 and can dynamically calculate values for all variables annually through 2100.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Historical Analysis and &amp;quot;Forecasts.&amp;quot;&#039;&#039;&#039; IFs also includes an extensive and growing historical data base starting in 1960. The data basis allows analysis of relationships among variables across countries and across time.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Instructional Use&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The standard modes for using IFs in a classroom are:&lt;br /&gt;
&lt;br /&gt;
1. Assigning class members to an issue area or topic. Consider identifying specific questions for them to address.&lt;br /&gt;
&lt;br /&gt;
2. Assigning class members to a country/geographic region. Again, specificity helps.&lt;br /&gt;
&lt;br /&gt;
Most often, students will work independently or in groups on projects and share information after completing them. It is possible, however, to have students work interactively, by assigning them topics or regions, letting them begin work, and then have the interacting groups (or individuals) create a collective model run with the changes that each group proposes by topic or region. That process, although more difficult to organize, allows the class as whole to investigate the interaction of their topics or regions (and to share learning about model use).&lt;br /&gt;
&lt;br /&gt;
There is a&amp;amp;nbsp;[http://portfolio.du.edu/bhughes web site]&amp;amp;nbsp;available in support of the educational use of IFs. You will find syllabi at that site. There are several [[Introduction_to_IFs#Publications_on_IFs|publications]] on IFs, including a book structured specifically for educational use.&lt;br /&gt;
&lt;br /&gt;
Donald Borock has described his classroom use of IFs in print. Borock, Donald. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot; Advances in Social Science and Computers 4, 103-127.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Acknowledgements&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The author gratefully recognizes critical contributions in the forms of:&lt;br /&gt;
&lt;br /&gt;
:1. Testing and suggestions for development of IFs in one or more of multiple generations. By Donald Borock, Richard Chadwick, William Dixon, Dale Rothman, Phil Schrodt, Douglas Stuart, Donald Sylvan, Jonathan Wilkenfeld, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
:2. Computer assistance across many releases. By Michael Niemann, Terrance Peet-Lukes, Douglas McClure, Mohammod Irfan, and Jose Solorzano.&lt;br /&gt;
&lt;br /&gt;
:3. Data gathering and general assistance. By James Chung, Padma Padula, Shannon Brady, David Horan, Michael Ferrier, Kay Drucker, Warren Christopher, and Anwar Hossain.&lt;br /&gt;
&lt;br /&gt;
:4. Long-term encouragement and support. By Harold Guetzkow, Karl Deutsch, Richard Chadwick, Gerald Barney, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
:5. Association in related world modeling projects and projects building upon IFs. By Mihajlo Mesarovic, Aldo Barsotti, Juan Huerta, John Richardson, Thomas Shook, Patricia Strauch, and other members of the World Integrated Model (WIM) team. By Stuart Bremer, Peter Brecke, Thomas Cusack, Wolf Dieter-Eberwein, Brian Pollins, and Dale Smith of the GLOBUS modeling project. By Evan Hillebrand, Paul Herman, and others of the IFs for SAG project. By Rob Lempert and Steve Bankes at RAND, Santa Monica. By Robert Pestel, Jonathan Cave, Ronald Inglehart, Sergei Parinov, Pentti Malaska, and many others in the IFs for TERRA project.&lt;br /&gt;
&lt;br /&gt;
:6. Financial assistance (without responsibility for the form of the evolving product). By the National Science Foundation, the Cleveland Foundation, the Exxon Education Foundation, the Kettering Family Foundation, the Pacific Cultural Foundation, the United States Institute of Peace, General Motors, the Strategic Assessments Group of the Central Intelligence Agency, the European Commission (Information Society Technology) Programme, the European Union Center of the University of Michigan, the National Intelligence Council (for web conversion), and Frederick S. Pardee. &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Feedback&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
Feedback on how to improve IFs is always appreciated, especially if you find something that is not working. Compliments are also accepted. Please contact. To send the IFs team an e-mail, click on&amp;amp;nbsp;[mailto:pardee.center@du.edu Pardee Center]&amp;amp;nbsp;in stand-alone versions or on the web.&lt;br /&gt;
&lt;br /&gt;
= [[Support_for_IFs_Use]] =&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Publications on IFs&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
To obtain additional information about IFs and its use, consult:&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes and Evan E. Hillebrand, &#039;&#039;&#039;Exploring and Shaping International Futures.&#039;&#039;&#039; Boulder, CO: Paradigm Publishers, 2006. Specifically, see chapter 4.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &#039;&#039;&#039;International Futures: Choices in the Face of Uncertainty,&#039;&#039;&#039; 3rd ed. Boulder, CO: Westview Press, 1999. This volume is built around IFs and contains detailed suggestions for its use. Version 3.17 of IFs, which runs under Windows 95, is distributed with the third edition of the book. The second edition contained a version for Windows 3.1, and the first edition ran under DOS. Chapter 4 of the 2nd edition of IFs included Flow Charts of Worldviews , reproduced now in this Help system.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &#039;&#039;&#039;Continuity and Change in World Politics,&#039;&#039;&#039; 4th ed. Englewood Cliffs, N.J.: Prentice Hall, 2000. IFs can also usefully supplement this textbook on global politics.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &amp;quot;The International Futures (IFs) Modeling Project. 1999. &#039;&#039;&#039;Simulation and Gaming&#039;&#039;&#039; 30, No. 3 (September): 304-326.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;IFs Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Alcamo, Joseph, Rik Leemans and Eric Kreileman, eds. 1998.&amp;amp;nbsp;&#039;&#039;Global Change Scenarios of the 21st Century: Results from the IMAGE 2.1 Model&#039;&#039;. The Netherlands: Pergamon.&lt;br /&gt;
&lt;br /&gt;
Alcamo, Joseph. 1994.&amp;amp;nbsp;&#039;&#039;IMAGE 2.0: Integrated Modeling of Global Climate Change&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
&lt;br /&gt;
Alexandratos, Nikos, ed. 1995.&amp;amp;nbsp;&#039;&#039;World Agriculture: Towards 2010&#039;&#039;&amp;amp;nbsp;(An FAO Study). New York: FAO and John Wiley and Sons.&lt;br /&gt;
&lt;br /&gt;
Allen, R. G. D. 1968.&amp;amp;nbsp;&#039;&#039;Macro-Economic Theory: A Mathematical Treatment&#039;&#039;. New York: St. Martin&#039;s Press.&lt;br /&gt;
&lt;br /&gt;
Avery, Dennis. 1995. &amp;quot;Saving the Planet with Pesticides,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 50-82.&lt;br /&gt;
&lt;br /&gt;
Bailey, Ronald, ed. 1995.&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;. New York: The Free Press.&lt;br /&gt;
&lt;br /&gt;
Barbieri, Kathleen. 1996. &amp;quot;Economic Interdependence: A Path to Peace or a Source of Interstate Conflict?&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Peace Research&#039;&#039;&amp;amp;nbsp;33: 29-50.&lt;br /&gt;
&lt;br /&gt;
Barker, T.S. and A.W.A. Peterson, eds. 1987.&amp;amp;nbsp;&#039;&#039;The Cambridge Multisectoral Dynamic Model of the British Economy&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Barney, Gerald O., W. Brian Kreutzer, and Martha J. Garrett, eds. 1991.&amp;amp;nbsp;&#039;&#039;Managing a Nation&#039;&#039;, 2nd ed. Boulder: Westview Press.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. 1997.&amp;amp;nbsp;&#039;&#039;Determinants of Economic Growth: A Cross-Country Empirical Study&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. and Xavier Sala-i-Martin. 1999.&amp;amp;nbsp;&#039;&#039;Economic Growth&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
&lt;br /&gt;
Bennett, D. Scott, and Allan Stam. 2003.&amp;amp;nbsp;&#039;&#039;The Behavioral Origins of War: Cumulation and Limits to Knowledge in Understanding International Conflict&#039;&#039;. Ann Arbor: University of Michigan Press&lt;br /&gt;
&lt;br /&gt;
Birg, Herwig. 1995.&amp;amp;nbsp;&#039;&#039;World Population Projections for the 21st Century&#039;&#039;. Frankfurt: Campus Verlag.&lt;br /&gt;
&lt;br /&gt;
Borock, Donald M. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot;&amp;amp;nbsp;&#039;&#039;Advances in Social Science and Computers&#039;&#039;&amp;amp;nbsp;4, 103-127.&lt;br /&gt;
&lt;br /&gt;
Bos, Eduard, My T. Vu, Ernest Massiah, and Rodolfo A. Bulatao. 1994.&amp;amp;nbsp;&#039;&#039;World Population Projections 1994-95 Edition&#039;&#039;&amp;amp;nbsp;[editions are biannual] Baltimore: Johns Hopkins Press.&lt;br /&gt;
&lt;br /&gt;
Boulding, Elise and Kenneth E. Boulding. 1995.&amp;amp;nbsp;&#039;&#039;The Future: Images and Processes&#039;&#039;. Thousand Oaks, CA: Sage Publications.&lt;br /&gt;
&lt;br /&gt;
Brecke, Peter. 1993. &amp;quot;Integrated Global Models that Run on Personal Computers,&amp;quot;&amp;amp;nbsp;&#039;&#039;Simulation&#039;&#039;60 (2).&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. 1977.&amp;amp;nbsp;&#039;&#039;Simulated Worlds: A Computer Model of National Decision-Making&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A., ed. 1987.&amp;amp;nbsp;&#039;&#039;The GLOBUS Model: Computer Simulation of World-wide Political and Economic Developments&#039;&#039;. Boulder, CO: Westview.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. and Walter Gruhn. 1988.&amp;amp;nbsp;&#039;&#039;Micro GLOBUS: A Computer Model of Long-Term Global Political and Economic Processes&#039;&#039;. Berlin: edition sigma.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. and Barry B. Hughes. 1990.&amp;amp;nbsp;&#039;&#039;Disarmament and Development: A Design for the Future?&#039;&#039;&amp;amp;nbsp;Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Brockmeier, Martina and Channing Arndt (presentor). 2002. Social Accounting Matrices. Powerpoint presentation on GTAP and SAMs (June 21). Found on the web.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1981.&amp;amp;nbsp;&#039;&#039;Building a Sustainable Society&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1988. &amp;quot;Analyzing the Demographic Trap,&amp;quot; in&amp;amp;nbsp;&#039;&#039;State of the World 1987&#039;&#039;, eds. Lester R. Brown and others. New York: W.W. Norton, pp. 20-37.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1995.&amp;amp;nbsp;&#039;&#039;Who Will Feed China?&#039;&#039;&amp;amp;nbsp;New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1996.&amp;amp;nbsp;&#039;&#039;Tough Choices: Facing the Challenge of Food Scarcity&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., et al. 1996&amp;amp;nbsp;&#039;&#039;State of the World 1996&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., Nicholas Lenssen, and Hal Kane. 1995.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1995. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., Christopher Flavin, and Hal Kane. 1996.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1996. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Burkhardt, Helmut. 1995. &amp;quot;Priorities for a Sustainable Civilization,&amp;quot; unpublished conference paper. Department of Physics, Ryerson Polytechnic University, Toronto, Canada.&lt;br /&gt;
&lt;br /&gt;
Bussolo, Maurizio, Mohamed Chemingui and David O’Connor. 2002. A Multi-Region Social Accounting Matrix (1995) and Regional Environmental General Equilibrium Model for India (REGEMI). Paris: OECD Development Centre (February). Available at&amp;amp;nbsp;[http://www.oecd.org/dev/technics www.oecd.org/dev/technics].&lt;br /&gt;
&lt;br /&gt;
British Petroleum Company. 1995.&amp;amp;nbsp;&#039;&#039;BP Statistical Review of World Energy&#039;&#039;. London: British Petroleum Company.&lt;br /&gt;
&lt;br /&gt;
Central Intelligence Agency (CIA). 1991.&amp;amp;nbsp;&#039;&#039;Handbook of Economic Statistics, 1991&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
&lt;br /&gt;
Central Intelligence Agency (CIA). 1994.&#039;&#039;&amp;amp;nbsp;The World Factbook 1994&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
&lt;br /&gt;
Chang, Sheldon S. L. 1961.&amp;amp;nbsp;&#039;&#039;Synthesis of Optimum Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Chenery, Hollis and Moises Syrquin. 1975.&amp;amp;nbsp;&#039;&#039;Patterns of Development 1950-1970&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Cipolla, Carlo M. 1962.&amp;amp;nbsp;&#039;&#039;The Economic History of World Population&#039;&#039;. Baltimore: Penguin.&lt;br /&gt;
&lt;br /&gt;
Cook, Earl. 1976.&amp;amp;nbsp;&#039;&#039;Man, Energy, Society&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
&lt;br /&gt;
Committee on the Strategic Assessment of the U.S. Department of Energy’s Coal Program. 1995.&amp;amp;nbsp;&#039;&#039;Coal: Energy for the Future&#039;&#039;. Washington, D.C.: National Academy Press.&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1981.&amp;amp;nbsp;&#039;&#039;The Global 2000 Report to the President&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1981b.&amp;amp;nbsp;&#039;&#039;Environmental Trends&#039;&#039;. Washington, D.C. (July).&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1991.&amp;amp;nbsp;&#039;&#039;21st Annual Report&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Crescenzi, Mark J.C. and Andrew J. Enterline. 2001. &amp;quot;Time Remembered: A Dynamic Model of Interstate Interaction,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;45, no. 3 (September): 409-431.&lt;br /&gt;
&lt;br /&gt;
Crosson, Pierre, and Jock R. Anderson. 1992.&amp;amp;nbsp;&#039;&#039;Resources and Global Food Prospects&#039;&#039;. Washington, D.C.: The World Bank. World Bank Technical Paper Number 184.&lt;br /&gt;
&lt;br /&gt;
Cusack, Thomas R. and Richard J. Stoll. 1990.&amp;amp;nbsp;&#039;&#039;Exploring Realpolitik: Probing International Relations with Computer Simulatio&#039;&#039;n. Boulder: Lynne Rienner Publishers.&lt;br /&gt;
&lt;br /&gt;
Dargay, Joyce and Dermot Gately. 1999. &amp;quot;Income’s Effect on Car and Vehicle Ownership, Worldwide: 1960-2015,&amp;quot;&amp;amp;nbsp;&#039;&#039;Transportation Research Part A&#039;&#039;&amp;amp;nbsp;33: 101-138.&lt;br /&gt;
&lt;br /&gt;
Dall, P., Kaspar, F. and Alcamo, J. 1998. &amp;quot;Modeling World-wide Water Availability and Water Use Under the Influence of Climate Change,&amp;quot;&amp;amp;nbsp;&#039;&#039;Proceedings of the Second International Conference on Climate and Water&#039;&#039;, July 17-20, Espoo, Finland.&lt;br /&gt;
&lt;br /&gt;
Dimaranan, Betina V. and Robert A. McDougall, eds. 2002.&amp;amp;nbsp;&#039;&#039;Global Trade, Assistance, and Production: The GTAP 5 Data Base&#039;&#039;. Center for Global Trade Analysis, Purdue University. Available at [http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp].&lt;br /&gt;
&lt;br /&gt;
Dowlatabadi, H., and Morgan, M.G. 1993. &amp;quot;A Model Framework for Integrated Studies of the Climate Problem,&amp;quot;&amp;amp;nbsp;&#039;&#039;Energy Policy&#039;&#039;&amp;amp;nbsp;(March): 209-221.&lt;br /&gt;
&lt;br /&gt;
Duchin, Faye. 1998.&amp;amp;nbsp;&#039;&#039;Structural Economics: Measuring Change in Technology, Lifestyles, and the Environment&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
&lt;br /&gt;
Edwards, Stephen R. 1995. &amp;quot;Conserving Biodiversity,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 212-265.&lt;br /&gt;
&lt;br /&gt;
Edmonds, J., and Reilly, J.M. 1985.&amp;amp;nbsp;&#039;&#039;Global Energy: Assessing the Future&#039;&#039;. Oxford, UK: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Edmonds, J., Pitcher, H. Rosenberg, N., and Wigley, T. &amp;quot;Design for the Global Change Assessment Model.&amp;quot;&amp;amp;nbsp;&#039;&#039;Integrative Assessment of Mitigation, Impacts and Adaptation to Climate Change&#039;&#039;. Laxenburg, Austria.&lt;br /&gt;
&lt;br /&gt;
Ehrlich, Paul R. and Anne H. Ehrlich. 1972.&amp;amp;nbsp;&#039;&#039;Population, Resources, Environment&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
&lt;br /&gt;
Eicher, Carl. 1982. &amp;quot;Facing up to Africa&#039;s Food Crisis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Foreign Affairs&#039;&#039;&amp;amp;nbsp;61, no. 1 (Fall): 151-74.&lt;br /&gt;
&lt;br /&gt;
Eberstadt, Nicholas. 1995. &amp;quot;Population, Food, and Income,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 8-47.&lt;br /&gt;
&lt;br /&gt;
Esty, Daniel C., Jack A. Goldstone, Ted Robert Gurr, Barbara Harff, Marc Levy, Geoffrey D. Dabelko, Pamela T. Surko, and Alan N. Unger. 1998. State Failure Task Force Report: Phase II Findings. Volume provided courtesy of Ted Robert Gurr.&lt;br /&gt;
&lt;br /&gt;
Flavin, Christopher. 1996. &amp;quot;Facing Up to the Risks of Climate Change,&amp;quot; in Lester R. Brown and others, eds., State of the World 1996 (New York: W.W. Norton), pp. 21-39.&lt;br /&gt;
&lt;br /&gt;
Forrester, Jay W. 1968.&amp;amp;nbsp;&#039;&#039;Principles of Systems&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Gilpin, Robert. 1981.&amp;amp;nbsp;&#039;&#039;War and Change in World Politics&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Globerman, Steven. 2000 (May). Linkages Between Technological Change and Productivity Growth. Industry Canada Research Publications Program: Occasional Paper 23.&lt;br /&gt;
&lt;br /&gt;
Grant, Lindsey. 1982.&amp;amp;nbsp;&#039;&#039;The Cornucopian Fallacies&#039;&#039;. Washington, D.C.: Environmental Fund.&lt;br /&gt;
&lt;br /&gt;
Griffith, Rachel, Stephen Redding, and John Van Reenen. 2000.&amp;amp;nbsp;&#039;&#039;Mapping the Two Faces of R&amp;amp;D: Productivity Growth in a Panel of OECD Industries&#039;&#039;. Institute for Fiscal Studies (January)&lt;br /&gt;
&lt;br /&gt;
Gwartney, James and Robert Lawson with Dexter Samida. 2000.&amp;amp;nbsp;&#039;&#039;Economic Freedom of the World: 2000 Annual Report&#039;&#039;. Vancouver, B.C.: the Fraser Institute.&lt;br /&gt;
&lt;br /&gt;
Hammond, Allen. 1998.&amp;amp;nbsp;&#039;&#039;Which World? Scenarios for the 21st Century&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
&lt;br /&gt;
Harff, Barbara, with Ted Robert Gurr and Alan Unger. 1999. Preconditions of Genocide and Politicide: 1955-1998. Paper prepared for the State Failure Task Force and provided courtesy of Barbara Harff and Ted Gurr.&lt;br /&gt;
&lt;br /&gt;
Henderson, Hazel. 1996. &amp;quot;Changing Paradigms and Indicators: Implementing Equitable, Sustainable and Participatory Development,&amp;quot; in Jo Marie Griesgraber and Bernhard G. Gunter,&amp;amp;nbsp;&#039;&#039;Development: New Paradigms and Principles for the 21st Century&#039;&#039;. East Haven, CT: Pluto Press, pp. 103-136.&lt;br /&gt;
&lt;br /&gt;
Herrera, Amilcar O., et al. 1976.&#039;&#039;&amp;amp;nbsp;Catastrophe or New Society? A Latin American World Model&#039;&#039;. Ottawa: International Development Research Centre.&lt;br /&gt;
&lt;br /&gt;
Hoekstra, A.Y. 1998.&amp;amp;nbsp;&#039;&#039;Perspectives on Water: An Integrated Model-Based Exploration of the Future&#039;&#039;. Utrecht, the Netherlands: International Books.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1980.&amp;amp;nbsp;&#039;&#039;World Modeling&#039;&#039;. Lexington, Mass: Lexington Books.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1982.&amp;amp;nbsp;&#039;&#039;International Futures Simulation: User&#039;s Manual&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985a.&amp;amp;nbsp;&#039;&#039;International Futures Simulation&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985b. &amp;quot;World Models: The Bases of Difference,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;29, 77-101.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985c.&amp;amp;nbsp;&#039;&#039;World Futures: A Critical Analysis of Alternatives&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1987. &amp;quot;Domestic Economic Processes,&amp;quot; in Stuart A. Bremer, ed.,&amp;amp;nbsp;&#039;&#039;The Globus Model: Computer Simulation of Worldwide Political Economic Development&#039;&#039;&amp;amp;nbsp;(Frankfurt and Boulder: Campus and Westview), pp. 39-158.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1988. &amp;quot;International Futures: History and Status,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Science Microcomputer Review&#039;&#039;&amp;amp;nbsp;6, 43-48.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1999. &amp;quot;The International Futures (IFs) Modeling Project.&#039;&#039;&amp;amp;nbsp;Simulation and Gaming&#039;&#039;&amp;amp;nbsp;Vol 30, No. 3 (September): 304-326.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1999.&amp;amp;nbsp;&#039;&#039;International Futures&#039;&#039;, 3rd edition Boulder: Westview Press, 1999.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2000.&amp;amp;nbsp;&#039;&#039;Continuity and Change in World Politics&#039;&#039;. Englewood Cliffs, N.J.: Prentice-Hall, fourth edition.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2001. &amp;quot;Global Social Transformation: The Sweet Spot, the Steady Slog, and the Systemic Shift,&amp;quot;&amp;amp;nbsp;&#039;&#039;Economic Development and Cultural Change&#039;&#039;&amp;amp;nbsp;49, No. 2 (January): 423-458.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2002.&amp;amp;nbsp;&#039;&#039;Theats and Opportunities Analysis&#039;&#039;. Living document prepared for the Strategic Assessments Group, Office of Transnational Issues, Central Intelligence Agency, August 2002.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. and Anwar Hossain. 2003. Long-Term Socio-Economic Modeling: With Universal, Globally-Integrated Social Accounting Matrices (SAMs) in a General Equilibrium Model Structure. IFs Project Living Document, University of Denver.&lt;br /&gt;
&lt;br /&gt;
Huth, Paul. 1996.&amp;amp;nbsp;&#039;&#039;Standing Your Ground: Territorial Disputes and International Conflict&#039;&#039;. Ann Arbor, MI: University of Michigan Press.&lt;br /&gt;
&lt;br /&gt;
Inglehart, Ronald. 1997.&amp;amp;nbsp;&#039;&#039;Modernization and Postmodernization: Cultural, Economic, and Political Change in 43 Societies&#039;&#039;. Ewing, NJ: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1995.&amp;amp;nbsp;&#039;&#039;Oil, Gas, and Coal Supply Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996.&amp;amp;nbsp;&#039;&#039;World Energy Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996b.&amp;amp;nbsp;&#039;&#039;The Strategic Value of Fossil Fuels: Challenges and Responses&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;International Financial Statistics&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;World Economic Outlook&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
Intergovernmental Panel on Climate Change (IPCC). 1995. Several volumes by various working groups. Published by Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Jansen, Karel and Rob Vos, eds. 1997.&amp;amp;nbsp;&#039;&#039;External Finance and Adjustment: Failure and Success in the Developing World&#039;&#039;. London: Macmillan Press Ltd.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1998.&amp;amp;nbsp;&#039;&#039;Modeling Global Change: The Art of Integrated Assessment Modelling&#039;&#039;. Cheltenham, UK: Edward Elgar.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1996.&amp;amp;nbsp;&#039;&#039;Meeting Targets: Tools to Support Integrated Modelling of Global Change&#039;&#039;. Den Haag: CIP-Gegevens Koninklijke Bibliotheek.&lt;br /&gt;
&lt;br /&gt;
Jansson, Kurt, Michael Harris, Angela Penrose. 1987.&amp;amp;nbsp;&#039;&#039;The Ethiopian Famine&#039;&#039;. London: Zed Books Ltd.&lt;br /&gt;
&lt;br /&gt;
Jeffreys, Kent. 1995. &amp;quot;Rescuing the Oceans,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 296-338.&lt;br /&gt;
&lt;br /&gt;
Jones, Daniel M., Stuart A. Bremer, and J. David Singer. 1996. &amp;quot;Militarized Interstate Disputes, 1816-1992: Rationale, Coding Rules, and Empirical Patterns,&amp;quot;&amp;amp;nbsp;&#039;&#039;Conflict Management and Peace Science&#039;&#039;&amp;amp;nbsp;XV, No. 2: 163-215.&lt;br /&gt;
&lt;br /&gt;
Khan, Haider A. 1998.&amp;amp;nbsp;&#039;&#039;Technology, Development and Democracy&#039;&#039;. Northhampton, Mass: Edward Elgar Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Kahn, Herman, William Brown, and Leon Martel. 1976.&amp;amp;nbsp;&#039;&#039;The Next 200 Years&#039;&#039;. New York: William Morrow.&lt;br /&gt;
&lt;br /&gt;
Kalymon, Basil A. 1975. &amp;quot;Economic Incentives in OPEC Oil Pricing Policy.&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Development Economics&#039;&#039;&amp;amp;nbsp;2: 337-362.&lt;br /&gt;
&lt;br /&gt;
Kaplan, Robert. 1994. &amp;quot;The Coming Anarchy,&amp;quot;&amp;amp;nbsp;&#039;&#039;The Atlantic Monthly&#039;&#039;&amp;amp;nbsp;273 (February): .&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999a. &amp;quot;Aggregating Governance Indicators&amp;quot;. World Bank Policy Research Department Working Paper No. 2195.&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999b. &amp;quot;Governance Matters&amp;quot;. World Bank Policy Research Department Working Paper No. 2196.&lt;br /&gt;
&lt;br /&gt;
Keepin, B. and B. Wynne. 1984. &amp;quot;Technical Analysis of the IIASA Energy Scenarios,&amp;quot;&amp;amp;nbsp;&#039;&#039;Nature&#039;&#039;312: 691-695.&lt;br /&gt;
&lt;br /&gt;
Kehoe, Timothy J. 1996. Social Accounting Matrices and Applied General Equilibrium Models. Federal Reserve Bank of Minneapolis, Working Paper 563.&lt;br /&gt;
&lt;br /&gt;
Kennedy, Paul. 1993.&amp;amp;nbsp;&#039;&#039;Preparing for the Twenty-First Century&#039;&#039;. New York: Random House.&lt;br /&gt;
&lt;br /&gt;
Klein, Lawrence R. and Fu-chen Lo, eds. 1995.&amp;amp;nbsp;&#039;&#039;Modeling Global Change&#039;&#039;. Tokyo: United Nations University Press.&lt;br /&gt;
&lt;br /&gt;
Kornai, J. 1971.&amp;amp;nbsp;&#039;&#039;Anti-Equilibrium&#039;&#039;. Amsterdam: North Holland.&lt;br /&gt;
&lt;br /&gt;
Kwasnicki, Witold and Halina Kwasnicka. 1996. &amp;quot;Long-Term Diffusion Factors of Technological Development: An Evolutionary Model and Case Study,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 31-57.&lt;br /&gt;
&lt;br /&gt;
Leontief, Wassily, Anne Carter and Peter Petri. 1977.&amp;amp;nbsp;&#039;&#039;The Future of the World Economy&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander H., and Elizabeth R. Ducot. 1976. &amp;quot;AGRIMOD: A Simulation Model for the Analysis of U.S. Food Policies.&amp;quot; Paper delivered at Conference on Systems Analysis of Grain Reserves, Joint Annual Meeting of GRSA and TIMS, Philadelphia, Pa., March 31-April 2.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander, H., et al. 1977. Energy in Agriculture: On Modeling Inputs in AGRIMOD. Final Report to U.S. Department of Energy. Palo Alto: Systems Control, Inc., August, available through NTIS.&lt;br /&gt;
&lt;br /&gt;
Lichbach, Mark Irving. 1989. &amp;quot;An Evaluation of ‘Does Economic Inequality Breed Political Conflict?,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;, Vol 41 , No. 4 (July 1989): 431-470.&lt;br /&gt;
&lt;br /&gt;
Liverman, Dianne. 1983.&amp;amp;nbsp;&#039;&#039;The Use of Global Simulation Models in Assessing Climate Impacts on the World Food System&#039;&#039;. Dissertation, University of California, Los Angeles.&lt;br /&gt;
&lt;br /&gt;
Londregan, John B. and Keith T. Poole. 1996. &amp;quot;Does High Income Promote Democrary?&amp;quot;,&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 1 (October): 1-30.&lt;br /&gt;
&lt;br /&gt;
MacKenzie, James J. 1996. &amp;quot;Oil as a Finite Resource: When is Global Production Likely to Peak?&amp;quot; Paper of the World Resources Institute. Washington, D.C.: WRI.&lt;br /&gt;
&lt;br /&gt;
Maddison, Angus. 1995.&amp;amp;nbsp;&#039;&#039;Monitoring the World Economy 1820-1992&#039;&#039;. Paris: OECD.&lt;br /&gt;
&lt;br /&gt;
Malthus, Thomas. 1798.&amp;amp;nbsp;&#039;&#039;An Essay on the Principle of Population as It Affects the Future Improvement of Society&#039;&#039;. London (reprinted many times).&lt;br /&gt;
&lt;br /&gt;
Mansfield, Edward D. 1994.&amp;amp;nbsp;&#039;&#039;Power, Trade, and War&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Marchetti, Cesare, Perrin S. Meyer, and Jesse H. Ausubel. 1996. &amp;quot;Human Population Dynamics Revisited with the Logistic Model: How Much Can be Modeled and Predicted?,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 1-30.&lt;br /&gt;
&lt;br /&gt;
Martens, Pim and Jan Rotmans, eds. 1999.&amp;amp;nbsp;&#039;&#039;Climate Change: An Integrated Perspective&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
&lt;br /&gt;
Martens, W.J.M. 1997. &amp;quot;Health Impacts of Climate Change and Ozone Depletion: An Eco-Epidemiological Approach,&amp;quot; Maastricht, the Netherlands: Maastricht University.&lt;br /&gt;
&lt;br /&gt;
Mason, Andrew. 1997. &amp;quot;The Role of Population Change in the Asian Economic Miracle,&amp;quot; Honolulu, Hawaii: East-West Center, AsiaPacific Issues, No. 33 (October), 8 pages.&lt;br /&gt;
&lt;br /&gt;
McMahon, Walter W. 1997.&amp;amp;nbsp;&#039;&#039;Education and Development: Measuring the Social Benefits&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, Jorgen Randers, and William K. Behrens, III. 1972.&amp;amp;nbsp;&#039;&#039;Limits to Growth&#039;&#039;. New York: Universe Books.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, and Jorgen Randers. 1992.&amp;amp;nbsp;&#039;&#039;Beyond the Limits&#039;&#039;. Post Mills, Vermont: Chelsea Green Publishing Company.&lt;br /&gt;
&lt;br /&gt;
Meadows, Dennis L. et al. 1974.&amp;amp;nbsp;&#039;&#039;Dynamics of Growth in a Finite World&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Mesarovic, Mihajlo D. and Eduard Pestel. 1974.&amp;amp;nbsp;&#039;&#039;Mankind at the Turning Point&#039;&#039;. New York: E.P. Dutton &amp;amp; Co.&lt;br /&gt;
&lt;br /&gt;
Mishkin, Eli. And Ludwig Braun, ed. 1961.&amp;amp;nbsp;&#039;&#039;Adaptive Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Moore, Will H., Ronny Lindstrom, and Valerie O’Regan. 1996. &amp;quot;Land Reform, Political Violence and the Economic Inequality-Political Conflict Nexus: A Longitudinal Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Interactions&#039;&#039;&amp;amp;nbsp;21, No. 4: 335-363.&lt;br /&gt;
&lt;br /&gt;
Mori, Shunsuke and Masato Takahaashi, 1997. An Integrated Assessment Model for the Evaluation of New Energy Technologies and Food Production, accepted by&amp;amp;nbsp;&#039;&#039;International Journal of Global Energy Issues&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
Naill, Roger F. 1977.&amp;amp;nbsp;&#039;&#039;Managing the Energy Transition&#039;&#039;. Vols. 1 and 2. Cambridge, Mass: Ballinger Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1992. &amp;quot;The DICE Model: Background and Structure of a Dynamic Integrated Climate Economy,&amp;quot; New Haven: Yale University.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1979.&amp;amp;nbsp;&#039;&#039;The Efficient Use of Energy Resources&#039;&#039;. New Haven, CT: Yale University Press.&lt;br /&gt;
&lt;br /&gt;
Oneal, John R. and Bruce M. Russett. 1997. The Classical Liberals were Right: Democracy, Interdependence, and Conflict, 1950-1985.&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;41, no. 2 (June): 267-294.&lt;br /&gt;
&lt;br /&gt;
Pan, Xiaoming. 2000 (January). &amp;quot;Social and Ecological Accounting Matrix: an Empirical Study for China,&amp;quot; paper submitted for the Thirteenth International Conference on Input-Output Techniques, Macerata, Italy, August 21-25, 2000.&lt;br /&gt;
&lt;br /&gt;
Pesaran, M. Hashem and G. C. Harcourt. 1999. Life and Work of John Richard Nicholas Stone.&lt;br /&gt;
&lt;br /&gt;
Pirages, Dennis. 1989.&amp;amp;nbsp;&#039;&#039;Global Technopolitics&#039;&#039;. Pacific Grove, Calif: Brooks/Cole Publishing.&lt;br /&gt;
&lt;br /&gt;
Prinn, R. H.J., A. Sokolov, C. Wand, X. Xiao, Z. Yang, R. Eckhaus, P. Stone, D. Ellerman, J Melilo, J. Fitzmaurice, D. Kicklighter, and Y. Liu. 1996. &amp;quot;Integrated Global System Model for Climate Policy Analysis: Model Framework and Sensitivity Analysis.&amp;quot; Cambridge, Mass: Global Change Center, Massachusetts Institute of Technology.&lt;br /&gt;
&lt;br /&gt;
Przeworski, Adam and Fernando Limongi. 1997. &amp;quot;Modernization: Theories and Facts,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 2 (January): 155-183.&lt;br /&gt;
&lt;br /&gt;
Population Reference Bureau. 1996. World Population Data Sheet 1996. Washington, D.C.: Population Reference Bureau.&lt;br /&gt;
&lt;br /&gt;
Postel, Sandra. 1996.&amp;amp;nbsp;&#039;&#039;Dividing the Waters: Food Security, Ecosystem Health, and the New Politics of Scarcity&#039;&#039;. Worldwatch Paper 132. Washington, D.C.: Worldwatch Institute, September.&lt;br /&gt;
&lt;br /&gt;
Pyatt, G. and J.I. Round, eds. 1985.&amp;amp;nbsp;&#039;&#039;Social Accounting Matrices: A Basis for Planning&#039;&#039;. Washington, D.C.: The World Bank.&lt;br /&gt;
&lt;br /&gt;
Raskin, P., T. Banuri, G. Gallopín, P. Gutman, A. Hammond, R. Kates, and R. Swart. 2001. Great Transition:&amp;amp;nbsp;&#039;&#039;The Promise and Lure of the Times Ahead&#039;&#039;. Forthcoming.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1990.&amp;amp;nbsp;&#039;&#039;Global Politics&#039;&#039;, 4th edition. Boston: Houghton Mifflin.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1995.&amp;amp;nbsp;&#039;&#039;Democracy and International Conflict&#039;&#039;. Columbia: University of South Carolina Press.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee and J. David Singer. 1973. &amp;quot; Measuring the Concentration of Power in the International System,&amp;quot;&#039;&#039;&amp;amp;nbsp;Sociological Methods and Research&#039;&#039;&amp;amp;nbsp;1, no. 4: 403-436. Reprinted in&amp;amp;nbsp;&#039;&#039;Measuring the Correlates of War&#039;&#039;, edited by J. David Singer and Paul Diehl. Ann Arbor: University of Michigan Press, 1990.&lt;br /&gt;
&lt;br /&gt;
Rayner. S. 1992. &amp;quot;Cultural Theory and Risk Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Theory of Risk&#039;&#039;, ed. G. D. Preagor. Westport, USA.&lt;br /&gt;
&lt;br /&gt;
Repetto, Robert and Duncan Austin. 1997.&amp;amp;nbsp;&#039;&#039;The Costs of Climate Protection&#039;&#039;. Washington, D.C.: World Resources Institute.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis Fry. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Chicago: Quadrangle Books.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis F. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Pittsburgh: Boxwood Press.&lt;br /&gt;
&lt;br /&gt;
Romer, Paul M. 1994. &amp;quot;The Origins of Endogenous Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Economic Perspectives&#039;&#039;Vol 8, No. 1 (Winter): 3-22.&lt;br /&gt;
&lt;br /&gt;
Root T. and Stephen Schneider. 1995. &amp;quot;Ecology and Climate: Research Strategies and Implications,&amp;quot; Science 269 (52): 334-341.&lt;br /&gt;
&lt;br /&gt;
Rosegrant, Mark W., Mercedita Agcaoili-Sombilla, and Nicostrato D. Perez. 1995. &amp;quot;Global Food Projections to 2020: Implications for Investment.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 5.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1999. Integrated Assessment Models: Uncertainty, Quality and Use. Maastricht, the Netherlands: Maastricht University, International Centre for Integrative Studies (ICIS), Working Paper 199-E005.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and Burt de Vries, eds. 1997.&amp;amp;nbsp;&#039;&#039;Perspectives on Global Change: The Targets Approach&#039;&#039;. Cambridge, UK: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and M.B.A. van Asselt. 1996. &amp;quot;Integrated Assessment: A Growing Child on its Way to Maturity,&amp;quot;&amp;amp;nbsp;&#039;&#039;Climatic Change&#039;&#039;&amp;amp;nbsp;34 (3-4): 327-336.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1990.&amp;amp;nbsp;&#039;&#039;IMAGE: An Integrated Model to Assess the Greenhouse Effect&#039;&#039;. Dordrecht, the Netherlands: Kluwer Academics.&lt;br /&gt;
&lt;br /&gt;
Saaty, Thomas L. 1996. The Analytic Network Process: Decision Making with Dependence and Feedback. Pittsburgh: RWS Publications.&lt;br /&gt;
&lt;br /&gt;
Schafer, Andreas and David G. Victor. 1997. The Future Mobility of the World Population. Massachusetts Institute of Technology and International Institute for Applied Systems Analysis, Discussion Paper 97-6-4 (revision 2, September).&lt;br /&gt;
&lt;br /&gt;
Scheer, Sara J. and Satya Yadav. 1996. &amp;quot;Land Degradation in the Developing World: Implications for Food, Agriculture, and the Environment to 2020.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 14.&lt;br /&gt;
&lt;br /&gt;
Schneider, Stephen. 1997. &amp;quot;Integrated Assessment Modeling of Climate Change: Transparent Rational Tool for Policy Making or Opaque Screen Hiding Value-Laden Assumptions?&amp;quot;&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(4): 229-250.&lt;br /&gt;
&lt;br /&gt;
Schwartz, Peter. 1996.&#039;&#039;&amp;amp;nbsp;The Art of the Long View.&#039;&#039;&amp;amp;nbsp;New York: Doubleday.&lt;br /&gt;
&lt;br /&gt;
Sedjo, Roger A. 1995. &amp;quot;Forests: Conflicting Signals,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 178-209.&lt;br /&gt;
&lt;br /&gt;
Shane, Harold G. and Gary A. Sojka. 1990. &amp;quot;John Elfreth Watkins, Jr.: Forgotten Genius of Forecasting,&amp;quot; in Edward Cornish, ed.,&#039;&#039;&amp;amp;nbsp;The 1990s and Beyond&#039;&#039;. Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Shaw, Timothy W. and Clement E. Adibe. 1995-96. &amp;quot;Africa and Global Developments in the Twenty-First Century,&amp;quot; International Journal 51 (Winter): 1-26.&lt;br /&gt;
&lt;br /&gt;
Siegmann, Heinrich. 1985.&amp;amp;nbsp;&#039;&#039;Recent Developments in World Modeling&#039;&#039;. Berlin: Science Center.&lt;br /&gt;
&lt;br /&gt;
Simon, Julian. 1981.&amp;amp;nbsp;&#039;&#039;The Ultimate Resource&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Singer, J. David, Stuart Bremer, and John Stuckey. 1972. &amp;quot;Capability Distribution, Uncertainty, and Major Power Wars, 1820-1965.&amp;quot; In Bruce Russett, ed.,&amp;amp;nbsp;&#039;&#039;Peace, War, and Numbers.&#039;&#039;&amp;amp;nbsp;Beverly Hills: Sage.&lt;br /&gt;
&lt;br /&gt;
Sivard, Ruth Leger. 1993.&amp;amp;nbsp;&#039;&#039;World Military and Social Expenditures 1993.&#039;&#039;&amp;amp;nbsp;Washington, D.C. 20007: World Priorities, Box 25140.&lt;br /&gt;
&lt;br /&gt;
Solow, Robert M. 1956. &amp;quot;A Contribution to the Theory of Economic Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Quarterly Journal of Economics&#039;&#039;&amp;amp;nbsp;70, 1 (February): 65-94.&lt;br /&gt;
&lt;br /&gt;
Stanford University. 1978.&amp;amp;nbsp;&#039;&#039;Stanford Pilot Energy/Economic Model&#039;&#039;. Stanford: Department of Research, Interim Report, Vol. 1.&lt;br /&gt;
&lt;br /&gt;
Stockholm International Peace Research Institute (SIPRI). 1994.&amp;amp;nbsp;&#039;&#039;SIPRI Yearbook&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Stone, Richard. 1986. &amp;quot;The Accounts of Society,&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Applied Econometrics&#039;&#039;&amp;amp;nbsp;1, no. 1 (January): 5-28.&lt;br /&gt;
&lt;br /&gt;
Strategic Assessments Group (SAG), Office of Transnational Issues, Directorate of Intelligence. 2001 (February). The Global Economy in the Long Term. OTI IR 2001-013.&lt;br /&gt;
&lt;br /&gt;
Systems Analysis Research Unit (SARU). 1977.&amp;amp;nbsp;&#039;&#039;SARUM 76 Global Modeling Project&#039;&#039;. Departments of the Environment and Transport, 2 Marsham Street, London, 3WIP 3EB.&lt;br /&gt;
&lt;br /&gt;
Tammen, Ronald L, Jacek Kugler, Douglas Lemke, Allan C. Stam III, Carole Alsharabati, Mark Andrew Abdollahian, Brian Efird, and A.F.K. Organski. 2000. Power Transitions: Strategies for the 21st Century. New York: Chatham House Publishers.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1975. &amp;quot;Theoretical Foundations and Technical Implications.&amp;quot; in Charles Blitzer, Peter Clark and Lance Taylor, eds.,&amp;amp;nbsp;&#039;&#039;Economy-Wide Models and Development Planning.&#039;&#039;&amp;amp;nbsp;Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1979.&amp;amp;nbsp;&#039;&#039;Macro Models for Developing Countries&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Thirlwall, A. P. 1977.&amp;amp;nbsp;&#039;&#039;Growth and Development&#039;&#039;. New York: John Wiley and Sons.&lt;br /&gt;
&lt;br /&gt;
Thompson, M. 1997. Cultural Theory and Integrated Assessment.&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(3): 139-150.&lt;br /&gt;
&lt;br /&gt;
Thompson, M., R. Ellis and A. Wildavsky. 1990.&amp;amp;nbsp;&#039;&#039;Cultural Theory&#039;&#039;. Boulder, Co: Westview Press.&lt;br /&gt;
&lt;br /&gt;
Thorbecke, Erik. 2001. &amp;quot;The Social Accounting Matrix: Deterministic or Stochastic Concept?&amp;quot;, paper prepared for a conference in honor of Graham Pyatt&#039;s retirement, at the Institute of Social Studies, The Hague, Netherlands (November 29 and 30). Available at [http://people.cornell.edu/pages/et17/etpapers.html http://people.cornell.edu/pages/et17/etpapers.html].&lt;br /&gt;
&lt;br /&gt;
United Nations, Department of Economic and Social Affairs. 1956.&amp;amp;nbsp;&#039;&#039;Methods of Population Projections by Sex and Age&#039;&#039;. New York: United Nations, ST/SOA Series A.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1992.&amp;amp;nbsp;&#039;&#039;Long-Range World Population Projections. Two Centuries of Population Growth: 1950-2150&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1993.&amp;amp;nbsp;&#039;&#039;World Population Prospects - the 1992 Revision&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations Development Program (UNDP). 1995.&amp;amp;nbsp;&#039;&#039;Human Development Report&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1992.&amp;amp;nbsp;&#039;&#039;Production Yearbook.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1995.&#039;&#039;&amp;amp;nbsp;World Agriculture: Towards 2010.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 1999. The World at Six Billion New York: UN.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2000. Replacement Migration: Is it a Solution to Declining and Ageing Populations? New York: UN.&lt;br /&gt;
&lt;br /&gt;
United States Arms Control and Disarmament Agency (ACDA). 1995.&amp;amp;nbsp;&#039;&#039;World Military Expenditures and Arms Transfers 1995&#039;&#039;. Washington, D.C.: Arms Control and Disarmament Agency.&lt;br /&gt;
&lt;br /&gt;
United States Bureau of the Census. 1991.&amp;amp;nbsp;&#039;&#039;World Population Profile: 1991&#039;&#039;. Report WP/91 Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Walters, Robert S. and David H. Blake. 1992.&amp;amp;nbsp;&#039;&#039;The Politics of Global Economic Relations&#039;&#039;, 4th edition. Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Waltz, Kenneth N. 1959. Man, the State, and War: A Theoretical Analysis. New York: Columbia University Press.&lt;br /&gt;
&lt;br /&gt;
Watkins, John Elfreth, Jr. 1990. &amp;quot;What May Happen in the Next Hundred Years,&amp;quot; in Edward Cornish, ed.,&amp;amp;nbsp;&#039;&#039;The 1990s and Beyond.&#039;&#039;&amp;amp;nbsp;Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Wildavsky, Aaron, and Ellen Tenenbaum. 1981.&amp;amp;nbsp;&#039;&#039;The Politics of Mistrust&#039;&#039;. Beverly Hills: Sage Publications.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1991b.&amp;amp;nbsp;&#039;&#039;World Tables 1991&#039;&#039;. New York: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1995&amp;amp;nbsp;&#039;&#039;World Development Report 1995&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
World Energy Council (WEC) Commission. 1993.&amp;amp;nbsp;&#039;&#039;Energy for Tomorrow’s World.&#039;&#039;&amp;amp;nbsp;New York: St. Martin’s Press.&lt;br /&gt;
&lt;br /&gt;
World Resources Institute (WRI). 1994.&amp;amp;nbsp;&#039;&#039;World Resources 1994-95.&#039;&#039;&amp;amp;nbsp;New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Wortman, Sterling and Ralph W. Cummings, Jr. 1978.&#039;&#039;&amp;amp;nbsp;To Feed This World&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Zinnes, Dina A. and John W. Gillespie, eds. 1976.&amp;amp;nbsp;&#039;&#039;Mathematical Models in International Relations&#039;&#039;&amp;amp;nbsp;(New York: Preaeger).&lt;br /&gt;
&lt;br /&gt;
== [[Development_Mode_Features|Development Mode Features]] ==&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=International_Futures_(IFs)&amp;diff=8323</id>
		<title>International Futures (IFs)</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=International_Futures_(IFs)&amp;diff=8323"/>
		<updated>2017-09-07T22:26:10Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&#039;&#039;&#039;Welcome to the International Futures (IFs) wiki.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
Consult the [//meta.wikimedia.org/wiki/Help:Contents User&#039;s Guide] for information on using the wiki software.&lt;br /&gt;
&lt;br /&gt;
[[Introduction_to_IFs|Introduction to IFs]]&amp;amp;nbsp;- Click here for an overview of the IFs system, including a description of the tool&#039;s&amp;amp;nbsp;purpose and major assumptions in its Base Case forecasts.&lt;br /&gt;
&lt;br /&gt;
[[Use_IFs_(Download_Version)|Use IFs (Download Version)]]&amp;amp;nbsp;- Click here for instructions on using the standalone desktop version of IFs.&lt;br /&gt;
&lt;br /&gt;
[[Use_IFs_(Online_Version)|Use IFs (Online Version)]]&amp;amp;nbsp;- Click here for instructions on using the web (cloud) version of IFs.&lt;br /&gt;
&lt;br /&gt;
[[Understand_the_Model|Understand IFs]]&amp;amp;nbsp;- Click here to access documentation&amp;amp;nbsp;on each of the major systems in IFs.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
[[Additional_resources|Additional resources]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Getting started ==&lt;br /&gt;
&lt;br /&gt;
*[//www.mediawiki.org/wiki/Manual:Configuration_settings Configuration settings list]&lt;br /&gt;
*[//www.mediawiki.org/wiki/Manual:FAQ MediaWiki FAQ]&lt;br /&gt;
*[https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]&lt;br /&gt;
*[//www.mediawiki.org/wiki/Localisation#Translation_resources Localise MediaWiki for your language]&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8322</id>
		<title>Introduction to IFs</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8322"/>
		<updated>2017-09-07T22:12:00Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Purposes&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;International Futures (IFs) is a tool for thinking about long-term global trends and planning more strategically for the&amp;amp;nbsp;future.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
IFs can help you:&lt;br /&gt;
&lt;br /&gt;
*Understand&amp;amp;nbsp;the state of major&amp;amp;nbsp;global systems&lt;br /&gt;
*Explore&amp;amp;nbsp;long-term trends and consider where they might take&amp;amp;nbsp;us&lt;br /&gt;
*Learn about the dynamic&amp;amp;nbsp;interactions between global systems&lt;br /&gt;
*Clarify long-term organizational goals/priorities&lt;br /&gt;
*Develop alternative scenarios (if-then statements) about the future&lt;br /&gt;
*Investigate how different groups (households, firms or governments) can shape the future&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;IFs development and analysis depend&amp;amp;nbsp;on core, underlying assumptions, including the following.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*Global issues are becoming&amp;amp;nbsp;more significant as the scope of human interaction and human impact on the broader environment grow.&lt;br /&gt;
*Goals and priorities for human systems are becoming clearer and are more frequently and consistently communicated.&lt;br /&gt;
*Understanding of the dynamics of human systems is improving&amp;amp;nbsp;rapidly.&lt;br /&gt;
*The domain of human choice and action is broadening.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;What issues can you&amp;amp;nbsp;investigate with IFs?&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*[[Environment]]: Atmospheric carbon dioxide levels, world forest area, fossil fuel usage&lt;br /&gt;
*[[Socio-Political|Socio-Political Change]]: Life expectancy, literacy rate, democracy level, status of women, value change&lt;br /&gt;
*[[Population|Demographics]]: Population levels and growth, fertility, mortality, migration&lt;br /&gt;
*[[Agriculture|Food and Agriculture]]: Land use and production levels, calorie availability, malnutrition rates&lt;br /&gt;
*[[Energy]]: Resource and production levels, demand patterns, renewable energy share&lt;br /&gt;
*[[Economics]]: Sectoral production, consumption, and trade patterns and structural change&lt;br /&gt;
*[[Interstate_Politics_(IP)|Geopolitics]]: Country and regional power levels&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Visual Representation&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
[[File:IFsOverviewChart.jpg|frame|center|Visual representation of IFs structure]]&lt;br /&gt;
&lt;br /&gt;
Among the philosophical premises of the International Futures (IFs) project is that the model cannot be a &amp;quot;black box&amp;quot; to users and be truly useful. Model users must be able to examine the structures of IFs in order (1) to have confidence in them, and (2) learn from them.&lt;br /&gt;
&lt;br /&gt;
There is available (see topics under Understanding the Mode in the contents of this help system):&lt;br /&gt;
&lt;br /&gt;
*[[Understand_IFs#Dominant_Relations|Dominant Relations]] of the model structure&lt;br /&gt;
*[[Understand_IFs#2.2|Structure-Based and Agent-Class Driven Modeling]]&lt;br /&gt;
*[[Understand_IFs#Equation_Notation|Equation Notation]]&lt;br /&gt;
*[[Introduction_to_IFs#IFs_Bibliography|IFs Bibliography]] of data and data sources&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Quick Survey&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;population&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents 22 age-sex cohorts to age 100+&lt;br /&gt;
*calculates change in fertility and mortality rates in response to income, income distribution, and analysis multipliers&lt;br /&gt;
*computes average life expectancy at birth, literacy rate, and overall measures of human development (HDI) and physical quality of life&lt;br /&gt;
*represents migration and HIV/AIDS&lt;br /&gt;
*includes a newly developing submodel of formal education across primary, secondary, and tertiary levels&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;economic&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents the economy in six sectors: agriculture, materials, energy, industry, services, and ICT (other sectors could be configured, using raw data from the GTAP project)&lt;br /&gt;
*computes and uses input-output matrices that change dynamically with development level&lt;br /&gt;
*is a general equilibrium-seeking model that does not assume exact equilibrium will exist in any given year; rather it uses inventories as buffer stocks and to provide price signals so that the model chases equilibrium over time&lt;br /&gt;
*contains an endogenous production function that represents contributions to growth in multifactor productivity from R&amp;amp;D, education, worker health, economic policies (&amp;quot;freedom&amp;quot;), and energy prices (the &amp;quot;quality&amp;quot; of capital)&lt;br /&gt;
*uses a Linear Expenditure System to represent changing consumption patterns&lt;br /&gt;
*utilizes a &amp;quot;pooled&amp;quot; rather than the bilateral trade approach for international trade&lt;br /&gt;
*is being imbedded during 2002 in a social accounting matrix (SAM) envelope that will tie economic production and consumption to intra-actor financial flows&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;agricultural&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents production, consumption and trade of crops and meat; it also carries ocean fish catch and aquaculture in less detail&lt;br /&gt;
*maintains land use in crop, grazing, forest, urban, and &amp;quot;other&amp;quot; categories&lt;br /&gt;
*represents demand for food, for livestock feed, and for industrial use of agricultural products&lt;br /&gt;
*is a partial equilibrium model in which food stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the agricultural sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;energy&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*portrays production of six energy types: oil, gas, coal, nuclear, hydroelectric, and other renewable&lt;br /&gt;
*represents consumption and trade of energy in the aggregate&lt;br /&gt;
*represents known reserves and ultimate resources of the fossil fuels&lt;br /&gt;
*portrays changing capital costs of each energy type with technological change as well as with draw-downs of resources&lt;br /&gt;
*is a partial equilibrium model in which energy stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the energy sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The two &#039;&#039;&#039;socio-political&#039;&#039;&#039; sub-modules:&lt;br /&gt;
&lt;br /&gt;
Within countries or geographic groupings&lt;br /&gt;
&lt;br /&gt;
*represents fiscal policy through taxing and spending decisions&lt;br /&gt;
*shows six categories of government spending: military, health, education, R&amp;amp;D, foreign aid, and a residual category&lt;br /&gt;
*represents changes in social conditions of individuals (like fertility rates or literacy levels), attitudes of individuals (such as the level of materialism/postmaterialism of a society from the World Value Survey), and the social organization of people (such as the status of women)&lt;br /&gt;
*represents the evolution of democracy&lt;br /&gt;
*represents the prospects for state instability or failure&lt;br /&gt;
&lt;br /&gt;
Between countries or groupings of countries&lt;br /&gt;
&lt;br /&gt;
*traces changes in power balances across states and regions&lt;br /&gt;
*allows exploration of changes in the level of interstate threat&lt;br /&gt;
*represents possible action-reaction processes and arms races with associated potential for conflict among countries&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;environmental&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows tracking of remaining resources of fossil fuels, of the area of forested land, of water usage, and of atmospheric carbon dioxide emissions&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;technology&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows changes in assumptions about rates of technological advance in agriculture, energy, and the broader economy&lt;br /&gt;
*explicitly represents the extent of electronic networking of individuals in societies&lt;br /&gt;
*is tied to the governmental spending model with respect to R&amp;amp;D spending&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Background&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
International Futures (IFs) has evolved since 1980 through three &amp;quot;generations,&amp;quot; with a fourth generation now taking form.&lt;br /&gt;
&lt;br /&gt;
The first generation had deep roots in the world models of the 1970s, including those of the Club of Rome. In particular, IFs drew on the Mesarovic-Pestel or World Integrated Model (Mesarovic and Pestel 1974). The author of IFs had contributed to that project, including the construction of the energy submodel. IFs consciously also drew on the Leontief World Model (Leontief et al. 1977), the Bariloche Foundation’s world model (Herrera et al. 1976), and Systems Analysis Research Unit Model (SARU 1977), following comparative analysis of those models by Hughes (1980). That generation was written in FORTRAN and available for use on main-frame computers through CONDUIT, an educational software distribution center at the University of Iowa. Although the primary use of that and subsequent generations was by students, IFs has always had some policy analysis capability that has appealed to specialists. For example, the U.S. Foreign Service Institute used the first generation of IFs in a mid-career training program.&lt;br /&gt;
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The second generation of International Futures moved to early microcomputers in 1985, using the DOS platform. It was a very simplified version of the original IFs without regional or country differentiation.&lt;br /&gt;
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The third generation, first available in 1993, became a full-scale microcomputer model. The third generation improved earlier representations of demographic, energy, and food systems, but added new environmental and socio-political content. It built upon the collaboration of the author with the GLOBUS project, and it adopted the economic submodel of GLOBUS (developed by the author). GLOBUS had been created with the inspiration of Karl Deutsch and under the leadership of Stuart Bremer (1987) at the Wissenschaftszentrum in Berlin.&lt;br /&gt;
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The third generation has produced three editions/major releases of IFs, each accompanied by a book also called International Futures (Hughes 1993, 1996, 1999). The second edition moved to a Visual Basic platform that allowed a much improved menu-driven interface, running under Windows. The third edition incorporated an early global mapping capability and an initial ability to do cross-sectional and longitudinal data analysis.&lt;br /&gt;
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The fourth generation has been taking shape since early 2000. It has been heavily influenced by the usage of the model in an increasingly policy-analysis mode by several important organizations. First, General Motors commissioned a specialized version of IFs named CoVaTrA (Consumer Values Trends Analysis) with a need for updated and extended demographic modeling and representation of value change. An alliance was established with the World Values Survey, directed by Ronald Inglehart, to create that version. Second, the Strategic Assessments Group of the Central Intelligence Agency commissioned a specialized version named IFs for SAG. The work involved in preparing that greatly extended and enhanced the socio-political representations of the model, both domestic and international. Third, the European Commission sponsored a project named TERRA which has led to a specialized version named IFs for TERRA. IFs for TERRA work led to enhancements across the model, including improved representation of economic sectors, updated IO matrices and a basic Social Accounting Matrix, GINI and Lorenz curves, and preparing for extended environmental impact representation (drawing upon the Advanced Sustainability Analysis framework of the Finland Futures Research Center).&lt;br /&gt;
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Throughout this emergence of a fourth generation IFs (incorporating all of the above elements for additional users) there has been also a heavy emphasis on enhanced usability. Ideas from Robert Pestel in the TERRA project led to the creation of a new tree-structure for scenario creation and management. Ideas from Ronald Inglehart led to the development of the Guided Use structure and a somewhat more game-like character within that structure. Inglehart also help arrange funding to support the programming of Guided Use through the European Union Center of the University of Michigan.&lt;br /&gt;
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The fifth version of IFs is currently in use and represents broad strides to improving the model and its usability. It is the first version of this software to be placed online due to the help of the National Intelligence Council ([http://www.ifs.du.edu http://www.ifs.du.edu]). Also, usability has been increased as Packaged Displays and Flex Packaged Displays were introduced that allowed for the creation of very specific lists of countries/regions, groups or Glists. A new education model has also been incorporated into the broader IFs model. New scenarios were created for UNEP (focusing on environmental change) and Pardee (focusing on poverty). Finally, one of the largest changes made was incorporating 182 countries into the Base-Case scenario used by IFs. Previous versions of IFs used broader regions to forecast global trends. This change also did away with the Student and Professional versions.&lt;br /&gt;
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= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Geographic Representation of the World&amp;lt;/span&amp;gt; =&lt;br /&gt;
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186 countries underpin the functioning of IFs and these countries can be displayed separately or as parts of larger groups that users can determine.&lt;br /&gt;
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&#039;&#039;Below is a visual representation of how different entities are organized into Countries/Regions, Groups or Glists:&#039;&#039;&lt;br /&gt;
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[[File:Geo 186.png|frame|right|Visual representation of IF&#039;s definition of regions/countries/groups/glists]]&lt;br /&gt;
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&#039;&#039;*Note: In older versions of IFs, Regions were used as intermediaries between Countries and Groups. In the future, they, or some similarly named unit, will be a sub-unit of Countries. Regions, acting as a sub-unit of Countries, are currently not a feature of IFs. See the image located at the bottom of this Help topic.&#039;&#039;&lt;br /&gt;
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When using IFs, there are many occasions where the user is asked whether or not they would like to display their results as a product of single countries, or larger groups. This is typically a toggle switch that moves between Country/Region and Groups, however, it might be a three-way-toggle that includes Country/Region, Group and Glist.&lt;br /&gt;
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Countries/Regions are currently the smallest geographical unit that users can represent. The ability to split countries down into smaller regions, or states, is under development. There are 186 different countries/regions that users can display.&lt;br /&gt;
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Groups are variably organized geographically or by memberships in international institutions/regimes. You can find out who is represented in each group and add or delete members by exploring the [[Extended_Features#Manage_Groups/Regions|Managing Regionalization]] function.&lt;br /&gt;
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Glists merge both Groups and Countries/Regions. These lists are mostly geographically bound. In the future, the Glist distinction will become more important as some users may want to place, for example, both the Indian state of Kerala in a Glist with Sri Lanka and Nepal.&lt;br /&gt;
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Users may also want to [[Extended_Features#Change_Grouping/Regionalization|create]] their own groups or [[Extended_Features#Identify_Groups_or_Country/Region_Members|explore]] what countries are members of what groups.&lt;br /&gt;
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= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Time Horizon&amp;lt;/span&amp;gt; =&lt;br /&gt;
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&#039;&#039;&#039;Future Forecasts.&#039;&#039;&#039; IFs begins computation with data from 2000 and can dynamically calculate values for all variables annually through 2100.&lt;br /&gt;
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&#039;&#039;&#039;Historical Analysis and &amp;quot;Forecasts.&amp;quot;&#039;&#039;&#039; IFs also includes an extensive and growing historical data base starting in 1960. The data basis allows analysis of relationships among variables across countries and across time.&amp;amp;nbsp;&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Instructional Use&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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The standard modes for using IFs in a classroom are:&lt;br /&gt;
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1. Assigning class members to an issue area or topic. Consider identifying specific questions for them to address.&lt;br /&gt;
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2. Assigning class members to a country/geographic region. Again, specificity helps.&lt;br /&gt;
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Most often, students will work independently or in groups on projects and share information after completing them. It is possible, however, to have students work interactively, by assigning them topics or regions, letting them begin work, and then have the interacting groups (or individuals) create a collective model run with the changes that each group proposes by topic or region. That process, although more difficult to organize, allows the class as whole to investigate the interaction of their topics or regions (and to share learning about model use).&lt;br /&gt;
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There is a&amp;amp;nbsp;[http://portfolio.du.edu/bhughes web site]&amp;amp;nbsp;available in support of the educational use of IFs. You will find syllabi at that site. There are several [[Introduction_to_IFs#Publications_on_IFs|publications]] on IFs, including a book structured specifically for educational use.&lt;br /&gt;
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Donald Borock has described his classroom use of IFs in print. Borock, Donald. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot; Advances in Social Science and Computers 4, 103-127.&lt;br /&gt;
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= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Acknowledgements&amp;lt;/span&amp;gt; =&lt;br /&gt;
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The author gratefully recognizes critical contributions in the forms of:&lt;br /&gt;
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:1. Testing and suggestions for development of IFs in one or more of multiple generations. By Donald Borock, Richard Chadwick, William Dixon, Dale Rothman, Phil Schrodt, Douglas Stuart, Donald Sylvan, Jonathan Wilkenfeld, and Ronald Inglehart.&lt;br /&gt;
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:2. Computer assistance across many releases. By Michael Niemann, Terrance Peet-Lukes, Douglas McClure, Mohammod Irfan, and Jose Solorzano.&lt;br /&gt;
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:3. Data gathering and general assistance. By James Chung, Padma Padula, Shannon Brady, David Horan, Michael Ferrier, Kay Drucker, Warren Christopher, and Anwar Hossain.&lt;br /&gt;
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:4. Long-term encouragement and support. By Harold Guetzkow, Karl Deutsch, Richard Chadwick, Gerald Barney, and Ronald Inglehart.&lt;br /&gt;
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:5. Association in related world modeling projects and projects building upon IFs. By Mihajlo Mesarovic, Aldo Barsotti, Juan Huerta, John Richardson, Thomas Shook, Patricia Strauch, and other members of the World Integrated Model (WIM) team. By Stuart Bremer, Peter Brecke, Thomas Cusack, Wolf Dieter-Eberwein, Brian Pollins, and Dale Smith of the GLOBUS modeling project. By Evan Hillebrand, Paul Herman, and others of the IFs for SAG project. By Rob Lempert and Steve Bankes at RAND, Santa Monica. By Robert Pestel, Jonathan Cave, Ronald Inglehart, Sergei Parinov, Pentti Malaska, and many others in the IFs for TERRA project.&lt;br /&gt;
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:6. Financial assistance (without responsibility for the form of the evolving product). By the National Science Foundation, the Cleveland Foundation, the Exxon Education Foundation, the Kettering Family Foundation, the Pacific Cultural Foundation, the United States Institute of Peace, General Motors, the Strategic Assessments Group of the Central Intelligence Agency, the European Commission (Information Society Technology) Programme, the European Union Center of the University of Michigan, the National Intelligence Council (for web conversion), and Frederick S. Pardee. &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
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= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Feedback&amp;lt;/span&amp;gt; =&lt;br /&gt;
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Feedback on how to improve IFs is always appreciated, especially if you find something that is not working. Compliments are also accepted. Please contact. To send the IFs team an e-mail, click on&amp;amp;nbsp;[mailto:pardee.center@du.edu Pardee Center]&amp;amp;nbsp;in stand-alone versions or on the web.&lt;br /&gt;
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= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Support for IFs Use&amp;lt;/span&amp;gt; =&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Publications on IFs&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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To obtain additional information about IFs and its use, consult:&lt;br /&gt;
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Barry B. Hughes and Evan E. Hillebrand, &#039;&#039;&#039;Exploring and Shaping International Futures.&#039;&#039;&#039; Boulder, CO: Paradigm Publishers, 2006. Specifically, see chapter 4.&lt;br /&gt;
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Barry B. Hughes, &#039;&#039;&#039;International Futures: Choices in the Face of Uncertainty,&#039;&#039;&#039; 3rd ed. Boulder, CO: Westview Press, 1999. This volume is built around IFs and contains detailed suggestions for its use. Version 3.17 of IFs, which runs under Windows 95, is distributed with the third edition of the book. The second edition contained a version for Windows 3.1, and the first edition ran under DOS. Chapter 4 of the 2nd edition of IFs included Flow Charts of Worldviews , reproduced now in this Help system.&lt;br /&gt;
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Barry B. Hughes, &#039;&#039;&#039;Continuity and Change in World Politics,&#039;&#039;&#039; 4th ed. Englewood Cliffs, N.J.: Prentice Hall, 2000. IFs can also usefully supplement this textbook on global politics.&lt;br /&gt;
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Barry B. Hughes, &amp;quot;The International Futures (IFs) Modeling Project. 1999. &#039;&#039;&#039;Simulation and Gaming&#039;&#039;&#039; 30, No. 3 (September): 304-326.&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;IFs Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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Alcamo, Joseph, Rik Leemans and Eric Kreileman, eds. 1998.&amp;amp;nbsp;&#039;&#039;Global Change Scenarios of the 21st Century: Results from the IMAGE 2.1 Model&#039;&#039;. The Netherlands: Pergamon.&lt;br /&gt;
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Alcamo, Joseph. 1994.&amp;amp;nbsp;&#039;&#039;IMAGE 2.0: Integrated Modeling of Global Climate Change&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
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Alexandratos, Nikos, ed. 1995.&amp;amp;nbsp;&#039;&#039;World Agriculture: Towards 2010&#039;&#039;&amp;amp;nbsp;(An FAO Study). New York: FAO and John Wiley and Sons.&lt;br /&gt;
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Allen, R. G. D. 1968.&amp;amp;nbsp;&#039;&#039;Macro-Economic Theory: A Mathematical Treatment&#039;&#039;. New York: St. Martin&#039;s Press.&lt;br /&gt;
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Avery, Dennis. 1995. &amp;quot;Saving the Planet with Pesticides,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 50-82.&lt;br /&gt;
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Bailey, Ronald, ed. 1995.&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;. New York: The Free Press.&lt;br /&gt;
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Barbieri, Kathleen. 1996. &amp;quot;Economic Interdependence: A Path to Peace or a Source of Interstate Conflict?&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Peace Research&#039;&#039;&amp;amp;nbsp;33: 29-50.&lt;br /&gt;
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Barker, T.S. and A.W.A. Peterson, eds. 1987.&amp;amp;nbsp;&#039;&#039;The Cambridge Multisectoral Dynamic Model of the British Economy&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
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Barney, Gerald O., W. Brian Kreutzer, and Martha J. Garrett, eds. 1991.&amp;amp;nbsp;&#039;&#039;Managing a Nation&#039;&#039;, 2nd ed. Boulder: Westview Press.&lt;br /&gt;
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Barro, Robert J. 1997.&amp;amp;nbsp;&#039;&#039;Determinants of Economic Growth: A Cross-Country Empirical Study&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
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Barro, Robert J. and Xavier Sala-i-Martin. 1999.&amp;amp;nbsp;&#039;&#039;Economic Growth&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
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Bennett, D. Scott, and Allan Stam. 2003.&amp;amp;nbsp;&#039;&#039;The Behavioral Origins of War: Cumulation and Limits to Knowledge in Understanding International Conflict&#039;&#039;. Ann Arbor: University of Michigan Press&lt;br /&gt;
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Birg, Herwig. 1995.&amp;amp;nbsp;&#039;&#039;World Population Projections for the 21st Century&#039;&#039;. Frankfurt: Campus Verlag.&lt;br /&gt;
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Borock, Donald M. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot;&amp;amp;nbsp;&#039;&#039;Advances in Social Science and Computers&#039;&#039;&amp;amp;nbsp;4, 103-127.&lt;br /&gt;
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Bos, Eduard, My T. Vu, Ernest Massiah, and Rodolfo A. Bulatao. 1994.&amp;amp;nbsp;&#039;&#039;World Population Projections 1994-95 Edition&#039;&#039;&amp;amp;nbsp;[editions are biannual] Baltimore: Johns Hopkins Press.&lt;br /&gt;
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Boulding, Elise and Kenneth E. Boulding. 1995.&amp;amp;nbsp;&#039;&#039;The Future: Images and Processes&#039;&#039;. Thousand Oaks, CA: Sage Publications.&lt;br /&gt;
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Brecke, Peter. 1993. &amp;quot;Integrated Global Models that Run on Personal Computers,&amp;quot;&amp;amp;nbsp;&#039;&#039;Simulation&#039;&#039;60 (2).&lt;br /&gt;
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Bremer, Stuart A. 1977.&amp;amp;nbsp;&#039;&#039;Simulated Worlds: A Computer Model of National Decision-Making&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
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Bremer, Stuart A., ed. 1987.&amp;amp;nbsp;&#039;&#039;The GLOBUS Model: Computer Simulation of World-wide Political and Economic Developments&#039;&#039;. Boulder, CO: Westview.&lt;br /&gt;
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Bremer, Stuart A. and Walter Gruhn. 1988.&amp;amp;nbsp;&#039;&#039;Micro GLOBUS: A Computer Model of Long-Term Global Political and Economic Processes&#039;&#039;. Berlin: edition sigma.&lt;br /&gt;
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Bremer, Stuart A. and Barry B. Hughes. 1990.&amp;amp;nbsp;&#039;&#039;Disarmament and Development: A Design for the Future?&#039;&#039;&amp;amp;nbsp;Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
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Brockmeier, Martina and Channing Arndt (presentor). 2002. Social Accounting Matrices. Powerpoint presentation on GTAP and SAMs (June 21). Found on the web.&lt;br /&gt;
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Brown, Lester R. 1981.&amp;amp;nbsp;&#039;&#039;Building a Sustainable Society&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
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Brown, Lester R. 1988. &amp;quot;Analyzing the Demographic Trap,&amp;quot; in&amp;amp;nbsp;&#039;&#039;State of the World 1987&#039;&#039;, eds. Lester R. Brown and others. New York: W.W. Norton, pp. 20-37.&lt;br /&gt;
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Brown, Lester R. 1995.&amp;amp;nbsp;&#039;&#039;Who Will Feed China?&#039;&#039;&amp;amp;nbsp;New York: W.W. Norton.&lt;br /&gt;
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Brown, Lester R. 1996.&amp;amp;nbsp;&#039;&#039;Tough Choices: Facing the Challenge of Food Scarcity&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
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Brown, Lester R., et al. 1996&amp;amp;nbsp;&#039;&#039;State of the World 1996&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
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Brown, Lester R., Nicholas Lenssen, and Hal Kane. 1995.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1995. New York: W.W. Norton.&lt;br /&gt;
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Brown, Lester R., Christopher Flavin, and Hal Kane. 1996.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1996. New York: W.W. Norton.&lt;br /&gt;
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Burkhardt, Helmut. 1995. &amp;quot;Priorities for a Sustainable Civilization,&amp;quot; unpublished conference paper. Department of Physics, Ryerson Polytechnic University, Toronto, Canada.&lt;br /&gt;
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Bussolo, Maurizio, Mohamed Chemingui and David O’Connor. 2002. A Multi-Region Social Accounting Matrix (1995) and Regional Environmental General Equilibrium Model for India (REGEMI). Paris: OECD Development Centre (February). Available at&amp;amp;nbsp;[http://www.oecd.org/dev/technics www.oecd.org/dev/technics].&lt;br /&gt;
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British Petroleum Company. 1995.&amp;amp;nbsp;&#039;&#039;BP Statistical Review of World Energy&#039;&#039;. London: British Petroleum Company.&lt;br /&gt;
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Central Intelligence Agency (CIA). 1991.&amp;amp;nbsp;&#039;&#039;Handbook of Economic Statistics, 1991&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
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Central Intelligence Agency (CIA). 1994.&#039;&#039;&amp;amp;nbsp;The World Factbook 1994&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
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Chang, Sheldon S. L. 1961.&amp;amp;nbsp;&#039;&#039;Synthesis of Optimum Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
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Chenery, Hollis and Moises Syrquin. 1975.&amp;amp;nbsp;&#039;&#039;Patterns of Development 1950-1970&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
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Cipolla, Carlo M. 1962.&amp;amp;nbsp;&#039;&#039;The Economic History of World Population&#039;&#039;. Baltimore: Penguin.&lt;br /&gt;
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Cook, Earl. 1976.&amp;amp;nbsp;&#039;&#039;Man, Energy, Society&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
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Committee on the Strategic Assessment of the U.S. Department of Energy’s Coal Program. 1995.&amp;amp;nbsp;&#039;&#039;Coal: Energy for the Future&#039;&#039;. Washington, D.C.: National Academy Press.&lt;br /&gt;
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Council on Environmental Quality (CEQ). 1981.&amp;amp;nbsp;&#039;&#039;The Global 2000 Report to the President&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
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Council on Environmental Quality (CEQ). 1981b.&amp;amp;nbsp;&#039;&#039;Environmental Trends&#039;&#039;. Washington, D.C. (July).&lt;br /&gt;
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Council on Environmental Quality (CEQ). 1991.&amp;amp;nbsp;&#039;&#039;21st Annual Report&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
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Crescenzi, Mark J.C. and Andrew J. Enterline. 2001. &amp;quot;Time Remembered: A Dynamic Model of Interstate Interaction,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;45, no. 3 (September): 409-431.&lt;br /&gt;
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Crosson, Pierre, and Jock R. Anderson. 1992.&amp;amp;nbsp;&#039;&#039;Resources and Global Food Prospects&#039;&#039;. Washington, D.C.: The World Bank. World Bank Technical Paper Number 184.&lt;br /&gt;
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Cusack, Thomas R. and Richard J. Stoll. 1990.&amp;amp;nbsp;&#039;&#039;Exploring Realpolitik: Probing International Relations with Computer Simulatio&#039;&#039;n. Boulder: Lynne Rienner Publishers.&lt;br /&gt;
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Dargay, Joyce and Dermot Gately. 1999. &amp;quot;Income’s Effect on Car and Vehicle Ownership, Worldwide: 1960-2015,&amp;quot;&amp;amp;nbsp;&#039;&#039;Transportation Research Part A&#039;&#039;&amp;amp;nbsp;33: 101-138.&lt;br /&gt;
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Dall, P., Kaspar, F. and Alcamo, J. 1998. &amp;quot;Modeling World-wide Water Availability and Water Use Under the Influence of Climate Change,&amp;quot;&amp;amp;nbsp;&#039;&#039;Proceedings of the Second International Conference on Climate and Water&#039;&#039;, July 17-20, Espoo, Finland.&lt;br /&gt;
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Dimaranan, Betina V. and Robert A. McDougall, eds. 2002.&amp;amp;nbsp;&#039;&#039;Global Trade, Assistance, and Production: The GTAP 5 Data Base&#039;&#039;. Center for Global Trade Analysis, Purdue University. Available at [http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp].&lt;br /&gt;
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Dowlatabadi, H., and Morgan, M.G. 1993. &amp;quot;A Model Framework for Integrated Studies of the Climate Problem,&amp;quot;&amp;amp;nbsp;&#039;&#039;Energy Policy&#039;&#039;&amp;amp;nbsp;(March): 209-221.&lt;br /&gt;
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Duchin, Faye. 1998.&amp;amp;nbsp;&#039;&#039;Structural Economics: Measuring Change in Technology, Lifestyles, and the Environment&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
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Edwards, Stephen R. 1995. &amp;quot;Conserving Biodiversity,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 212-265.&lt;br /&gt;
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Edmonds, J., and Reilly, J.M. 1985.&amp;amp;nbsp;&#039;&#039;Global Energy: Assessing the Future&#039;&#039;. Oxford, UK: Oxford University Press.&lt;br /&gt;
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Edmonds, J., Pitcher, H. Rosenberg, N., and Wigley, T. &amp;quot;Design for the Global Change Assessment Model.&amp;quot;&amp;amp;nbsp;&#039;&#039;Integrative Assessment of Mitigation, Impacts and Adaptation to Climate Change&#039;&#039;. Laxenburg, Austria.&lt;br /&gt;
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Ehrlich, Paul R. and Anne H. Ehrlich. 1972.&amp;amp;nbsp;&#039;&#039;Population, Resources, Environment&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
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Eicher, Carl. 1982. &amp;quot;Facing up to Africa&#039;s Food Crisis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Foreign Affairs&#039;&#039;&amp;amp;nbsp;61, no. 1 (Fall): 151-74.&lt;br /&gt;
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Eberstadt, Nicholas. 1995. &amp;quot;Population, Food, and Income,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 8-47.&lt;br /&gt;
&lt;br /&gt;
Esty, Daniel C., Jack A. Goldstone, Ted Robert Gurr, Barbara Harff, Marc Levy, Geoffrey D. Dabelko, Pamela T. Surko, and Alan N. Unger. 1998. State Failure Task Force Report: Phase II Findings. Volume provided courtesy of Ted Robert Gurr.&lt;br /&gt;
&lt;br /&gt;
Flavin, Christopher. 1996. &amp;quot;Facing Up to the Risks of Climate Change,&amp;quot; in Lester R. Brown and others, eds., State of the World 1996 (New York: W.W. Norton), pp. 21-39.&lt;br /&gt;
&lt;br /&gt;
Forrester, Jay W. 1968.&amp;amp;nbsp;&#039;&#039;Principles of Systems&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Gilpin, Robert. 1981.&amp;amp;nbsp;&#039;&#039;War and Change in World Politics&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Globerman, Steven. 2000 (May). Linkages Between Technological Change and Productivity Growth. Industry Canada Research Publications Program: Occasional Paper 23.&lt;br /&gt;
&lt;br /&gt;
Grant, Lindsey. 1982.&amp;amp;nbsp;&#039;&#039;The Cornucopian Fallacies&#039;&#039;. Washington, D.C.: Environmental Fund.&lt;br /&gt;
&lt;br /&gt;
Griffith, Rachel, Stephen Redding, and John Van Reenen. 2000.&amp;amp;nbsp;&#039;&#039;Mapping the Two Faces of R&amp;amp;D: Productivity Growth in a Panel of OECD Industries&#039;&#039;. Institute for Fiscal Studies (January)&lt;br /&gt;
&lt;br /&gt;
Gwartney, James and Robert Lawson with Dexter Samida. 2000.&amp;amp;nbsp;&#039;&#039;Economic Freedom of the World: 2000 Annual Report&#039;&#039;. Vancouver, B.C.: the Fraser Institute.&lt;br /&gt;
&lt;br /&gt;
Hammond, Allen. 1998.&amp;amp;nbsp;&#039;&#039;Which World? Scenarios for the 21st Century&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
&lt;br /&gt;
Harff, Barbara, with Ted Robert Gurr and Alan Unger. 1999. Preconditions of Genocide and Politicide: 1955-1998. Paper prepared for the State Failure Task Force and provided courtesy of Barbara Harff and Ted Gurr.&lt;br /&gt;
&lt;br /&gt;
Henderson, Hazel. 1996. &amp;quot;Changing Paradigms and Indicators: Implementing Equitable, Sustainable and Participatory Development,&amp;quot; in Jo Marie Griesgraber and Bernhard G. Gunter,&amp;amp;nbsp;&#039;&#039;Development: New Paradigms and Principles for the 21st Century&#039;&#039;. East Haven, CT: Pluto Press, pp. 103-136.&lt;br /&gt;
&lt;br /&gt;
Herrera, Amilcar O., et al. 1976.&#039;&#039;&amp;amp;nbsp;Catastrophe or New Society? A Latin American World Model&#039;&#039;. Ottawa: International Development Research Centre.&lt;br /&gt;
&lt;br /&gt;
Hoekstra, A.Y. 1998.&amp;amp;nbsp;&#039;&#039;Perspectives on Water: An Integrated Model-Based Exploration of the Future&#039;&#039;. Utrecht, the Netherlands: International Books.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1980.&amp;amp;nbsp;&#039;&#039;World Modeling&#039;&#039;. Lexington, Mass: Lexington Books.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1982.&amp;amp;nbsp;&#039;&#039;International Futures Simulation: User&#039;s Manual&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985a.&amp;amp;nbsp;&#039;&#039;International Futures Simulation&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985b. &amp;quot;World Models: The Bases of Difference,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;29, 77-101.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985c.&amp;amp;nbsp;&#039;&#039;World Futures: A Critical Analysis of Alternatives&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1987. &amp;quot;Domestic Economic Processes,&amp;quot; in Stuart A. Bremer, ed.,&amp;amp;nbsp;&#039;&#039;The Globus Model: Computer Simulation of Worldwide Political Economic Development&#039;&#039;&amp;amp;nbsp;(Frankfurt and Boulder: Campus and Westview), pp. 39-158.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1988. &amp;quot;International Futures: History and Status,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Science Microcomputer Review&#039;&#039;&amp;amp;nbsp;6, 43-48.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1999. &amp;quot;The International Futures (IFs) Modeling Project.&#039;&#039;&amp;amp;nbsp;Simulation and Gaming&#039;&#039;&amp;amp;nbsp;Vol 30, No. 3 (September): 304-326.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1999.&amp;amp;nbsp;&#039;&#039;International Futures&#039;&#039;, 3rd edition Boulder: Westview Press, 1999.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2000.&amp;amp;nbsp;&#039;&#039;Continuity and Change in World Politics&#039;&#039;. Englewood Cliffs, N.J.: Prentice-Hall, fourth edition.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2001. &amp;quot;Global Social Transformation: The Sweet Spot, the Steady Slog, and the Systemic Shift,&amp;quot;&amp;amp;nbsp;&#039;&#039;Economic Development and Cultural Change&#039;&#039;&amp;amp;nbsp;49, No. 2 (January): 423-458.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2002.&amp;amp;nbsp;&#039;&#039;Theats and Opportunities Analysis&#039;&#039;. Living document prepared for the Strategic Assessments Group, Office of Transnational Issues, Central Intelligence Agency, August 2002.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. and Anwar Hossain. 2003. Long-Term Socio-Economic Modeling: With Universal, Globally-Integrated Social Accounting Matrices (SAMs) in a General Equilibrium Model Structure. IFs Project Living Document, University of Denver.&lt;br /&gt;
&lt;br /&gt;
Huth, Paul. 1996.&amp;amp;nbsp;&#039;&#039;Standing Your Ground: Territorial Disputes and International Conflict&#039;&#039;. Ann Arbor, MI: University of Michigan Press.&lt;br /&gt;
&lt;br /&gt;
Inglehart, Ronald. 1997.&amp;amp;nbsp;&#039;&#039;Modernization and Postmodernization: Cultural, Economic, and Political Change in 43 Societies&#039;&#039;. Ewing, NJ: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1995.&amp;amp;nbsp;&#039;&#039;Oil, Gas, and Coal Supply Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996.&amp;amp;nbsp;&#039;&#039;World Energy Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996b.&amp;amp;nbsp;&#039;&#039;The Strategic Value of Fossil Fuels: Challenges and Responses&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;International Financial Statistics&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;World Economic Outlook&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
Intergovernmental Panel on Climate Change (IPCC). 1995. Several volumes by various working groups. Published by Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Jansen, Karel and Rob Vos, eds. 1997.&amp;amp;nbsp;&#039;&#039;External Finance and Adjustment: Failure and Success in the Developing World&#039;&#039;. London: Macmillan Press Ltd.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1998.&amp;amp;nbsp;&#039;&#039;Modeling Global Change: The Art of Integrated Assessment Modelling&#039;&#039;. Cheltenham, UK: Edward Elgar.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1996.&amp;amp;nbsp;&#039;&#039;Meeting Targets: Tools to Support Integrated Modelling of Global Change&#039;&#039;. Den Haag: CIP-Gegevens Koninklijke Bibliotheek.&lt;br /&gt;
&lt;br /&gt;
Jansson, Kurt, Michael Harris, Angela Penrose. 1987.&amp;amp;nbsp;&#039;&#039;The Ethiopian Famine&#039;&#039;. London: Zed Books Ltd.&lt;br /&gt;
&lt;br /&gt;
Jeffreys, Kent. 1995. &amp;quot;Rescuing the Oceans,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 296-338.&lt;br /&gt;
&lt;br /&gt;
Jones, Daniel M., Stuart A. Bremer, and J. David Singer. 1996. &amp;quot;Militarized Interstate Disputes, 1816-1992: Rationale, Coding Rules, and Empirical Patterns,&amp;quot;&amp;amp;nbsp;&#039;&#039;Conflict Management and Peace Science&#039;&#039;&amp;amp;nbsp;XV, No. 2: 163-215.&lt;br /&gt;
&lt;br /&gt;
Khan, Haider A. 1998.&amp;amp;nbsp;&#039;&#039;Technology, Development and Democracy&#039;&#039;. Northhampton, Mass: Edward Elgar Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Kahn, Herman, William Brown, and Leon Martel. 1976.&amp;amp;nbsp;&#039;&#039;The Next 200 Years&#039;&#039;. New York: William Morrow.&lt;br /&gt;
&lt;br /&gt;
Kalymon, Basil A. 1975. &amp;quot;Economic Incentives in OPEC Oil Pricing Policy.&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Development Economics&#039;&#039;&amp;amp;nbsp;2: 337-362.&lt;br /&gt;
&lt;br /&gt;
Kaplan, Robert. 1994. &amp;quot;The Coming Anarchy,&amp;quot;&amp;amp;nbsp;&#039;&#039;The Atlantic Monthly&#039;&#039;&amp;amp;nbsp;273 (February): .&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999a. &amp;quot;Aggregating Governance Indicators&amp;quot;. World Bank Policy Research Department Working Paper No. 2195.&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999b. &amp;quot;Governance Matters&amp;quot;. World Bank Policy Research Department Working Paper No. 2196.&lt;br /&gt;
&lt;br /&gt;
Keepin, B. and B. Wynne. 1984. &amp;quot;Technical Analysis of the IIASA Energy Scenarios,&amp;quot;&amp;amp;nbsp;&#039;&#039;Nature&#039;&#039;312: 691-695.&lt;br /&gt;
&lt;br /&gt;
Kehoe, Timothy J. 1996. Social Accounting Matrices and Applied General Equilibrium Models. Federal Reserve Bank of Minneapolis, Working Paper 563.&lt;br /&gt;
&lt;br /&gt;
Kennedy, Paul. 1993.&amp;amp;nbsp;&#039;&#039;Preparing for the Twenty-First Century&#039;&#039;. New York: Random House.&lt;br /&gt;
&lt;br /&gt;
Klein, Lawrence R. and Fu-chen Lo, eds. 1995.&amp;amp;nbsp;&#039;&#039;Modeling Global Change&#039;&#039;. Tokyo: United Nations University Press.&lt;br /&gt;
&lt;br /&gt;
Kornai, J. 1971.&amp;amp;nbsp;&#039;&#039;Anti-Equilibrium&#039;&#039;. Amsterdam: North Holland.&lt;br /&gt;
&lt;br /&gt;
Kwasnicki, Witold and Halina Kwasnicka. 1996. &amp;quot;Long-Term Diffusion Factors of Technological Development: An Evolutionary Model and Case Study,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 31-57.&lt;br /&gt;
&lt;br /&gt;
Leontief, Wassily, Anne Carter and Peter Petri. 1977.&amp;amp;nbsp;&#039;&#039;The Future of the World Economy&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander H., and Elizabeth R. Ducot. 1976. &amp;quot;AGRIMOD: A Simulation Model for the Analysis of U.S. Food Policies.&amp;quot; Paper delivered at Conference on Systems Analysis of Grain Reserves, Joint Annual Meeting of GRSA and TIMS, Philadelphia, Pa., March 31-April 2.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander, H., et al. 1977. Energy in Agriculture: On Modeling Inputs in AGRIMOD. Final Report to U.S. Department of Energy. Palo Alto: Systems Control, Inc., August, available through NTIS.&lt;br /&gt;
&lt;br /&gt;
Lichbach, Mark Irving. 1989. &amp;quot;An Evaluation of ‘Does Economic Inequality Breed Political Conflict?,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;, Vol 41 , No. 4 (July 1989): 431-470.&lt;br /&gt;
&lt;br /&gt;
Liverman, Dianne. 1983.&amp;amp;nbsp;&#039;&#039;The Use of Global Simulation Models in Assessing Climate Impacts on the World Food System&#039;&#039;. Dissertation, University of California, Los Angeles.&lt;br /&gt;
&lt;br /&gt;
Londregan, John B. and Keith T. Poole. 1996. &amp;quot;Does High Income Promote Democrary?&amp;quot;,&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 1 (October): 1-30.&lt;br /&gt;
&lt;br /&gt;
MacKenzie, James J. 1996. &amp;quot;Oil as a Finite Resource: When is Global Production Likely to Peak?&amp;quot; Paper of the World Resources Institute. Washington, D.C.: WRI.&lt;br /&gt;
&lt;br /&gt;
Maddison, Angus. 1995.&amp;amp;nbsp;&#039;&#039;Monitoring the World Economy 1820-1992&#039;&#039;. Paris: OECD.&lt;br /&gt;
&lt;br /&gt;
Malthus, Thomas. 1798.&amp;amp;nbsp;&#039;&#039;An Essay on the Principle of Population as It Affects the Future Improvement of Society&#039;&#039;. London (reprinted many times).&lt;br /&gt;
&lt;br /&gt;
Mansfield, Edward D. 1994.&amp;amp;nbsp;&#039;&#039;Power, Trade, and War&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Marchetti, Cesare, Perrin S. Meyer, and Jesse H. Ausubel. 1996. &amp;quot;Human Population Dynamics Revisited with the Logistic Model: How Much Can be Modeled and Predicted?,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 1-30.&lt;br /&gt;
&lt;br /&gt;
Martens, Pim and Jan Rotmans, eds. 1999.&amp;amp;nbsp;&#039;&#039;Climate Change: An Integrated Perspective&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
&lt;br /&gt;
Martens, W.J.M. 1997. &amp;quot;Health Impacts of Climate Change and Ozone Depletion: An Eco-Epidemiological Approach,&amp;quot; Maastricht, the Netherlands: Maastricht University.&lt;br /&gt;
&lt;br /&gt;
Mason, Andrew. 1997. &amp;quot;The Role of Population Change in the Asian Economic Miracle,&amp;quot; Honolulu, Hawaii: East-West Center, AsiaPacific Issues, No. 33 (October), 8 pages.&lt;br /&gt;
&lt;br /&gt;
McMahon, Walter W. 1997.&amp;amp;nbsp;&#039;&#039;Education and Development: Measuring the Social Benefits&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, Jorgen Randers, and William K. Behrens, III. 1972.&amp;amp;nbsp;&#039;&#039;Limits to Growth&#039;&#039;. New York: Universe Books.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, and Jorgen Randers. 1992.&amp;amp;nbsp;&#039;&#039;Beyond the Limits&#039;&#039;. Post Mills, Vermont: Chelsea Green Publishing Company.&lt;br /&gt;
&lt;br /&gt;
Meadows, Dennis L. et al. 1974.&amp;amp;nbsp;&#039;&#039;Dynamics of Growth in a Finite World&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Mesarovic, Mihajlo D. and Eduard Pestel. 1974.&amp;amp;nbsp;&#039;&#039;Mankind at the Turning Point&#039;&#039;. New York: E.P. Dutton &amp;amp; Co.&lt;br /&gt;
&lt;br /&gt;
Mishkin, Eli. And Ludwig Braun, ed. 1961.&amp;amp;nbsp;&#039;&#039;Adaptive Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Moore, Will H., Ronny Lindstrom, and Valerie O’Regan. 1996. &amp;quot;Land Reform, Political Violence and the Economic Inequality-Political Conflict Nexus: A Longitudinal Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Interactions&#039;&#039;&amp;amp;nbsp;21, No. 4: 335-363.&lt;br /&gt;
&lt;br /&gt;
Mori, Shunsuke and Masato Takahaashi, 1997. An Integrated Assessment Model for the Evaluation of New Energy Technologies and Food Production, accepted by&amp;amp;nbsp;&#039;&#039;International Journal of Global Energy Issues&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
Naill, Roger F. 1977.&amp;amp;nbsp;&#039;&#039;Managing the Energy Transition&#039;&#039;. Vols. 1 and 2. Cambridge, Mass: Ballinger Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1992. &amp;quot;The DICE Model: Background and Structure of a Dynamic Integrated Climate Economy,&amp;quot; New Haven: Yale University.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1979.&amp;amp;nbsp;&#039;&#039;The Efficient Use of Energy Resources&#039;&#039;. New Haven, CT: Yale University Press.&lt;br /&gt;
&lt;br /&gt;
Oneal, John R. and Bruce M. Russett. 1997. The Classical Liberals were Right: Democracy, Interdependence, and Conflict, 1950-1985.&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;41, no. 2 (June): 267-294.&lt;br /&gt;
&lt;br /&gt;
Pan, Xiaoming. 2000 (January). &amp;quot;Social and Ecological Accounting Matrix: an Empirical Study for China,&amp;quot; paper submitted for the Thirteenth International Conference on Input-Output Techniques, Macerata, Italy, August 21-25, 2000.&lt;br /&gt;
&lt;br /&gt;
Pesaran, M. Hashem and G. C. Harcourt. 1999. Life and Work of John Richard Nicholas Stone.&lt;br /&gt;
&lt;br /&gt;
Pirages, Dennis. 1989.&amp;amp;nbsp;&#039;&#039;Global Technopolitics&#039;&#039;. Pacific Grove, Calif: Brooks/Cole Publishing.&lt;br /&gt;
&lt;br /&gt;
Prinn, R. H.J., A. Sokolov, C. Wand, X. Xiao, Z. Yang, R. Eckhaus, P. Stone, D. Ellerman, J Melilo, J. Fitzmaurice, D. Kicklighter, and Y. Liu. 1996. &amp;quot;Integrated Global System Model for Climate Policy Analysis: Model Framework and Sensitivity Analysis.&amp;quot; Cambridge, Mass: Global Change Center, Massachusetts Institute of Technology.&lt;br /&gt;
&lt;br /&gt;
Przeworski, Adam and Fernando Limongi. 1997. &amp;quot;Modernization: Theories and Facts,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 2 (January): 155-183.&lt;br /&gt;
&lt;br /&gt;
Population Reference Bureau. 1996. World Population Data Sheet 1996. Washington, D.C.: Population Reference Bureau.&lt;br /&gt;
&lt;br /&gt;
Postel, Sandra. 1996.&amp;amp;nbsp;&#039;&#039;Dividing the Waters: Food Security, Ecosystem Health, and the New Politics of Scarcity&#039;&#039;. Worldwatch Paper 132. Washington, D.C.: Worldwatch Institute, September.&lt;br /&gt;
&lt;br /&gt;
Pyatt, G. and J.I. Round, eds. 1985.&amp;amp;nbsp;&#039;&#039;Social Accounting Matrices: A Basis for Planning&#039;&#039;. Washington, D.C.: The World Bank.&lt;br /&gt;
&lt;br /&gt;
Raskin, P., T. Banuri, G. Gallopín, P. Gutman, A. Hammond, R. Kates, and R. Swart. 2001. Great Transition:&amp;amp;nbsp;&#039;&#039;The Promise and Lure of the Times Ahead&#039;&#039;. Forthcoming.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1990.&amp;amp;nbsp;&#039;&#039;Global Politics&#039;&#039;, 4th edition. Boston: Houghton Mifflin.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1995.&amp;amp;nbsp;&#039;&#039;Democracy and International Conflict&#039;&#039;. Columbia: University of South Carolina Press.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee and J. David Singer. 1973. &amp;quot; Measuring the Concentration of Power in the International System,&amp;quot;&#039;&#039;&amp;amp;nbsp;Sociological Methods and Research&#039;&#039;&amp;amp;nbsp;1, no. 4: 403-436. Reprinted in&amp;amp;nbsp;&#039;&#039;Measuring the Correlates of War&#039;&#039;, edited by J. David Singer and Paul Diehl. Ann Arbor: University of Michigan Press, 1990.&lt;br /&gt;
&lt;br /&gt;
Rayner. S. 1992. &amp;quot;Cultural Theory and Risk Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Theory of Risk&#039;&#039;, ed. G. D. Preagor. Westport, USA.&lt;br /&gt;
&lt;br /&gt;
Repetto, Robert and Duncan Austin. 1997.&amp;amp;nbsp;&#039;&#039;The Costs of Climate Protection&#039;&#039;. Washington, D.C.: World Resources Institute.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis Fry. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Chicago: Quadrangle Books.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis F. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Pittsburgh: Boxwood Press.&lt;br /&gt;
&lt;br /&gt;
Romer, Paul M. 1994. &amp;quot;The Origins of Endogenous Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Economic Perspectives&#039;&#039;Vol 8, No. 1 (Winter): 3-22.&lt;br /&gt;
&lt;br /&gt;
Root T. and Stephen Schneider. 1995. &amp;quot;Ecology and Climate: Research Strategies and Implications,&amp;quot; Science 269 (52): 334-341.&lt;br /&gt;
&lt;br /&gt;
Rosegrant, Mark W., Mercedita Agcaoili-Sombilla, and Nicostrato D. Perez. 1995. &amp;quot;Global Food Projections to 2020: Implications for Investment.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 5.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1999. Integrated Assessment Models: Uncertainty, Quality and Use. Maastricht, the Netherlands: Maastricht University, International Centre for Integrative Studies (ICIS), Working Paper 199-E005.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and Burt de Vries, eds. 1997.&amp;amp;nbsp;&#039;&#039;Perspectives on Global Change: The Targets Approach&#039;&#039;. Cambridge, UK: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and M.B.A. van Asselt. 1996. &amp;quot;Integrated Assessment: A Growing Child on its Way to Maturity,&amp;quot;&amp;amp;nbsp;&#039;&#039;Climatic Change&#039;&#039;&amp;amp;nbsp;34 (3-4): 327-336.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1990.&amp;amp;nbsp;&#039;&#039;IMAGE: An Integrated Model to Assess the Greenhouse Effect&#039;&#039;. Dordrecht, the Netherlands: Kluwer Academics.&lt;br /&gt;
&lt;br /&gt;
Saaty, Thomas L. 1996. The Analytic Network Process: Decision Making with Dependence and Feedback. Pittsburgh: RWS Publications.&lt;br /&gt;
&lt;br /&gt;
Schafer, Andreas and David G. Victor. 1997. The Future Mobility of the World Population. Massachusetts Institute of Technology and International Institute for Applied Systems Analysis, Discussion Paper 97-6-4 (revision 2, September).&lt;br /&gt;
&lt;br /&gt;
Scheer, Sara J. and Satya Yadav. 1996. &amp;quot;Land Degradation in the Developing World: Implications for Food, Agriculture, and the Environment to 2020.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 14.&lt;br /&gt;
&lt;br /&gt;
Schneider, Stephen. 1997. &amp;quot;Integrated Assessment Modeling of Climate Change: Transparent Rational Tool for Policy Making or Opaque Screen Hiding Value-Laden Assumptions?&amp;quot;&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(4): 229-250.&lt;br /&gt;
&lt;br /&gt;
Schwartz, Peter. 1996.&#039;&#039;&amp;amp;nbsp;The Art of the Long View.&#039;&#039;&amp;amp;nbsp;New York: Doubleday.&lt;br /&gt;
&lt;br /&gt;
Sedjo, Roger A. 1995. &amp;quot;Forests: Conflicting Signals,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 178-209.&lt;br /&gt;
&lt;br /&gt;
Shane, Harold G. and Gary A. Sojka. 1990. &amp;quot;John Elfreth Watkins, Jr.: Forgotten Genius of Forecasting,&amp;quot; in Edward Cornish, ed.,&#039;&#039;&amp;amp;nbsp;The 1990s and Beyond&#039;&#039;. Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Shaw, Timothy W. and Clement E. Adibe. 1995-96. &amp;quot;Africa and Global Developments in the Twenty-First Century,&amp;quot; International Journal 51 (Winter): 1-26.&lt;br /&gt;
&lt;br /&gt;
Siegmann, Heinrich. 1985.&amp;amp;nbsp;&#039;&#039;Recent Developments in World Modeling&#039;&#039;. Berlin: Science Center.&lt;br /&gt;
&lt;br /&gt;
Simon, Julian. 1981.&amp;amp;nbsp;&#039;&#039;The Ultimate Resource&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Singer, J. David, Stuart Bremer, and John Stuckey. 1972. &amp;quot;Capability Distribution, Uncertainty, and Major Power Wars, 1820-1965.&amp;quot; In Bruce Russett, ed.,&amp;amp;nbsp;&#039;&#039;Peace, War, and Numbers.&#039;&#039;&amp;amp;nbsp;Beverly Hills: Sage.&lt;br /&gt;
&lt;br /&gt;
Sivard, Ruth Leger. 1993.&amp;amp;nbsp;&#039;&#039;World Military and Social Expenditures 1993.&#039;&#039;&amp;amp;nbsp;Washington, D.C. 20007: World Priorities, Box 25140.&lt;br /&gt;
&lt;br /&gt;
Solow, Robert M. 1956. &amp;quot;A Contribution to the Theory of Economic Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Quarterly Journal of Economics&#039;&#039;&amp;amp;nbsp;70, 1 (February): 65-94.&lt;br /&gt;
&lt;br /&gt;
Stanford University. 1978.&amp;amp;nbsp;&#039;&#039;Stanford Pilot Energy/Economic Model&#039;&#039;. Stanford: Department of Research, Interim Report, Vol. 1.&lt;br /&gt;
&lt;br /&gt;
Stockholm International Peace Research Institute (SIPRI). 1994.&amp;amp;nbsp;&#039;&#039;SIPRI Yearbook&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Stone, Richard. 1986. &amp;quot;The Accounts of Society,&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Applied Econometrics&#039;&#039;&amp;amp;nbsp;1, no. 1 (January): 5-28.&lt;br /&gt;
&lt;br /&gt;
Strategic Assessments Group (SAG), Office of Transnational Issues, Directorate of Intelligence. 2001 (February). The Global Economy in the Long Term. OTI IR 2001-013.&lt;br /&gt;
&lt;br /&gt;
Systems Analysis Research Unit (SARU). 1977.&amp;amp;nbsp;&#039;&#039;SARUM 76 Global Modeling Project&#039;&#039;. Departments of the Environment and Transport, 2 Marsham Street, London, 3WIP 3EB.&lt;br /&gt;
&lt;br /&gt;
Tammen, Ronald L, Jacek Kugler, Douglas Lemke, Allan C. Stam III, Carole Alsharabati, Mark Andrew Abdollahian, Brian Efird, and A.F.K. Organski. 2000. Power Transitions: Strategies for the 21st Century. New York: Chatham House Publishers.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1975. &amp;quot;Theoretical Foundations and Technical Implications.&amp;quot; in Charles Blitzer, Peter Clark and Lance Taylor, eds.,&amp;amp;nbsp;&#039;&#039;Economy-Wide Models and Development Planning.&#039;&#039;&amp;amp;nbsp;Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1979.&amp;amp;nbsp;&#039;&#039;Macro Models for Developing Countries&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Thirlwall, A. P. 1977.&amp;amp;nbsp;&#039;&#039;Growth and Development&#039;&#039;. New York: John Wiley and Sons.&lt;br /&gt;
&lt;br /&gt;
Thompson, M. 1997. Cultural Theory and Integrated Assessment.&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(3): 139-150.&lt;br /&gt;
&lt;br /&gt;
Thompson, M., R. Ellis and A. Wildavsky. 1990.&amp;amp;nbsp;&#039;&#039;Cultural Theory&#039;&#039;. Boulder, Co: Westview Press.&lt;br /&gt;
&lt;br /&gt;
Thorbecke, Erik. 2001. &amp;quot;The Social Accounting Matrix: Deterministic or Stochastic Concept?&amp;quot;, paper prepared for a conference in honor of Graham Pyatt&#039;s retirement, at the Institute of Social Studies, The Hague, Netherlands (November 29 and 30). Available at [http://people.cornell.edu/pages/et17/etpapers.html http://people.cornell.edu/pages/et17/etpapers.html].&lt;br /&gt;
&lt;br /&gt;
United Nations, Department of Economic and Social Affairs. 1956.&amp;amp;nbsp;&#039;&#039;Methods of Population Projections by Sex and Age&#039;&#039;. New York: United Nations, ST/SOA Series A.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1992.&amp;amp;nbsp;&#039;&#039;Long-Range World Population Projections. Two Centuries of Population Growth: 1950-2150&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1993.&amp;amp;nbsp;&#039;&#039;World Population Prospects - the 1992 Revision&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations Development Program (UNDP). 1995.&amp;amp;nbsp;&#039;&#039;Human Development Report&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1992.&amp;amp;nbsp;&#039;&#039;Production Yearbook.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1995.&#039;&#039;&amp;amp;nbsp;World Agriculture: Towards 2010.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 1999. The World at Six Billion New York: UN.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2000. Replacement Migration: Is it a Solution to Declining and Ageing Populations? New York: UN.&lt;br /&gt;
&lt;br /&gt;
United States Arms Control and Disarmament Agency (ACDA). 1995.&amp;amp;nbsp;&#039;&#039;World Military Expenditures and Arms Transfers 1995&#039;&#039;. Washington, D.C.: Arms Control and Disarmament Agency.&lt;br /&gt;
&lt;br /&gt;
United States Bureau of the Census. 1991.&amp;amp;nbsp;&#039;&#039;World Population Profile: 1991&#039;&#039;. Report WP/91 Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Walters, Robert S. and David H. Blake. 1992.&amp;amp;nbsp;&#039;&#039;The Politics of Global Economic Relations&#039;&#039;, 4th edition. Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Waltz, Kenneth N. 1959. Man, the State, and War: A Theoretical Analysis. New York: Columbia University Press.&lt;br /&gt;
&lt;br /&gt;
Watkins, John Elfreth, Jr. 1990. &amp;quot;What May Happen in the Next Hundred Years,&amp;quot; in Edward Cornish, ed.,&amp;amp;nbsp;&#039;&#039;The 1990s and Beyond.&#039;&#039;&amp;amp;nbsp;Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Wildavsky, Aaron, and Ellen Tenenbaum. 1981.&amp;amp;nbsp;&#039;&#039;The Politics of Mistrust&#039;&#039;. Beverly Hills: Sage Publications.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1991b.&amp;amp;nbsp;&#039;&#039;World Tables 1991&#039;&#039;. New York: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1995&amp;amp;nbsp;&#039;&#039;World Development Report 1995&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
World Energy Council (WEC) Commission. 1993.&amp;amp;nbsp;&#039;&#039;Energy for Tomorrow’s World.&#039;&#039;&amp;amp;nbsp;New York: St. Martin’s Press.&lt;br /&gt;
&lt;br /&gt;
World Resources Institute (WRI). 1994.&amp;amp;nbsp;&#039;&#039;World Resources 1994-95.&#039;&#039;&amp;amp;nbsp;New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Wortman, Sterling and Ralph W. Cummings, Jr. 1978.&#039;&#039;&amp;amp;nbsp;To Feed This World&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Zinnes, Dina A. and John W. Gillespie, eds. 1976.&amp;amp;nbsp;&#039;&#039;Mathematical Models in International Relations&#039;&#039;&amp;amp;nbsp;(New York: Preaeger).&lt;br /&gt;
&lt;br /&gt;
== [[Development_Mode_Features|Development Mode Features]] ==&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Infrastructure&amp;diff=8321</id>
		<title>Infrastructure</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Infrastructure&amp;diff=8321"/>
		<updated>2017-09-07T22:11:33Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
The most recent and complete infrastructure model documentation is available on Pardee&#039;s [http://pardee.du.edu/ifs-infrastructure-model-documentation website]. Although the text in this interactive system is, for some IFs models, often significantly out of date, you may still find the basic description useful to you.&lt;br /&gt;
&lt;br /&gt;
The current version of the infrastructure model within IFs was developed in concert with the production of &#039;&#039;Building Global Infrastructure&#039;&#039;, the fourth volume in the Patterns of Potential Human Progress series (Rothman et al 2013). Further details on the model and analyses can be found in that volume.&lt;br /&gt;
&lt;br /&gt;
The purpose of the infrastructure model is to forecast the following:&lt;br /&gt;
&lt;br /&gt;
#the amount of particular forms of infrastructure;&lt;br /&gt;
#the level of access to these particular forms of infrastructure;&lt;br /&gt;
#the level of spending on infrastructure; and&lt;br /&gt;
#the effect of infrastructure development on other socio-economic and environmental systems&lt;br /&gt;
&lt;br /&gt;
The infrastructure model includes parameters that allow users to explore a range of alternative scenarios around infrastructure. These can be used to ask questions such as:&lt;br /&gt;
&lt;br /&gt;
#What would be the costs and benefits if countries were to accelerate infrastructure development above that seen in the Base Case?&lt;br /&gt;
#What if the unit costs of infrastructure development or infrastructure lifetimes were to differ from the assumptions used in the Base Case?&lt;br /&gt;
#What if the impacts of infrastructure development on economic productivity and health were to differ from the assumptions used in the Base Case?&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Unlike many previous studies, which estimate only the demand for infrastructure, IFs forecasts a path jointly determined by both the demand for infrastructure and the funding available to meet that demand. Therefore, the amount of infrastructure forecasted in IFs in each year explicitly accounts for expected fiscal constraints. Furthermore, the socio-economic and environmental effects of infrastructure feed forward to the drivers of infrastructure demand and supply in future years.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
The figure below provides an overview of the infrastructure model within IFs. In brief, the infrastructure modeling in IFs involves moving through the following sequence for each forecast year:&lt;br /&gt;
&lt;br /&gt;
#Estimating the expected levels of infrastructure&lt;br /&gt;
#Translating the expected levels of infrastructure into financial requirements&lt;br /&gt;
#Balancing the financial requirements with available resources&lt;br /&gt;
#Forecasting the actual levels of attained infrastructure&lt;br /&gt;
#Estimating the social, economic, and environmental impacts of the attained infrastructure&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Each of these steps are described in more detail below. [[File:Health16.png|frame|center|Visual representation of the infrastructure model]]&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Structure and Agent System: Infrastructure&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot; border=&amp;quot;0&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width: 50%&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;System/Subsystem&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Infrastructure&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| &amp;lt;div&amp;gt;&#039;&#039;&#039;Organizing Structure&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;amp;nbsp;&lt;br /&gt;
|-&lt;br /&gt;
| &amp;lt;div&amp;gt;&#039;&#039;&#039;Stocks&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Physical infrastructure, Access rates&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| &amp;lt;div&amp;gt;&#039;&#039;&#039;Flows&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Spending (public and private on ‘core’ infrastructure; public on ‘other’ infrastructure)&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| &amp;lt;div&amp;gt;&#039;&#039;&#039;Key Aggregate&amp;amp;nbsp;&#039;&#039;&#039; &#039;&#039;&#039;Relationships&amp;amp;nbsp;&#039;&#039;&#039;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;(illustrative, not comprehensive)&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Demand for physical infrastructure and access changes with population, income, and other societal changes&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;More infrastructure helps economic growth and reduces health effects from specific diseases&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Public spending available for infrastructure rises with income level&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Public spending leverages private spending&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Lack (surplus) of public spending on ‘core’ infrastructure hurts (helps) infrastructure development&amp;amp;nbsp;&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| &amp;lt;div&amp;gt;&#039;&#039;&#039;Key Agent-Class Behavior&amp;amp;nbsp;&#039;&#039;&#039; &#039;&#039;&#039;Relationships&#039;&#039;&#039;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;(illustrative, not comprehensive)&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &lt;br /&gt;
Government revenue and expenditure on infrastructure&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Infrastructure Types&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
IFs distinguishes between ‘core’ and ‘other’ infrastructure. Core infrastructure refers to those types of infrastructure that we represent explicitly in IFs—roads, electricity generation, improved water and sanitation, and ICT. Other infrastructure refers to those types that we do not represent explicitly—e.g., railroads, ports, airports, and types of infrastructure yet to be envisioned. The choice of what to include as core infrastructure reflects the availability of historical data and understanding of what can be modelled.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Infrastructure Access and Stocks&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The table below summarizes the primary variables in IFs related to infrastructure stocks and access. From these and other variables forecasted by IFs, we are able to calculate numerous other indicators—for example, the number of persons with access to electricity.&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%; border: 1px solid #cccccc&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; | &amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; | &#039;&#039;&#039;Variable Name in IFs (dimensions)&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; | &#039;&#039;&#039;Description&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; | &#039;&#039;&#039;Units&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; rowspan=&amp;quot;11&amp;quot; | &#039;&#039;&#039;Access&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | &#039;&#039;&#039;INFRAROADRAI*&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Access to rural roads&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | percentage of rural population living within 2 kilometers of an all-season road&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | INFRAELECACC* (rural, urban, total)&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Access to electricity&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | percentage of population with access&amp;amp;nbsp;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | ENSOLFUEL&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Solid fuel use&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | percentage of population using solid fuels as their main household energy source&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | WATSAFE* (none, other improved, piped)&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Access to improved water&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | percentage of population with access by type&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | SANITATION* (other unimproved, shared, improved)&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Access to improved sanitation&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | percentage of population with access by type&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | WATWASTE&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Access to wastewater collection connection&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | percentage of population with wastewater collection&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | WATWASTETREAT*&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Access to wastewater treatment&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | percentage of population with wastewater treatment&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | INFRATELE*&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Fixed telephone lines&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | lines per 100 persons&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | ICTBROAD*&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Fixed broadband subscriptions&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | subscriptions per 100 persons&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | ICTMOBIL*&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Mobile telephone subscriptions&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | subscriptions per 100 persons&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | ICTBROADMOBIL*&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Mobile broadband subscriptions&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | subscriptions per 100 persons&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; rowspan=&amp;quot;4&amp;quot; | &#039;&#039;&#039;Physical Stocks&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | &#039;&#039;&#039;INFRAROAD*&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Total road density&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | kilometers per 1000 hectares&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | INFRAROADPAVEDPCNT*&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Percentage of roads paved&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | percentage&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | INFRAELECGENCAP*&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Electricity generation capacity per capita&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | kilowatts per person&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | LANDIRAREAEQUIP&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Area equipped with irrigation&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | 1000 hectares&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; colspan=&amp;quot;4&amp;quot; | &#039;&#039;&#039;*Note: Each of these variables has a companion variable with the extension DEM; for example, the variable INFRAROADRAI has a companion variable named INFRAROADRAIDEM. These companion variables indicate the amount of the infrastructure stock or access that would be expected to exist in the absence of financial constraints.&#039;&#039;&#039;&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Infrastructure Spending&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The following table summarizes the primary variables in IFs related to infrastructure spending. As with the access and stock variables, from these and other variables forecasted in IFs, we are able to calculate numerous other indicators—for example, the ratio of total public to private spending on infrastructure. Please note that although we do not represent these other forms of infrastructure explicitly, we do estimate spending on them in order to avoid almost certainly underrepresenting the total demand for infrastructure. This is given by the variable GDS(InfraOther).&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%; border: 1px solid #cccccc&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; | &#039;&#039;&#039;Variable Name in IFs&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; | &#039;&#039;&#039;Description&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; | &#039;&#039;&#039;Units&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | GDS (infrastructure, infraother)&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Government consumption, by category&amp;lt;sup&amp;gt;[1]&amp;lt;/sup&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | billion dollars&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | INFRAINVESTMAINT&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Total (public plus private) investment for infrastructure maintenance, by type of infrastructure&amp;lt;sup&amp;gt;[2]&amp;lt;/sup&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | billion dollars&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | INFRAINVESTMAINTPUB&amp;lt;sup&amp;gt;[3]&amp;lt;/sup&amp;gt;&amp;lt;br/&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Public investment for infrastructure maintenance, by type of infrastructure&amp;lt;sup&amp;gt;[2]&amp;lt;/sup&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | billion dollars&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | INFRAINVESTNEW&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Total (public plus private) investment for construction of new infrastructure, by type of infrastructure&amp;lt;sup&amp;gt;[2]&amp;lt;/sup&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | billion dollars&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | INFRAINVESTNEWPUB&amp;lt;sup&amp;gt;[3]&amp;lt;/sup&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Public investment for construction of new infrastructure, by type of infrastructure&amp;lt;sup&amp;gt;[2]&amp;lt;/sup&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | billion dollars&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; colspan=&amp;quot;3&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;&amp;lt;sup&amp;gt;[1]&amp;lt;/sup&amp;gt; The categories are military, health, education, R&amp;amp;D, Infrastructure, InfraOther, Other, and Total.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&amp;lt;sup&amp;gt;[2]&amp;lt;/sup&amp;gt; The types of infrastructure included are RoadPaved, RoadUnPaved, ElectricityGen, ElectricityAccRural, ElectricityAccUrban, Irrigation, SafeWaterHH, SafeWaterImproved, SanitationHH, SanitationImproved, WasteWater, Telephone, Mobile, Broadband, BroadbandMobile, and Total. Currently, no cost is assumed for access to Unimproved water, Other unimproved sanitation, solid fuel use, or a wastewater collection connection. &amp;lt;sup&amp;gt;[3]&amp;lt;/sup&amp;gt; Each of these variables has a companion variable, which indicates the amount of public investment that is desired based upon the expected levels of infrastructure. &amp;amp;nbsp;For INFRAINVESTMAINTPUB, the companion variable is named INFRABUDDEMMNT and for INFRAINVESTNEWPUB, the companion variable is named INFRABUDDEMNEW. The differences between the desired and actual amounts of public investment result from the budgeting process described below.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&amp;lt;sup&amp;gt;[3]&amp;lt;/sup&amp;gt;&amp;amp;nbsp;Each of these variables has a companion variable, which indicates the amount of public investment that is desired based upon the expected levels of infrastructure. &amp;amp;nbsp;For INFRAINVESTMAINTPUB, the companion variable is named INFRABUDDEMMNT and for INFRAINVESTNEWPUB, the companion variable is named INFRABUDDEMNEW. The differences between the desired and actual amounts of public investment result from the budgeting process described below.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Forward Links from Infrastructure&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Although there are a wide range of potential social, economic, and environmental impacts of infrastructure, we limit our modeling of the direct effects of infrastructure to its effects on economic productivity and a small set of health impacts. Currently, the empirical research on these effects are more advanced—and the effects themselves more amenable to modeling—than the direct effects of infrastructure on factors such as income inequality, educational attainment, or governance. To the extent direct effects and other aspects, such as spending on infrastructure that reduces spending on other categories, affect other systems included in IFs, infrastructure will have a number of indirect effects.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Sources of Infrastructure Data&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Infrastructure Stocks and Access&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
In terms of historical data on infrastructure stocks and access, we can turn to various international organizations with specific emphases. These include the International Road Federation (IRF) for transportation, the International Energy Agency (IEA) for energy, and the International Telecommunication Union (ITU) for telecommunications. No one organization focuses on water and sanitation systems, but a number of different organizations, such as the Joint Monitoring Programme (JMP) of WHO and the United Nations Children’s Fund (UNICEF), the United Nations Statistics Division, and the United Nations Food and Agriculture Organization (FAO), maintain global data related to certain aspects of water infrastructure. The table below summarizes a number of the datasets these groups maintain.&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%; border: 1px solid #cccccc&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Infrastructure Type&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Organization&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Spatial Coverage&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Temporal Coverage&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Infrastructure Coverage&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; rowspan=&amp;quot;2&amp;quot; valign=&amp;quot;middle&amp;quot; | Transportation&amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;International Road Federation&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Global&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data: 1968–2009&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Total road network length, percent of road network paved, and road density&amp;amp;nbsp;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | World Bank&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Global&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Data for most recent year only&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Percentage of rural population with access to an all-season road&amp;amp;nbsp;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; rowspan=&amp;quot;2&amp;quot; valign=&amp;quot;middle&amp;quot; | Electricity and Energy&amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;United States Energy Information Administration&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Global&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data: 1980–2010&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Total installed electricity generation capacity and generation capacity by energy type&amp;amp;nbsp;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | International Energy Agency&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Global&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data: 1960–2009&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Electricity production by source type; total electricity production; percent of total, urban, and rural population with access to electricity&amp;amp;nbsp;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; rowspan=&amp;quot;3&amp;quot; valign=&amp;quot;middle&amp;quot; | Water and Sanitation&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;WHO and UNICEF Joint Monitoring Programme for Water Supply and Sanitation&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Global&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data: 1990−2010&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Percent of population with access to improved, piped, other improved, and unimproved water, and to sanitation facilities&amp;amp;nbsp;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Food and Agriculture Organization AQUASTAT database&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Global&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data: 1960–2010&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Percent of arable land equipped for irrigation and water use/withdrawals by sector&amp;amp;nbsp;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | United Nations Statistics Division&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Global&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Data for most recent year available only&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Percent of population with wastewater connection and percent with connection to wastewater treatment&amp;amp;nbsp;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Information and Communication Technologies&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;International Telecommunication Union&#039;&#039;&#039;&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Global&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data: 1960–2011&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Number of telephone mainlines, cell phone subscriptions, broadband subscriptions, mobile broadband subscriptions, and number of computer/internet users&amp;amp;nbsp;&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
In addition to these primary data sources, the World Bank’s World Development Indicators (WDI) and the World Resources Institute’s Earth Trends databases act as clearinghouses for much of the same data. We can turn also to Canning (1998), Canning and Farahani (2007), and Estache and Goicoechea (2005),who have drawn on these and other sources in attempts to create global databases of infrastructure stocks and access, increase the number of years covered for certain time-series while maintaining consistent definitions, and correct errors. Further, as part of the Africa Infrastructure Country Diagnostic (AICD), the World Bank and the African Development Bank developed an extensive database on infrastructure in Africa. Finally, G. Hughes, Chinowsky, and Strzepek (2009) and Calderón and Servén (2010a; 2010b), among others, have used and modified a number of these databases in their own studies.&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Infrastructure Spending&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
There exist relative little organized historical data on infrastructure spending. In considering public investment in infrastructure (PII), some researchers have used other measures in the Systems of National Accounts, usually fixed capital formation or government outlays by economic sector, as proxies (Agénor, Nabli, and Yousef 2007; Cavallo and Daude 2008; Organisation for Economic Co-operation and Development 2009a; Ter-Minassian and Allen 2004). Lora (2007: 7), however, strongly argued against this practice&lt;br /&gt;
&lt;br /&gt;
:because capital expenditures by the central or the consolidated government as measured by the International Monetary Fund’s Government Financial Statistics . . . are a very poor measure of actual PII, which in many countries is mostly undertaken by state-owned enterprises or local governments whose operations are not well captured by this source.&lt;br /&gt;
&lt;br /&gt;
Estache (2010: 67) adds:&lt;br /&gt;
&lt;br /&gt;
:Neither the national accounts nor the IMF [International Monetary Fund] Government Finance Statistics (GFS) report a disaggregation of total and public investment data detailed enough to allow identifying every infrastructure sub-sector. In national accounts, energy data cover both electricity and gas but also all primary-energy related products such as petroleum. Similarly, the data do not really distinguish between transport and communication. Water expenditures can be hidden in public works or even in health expenditures.&lt;br /&gt;
&lt;br /&gt;
The World Bank does collect data on private investment in infrastructure in its Private Participation in Infrastructure Project Database. Unfortunately, limitations to this database make us hesitant to rely on it as a primary source of data on infrastructure investment. First, it provides data only on projects in low and middle-income countries in which there is private participation. Second, the amounts in the database primarily reflect commitments, not actual investments. Third, it relies exclusively on information that is made publicly available. Finally, the Bank itself states that it “should not be seen as a fully comprehensive resource.”&lt;br /&gt;
&lt;br /&gt;
This leaves us needing to rely on national, regional, and global studies and reports that provide estimates of infrastructure spending. Given their varied purposes, these studies and reports tend to differ in a number of significant dimensions: temporal coverage; types of infrastructure included; sources of funding (e.g., public versus private); and purpose of expenditure (e.g., new construction versus maintenance). Therefore, we need to be careful in comparing data across studies and in drawing conclusions from them. Even so, they provide a starting point for our exploration. The following table lists a number of these studies and summarizes some of the major elements in their approaches.&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 750px; height: 1011px; border: 1px solid #cccccc&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; height: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Study&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; width: 500px&amp;quot; scope=&amp;quot;colgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Spatial Coverage&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; height: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Temporal Coverage&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; height: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Infrastructure Coverage&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; height: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Source of Funds&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; height: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Purpose of Expenditure&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Trends in Transport Infrastructure Investment 1995–2009 (International Transport Forum and Organisation for Economic Co-operation and Development 2011)&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; width: 500px&amp;quot; scope=&amp;quot;colgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Albania, Australia, Austria, Azerbaijan, Belgium, Bosnia, Bulgaria, Canada, Croatia, Czech Republic, Denmark, Estonia, Finland, , France, Georgia, Germany, Greece, Hungary, Iceland, India, Ireland, Italy, Japan, Korea, Latvia, Liechtenstein, Lithuania, Luxembourg, Macedonia, Malta, Mexico, Moldova, Montenegro, Netherlands, New Zealand, Norway, Poland, Portugal, Romania, Russia, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data: 1992–2009&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Separate data for rail, road, inland waterways, maritime ports, and airports&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Combined public and private sources for investment; only spending by public authorities for maintenance&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Separate data for investment and maintenance&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Africa Infrastructure Country Diagnostic ([http://www.-infrastructureafrica.-org/aicd/tools/data) http://www.-infrastructureafrica.-org/aicd/tools/data]);&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; width: 500px&amp;quot; scope=&amp;quot;colgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Benin, Botswana, Burkina Faso, Cameroon, Cape Verde, Chad, Congo, Côte d’Ivoire, Democratic Republic of the Congo, Ethiopia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mozambique, Namibia, Nigeria, Rwanda, Senegal, South Africa, Tanzania, Uganda, Zambia&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual average for one period: 2001–2006&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Separate data for electricity, ICT, irrigation, transportation, and water supply and sanitation&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Public and private&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Separate data for new construction and for operation and maintenance&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Infrastructure in Latin America (Calderón and Servén 2010b)&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; width: 500px&amp;quot; scope=&amp;quot;colgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Argentina, Brazil, Chile, Colombia, Mexico, Peru&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data: 1980–2006&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Separate data for telecommunications, power generation, land transportation (roads and railways), and water and sanitation&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Separate data for public and private&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Total spending (construction, operations, and maintenance)&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Public Spending on Transportation and Water Infrastructure (Congressional Budget Office 2010)&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; width: 500px&amp;quot; scope=&amp;quot;colgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | United States&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data: 1956–2007&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Separate data for highways, mass transit, rail, aviation, water transportation, water resources, and water supply and wastewater treatment&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Public only, broken down by (1) federal, and (2) state and local&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Separate data for capital expenditures and for operation and maintenance&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Infrastructure Development in India and China—A Comparative Analysis (Kim and Nangia 2010)&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; width: 500px&amp;quot; scope=&amp;quot;colgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | China, India&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data: 1985–2006&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Combined data for electricity, water, gas, transport, and communications&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Combined public and private&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Not stated&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Going for Growth: Economic Policy Reforms (Organisation for Economic Co-operation and Development 2009a)&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; width: 500px&amp;quot; scope=&amp;quot;colgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Australia, Austria, Belgium, Canada, Finland, France, Iceland, Ireland, Italy, Netherlands, New Zealand, Norway, South Korea, Spain, Sweden, United Kingdom, United States&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual averages for four periods: 1970–1979, 1980–1989, 1990–1999, 2000–2006&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Aggregate data provided separately for (1) electricity, gas, and water, and (2) transport and communications&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Combined public and private&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Aggregate investment (from national accounts)&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Connecting East Asia: A New Framework for Infrastructure (Asian Development Bank, Japan Bank for International Cooperation, and World Bank 2005)&#039;&#039;&#039;&amp;lt;br/&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; width: 500px&amp;quot; scope=&amp;quot;colgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Cambodia, China, Indonesia, Laos, Mongolia, Philippines, Thailand, Vietnam&amp;lt;br/&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data for select years: 1998, 2003&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Separate data for transportation, telecommunications, water and sanitation, other urban infrastructure, and power&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Separate data for national government, local government, state owned enterprises, and private&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Not stated&amp;lt;br/&amp;gt;&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Dominant Relations: Infrastructure&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The dominant relations in the Infrastructure model are those that determine the expected levels of infrastructure stocks and access, spending on infrastructure, and the impacts of infrastructure on health and productivity. The expected levels of infrastructure stocks and access are influenced by socio-economic factors related to population, economic activity, governance, and educational attainment. In almost every case there are also path dependencies that supplement the basic relationships, reflecting the considerable inertia in infrastructure development.&lt;br /&gt;
&lt;br /&gt;
Spending on infrastructure is divided into private and public spending, with the latter further divided into ‘core’ and ‘other’ infrastructure. ‘Core’ infrastructure refers to those types of infrastructure that are explicitly represented in the model; ‘other’ infrastructure refers to those types of infrastructure that are not explicitly represented in the model (see [[Infrastructure#Structure_and_Agent_System:_Infrastructure|Infrastructure Types]]). Public spending on core infrastructure, GDS(Infra), is driven by the required spending to meet the expected levels of infrastructure (INFRABUDDEMMNT and INFRABUDDEMNEW), total government consumption (GOVCON), and the demands on government consumption from other categories. Public spending on other infrastructure, GDS(InfraOther), is driven by average GDP per capita (GDPPCP), total government consumption (GOVCON), and the demands on government consumption from other categories. Deficits and surpluses of government funds will affect the actual levels of funds allocated for both core and other infrastructure. The public spending on core infrastructure leverages a certain amount of private spending on core infrastructure, with the amount leveraged depending upon historical relationships found in the literature, which nominally reflect the variation in public and private returns between particular types of infrastructure. Finally, in recognition of the incremental approaches that public budgeting decisions usually follow, our model avoids unusually sharp increases in public spending on infrastructure by smoothing it out over time.&lt;br /&gt;
&lt;br /&gt;
Infrastructure development directly affects multifactor productivity, with this effect being treated separately for non-ICT and ICT related infrastructure. The use of solid fuels in the home and access to improved water and sanitation directly affect human health through their effects on the mortality and morbidity rates of specific diseases—diarrheal diseases, acute respiratory infections, and respiratory diseases.&lt;br /&gt;
&lt;br /&gt;
For detailed discussion of the model&#039;s causal dynamics, see the discussions of [[Infrastructure#Infrastructure_Flow_Charts|flow charts]] (block diagrams) and [[Infrastructure#Infrastructure_Equations|equations]].&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Initializing the Infrastructure Data&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The IFs preprocessor uses historical data to prepare data for the base year of the model, currently 2010. We describe the general workings of the IFs preprocessor [http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf here]. However, there are some peculiarities in the infrastructure model, specifically related to the initialization of the variables related to spending on infrastructure.&lt;br /&gt;
&lt;br /&gt;
Because of the paucity and inconsistency of the historical data on infrastructure spending discussed above, IFs does not use actual historical data on spending, but rather estimates spending in the first year of the model based upon data on the stocks of and access to infrastructure after the pre-processor has filled any gaps in the historical data. The procedure is as follows:&lt;br /&gt;
&lt;br /&gt;
*We assume that: 1) the amount of infrastructure requiring maintenance in the base year is given by the level of infrastructure in the previous year (2009) times a factor based on the lifetime of the infrastructure (see table 5 below), and 2) the amount of newly constructed infrastructure is the difference between the amount of infrastructure in the base year (2010) and the previous year (2009).&lt;br /&gt;
*Total spending on maintenance, &#039;&#039;INFRAINVESTMAINT&#039;&#039;, is estimated as the amount of infrastructure requiring maintenance times the unit cost for each type of infrastructure (see Table 6 below).&lt;br /&gt;
*Total spending on new construction, &#039;&#039;INFRAINVESTNEW&#039;&#039;, is estimated as the amount of new construction times the unit cost for each type of infrastructure. If the amount of newly constructed infrastructure is less than or equal to zero, spending on that type of infrastructure is set to zero.&lt;br /&gt;
*For each type of infrastructure, public spending on maintenance, &#039;&#039;INFRAINVESTMAINTPUB&#039;&#039;, and new construction, &#039;&#039;INFRAINVESTNEWPUB&#039;&#039;, are estimated by multiplying the total spending by infrastructure specific parameters, &#039;&#039;&#039;&#039;&#039;infrainvmaintpubshrm&#039;&#039; &#039;&#039;&#039; and &#039;&#039;&#039;&#039;&#039;infrainvnewpubshrm&#039;&#039; &#039;&#039;&#039;, indicating the share of total spending that is assumed to be public.&lt;br /&gt;
*The sum of estimated public spending on maintenance and new construction, across all types of core infrastructure, provides an initial estimate of government consumption for core infrastructure, &#039;&#039;GDS(Infrastructure)&#039;&#039;.&lt;br /&gt;
*If, in the first year budgeting process, total estimated government consumption on core infrastructure is reduced, an infrastructure cost adjustment factor, &#039;&#039;INFRACOSTADJFAC&#039;&#039;, is calculated as the ratio of the final to the initial value of &#039;&#039;GDS(Infrastructure)&#039;&#039;. The value of &#039;&#039;INFRACOSTADJFAC&#039;&#039; is also used to adjust infrastructure spending in future years. It gradually converges to 1 over the time period given by the parameter &#039;&#039;&#039;&#039;&#039;infracostadjfacconvtime&#039;&#039; &#039;&#039;&#039;.&lt;br /&gt;
*The initial estimates of &#039;&#039;INFRAINVESTMAINT&#039;&#039;, &#039;&#039;INFRAINVESTNEW&#039;&#039;, &#039;&#039;INFRAINVESTMAINTPUB&#039;&#039;, and &#039;&#039;INFRAINVESTNEWPUB&#039;&#039; are each multiplied by &#039;&#039;INFRACOSTADJFAC&#039;&#039; to calculate their final values.&lt;br /&gt;
*The initial value of public spending on other infrastructure, &#039;&#039;GDS(InfraOther&#039;&#039; &#039;&#039;&#039;)&#039;&#039;&#039;, is calculated as a function of average income, &#039;&#039;GDPPCP&#039;&#039;, multiplied by &#039;&#039;INFRACOSTADJFAC&#039;&#039;. This function is:&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GDS(InfraOther)_{r,t}=GDP_{r,t}*(1.8162+0.061*ln(GDPPCP_{r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:GDS(InfraOther) = government spending on other infrastructure in billion constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:GDP = gross domestic product at market exchange rates in billion constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Infrastructure Flow Charts&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt;&amp;amp;nbsp; ===&lt;br /&gt;
&lt;br /&gt;
The introduction provided an overview of the infrastructure model within IFs, noting that this involves moving through the following sequence for each forecast year. This section describes each of these five steps:&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;1. Estimating the Expected Levels of Infrastructure&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
At the core of our forecasts of the expected levels of infrastructure is a set of estimated equations embedded within a set of accounting relationships. The equations are presented [[Infrastructure#Infrastructure_Equations|here]].&lt;br /&gt;
&lt;br /&gt;
Additional elements beyond the estimated equations are involved in specifying the expected values of infrastructure, and we handle some of these elements algorithmically. For instance, the base year calculated estimations will most often not match exactly the historical data for countries in the base year.&amp;lt;sup&amp;gt;[1]&amp;lt;/sup&amp;gt; Each country has peculiarities that differentiate it from the “typical pattern”; among the factors not captured by our equations for estimating the base year country values are many aspects of geography, culture, and unique historical development paths. And sometimes, of course, data errors account for such differences.&lt;br /&gt;
&lt;br /&gt;
To deal with this issue of differences between our estimated values and reported data in the base year, the model calculates an additive or a multiplicative country and variable specific shift factor representing that difference; we allow those shift factors to gradually diminish over time, thereby causing countries to approach the expected value function. Among the reasons for allowing convergence is that we quite consistently see that the patterns of higher-income countries are more similar and more like those of our general equations than are those of lower-income countries. On the assumption that countries will seldom abandon infrastructure they have already developed, however, our downward convergence is extremely slow relative to our upward convergence.&lt;br /&gt;
&lt;br /&gt;
A second instance in which we make adjustments to our core estimated equations is when the dynamic trajectory of demand/supply growth in a country in recent years is inconsistent with the forecasts produced by the equations. For instance, a policy-based surge of infrastructure development like that seen recently in China may result in a historical growth rate well above the one that our functions produce in the first years of our forecasting. Making a simplifying assumption that these growth rates will change only gradually, we estimate the growth rate of physical infrastructure stock using the historical data over three to five recent years and incorporate that growth rate in the demand estimation through a moving average-based extrapolative formulation.&lt;br /&gt;
&lt;br /&gt;
We make a final adjustment in those cases where we wish to modify the estimates of expected infrastructure for scenario analysis. This can be accomplished in several ways. First, most of the estimates can be adjusted with the use of a simple multiplier. Second, we can stipulate specific levels for specific types of infrastructure in a specific future year; in this case, the model will automatically forecast a linear approach to the targeted level from the base year. Third, we can modify both the rates at which the country shift factors converge and the levels, in relation to the expected values, to which the shift factors converge. For example, we can drive the shift factors to those of the best performing countries, i.e., those that perform better than expected, by a certain date. This will, in turn, affect the levels to which the physical infrastructures themselves converge (see [[Understand_IFs#Standard_Error_Targeting|Standard Error Targeting]]).&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Transportation&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
The primary indicators of transportation infrastructure included in IFs are: 1) the total road density in kilometers per 1000 hectares, &#039;&#039;INFRAROAD&#039;&#039;, 2) the percentage of roads that are paved, &#039;&#039;INFRAROADPAVEDPCNT&#039;&#039;, and 3) the Rural Access Index, &#039;&#039;INFRAROADRAI&#039;&#039;, the percentage of the rural population living within two kilometers of an all-season road. From these, we can calculate additional indicators, such as the expected lengths of paved and unpaved roads.&lt;br /&gt;
&lt;br /&gt;
The general sequence of calculations for estimating the expected values of these variables is shown in the figure below. We begin by estimating road density (&#039;&#039;INFRAROAD&#039;&#039;) as a function of income density, population density, and land area. The percentage of roads that are paved (&#039;&#039;INFRAROADPAVEDPCNT&#039;&#039;) is then calculated as a function of the estimated road density, GDP per capita (&#039;&#039;GDPPCP&#039;&#039;), population (&#039;&#039;POP&#039;&#039;), and land area (&#039;&#039;LANDAREA&#039;&#039;). In parallel, the Rural Access Index(&#039;&#039;INFRAROADRAI&#039;&#039;) is calculated as a function of the estimated road density (kilometers per person) and income density (dollars per hectare).&lt;br /&gt;
&lt;br /&gt;
[[File:Infrastructure2.png|frame|center|Visual representation of transportation infrastructure]]&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Electricity&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Our focus in the energy sector is on the generation and use of electricity. In terms of physical infrastructure, the key indicator we forecast is the level of electricity generation capacity, &#039;&#039;INFRAELECGENCAP&#039;&#039;. From the user perspective, we forecast the percentage of the rural and urban populations that have access to electricity, &#039;&#039;INFRAELECACC(rural)&#039;&#039; and &#039;&#039;INFRAELECACC(urban)&#039;&#039;. These access rates, in combination with the forecasts for population and average household size, are used to calculate the number of household connections, which drive the cost calculations described below. Finally, given its connection to electricity access, we also forecast the percentage of the population that uses solid fuels as the main source of energy, &#039;&#039;ENSOLFUEL&#039;&#039;. At the moment, no physical infrastructure is associated with solid fuels, so this value does not enter into the cost calculations.&lt;br /&gt;
&lt;br /&gt;
The following figure presents an overview of the submodel that forecasts access to electricity and electricity generation capacity in IFs. It is fully integrated with the larger IFs system, which provides forecasts of critical variables such as energy demand, energy production by primary type, poverty, and governance character. The electricity submodel contains three components—estimating consumption, estimating production, and sending a signal for additional generation capacity in the case of a gap between production and consumption.&lt;br /&gt;
&lt;br /&gt;
Beginning with consumption, we first estimate the percentage of the population with access to electricity (&#039;&#039;INFRAELECACC&#039;&#039;). This is forecast as a function of poverty levels (&#039;&#039;INCOMELT1CS/POP)&#039;&#039; and a measure of government effectiveness (&#039;&#039;GOVEFFECT&#039;&#039;). The levels of access, along with average income (&#039;&#039;GDPPCP&#039;&#039;) determine the share of the population using Solid Fuel for heating and cooking (&#039;&#039;ENSOLFUEL&#039;&#039;). Next, the levels of access and average income (&#039;&#039;GDPPCP&#039;&#039;), along with the historic ratios of fossil fuel and non-fossil fuel production to total primary energy use (&#039;&#039;FossilFuelShare &#039;&#039;and &#039;&#039;NonFossilFuelShare&#039;&#039;), are used to forecast the expected ratio of electricity use to total primary energy use (&#039;&#039;INFRAELECSHRENDEM&#039;&#039;). With this ratio and the level of total primary energy use (&#039;&#039;ENDEMSH&#039;&#039;)&amp;lt;sup&amp;gt;[2]&amp;lt;/sup&amp;gt;, forecast elsewhere in IFs, we then calculate the desired electricity use (&#039;&#039;INFRAELEC * POP&#039;&#039;).&lt;br /&gt;
&lt;br /&gt;
The amount of domestically produced electricity (&#039;&#039;INFRAELECPROD&#039;&#039;) is determined by the existing generation capacity (&#039;&#039;INFRAELECGENCAP&#039;&#039;), adjusted by a capacity utilization factor (&#039;&#039;INFRAELECTADJFACT&#039;&#039;). We estimate the initial capacity utilization factor for each country based on historical data related to generating capacity and electricity production. Over the forecast horizon, the capacity utilization factor is assumed to converge, over a 50 year period, to a global average value, 0.55, which we derived from current data on generation capacity and production in high-income countries. We also account for transmission and distribution loss (&#039;&#039;INFRAELECTRANLOSS&#039;&#039;), which we forecast as a function of average income (&#039;&#039;GDPPCP&#039;&#039;) and a measure of governance regulatory quality (&#039;&#039;GOVREGQUAL&#039;&#039;). This allows us to calculate post-loss production of electricity.&lt;br /&gt;
&lt;br /&gt;
The desired electricity use can be met by either the domestic post-loss production or imports. Similarly, the post-loss production can be used for either domestic use or exports. At the moment, we assume that the imports are available, when necessary, and that any excess post-loss production can be exported; i.e., we do not attempt to balance the trade in electricity. In parallel, we use the ratio of desired electricity use to post-loss production (&#039;&#039;INFRAELECCONSPRODRATIO&#039;&#039;) as a driver of future levels of generating capacity. Each year the computed ratio is compared to a historical value calculated in the pre-processor. We make the simplifying assumption that countries wish to keep this ratio constant over time. A growing ratio implies that domestic consumption is increasing at a faster rate than domestic production, which sends a signal indicating a desire to build additional capacity. A declining ratio implies that domestic consumption is increasing at a slower rate than domestic production. While this could send a signal to remove existing capacity, the model does not do so; rather it calls for no new construction and less than full replacement of depreciated capacity. Over time, this should bring the production and use back into historical balance.&lt;br /&gt;
&lt;br /&gt;
[[File:Infra3.png|frame|center|964x672px|Visual representation of electricity infrastructure]]&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Water and Sanitation&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
==== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Access to Water, Access to Sanitation, and Wastewater Treatment&amp;lt;/span&amp;gt; ====&lt;br /&gt;
&lt;br /&gt;
The key access indicators we include for water and sanitation infrastructure are the percentages of the population with access to different levels of improved drinking water and sanitation and whose wastewater is collected and subsequently treated. The physical quantities include the number of connections providing these services and the amount of land that is equipped for irrigation.[[File:Infra4.png|frame|right|Visual representation of water and sanitation infrastructure]]&lt;br /&gt;
&lt;br /&gt;
We originally introduced forecasts of access to improved sources of drinking water and sanitation into IFs in support of the third volume in the PPHP series, &#039;&#039;Improving Global Health&#039;&#039; (Hughes, Kuhn, et al. 2011), because of the health risks associated with a lack of clean water and/or improved sanitation. We have extended this portion of the model to include forecasts of the share of wastewater that is collected and then treated prior to being returned to the environment. In addition, we have added a component to forecast the area equipped for irrigation.&lt;br /&gt;
&lt;br /&gt;
The WHO and UNICEF (2013) use the concept of “ladders” for drinking water sources and sanitation systems. They currently include four steps for both drinking water (surface water, unimproved, other improved, and piped on premises) and sanitation (open defecation, unimproved, shared, and improved). As countries develop, more of their citizens ascend these ladders. We have combined these into three categories each; for drinking water these are unimproved, other improved, and piped; for sanitation, these are other unimproved, shared, and improved. Notably, using international standards, estimates of the total population with access to improved sanitation does not include the shared category.&lt;br /&gt;
&lt;br /&gt;
We forecast the shares of the population in each of the water and sanitation ladder categories using average income, poverty levels (measured as the percentage of the population living on less than $1.25 per day), educational attainment (measured as the average number of years of formal education for adults over 25), and public health expenditures as explanatory variables (see the next figure). These results then feed into the forecasts of the percentage of population with wastewater collection and wastewater treatment.&lt;br /&gt;
&lt;br /&gt;
Finally, these access rates, in combination with the forecasts for population and average household size, are used to calculate the number of safe water, sanitation, and wastewater treatment connections, which drive the cost calculations described below.&lt;br /&gt;
&lt;br /&gt;
==== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;Area Equipped for Irrigation&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt; ====&lt;br /&gt;
&lt;br /&gt;
There have been few forecasts of the area equipped for irrigation, and those that do exist tend to be based on very detailed analyses of specific situations. In a recent report from the United Nations Food and Agriculture Organization (FAO) looking out to the year 2050, Bruinsma (2011: 251) stated that the “projections of irrigation presented in this section are based on scattered information about existing irrigation expansion plans in different countries, potentials for expansion (including water availability) and the need to increase crop production.” Another report looking at global agriculture over the next half century (Nelson et al. 2010), this one from the International Food Policy Research Institute, relies on exogenous assumptions of the growth in irrigated area. The authors do not specify the source of these assumptions, but some of the same authors (You et al. 2011) have reported on the irrigation potential for Africa, basing their conclusions on agronomic, hydrological, and economic factors.&lt;br /&gt;
&lt;br /&gt;
Rather than attempt to replicate the level of detailed analysis of most previous studies, we forecast the area equipped for irrigation based on data from the FAO’s FAOSTAT and AQUASTAT databases on historical irrigation patterns and the area that could potentially be equipped for irrigation. These data are incomplete; for area equipped for irrigation, data are provided for 168 of the 186 countries included in IFs, and for the potentially irrigable area, data are provided for 117 of 186 countries. In our examination of these historical data, we found that a number of countries had already reached an apparent plateau in the amount of area equipped for irrigation that was often well below the potential indicated. For example, Argentina’s equipped area has stayed at a bit over 1.5 million hectares since the late 1970s, even though its potential is given as more than 6 million hectares. Why a country saturates below its ultimate potential is often unclear, but one obvious reason for some countries is that they receive enough rainfall to not warrant further irrigation.&lt;br /&gt;
&lt;br /&gt;
In any case, once we have determined an appropriate saturation level for each country and a recent historical growth rate, we assume that the expected area equipped for irrigation gradually approaches the saturation level. The rate of growth starts at the historical growth rate, with the growth rate slowing as the saturation level is approached. The user can modify this path using the parameter&amp;amp;nbsp;&#039;&#039;&#039;&#039;&#039;ladirareaequipm&#039;&#039; &#039;&#039;&#039;, which acts as a multiplier. Still, the amount of area equipped for irrigation cannot exceed the specified saturation level for the country.&lt;br /&gt;
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==== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;ICT&amp;lt;/span&amp;gt; ====&lt;br /&gt;
&lt;br /&gt;
We forecast four basic indicators of ICT infrastructure: fixed telephone lines, fixed broadband subscriptions, mobile telephone subscriptions, and mobile broadband subscriptions, all per 100 persons. Our forecasts for the expected levels of these different forms of ICT infrastructure are driven in part by cross-sectional relationships with average income and government regulatory quality. As the next figure illustrates, however, there are also interactions among the different forms of ICT.[[File:Infra5.png|frame|right|Visual representation of ICT infrastructure]]&lt;br /&gt;
&lt;br /&gt;
For each technology, we found strong relationships indicating that usage levels (our proxies in this case for access) increase with rises in average income and governance regulatory quality; in the case of fixed broadband, we also found urbanization to be important, as one might expect for a technology whose installation is supported by population density.&lt;br /&gt;
&lt;br /&gt;
As for the interactions between the different forms of ICT, we start with fixed telephone lines. Given the potential for substitution by mobile telephone lines, we assume that the demand for fixed telephone lines will decline as mobile usage increases. Already we see this happening in the data, especially, but not exclusively, in high-income countries. Our analysis of the historical data indicates a level of approximately 30 mobile telephone subscriptions per 100 persons as the point at which fixed-line telephone decline begins, so we build this into our forecasts algorithmically. We do not expect that fixed telephone line usage will completely disappear. Rather, we assume arbitrarily that it will settle at a low level; this is set by default to 2.5 lines per 100 persons. Furthermore, we also assume that: (1) mobile broadband subscriptions will never exceed mobile telephone subscriptions; and (2) any decline in fixed telephone lines will boost the growth in fixed broadband because countries that have existing investments in fixed-line infrastructure are able to leverage these networks to provide broadband access with rather modest investments.&lt;br /&gt;
&lt;br /&gt;
The cross-sectional relationships with income do not remain static across time for mobile phones, fixed broadband, and mobile broadband. The last figure shows this for mobile telephone subscriptions. The individual points reflect historical data for country access rates for the years 2000, 2005, and 2010. The lines are logarithmic curves fit through these data. The upward shift over time reflects advances in information and communication technologies that are making ICT cheaper and more accessible around the world. These advances are, in turn, driven by various systemic factors ranging from product and process innovation to network effects.[[File:Infra6.png|frame|right|Example of saturation levels]]&lt;br /&gt;
&lt;br /&gt;
In order to capture the effect of this rapid change in our forecasts of future access, we combine the use of the cross-sectional function with an algorithmic approach that simulates the upward shift of the curves for mobile phones, fixed broadband, and mobile broadband. The algorithmic element assumes a standard technology diffusion process in which the growth in penetration rate associated with the technological shift rises from a low annual percentage point increase at low levels of penetration to a maximum at the middle of the range (the inflection point) and falls again as saturation is approached. For each of the three technologies, we have looked at historical patterns to estimate the minimum and maximum growth rates, expressed as annual percentage points of absolute change.&lt;br /&gt;
&lt;br /&gt;
The choice of saturation levels is obviously quite important. Data from the International Telecommunications Union show penetration rates for mobile phones that exceed 100 subscriptions per 100 persons (e.g., approaching 200 in Hong Kong). At the same time, some countries (e.g., Denmark) seem to be reaching a saturation level for fixed broadband well below 100 subscriptions per 100 persons. Uncertainty remains over the proper level of saturation to assume for these subscriptions, and therefore, different researchers use different values. Specifically, we define saturation as 50 subscriptions per 100 persons for fixed broadband and 150 subscriptions per 100 persons for both mobile technologies. In addition, we assume that mobile broadband penetration cannot exceed mobile phone penetration.&lt;br /&gt;
&lt;br /&gt;
Similarly to the other access rates, the numbers of lines and subscriptions per 100 persons, in combination with the forecasts for population, are used to calculate the absolute number of lines and subscriptions, which drive the cost calculations described in the diagram below.&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
[1]&amp;amp;nbsp;Not all countries have data for all indicators included in the model in the base year. IFs includes a preprocessor that uses a series of algorithms that draw on historical data for previous years, the estimated equations, and other factors to initialize these missing data.&lt;br /&gt;
&lt;br /&gt;
[2]&amp;amp;nbsp;&#039;&#039;ENDEMSH&#039;&#039; is an adjusted value of &#039;&#039;ENDEM&#039;&#039;, which takes into account the differences between the base year values for total primary energy use from historic data and the base year values calculated in the pre-processor, which adjusts for differences between the physical and financial data on energy trade. The ratio of &#039;&#039;ENDEMSH&#039;&#039; to &#039;&#039;ENDEM&#039;&#039; gradually converges to 1 over a number of years given by the parameter &#039;&#039;&#039;&#039;&#039;enconv&#039;&#039; &#039;&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;2. Translating the Expected Levels of Infrastructure into Financial Requirements&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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In estimating the financial requirements to achieve the expected levels of infrastructure, we adopt the approach introduced by Fay (2001) and Fay and Yepes (2003) described earlier. In this approach, there are two components to the financial requirements for each type of infrastructure each year. First there is the cost of maintenance/renewal of existing infrastructure. Second, there is the cost of new construction. These then need to be separated into public and private shares. The following figure shows the general process for each type of infrastructure.&lt;br /&gt;
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[[File:Infra7.png|frame|center|Visual representation of financial requirement estimation]]&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Estimating the financial requirements for new construction&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
For each type of infrastructure, the existing level of physical infrastructure is subtracted from the forecasted level and the difference is multiplied by the unit cost (see the table below for the list of the parameters that store the information on the unit costs). The results are then summed across the different types of infrastructure to calculate the total demand for funding for new construction. In a slight variation, rather than calculate the growth of the physical stock, Stambrook (2006) first calculated the asset value of the existing road stock by multiplying the level of the physical stock by a unit cost. He then directly forecasted the growth of this asset value, which was assumed to be equal to the investment requirements.&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Estimating the financial requirements for maintenance/renewal&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Although we use the term “maintenance” for this second set of infrastructure funding requirements, different studies use different nomenclature. Bhattacharyay (2010), Fay and Yepes (2003), Kohli and Basil (2011), and Yepes (2005), all use “maintenance”; Chatterton and Puerto (2006) refer to “rehabilitation.” Yepes (2008) refers to “maintenance and rehabilitation.” Finally, G. Hughes, Chinowsky, and Strzepek (2009) provide separate estimates for replacement and for maintenance. In general, however, the methodology for the estimation of the funding requirements is the same across all studies. For each type of infrastructure, the funding is determined as a percentage of the dollar value of the existing infrastructure. The dollar value is given as the amount of infrastructure in physical units multiplied by the same unit cost used for estimating the funding for new construction. The percentage is based on the average lifetime of the particular infrastructure (see Table 6 for the list of the parameters that store the infrastructure lifetimes in IFs). Fay and Yepes (2003: 10) referred to this as “the minimum annual average expenditure on maintenance, below which the network’s functionality will be threatened.” Later authors have more specifically related the percentage to the depreciation rate or average expected lifetime of each type of infrastructure (Chatterton and Puerto 2006; Yepes 2005, 2008).&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Separating the financial requirements into public and private shares&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
In the real world funding for infrastructure comes from both public and private sources, so we separate the funding requirements into public and private components. We assume a specific share of public and private funding for each type of infrastructure. This, in effect, implies that public spending on infrastructure leverages a certain amount of private spending. These shares differ by type of infrastructure, but are constant across countries and time. The share parameters are &#039;&#039;&#039;&#039;&#039;infrainvmaintpubshrm&#039;&#039; &#039;&#039;&#039; and &#039;&#039;&#039;&#039;&#039;infrainvnewpubshrm&#039;&#039; &#039;&#039;&#039;, each of which is a vector, with the dimension representing the type of infrastructure. The balancing of the financial requirements with the available resources included in IFs and described in the next section only considers the public sector.&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%; border: 1px solid #cccccc&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Infrastructure Type (unit)&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Unit Cost Parameters&amp;lt;sup&amp;gt;[1]&amp;lt;/sup&amp;gt;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Lifetime Parameter&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Paved road (kilometer)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infraroadpavedcostlower, infraroadpavedcostm, infraroadpavedcostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infraroadpavedlife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Unpaved road (kilometer)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infraroadunpavedcostlower, infraroadunpavedcostm, infraroadunpavedcostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infraroadunpavedlife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Electricity generation (megawatt)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infraelecgencostlower, infraelecgencostm, infraelecgencostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infraelecgenlife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Rural electricity (connection)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infraelecaccruralcostlower, infraelecaccruralcostm, infraelecaccruralcostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infraelecaccrurallife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Urban electricity (connection)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infraelecaccurbancostlower, infraelecaccurbancostm, infraelecaccurbancostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infraelecaccurbanlife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Irrigation equipment (hectare)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;landircostlower, landircostm, landircostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;landirlife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Improved water (connection)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;watsafeimpcostlower, watsafeimpcostm, watsafeimpcostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;watsafeimplife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Piped water (connection)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;watsafecostlower, watsafecostm, watsafecostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;watsafelife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Shared sanitation (connection)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;sanitationimpcostlower, sanitationimpcostm, sanitationimpcostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;sanitationimplife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Improved sanitation (connection)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;sanitationcostlower, sanitationcostm, sanitationcostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;sanitationlife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Wastewater treatment (connection)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;watwastetreatcostlower, watwastetreatcostm, watwastetreatcostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;watwastetreatlife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Fixed telephone (line)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infratelecostlower, infratelecostm, infratelecostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infratelelife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Fixed broadband (subscription)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;ictbroadcostlower, ictbroadcostm, ictbroadcostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;ictbroadlife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Mobile phone (subscription)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;ictmobilcostlower, ictmobilcostm, ictmobilcostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;ictmobillife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Mobile broadband (subscription)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;ictbroadmobilcostlower, ictbroadmobilcostm, ictbroadmobilcostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;ictbroadmobillife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; colspan=&amp;quot;3&amp;quot; valign=&amp;quot;middle&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;&amp;lt;span style=&amp;quot;font-size:small;&amp;quot;&amp;gt;[1] The actual unit costs can change as a function of GDP per capita (&#039;&#039;GDPPCP&#039;&#039;). For a given type of infrastructure, below a given level of &#039;&#039;GDPPCP&#039;&#039;, the unit cost takes on the value specified by the parameter ending with ‘lower’. Above a given level of &#039;&#039;GDPPCP&#039;&#039;, the unit cost takes on the value specified by the parameter ending with ‘upper’. Between these two values of &#039;&#039;GDPPCP&#039;&#039;, the unit cost changes in a linear fashion between the ‘lower’ and ‘upper’ value as a function of &#039;&#039;GDPPCP&#039;&#039;. Currently, the lower and upper thresholds for &#039;&#039;GDPPCP&#039;&#039; are hard coded in the model and vary by type of infrastructure.&amp;lt;/span&amp;gt;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&amp;lt;span style=&amp;quot;font-size:small;&amp;quot;&amp;gt;[2] The unit cost parameters ending in ‘m’ are multipliers that can be used to change the unit cost directly.&amp;lt;/span&amp;gt;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&amp;lt;span style=&amp;quot;font-size:small;&amp;quot;&amp;gt;[3] As described in the discussion on initializing the infrastructure data for IFs, the unit costs are also multiplied by the variable &#039;&#039;INFRACOSTADJFAC&#039;&#039;, which is calculated in the first year of the model as part of balancing the government spending in that year. This variable always has a value between 0 and 1, and gradually converges to 1 over the time period given by the parameter &#039;&#039;infracostadjfacconvtime&#039;&#039; .&amp;lt;/span&amp;gt;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&amp;lt;/div&amp;gt;&amp;lt;br/&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;3. Determining the Actual Funds for Infrastructure Spending&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
There is no guarantee that the requirements for infrastructure funds will match those made available. In determining whether this is the case, we focus on the public spending for infrastructure. In IFs, government domestic revenues and net foreign aid are summed into government expenditure (GOVEXP), which is then allocated between transfers, (GOVHHTRN - pensions and other social payments) and direct government spending (GOVCON). The latter is divided among broad categories— defense, education, health, research and development, core infrastructure, other infrastructure, and a residual category of other government spending. It is through this process of allocating government revenues that the amount of public funding for infrastructure ultimately is determined. IFs allows some imbalance between revenues and total expenditures year to year, but neither debt nor surpluses can accumulate indefinitely; as their percentages of GDP change, signals adjust revenues and expenditures over time.&lt;br /&gt;
&lt;br /&gt;
The figure below illustrates how the actual public funds available for core infrastructure are determined starting from the public funds required for core infrastructure estimated in the previous step. During this step, the amount of public funds available for other infrastructure is also determined.&lt;br /&gt;
&lt;br /&gt;
[[File:Infra8.png|frame|right|Visual representation of public spending for infrastructure]]&lt;br /&gt;
&lt;br /&gt;
Prior to the budget algorithm, the public funds required for core infrastructure can be modified by a spending multiplier, &#039;&#039;&#039;&#039;&#039;gdsm(Infrastructure)&#039;&#039; &#039;&#039;&#039;, to determine the public funds desired for core infrastructure. Similarly, the public funds desired for other infrastructure, which are initially estimated as function of GDP per capita, can be modified by a spending multiplier, &#039;&#039;&#039;&#039;&#039;gdsm(InfraOther&#039;&#039; &#039;&#039;&#039; &#039;&#039;)&#039;&#039;. Finally, the parameter &#039;&#039;&#039;&#039;&#039;infrabudsdrat&#039;&#039; &#039;&#039;&#039; can be used to indicate the priority that should be given to core and other infrastructure in the budget allocation process (it affects both categories equally).&lt;br /&gt;
&lt;br /&gt;
The budget algorithm takes this information, along with the public funds desired for other categories and government consumption to determine the public funds available for core and other infrastructure. First, a fraction, defined by &#039;&#039;&#039;&#039;&#039;infrabudsdrat&#039;&#039; &#039;&#039;&#039; divided by 1, of the public funds desired for core and other infrastructure, up to the level of total government consumption, is allocated to these categories and removed from total government consumption ([[Education#Education_Financial_Flow|there is a similar parameter, &#039;&#039;&#039;&#039;&#039;edbudgon&#039;&#039; &#039;&#039;&#039;, discussed in the Education section of the Help system]]). The remaining government consumption is allocated to the various categories based upon their desired levels of funding (at this point, the amounts of desired funding for core and other infrastructure does not include the amounts already set aside). In the case of demand-supply mismatches, the subtractions or additions are allocated to each category based on their relative shares of the total desired funding. There is also a minimal level of funds allocated to each category; i.e., each category will receive at least some funds. (Note, [[Governance#Equations:_Broader_Regime_Capacity|the budget allocation process is described in more detail in the governance section of this Help system.]])&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;4. Determining the Forecasted Levels of Infrastructure Spending and Attainment&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Once the level of public funds available for core infrastructure is determined, we can forecast the levels of infrastructure that will be attained. If there is a match between the estimated funding requirements and the estimated funding available, the process is fairly straightforward. In the case where there is a demand-supply mismatch, the forecasting becomes more complicated. The following figure presents this process.&lt;br /&gt;
&lt;br /&gt;
[[File:Infra9.png|frame|right|Visual representation of forecasted levels of infrastructure spending and attainment]]&lt;br /&gt;
&lt;br /&gt;
Recognizing that the infrastructure sector may not be able to manage rapid increases in public funding, we first smooth the actual provision of the public funds. Specifically, if the public funds available in the current year dramatically exceed the amount spent in the previous year, a portion of the available funds are held in reserve (in a lockbox). The threshold for this increase is half a percent of GDP. The funds in the lockbox are gradually released over time. The amount released from the lockbox depends on the amount in the lockbox and a fixed coefficient, InfraSpndBoxUnloadFactor, indicating the fraction that can be released in any year. This value is currently hard coded at 0.2. The amount of public funds available in the present year for core infrastructure is, therefore, the sum of the public funds coming out of the budget process for the current year not put in reserve plus the funds released from the lockbox in the current year. This amount is then compared to the public funds required for core infrastructure determined previously.&lt;br /&gt;
&lt;br /&gt;
In the case of an exact match between the public funds available in the present year for core infrastructure exactly matches the public funds required, the amounts of public and private spending on new construction and maintenance/renewal are exactly the amounts required. Similarly, the levels of infrastructure attained exactly match those expected (see again earlier figures on expected levels of infrastructure).&lt;br /&gt;
&lt;br /&gt;
In the case of a budget shortfall, we make three simplifying assumptions. First, we assume that all forms of infrastructure are affected equally; specifically, each receives the same proportionate cut in the amount of public funding received. Second, with the exception of ICT infrastructure (fixed and mobile telephones and broadband), we assume that the amount of private funding is reduced by the same proportion. This is based on our premise, stated earlier, that public funding for infrastructure leverages private spending, so less public funding also means less private spending. We make the exception for ICT because this is a less-tenable assumption for that sector given the degree to which private spending historically has driven ICT development. Specifically, private funding for ICT is not reduced even in the case of a reduction of public funding. Third, we assume that the reductions in funding affect spending on both maintenance and new construction equally. The net result is that there will be less new construction of infrastructure than desired, as well as less maintenance of existing infrastructure. This can lead to an absolute decline in some forms of infrastructure when the new construction is not enough to make up for the amount of infrastructure lost due to inadequate maintenance.&lt;br /&gt;
&lt;br /&gt;
This is slightly altered when targets are set for infrastructure. Here, an algorithm is used that first tries to ensure that the funds are used to provide the levels of construction and maintenance implied by the expected values estimated in the absence of a target. In this way, infrastructures with high targets are not favored over other forms of infrastructure. Any remaining funds are then distributed among all other infrastructure types, with their shares being proportional to the funds required to achieve the expected levels of construction and maintenance implied by the target.&lt;br /&gt;
&lt;br /&gt;
A further effect of a budget shortfall is that when infrastructure stocks do not achieve their expected levels, there is a feedback to our access measures.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;When there is a budget surplus, the extra funds go to additional new construction because the maintenance/renewal requirements are already covered. The surplus is spread across the different forms of infrastructure using the following logic. First, r&amp;lt;/span&amp;gt; &amp;lt;span&amp;gt;oads and electricity generation are allocated shares of the excess funds determined by their historical shares in total infrastructure spending. Second, the remainder of the excess funds is disbursed among the infrastructures that involve access. They are used to meet the gap to universal (stipulated) access rate with a cap on how much of the gap can be met each year.&amp;lt;/span&amp;gt; &amp;lt;span&amp;gt;Private funding is not affected by increases in public funding from “surplus funds.”&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;5. Estimating the Social, Economic, and Environmental Impacts of the Attainable Infrastructure&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
There a number of possible social, economic, and environmental impacts of infrastructure. We divided these into impacts on economic growth, income distribution, health, education, governance, and the environment. Given the limited empirical support for many of these linkages and, thus, a high level of uncertainty about whether and how to represent them, we have limited our inclusion of direct links from infrastructure to the links from infrastructure to economic growth and health. Important indirect linkages supplement the direct linkages that we describe here. For example, the forward linkages from economic growth to environmental impact (via paths such as increased energy use and food demand) and from improved health to demographic change are present in the current model. In fact, the indirect linkages via both of these paths are pervasive across the model.&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Impacts on productivity and economic growth&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
We estimate the impact of infrastructure on economic growth through its effect on multifactor productivity. Most economic models relate aggregate growth to changes in factors of production, typically capital (K) and labor (L), and an additional component, which is variously called the Solow residual, the technological change parameter, total factor productivity (TFP) or multifactor productivity (MFP); here we use the MFP label. Analyses have long shown that MFP can be quite large (Solow 1956; 1957). Within IFs, we treat MFP as an endogenous variable that human capital, social capital, physical capital, and knowledge capital influence (Hughes 2007). Infrastructure is a key component of physical capital, along with natural resources. The impact of the latter is represented through the effect of energy prices on [[Economics#Multifactor_Productivity|MFP]].&lt;br /&gt;
&lt;br /&gt;
In estimating the impact of infrastructure on MFP, we relate the impact to measures of physical infrastructure and not to measures of infrastructure spending. Because of the interaction effects across infrastructure types, we do not attempt to estimate the impact of individual forms of infrastructure but rather estimate the impact as a function of a composite index of infrastructure. Due to the very different historical and expected growth patterns of more traditional infrastructure—transportation, energy and water—vis-à-vis ICT, we create a separate index for ICT and link it to the physical capital component of MFP (MFPPC) in a different way.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Traditional Infrastructure – Transportation, Electricity, and Water and Sanitation&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
For the more traditional forms of infrastructure—transportation, electricity, and water and sanitation, we first construct a set of component indices—&#039;&#039;INFRAINDTRAN&#039;&#039;, &#039;&#039;INFRAINDELEC&#039;&#039;, and &#039;&#039;INFRAINDWATSAN&#039;&#039; (see the figure below). These are then aggregated into an overall index, &#039;&#039;INFRAINDTRAD&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
In order to construct these indices, we followed the approach presented in Calderón and Servén (2010a). This begins with basic measures of infrastructure, e.g., the number of telephone lines, the amount of electricity generating capacity, and the length of the road network. These measures are ‘standardized’, as follows:&lt;br /&gt;
&lt;br /&gt;
#If the indicator is not already normalized by a meaningful scaling factor, e.g., land area or total population, calculate an appropriate normalized value. This is based on the notion that, for example, it makes more sense to compare countries based on the number of telephones per person rather than the total number of telephones. The following figure shows the normalized indicators used for each of the component indices.&lt;br /&gt;
#The logarithms of the normalized indicators are calculated.&lt;br /&gt;
#The mean and standard deviation for each of the normalized and logged indicators in the year 2010 are calculated in the pre-processor. These are stored in the vectors &#039;&#039;INFRAINDTRANCOMPMEANI&#039;&#039;, &#039;&#039;INFRAINDTRANCOMPSDI&#039;&#039;, &#039;&#039;INFRAINDELECCOMPMEANI&#039;&#039;, &#039;&#039;INFRAINDELECCOMPSDI&#039;&#039;, &#039;&#039;INFRAINDWATSANCOMPMEANI&#039;&#039;, and &#039;&#039;INFRAINDWATSANCOMPSDI&#039;&#039;, each of which has an entry for each indicator included in the component index.&lt;br /&gt;
#In each forecast year, a z-value for each of the normalized indicators is calculated by subtracting the mean value for the year 2010 and then dividing by the standard deviation for the year. This provides a more standardized measure of the difference across countries and is independent of the original units of measure. If a country has negative (positive) z-value for a particular indicator, this indicates that its level of that indicator is smaller (greater) than it was for the average country in 2010. By definition, the aggregated z-value for the world for each indicator in 2010 is equal to 0.[[File:Infra10.png|frame|right|Visual representation of the basic measures of infrastructure]]&lt;br /&gt;
&lt;br /&gt;
The component indices are then calculated as a weighted sum of the z-values for the normalized indicators used for each of the component indices. The weights are given by the parameters &#039;&#039;&#039;&#039;&#039;infraindtrancompwt&#039;&#039; &#039;&#039;&#039;, &#039;&#039;&#039;&#039;&#039;infraindeleccompwt&#039;&#039; &#039;&#039;&#039;, and &#039;&#039;&#039;&#039;&#039;infraindwatsancompwt&#039;&#039; &#039;&#039;&#039;, where, once again each of these is a vector with an entry for each indicator included in the component index. Finally, the overall traditional infrastructure index, &#039;&#039;INFRAINDTRAD&#039;&#039;, is calculated as a weighted sum of the component indices. The weights are given by the parameter &#039;&#039;&#039;&#039;&#039;infraindtradcompwt&#039;&#039; &#039;&#039;&#039;, which is a vector with three entries, one for each of the component indices.&lt;br /&gt;
&lt;br /&gt;
As with the z-values for the individual indicators, a negative (positive) value of for one the component indices or the overall index implies that a country ranks below (above) the average country in 2010 for that indicator. Furthermore, by definition, the aggregated value for the world for each index in 2010 is equal to 0.&lt;br /&gt;
&lt;br /&gt;
We use the overall Traditional Infrastructure Index to calculate the impact of traditional infrastructure on MFP in the same way as we do for most factors that influence MFP. As described by Hughes (2007: 15–16), we do this by comparing the value of &#039;&#039;INFRAINDTRAD&#039;&#039; to the value of &#039;&#039;INFRAINDTRADEXP&#039;&#039;, which is calculated using a benchmark function&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt;&amp;amp;nbsp;that indicates what value we would expect to see for a country given its current level of GDP per capita (see the figure below). A country whose index falls above (below) the benchmark value receives a boost to (reduction from) its MFP. For example, Gabon and Latvia have similar levels of GDP per capita in 2010, but Latvia’s Traditional Infrastructure Index falls well above the benchmark line, while Gabon’s falls well below. Thus, the former will receive a boost to its MFP due to traditional infrastructure, while the latter will receive a reduction.&lt;br /&gt;
&lt;br /&gt;
The size of the boost or reduction depends on the distance from the benchmark value, &#039;&#039;INFRAINDTRAD&#039;&#039; – &#039;&#039;INFRAINDTRADEXP&#039;&#039;, and a factor relating this distance to productivity, which is given by the parameter &#039;&#039;&#039;&#039;&#039;mfpinfrindtrad&#039;&#039; &#039;&#039;&#039;. Calderón and Servén (2010a: i35) presented a value of 2.193 as their estimate of the increase in annual average growth rate of GDP per capita for an increase in 1 unit of their index. Based on this, we use a default value of 2 for the effect of traditional infrastructure on MFP. Specifically, if the value of the Traditional Infrastructure Index for a country is a full point above its expected value in a given year, it would receive a 2 percentage point boost to its MFP, which roughly translates into the same increase in growth in GDP per capita, over the coming year. The model user can change this value, allowing for exploration of the sensitivity of model results to the traditional infrastructure parameter.&lt;br /&gt;
&lt;br /&gt;
[[File:Infra11.png|frame|center|Traditional infrastructure index]]ICT&lt;br /&gt;
&lt;br /&gt;
The ICT Index, &#039;&#039;INFRAINDICT&#039;&#039; &amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[2]&amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt;, is calculated as a weighted average of the subscription rates for three of the four different kinds of ICT – mobile phones, fixed broadband, and mobile broadband. Since the subscription rates for mobile phones and mobile broadband saturate at 150 per 100 persons, their values are first multiplied by 2/3 so that they range from 0 to 100. The weights are given by the parameter &#039;&#039;&#039;&#039;&#039;infraindictcompwt&#039;&#039; &#039;&#039;&#039;, which is a vector with three entries, one for each of the component indices. By default, these values are set to 1, indicating equal weighting.&lt;br /&gt;
&lt;br /&gt;
When considering the impact of ICT infrastructure on MFP, using the same approach as for traditional infrastructure would be problematic. Our formulation for forecasting ICT infrastructure includes a technology shift factor. Therefore, any relationship between GDP per capita and the expected level of ICT would not remain stable over time; for example, a country with a GDP per capita of $5,000 in 2015 would be expected to have more ICT infrastructure than a country with a GDP per capita of $5,000 in 2010.&lt;br /&gt;
&lt;br /&gt;
We therefore associate the growth contribution from ICT advances with annual changes in the ICT Index, rather than with the level of the index as we do for traditional infrastructure. We multiply the annual unit change in the ICT Index by the parameter &#039;&#039;&#039;&#039;&#039;mfpinfrindict&#039;&#039; &#039;&#039;&#039;. Qiang, Rossotto and Kimura (2009: 45) estimated that each 10 percent increase in broadband penetration in developing countries increased the growth rate of per capita GDP by 1.38 percentage points (by 1.21 percentage points for developed countries) during the 1980 to 2006 period. We arbitrarily reduced the impact by using a default value of 0.8 because our index is a mixture of several types of ICT infrastructures, not all of which might have as strong an impact on economic productivity as does broadband. Thus, a 10 point increase in the value of the ICT index would result in a 0.8 addition to MFP, or an approximate increase of 0.8 percent in GDP per capita.&lt;br /&gt;
&lt;br /&gt;
There is one obviously questionable implication of this approach. When a country reaches saturation in the ICT Index, it will no longer receive a productivity boost from ICT. Given the current rapid increase in mobile telephones and mobile broadband that together make up two-thirds of the ICT Index, we see in most scenarios a near-term boost to MFP from ICT in much of the world, followed by little or no contribution later in the horizon. Our uncertainty with respect to appropriate treatment of the longer-term contribution of ICT points to one of the limitations of trying to forecast rapidly changing technologies.&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Impacts on health&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
There are many ways in which infrastructure can affect human health. We have chosen to limit our inclusion of these effects to a small set, specifically the impact of (1) unsafe water, sanitation, and hygiene directly on diarrheal diseases, and indirectly on diseases related to undernutrition; and (2) indoor air pollution on respiratory infections, such as pneumonia, and respiratory diseases, such as chronic obstructive pulmonary disease. These health outcomes are influenced directly by infrastructure via our measures of access to improved sources of drinking water and sanitation and the use of solid fuels in the home. These measures serve as proxies for the environmental health risks linked to infrastructure in IFs. We explored these effects in a previous volume in this series, &#039;&#039;Improving Global Health &#039;&#039;(B.Hughes, Kuhn, et al. 2011: 95–100), and have some confidence in the reasonableness of our results.&lt;br /&gt;
&lt;br /&gt;
Our approach for estimating the impact of these health risks is described in the [[Health#Proximate_Drivers_and_Risk-Specific_Population_Attributable_Fractions|health documentation]]. Therefore, we provide only a brief overview here. In general, we compare the forecasted values of these infrastructure indicators to values that we would anticipate based only on income and educational attainment (distal drivers). If the estimated and expected values differ, we adjust the levels of mortality and morbidity for the associated diseases forecasted based only on the distal drivers. For example, if the levels of access to improved sources of water and sanitation are higher than expected, we reduce the mortality rate from diarrheal diseases. The amount by which the mortality rate is reduced is based on the analysis presented in the Comparative Risk Analysis work of the World Health Organization (Ezzati et al. 2004). This general approach, comparing forecasted values with expected ones and translating the difference into impact in a forward linkage, is fundamentally similar to the method described above for linking infrastructure development and economic productivity.&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt;&amp;amp;nbsp;&amp;lt;span&amp;gt;This benchmark function is actually the combination of two functions: 1) INFRAINDTRADEXP = -0.881 + 0.519 * GDPPCPPP at levels of GDPPCPPP below $5000 and 2) INFRAINDTRADEXP = 2.767 + 0.225 * GDPPCPPP at levels of GDPPCPPP above $40000, with blending between these two thresholds.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[2]&amp;lt;/span&amp;gt;&amp;amp;nbsp;&amp;lt;span&amp;gt;A separate index, &amp;lt;/span&amp;gt; &#039;&#039;&amp;lt;span&amp;gt;INFRAINDICTZ&amp;lt;/span&amp;gt; &#039;&#039; &amp;lt;span&amp;gt;, is also calculated following the same approach as for the component indices of traditional infrastructure. This is only used for display purposes.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Infrastructure Equations&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
The primary equations in the infrastructure model in IFs are those for estimating the expected levels of infrastructure stocks or access. Each of the estimated equations relates one aspect of physical infrastructure to specific economic, structural, and demographic drivers; in some cases these equations also include other types of infrastructure, creating explicit linkages across those infrastructures. While a number of earlier studies did provide equations for forecasting future levels of some of the types of physical infrastructure we include, we chose to undertake our own analyses for the purposes of this volume. This allowed us to use more recent data to drive the relationships than earlier studies and to better integrate the resulting relationships within the broader IFs system.&lt;br /&gt;
&lt;br /&gt;
Our choices of the driving variables ultimately included in the equations were influenced by theoretical considerations, previous efforts, the availability of data, and, of course, the analytical results themselves. These factors also influenced our choices of functional forms. In particular, for variables that have natural minimums and maximums, such as the percentage of population with access to electricity, we use functional forms that guarantee that the forecasted values fall in this range.&lt;br /&gt;
&lt;br /&gt;
The basic equations shown below provide only the initial estimates of the expected levels of the specific infrastructure stock or access.&amp;amp;nbsp;[[Understand_IFs#Specialized_Functions|The final values are adjusted based upon a number of algorithmic and scenario-specific processes, including the use of shift factors, multipliers, extrapolative formulations, targeting processes.]]&amp;amp;nbsp;Some key aspects of these algorithmic processes, including key parameters available to the user for scenario development, are provided below the definitions of the variables used in the basic equations. Finally, the nature of the data used for estimation, the model fitted, and the R-squared values for a fit of the predicted against the actual historical values used for our estimations are also provided.&lt;br /&gt;
&lt;br /&gt;
As with the flow charts, this section presents the equations grouped by the four categories of infrastructure: transportation, electricity, water and sanitation, and ICT. Unless specified otherwise, in all of the following equations, the subscripts r and t refer to region/country and time/year, respectively.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
For help understanding the equations see [[Understand_IFs#Equation_Notation|Notation]].&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Equations: Transportation Infrastructure&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The estimated equations for transportation infrastructure in IFs are: 1) the total road density in kilometers per 1000 hectares, &#039;&#039;INFRAROAD&#039;&#039;, 2) the percentage of roads that are paved, &#039;&#039;INFRAROADPAVEDPCNT&#039;&#039;, and 3) the Rural Access Index, &#039;&#039;INFRAROADRAI&#039;&#039;, the percentage of the rural population living within two kilometers of an all-season road. From these we can calculate other transportation indicators.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Total road density&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ln(INFRAROAD_{r,t})=-2.539+0.483*ln(\frac{GDPP_{r,t}}{LANDAREA_{r,t}})+0.183*ln(\frac{POP_{r,t}}{LANDAREA_{r,t}}-0.102*ln(LANDAREA_{r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:INFRAROAD = road network density in kilometers per 1,000 hectares&lt;br /&gt;
&lt;br /&gt;
:GDPP = gross domestic product at purchasing power parity in billion constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:LANDAREA = land area in 10,000 square kilometers (million hectares)&lt;br /&gt;
&lt;br /&gt;
:POP = total population in million persons&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;uses extrapolative formulation: &#039;&#039;&#039;extmafuncroad, extmaposnconvtimeroad, extmaposnroad&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;additive shift factor: RoadDensShift, downward shift over 300 years, upward shift over 40 years&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;infraroadm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline in the absence of a target or multiplier or lack of finance for maintenance&#039;&#039;&lt;br /&gt;
*&#039;&#039;pooled cross-sectional data, OLS regression, R-squared = 0.79&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Percentage of total roads that are paved&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;INFRAROADPAVEDPCNT_{r,t}=\frac{100}{1+e^{-(-1.022+0.833*GDPPCP_{r,t}+0.756*POP_{r,t}-0.726*LANDAREA_{r,t}-0.267*INFRAROAD_{r,t}})}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:INFRAROADPAVEDPCNT = road network, paved percent in percentage&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:LANDAREA = land area in 10,000 square kilometers (million square hectares)&lt;br /&gt;
&lt;br /&gt;
:POP = total population in million persons&lt;br /&gt;
&lt;br /&gt;
:INFRAROAD = road network density in kilometers per 1,000 hectares&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;uses extrapolative formulation: &#039;&#039;&#039;extmafuncroadpaved, extmaposnconvtimeroadpaved, extmaposnroadpaved&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;additive shift factor: INFRARoadPavedPcntShift, downward shift over 500 years, upward shift over 50 years&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;infraroadpavedpcntm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline in the absence of a target or multiplier or lack of finance for maintenance&#039;&#039;&lt;br /&gt;
*&#039;&#039;&amp;lt;span&amp;gt;pooled cross-sectional data, OLS regression, R-squared = 0.45&amp;lt;/span&amp;gt;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Rural Access Index&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;INFRAROADRAI_{r,t}=100*e^{-3.558+1.328*ln(\frac{GDPPC_{r,t}*1000}{LANDAREA_{r,t}})+0.239*ln(INFRAROAD_{r,t}*\frac{LANDAREA_{r,t}*1000}{POP_{r,t}})}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:INFRAROADRAI = Rural Access Index, percent of rural population living within 2 kilometers of an all-weather road in percentage&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:LANDAREA = land area in 10,000 square kilometers (million square hectares)&lt;br /&gt;
&lt;br /&gt;
:POP = total population in million persons&lt;br /&gt;
&lt;br /&gt;
:INFRAROAD = road network density in kilometers per 1,000 hectares&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;additive shift factor: INFRAROADRAIShift, downward shift over 500 years, upward shift over 50 years&#039;&#039;&lt;br /&gt;
*&#039;&#039;there is currently no multiplier for INFRAROADRAI&#039;&#039;&lt;br /&gt;
*&#039;&#039;targeting parameters: &#039;&#039;&#039;infraroadraitrgtval, infraroadraitrgtyr, infraroadraisetar, infraroadraiseyrtar&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline unless lack of finance for maintenance&#039;&#039;&lt;br /&gt;
*&#039;&#039;cross-sectional data, OLS regression, R-squared = 0.51&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Equations: Energy Infrastructure&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Percentage of urban population with access to electricity&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;INFRAELECACC(urban)_{r,t}=\frac{100}{1+e^{-(1.144-4.858*\frac{INCOMELT1CS_{r,t-1}}{POP_{r,t-1}}+0.837*GOVEFFECT_{r,t})}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:INFRAELECACC(urban) = percent of urban population with access to electricity in percentage&lt;br /&gt;
&lt;br /&gt;
:INCOMELT1CS = population with income less than $1.25 per day, cross sectional computation in millions&lt;br /&gt;
&lt;br /&gt;
:POP = total population in million persons&lt;br /&gt;
&lt;br /&gt;
:GOVEFFECT = government effectiveness using the World Bank WGI scale, shifting it 2.5 points so that it runs from 0-5 instead of from -2.5 to 2.5&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;additive shift factor: INFRAELECACCShift(R%, Urban), downward shift over 500 years, upward shift over 50 years&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;infraelecaccm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;targeting parameters: &#039;&#039;&#039;infraelecacctrgtval, infraelecacctrgtyr, infraelecaccsetar, infraelecaccseyrtar&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline in the absence of a target or multiplier or lack of finance for maintenance&#039;&#039;&lt;br /&gt;
*&#039;&#039;cross-sectional data, GLM regression, R-squared = 0.68&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Percentage of rural population with access to electricity&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;INFRAELECACC(rural)_{r,t}=\frac{100}{1+e^{-(-0.500-6.925*\frac{INCOMELT1CS_{r,t-1}}{POP_{r,t-1}}+0.858*GOVEFFECT_{r,t})}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:INFRAELECACC(rural) = percent of urban population with access to electricity in percentage&lt;br /&gt;
&lt;br /&gt;
:INCOMELT1CS = population with income less than $1.25 per day, cross sectional computation in millions&lt;br /&gt;
&lt;br /&gt;
:POP = total population in million persons&lt;br /&gt;
&lt;br /&gt;
:GOVEFFECT = government effectiveness using the World Bank WGI scale, shifting it 2.5 points so that it runs from 0-5 instead of from -2.5 to 2.5&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;shift factor: INFRAELECACCShift(R%, Urban), downward shift over 500 years, upward shift over 50 years&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;infraelecaccm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;targeting parameters: &#039;&#039;&#039;infraelecacctrgtval, infraelecacctrgtyr, infraelecaccsetar, infraelecaccseyrtar&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline in the absence of a target or multiplier or lack of finance for maintenance&#039;&#039;&lt;br /&gt;
*&#039;&#039;cross-sectional data, GLM regression, R-squared = 0.77&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Ratio of electricity use to total primary energy demand&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENELECSHRENDEM_{r,t}=0.979*GDPPCP_{r,t}^{0.275}*INFRAELECACC(national)_{r,t}^{0.492}*FossilShare_{r,t=1}^{-0.077}*NonFossilShare_{r,t=1}^{0.123}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;FossilShare_{r,t=1}=\frac{ENP(oil)_{r,t=1}+ENP(gas)_{r,t=1}+ENP(coal)_{r,t=1}}{ENDEMSH_{r,t=1}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;NonFossilShare_{r,t=1}=\frac{ENP(hydro)_{r,t=1}+ENP(renew)_{r,t=1}}{ENDEMSH_{r,t=1}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:ENELECSHRENDEM = ratio of electricity use to total primary energy demand, in percentage&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:INFRAELECACC(national) = percent of total population with access to electricity in percentage&lt;br /&gt;
&lt;br /&gt;
:FossilShare = ratio of fossil fuel production to total primary energy demand in base year, as a fraction&lt;br /&gt;
&lt;br /&gt;
:NonFossilShare= ratio of hydroelectric and renewable energy production to total primary energy demand in base year, as a fraction&lt;br /&gt;
&lt;br /&gt;
:ENP = energy production for oil, gas, coal, hydro, and other renewables in billion barrels of oil equivalent&lt;br /&gt;
&lt;br /&gt;
:ENDEM = total primary energy use in billion barrels of oil equivalent&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;uses an extrapolative formulation:&#039;&#039; &#039;&#039;&#039;&#039;&#039;extmafuncenelecshr, extmaposnconvtimeenelecshr, extmaposnenelecshr&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;no shift factor&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;enelecshrendemm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline in the absence of a target or multiplier or lack of finance for maintenance&#039;&#039;&lt;br /&gt;
*&#039;&#039;cross-sectional data, OLS regression, R-squared = 0.65&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
As described in the flowchart for electricity the value of ENELECSHRENDEM is used to calculate the value of desired electricity use, given by INFRAELEC * POP, where INFRAELEC is electricity consumption per capita in kilowatt-hours and POP is total population in million persons. INFRAELEC is in initially calculated as:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;INFRAELEC_{r,t}=ENELECSHRENDEM_{r,t}*\frac{ENDEM_{r,t}*EnDemDFRIVal_{r,t}*17,000}{POP_{r,t}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:INFRAELEC = electricity consumption per capita in kilowatt-hours&lt;br /&gt;
&lt;br /&gt;
:ENDEM = total primary energy use in billion barrels of oil equivalent&lt;br /&gt;
&lt;br /&gt;
:EnDemDFRI = a multiplicative shift factor based on the ratio of the actual energy consumption in physical units in the historical data to the apparent energy consumption calculated in the pre-processor as part of adjusting the physical data to match the financial data on energy imports and exports; this converges to a value of 1 over a number of years given by the parameter enconv&lt;br /&gt;
&lt;br /&gt;
:17,000 = the conversion factor from barrels of oil equivalent to kilowatt-hours&lt;br /&gt;
&lt;br /&gt;
:POP = total population in million persons&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;&amp;lt;span&amp;gt;an additional multiplicative shift factor, InfraElecRI, which converges over 40 years to a value of 1, is used to further adjust the estimate of INFRAELEC&amp;lt;/span&amp;gt;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Percentage of electricity lost in transmission and distribution&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;INFRAELECTRANLOSS_{r,t}=e^{(3.125-0.026*GDPPCP_{r,t}-0.125*GOVREGQUAL_{r,t})}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:INFRAELECTRANLOSS = transmission and distribution loss as a percentage of total electricity production, in percentage&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:GOVEREGQUAL = government regulatory quality using the World Bank WGI scale, shifting it 2.5 points so that it runs from 0-5 instead of from -2.5 to 2.5&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;uses extrapolative formulation: &#039;&#039;&#039;extmafuncelectran, extmaposnconvtimeelectran, extmaposnelectran&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;additive shift factor: INFRAELECTRANLOSSShift, converges downward over 50 years, upward over 500 years&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;infraelectranlossm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;bound between 3 and 90&#039;&#039;&lt;br /&gt;
*&#039;&#039;&#039;&#039;&amp;lt;span&amp;gt;pooled cross-sectional data, OLS regression, R-squared = 0.85&amp;lt;/span&amp;gt; &#039;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Percentage of population primarily using solid fuels for heating and cooking&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENSOLFUEL_{r,t}=\frac{100}{1+e^{-(2.823+0.166*GDPPCP_{r,t}+0.032*INFRAELECACC(national)_{r,t})}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:ENSOLFUEL = ratio of electricity use to total primary energy demand, in percentage&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:INFRAELECACC(national) = percent of total population with access to electricity in percentage&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;multiplicative shift factor: ENSOLFUELShift; never converges&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;ensolfuelm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;targeting parameters: &#039;&#039;&#039;ensolfuelsetar, ensolfueltrgtyr, ensolfuelsetar, ensolfuelseyrtar&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;hold switch: &#039;&#039;&#039;ensolflhldsw&#039;&#039;&#039;, fixes value of ENSOLFUEL at initial year value&#039;&#039;&lt;br /&gt;
*&#039;&#039;cross-sectional data, GLM regression, R-squared = 0.81&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Equations: Water and Sanitation Infrastructure&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Percentage of population with access to improved drinking water and sanitation&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
For access to water and sanitation, we use a nominal logistic model to determine the share of the population in each category of access. For both water and sanitation, the number of categories is 3. For water these are no improved access, other improved access, and piped; for sanitation they are other unimproved access, shared access, and improved access.&lt;br /&gt;
&lt;br /&gt;
The values p&amp;lt;sub&amp;gt;i&amp;lt;/sub&amp;gt; shown below represent the share of population with access to each of these categories. The resulting values of p&amp;lt;sub&amp;gt;i&amp;lt;/sub&amp;gt;will all fall between 0 and 1 and sum to 1. These are then multiplied by100 in order to obtain values that range between 0 and 100 and sum to 100.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;P_i=\frac{S_i}{1+\sum^2_{i=1^{S_i}}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:for i = 1 to 2&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;and&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;P_3=1-\sum^2_{i=1^{P_i}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;S_i=e^{(a_i+\sum^n_{j=1}b_{i,j}*x_j)}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:for i = 1 to 2&lt;br /&gt;
&lt;br /&gt;
:n is the number of explanatory variables&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%; border: 1px solid #cccccc&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; colspan=&amp;quot;6&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Estimated coefficients&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Intercept&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;EDYRSAGE25&#039;&#039; &amp;lt;sub&amp;gt;r,t&amp;lt;/sub&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;GDPPCP&#039;&#039; &amp;lt;sub&amp;gt;r,t&amp;lt;/sub&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;INCOMELT1CS&#039;&#039; &amp;lt;sub&amp;gt;r,t&amp;lt;/sub&amp;gt; &#039;&#039;/ POP&#039;&#039; &amp;lt;sub&amp;gt;r,t&amp;lt;/sub&amp;gt; * 100&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;GDS(health)&#039;&#039; &amp;lt;sub&amp;gt;r,t&amp;lt;/sub&amp;gt; &#039;&#039;/ GDP&#039;&#039; &amp;lt;sub&amp;gt;r,t&amp;lt;/sub&amp;gt; * 100&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; colspan=&amp;quot;6&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Water&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | s0&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 0.47200933&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | -0.4414453&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | -0.7033376&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; | 0.0253734&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; | -0.1616335&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | s1&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 1.17414971&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 0.13867779&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | -1.1508133&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; | 0.01181508&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; | -0.2769033&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; colspan=&amp;quot;6&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Sanitation&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | s0&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 0.73081107&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | -0.6420051&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | -0.4497351&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; | 0.02170283&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; | -0.1562885&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | s1&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | -2.1593291&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 0.22539909&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | -0.3555466&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; | 0.02823687&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; | -0.1579957&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
:EDYEARSAGE25 = mean years of education for adults over the age of 25, in years&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:INCOMELT1CS = population with income less than $1.25 per day, cross sectional computation in millions&lt;br /&gt;
&lt;br /&gt;
:POP = total population in million persons&lt;br /&gt;
&lt;br /&gt;
:GDS(health) = government expenditure on health in billion constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:GDP = gross domestic product at market exchange rates in billion constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;additive shift factors: WATSAFEshift and SANITATIONShift; converge over &#039;&#039;&#039;watsanconv&#039;&#039;&#039; years for high and low categories; for intermediate categories, convergence time is 20 years for positive shift factors and 50 years for negative shift factors&#039;&#039;&lt;br /&gt;
*&#039;&#039;multipliers; &#039;&#039;&#039;watsafem&#039;&#039;&#039; and &#039;&#039;&#039;sanitationm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;targeting parameters: &#039;&#039;&#039;sanitationtrgtval, sanitationtrgtyr,&#039;&#039;&#039; &#039;&#039;&#039;sanitnoconsetar, sanitnoconseyrtar, sanitimpconsetar, sanitnoconsetar, sanitnoconseyrtar, watsafetrgtval, watsafetrgtyr, watsafehhconsetar, watsafeimpconsetar, watsafenoconsetar, watsafenoconseyrtar,&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;hold switches: &#039;&#039;&#039;watsafhldsw&#039;&#039;&#039; and &#039;&#039;&#039;sanithldsw,&#039;&#039;&#039; , fixes value of WATSAFE and SANITATION at initial year value&#039;&#039;&lt;br /&gt;
*&#039;&#039;values are normalized so that the three categories for water and sanitation each sum to 100&#039;&#039;&lt;br /&gt;
*&#039;&#039;pooled cross-sectional data, nominal logistic regression, R-squared = 0.85 for safe water, 0.87 for sanitation&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Percentage of population with wastewater collection&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WATWASTE_{r,t}=\frac{SANITATION(improved)_{r,t}}{1+e^{-(-2.4+0.043*GDPPCP_{r,t}+0.042*\frac{POPURBAN_{r,t}}{POP_{r,t}})}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:WATWASTE = percent of population with wastewater collection, in percentage&lt;br /&gt;
&lt;br /&gt;
:SANITATION(improved) = percent of population with access to improved sanitation, in percentage&lt;br /&gt;
&lt;br /&gt;
:POPURBAN = urban population in million persons&lt;br /&gt;
&lt;br /&gt;
:POP = total population in million persons&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;uses extrapolative formulation – coefficients are hard coded&#039;&#039;&lt;br /&gt;
*&#039;&#039;additive shift factor: WatWasteColShift; converge upward over 25 years, downward over 250 years&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;watwastem&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;no targeting parameters&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to exceed SANITATION(improved)&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline in the absence of a target or multiplier or lack of finance for maintenanc&#039;&#039;&lt;br /&gt;
*&#039;&#039;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;pooled cross-sectional data, OLS regression with country random effect, R-squared = 0.34&amp;lt;/span&amp;gt;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Percentage of population with wastewater treatment&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WATWASTETREAT_{r,t}=\frac{100}{1+e^{-(-2.482+0.038*GDPPCP_{r,t}+0.029*WATWASTE_{r,t})}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:WATWASTETREAT = percent of population with wastewater treatment, in percentage&lt;br /&gt;
&lt;br /&gt;
:WATWASTE = percent of population with wastewater collection, in percentage&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;additive shift factor: WatWasteTreatShift; converge upward over 25 years, downward over 250 years&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;watwastetreatm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;targeting parameters: &#039;&#039;&#039;watwastetreatsetar&#039;&#039;&#039;. &#039;&#039;&#039;watwastetreatseyrtar&#039;&#039;&#039; (no targeting parameters for absolute targets)&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to exceed WATWASTETREAT&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline in the absence of a target or multiplier or lack of finance for maintenance&#039;&#039;&lt;br /&gt;
*&#039;&#039;pooled cross-sectional data, GLM regression, R-squared = 0.59&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Equations: ICT Infrastructure&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Fixed telephone lines per 100 persons&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;INFRATELE_{r,t}=1.030+2.554*GDPPCP_{r,t}-0.033*GDPPCP^2_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:INFRATELE = fixed telephone lines per 100 persons&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;uses extrapolative formulation – parameters are hard coded&#039;&#039;&lt;br /&gt;
*&#039;&#039;no shift factor&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;infratelem&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;no targeting parameters&#039;&#039;&lt;br /&gt;
*&#039;&#039;when ICTMOBIL reaches 30, if INFRATELE &amp;gt; 2.5 value will fall to level of 2.5 over time period given by &#039;&#039;&#039;infrateledtfp&#039;&#039;&#039;: if INFRATELE &amp;lt; 2.5, then can continue to grow&#039;&#039;&lt;br /&gt;
*&#039;&#039;cross-sectional data, OLS regression, R-squared = 0.70&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Mobile telephone subscriptions per 100 persons&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ICTMOBIL_{r,t}=43.938+23.919*ln(GDPPCP_{r,t})+1.405*GOVREGQUAL_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:ICTMOBIL = mobile phone subscriptions per 100 persons&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:GOVEREGQUAL = government regulatory quality using the World Bank WGI scale, shifting it 2.5 points so that it runs from 0-5 instead of from -2.5 to 2.5&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;additive shift factor: MOBILshift; converge upward over 100 years, no convergence downward&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;ictmobilm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;targeting parameters: &#039;&#039;&#039;ictmobilsetar&#039;&#039;&#039;. &#039;&#039;&#039;ictmobilseyrtar&#039;&#039;&#039; (no targeting parameters for absolute targets)&#039;&#039;&lt;br /&gt;
*&#039;&#039;tech shift parameters: &#039;&#039;&#039;ictmobiltecinflection, ictmobiltechighrt, ictmobilteclowrt&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;saturation level: &#039;&#039;&#039;ictmobilsaturation&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline in the absence of a target or multiplier or lack of finance for maintenance&#039;&#039;&lt;br /&gt;
*&#039;&#039;cross-sectional data, OLS regression, R-squared = 0.53&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Fixed broadband subscriptions per 100 persons&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ICTBROAD_{r,t}=-12.581+2.534*ln(GDPPCP_{r,t})+6.496*GOVREGQUAL_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:ICTBROAD = fixed broadband subscriptions per 100 persons&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:GOVEREGQUAL = government regulatory quality using the World Bank WGI scale, shifting it 2.5 points so that it runs from 0-5 instead of from -2.5 to 2.5&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;additive shift factor: BROADshift; converges over 100 years (both upwards and downwards)&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;ictbroadm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;targeting parameters: &#039;&#039;&#039;ictbroadsetar, ictbroadseyrtar&#039;&#039;&#039; (no targeting parameters for absolute targets)&#039;&#039;&lt;br /&gt;
*&#039;&#039;urbanization increases growth using parameters &#039;&#039;&#039;ictbroadurimpmin&#039;&#039;&#039; and &#039;&#039;&#039;ictbroadurimpmax&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;as INFRATELE falls, this boosts growth of fixed broadband using the parameter &#039;&#039;&#039;ictbroadfromtelem&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;tech shift parameters: &#039;&#039;&#039;ictbroadtecinflection, ictbroadtechighrt, ictbroadteclowrt&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;saturation level: given by ictbroadcap (not in common block, currently hard coded as 50)&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline in the absence of a target or multiplier or lack of finance for maintenance&#039;&#039;&lt;br /&gt;
*&#039;&#039;cross-sectional data, OLS regression, R-squared = 0.74&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Mobile broadband subscriptions per 100 persons&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ICTBROADMOBIL_{r,t}=-21.827+9.139*ln(GDPPCP_{r,t})+9.357*GOVREGQUAL_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:ICTBROADMOBIL = mobile broadband subscriptions per 100 persons&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:GOVEREGQUAL = government regulatory quality using the World Bank WGI scale, shifting it 2.5 points so that it runs from 0-5 instead of from -2.5 to 2.5&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;additive shift factor: BroadMOBILshift; converge upward over 100 years, no convergence downward&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;ictbroadmobilm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;targeting parameters: &#039;&#039;&#039;ictbroadmobiltrgtval, ictbroadmobiltrgtyr, ictbroadmobilsetar, ictbroadmobilseyrtar&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;tech shift parameters: &#039;&#039;&#039;ictbroadmobiltecinflection, ictbroadmobiltechighrt, ictbroadmobilteclowrt&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;saturation level: &#039;&#039;&#039;ictmobilsaturation&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to exceed ICTMOBIL&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline in the absence of a target or multiplier or lack of finance for maintenance&#039;&#039;&lt;br /&gt;
*&#039;&#039;cross-sectional data, OLS regression, R-squared = 0.70&#039;&#039;&lt;br /&gt;
&amp;lt;/div&amp;gt;&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Infrastructure Bibliography&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
Agénor, Pierre-Richard, Mustapha Kamel Nabli, and Tarik M. Yousef. 2007. “Public Infrastructure and Private Investment in the Middle East and North Africa.” In Mustapha Kamel Nabli, ed.,. Breaking the Barriers to Higher Economic Growth: Better Governance and Deeper Reforms in the Middle East and North Africa. Washington, DC: World Bank Publications, 399–422.&lt;br /&gt;
&lt;br /&gt;
Asian Development Bank, Japan Bank for International Cooperation, and World Bank. 2005.&amp;amp;nbsp;&#039;&#039;Connecting East Asia: A New Framework for Infrastructure&#039;&#039;. Tokyo: Asian Development Bank, Japan Bank for International Cooperation, and World Bank.&amp;amp;nbsp;[http://siteresources.worldbank.org/INTEASTASIAPACIFIC/Resources/Connecting-East-Asia.pdf http://siteresources.worldbank.org/INTEASTASIAPACIFIC/Resources/Connecting-East-Asia.pdf].&lt;br /&gt;
&lt;br /&gt;
Bhattacharyay, Biswa Nath. 2010. “Estimating Demand for Infrastructure in Energy, Transport, Telecommunications, Water and Sanitation in Asia and the Pacific: 2010-2020”. Working Paper no. 248. Asian Development Bank Institute, Tokyo.&amp;amp;nbsp;[http://www.adbi.org/working-paper/2010/09/09/4062.infrastructure.demand.asia.pacific/ http://www.adbi.org/working-paper/2010/09/09/4062.infrastructure.demand.asia.pacific/].&lt;br /&gt;
&lt;br /&gt;
Bruinsma, Jelle. 2011. “The Resources Outlook: By How Much Do Land, Water and Crop Yields Need to Increase by 2050?” In Piero Conforti, ed.,.&amp;amp;nbsp;&#039;&#039;Looking Ahead in World Food and Agriculture: Perspectives to 2050&#039;&#039;. Rome: Food and Agriculture Organization of the United Nations (FAO), 233–275.&amp;amp;nbsp;[http://www.fao.org/docrep/014/i2280e/i2280e.pdf http://www.fao.org/docrep/014/i2280e/i2280e.pdf].&lt;br /&gt;
&lt;br /&gt;
Calderón, César, and Luis Servén. 2010a. “Infrastructure and Economic Development in Sub-Saharan Africa.”&amp;amp;nbsp;&#039;&#039;Journal of African Economies&#039;&#039;&amp;amp;nbsp;19(Supplement 1): i13–i87. doi:10.1093/jae/ejp022.&amp;amp;nbsp;[http://jae.oxfordjournals.org/cgi/content/abstract/19/suppl_1/i13 http://jae.oxfordjournals.org/cgi/content/abstract/19/suppl_1/i13].&lt;br /&gt;
&lt;br /&gt;
Calderón, César, and Luis Servén. 2010b. “Infrastructure in Latin America”. World Bank Policy Research Working Paper. Report Number 5317. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Canning, David. 1998. “A Database of World Stocks of Infrastructure, 1950-1995.”&amp;amp;nbsp;&#039;&#039;The World Bank Economic Review&#039;&#039;&amp;amp;nbsp;12(3): 529–548.&lt;br /&gt;
&lt;br /&gt;
Canning, David, and Mansour Farahani. 2007. “A Database of World Stocks of Infrastructure: Update 1950-2005”. Harvard School of Public Health, Boston, MA.&amp;amp;nbsp;[http://www.hsph.harvard.edu/faculty/david-canning/data-sets/ http://www.hsph.harvard.edu/faculty/david-canning/data-sets/].&lt;br /&gt;
&lt;br /&gt;
Cavallo, Eduardo Alfredo, and Christian Daude. 2008. “Public Investment in Developing Countries: A Blessing or a Curse?” RES Working Paper #4597. Inter-American Development Bank (IADB) - Research Department, OECD, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Chatterton, Isabe, and Olga S. Puerto. 2006.&amp;amp;nbsp;&#039;&#039;Estimation of Infrastructure Investment Needs in the South Asia Region: Executive Summary&#039;&#039;. Washington, DC: World Bank.&amp;amp;nbsp;[http://siteresources.worldbank.org/INTSARREGTOPTRANSPORT/Resources/Inf_Investment_Needs_IC_version4.pdf http://siteresources.worldbank.org/INTSARREGTOPTRANSPORT/Resources/Inf_Investment_Needs_IC_version4.pdf].&lt;br /&gt;
&lt;br /&gt;
Congressional Budget Office. 2010.&amp;amp;nbsp;&#039;&#039;Public Spending on Transportation and Water Infrastructure&#039;&#039;. Washington, DC: Congressional Budget Office.&amp;amp;nbsp;[http://www.cbo.gov/publication/21902 http://www.cbo.gov/publication/21902].&lt;br /&gt;
&lt;br /&gt;
Estache, Antonio, and Ana Goicoechea. 2005. “A Research Database on Infrastructure Economic Performance”. Policy Research Working Paper no. 3643. World Bank, Washington, DC.&amp;amp;nbsp;[http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2005/06/16/000016406_20050616100502/Rendered/PDF/wps3643.pdf http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2005/06/16/000016406_20050616100502/Rendered/PDF/wps3643.pdf].&lt;br /&gt;
&lt;br /&gt;
Ezzati, Majid, Alan D. Lopez, Anthony Rodgers, and Christopher J. L. Murray, eds. 2004.&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors&#039;&#039;. Geneva, Switzerland: World Health Organization (WHO).&lt;br /&gt;
&lt;br /&gt;
Fay, Marianne. 2001. “Financing the Future: Infrastructure Needs in Latin America, 2000-05”. Policy Research Working Paper no. 2545. World Bank, Washington, DC.&amp;amp;nbsp;[http://elibrary.worldbank.org/docserver/download/2545.pdf?expires=1375200693&amp;amp;id=id&amp;amp;accname=guest&amp;amp;checksum=DB7E5FB146EE28C93511B5ADAB2FD3CB http://elibrary.worldbank.org/docserver/download/2545.pdf?expires=1375200693&amp;amp;id=id&amp;amp;accname=guest&amp;amp;checksum=DB7E5FB146EE28C93511B5ADAB2FD3CB].&lt;br /&gt;
&lt;br /&gt;
Fay, Marianne, and Tito Yepes. 2003. “Investing in Infrastructure: What Is Needed from 2000 to 2010?” Policy Research Working Paper no. 3102. World Bank, Washington, DC. RePEc.&amp;amp;nbsp;[http://ideas.repec.org/p/wbk/wbrwps/3102.html http://ideas.repec.org/p/wbk/wbrwps/3102.html].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2007. “Forecasting Global Economic Growth with Endogenous Multifactor Productivity: The International Futures (IFs) Approach”. Pardee Center for International Futures Working Paper, University of Denver. Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., Devin Joshi, Jonathan Moyer, Timothy Sisk and José Roberto Solórzano. 2014. Strengthening Governance Globally. vol. 5, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Hughes, Gordon, Paul Chinowsky, and Ken Strzepek. 2009. “The Costs of Adapting to Climate Change for Infrastructure”. Economics of Adaptation to Climate Change Discussion Paper no. 2. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
International Transport Forum, and Organisation for Economic Cooperation and Development (OECD). 2011. “Trends in Transport Infrastructure Investment 1995-2009”. Paris.&lt;br /&gt;
&lt;br /&gt;
Kohli, Harpaul Alberto, and Phillip Basil. 2011. “Requirements for Infrastructure Investment in Latin America Under Alternate Growth Scenarios.”&amp;amp;nbsp;&#039;&#039;Global Journal of Emerging Market Economies&#039;&#039;&amp;amp;nbsp;3(1): 59 –110. doi:10.1177/097491011000300103.&amp;amp;nbsp;[http://eme.sagepub.com/content/3/1/59.abstract http://eme.sagepub.com/content/3/1/59.abstract].&lt;br /&gt;
&lt;br /&gt;
Kim, M. Julie, and Rita Nangia. 2010. “Infrastructure Development in India and China—A Comparative Analysis.” In William Ascher and Corinne Krupp, eds.,.&amp;amp;nbsp;&#039;&#039;Physical Infrastructure Development: Balancing The Growth, Equity, and Environmental Imperatives&#039;&#039;. New York, NY: Palgrave Macmillan, 97–140.&lt;br /&gt;
&lt;br /&gt;
Lora, Eduardo A. 2007.&amp;amp;nbsp;&#039;&#039;Public Investment in Infrastructure in Latin America: Is Debt the Culprit?&#039;&#039;&amp;amp;nbsp;Inter-American Development Bank Working Paper. Washington, DC: Inter-American Development Bank (IADB) - Research Department.&lt;br /&gt;
&lt;br /&gt;
Nelson, Gerald C., Mark W. Rosegrant, Amanda Palazzo, Ian Gray, Christina Ingersoll, Richard Robertson, Simla Tokgoz, Tingju Zhu, Timothy B. Sulser, Claudia Ringler, Siwa Msangi, and Liangzhi You. 2010.&amp;amp;nbsp;&#039;&#039;Food Security, Farming, and Climate Change to 2050: Scenarios, Results, Policy Options&#039;&#039;. Washington, DC: International Food Policy Research Institute.&amp;amp;nbsp;[http://www.ifpri.org/publication/food-security-farming-and-climate-change-2050 http://www.ifpri.org/publication/food-security-farming-and-climate-change-2050].&lt;br /&gt;
&lt;br /&gt;
Organisation for Economic Co-operation and Development. 2006.&amp;amp;nbsp;&#039;&#039;Infrastructure to 2030 Volume 1: Telecom, Land Transport, Water and Electricity&#039;&#039;. Infrastructure to 2030. Paris: Organisation for Economic Cooperation and Development.&lt;br /&gt;
&lt;br /&gt;
Organisation for Economic Co-operation and Development. 2009.&amp;amp;nbsp;&#039;&#039;Going for Growth: Economic Policy Reforms&#039;&#039;. Paris: Organisation for Economic Cooperation and Development (OECD).&lt;br /&gt;
&lt;br /&gt;
Qiang, Christine Zhen-Wei, Carlo M. Rossotto, and Kaoru Kimura. 2009. “Economic Impacts of Broadband.” In World Bank, ed.,.&amp;amp;nbsp;&#039;&#039;2009 Information and Communications for Development: Extending Reach and Increasing Impact&#039;&#039;. Washington, DC: World Bank, 35–50.&lt;br /&gt;
&lt;br /&gt;
Rothman, Dale S. Mohammod T. Irfan, Eli Margolese-Malin, Barry B. Hughes, Jonathan Moyer, and Janet Dickson. 2013.&amp;amp;nbsp;&#039;&#039;Building Global Infrastructure.&amp;amp;nbsp;&#039;&#039;vol. 4, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press. Stambrook, David. 2006. “Key Factors Driving the Future Demand for Surface Transport Infrastructure and Services.” In Organisation for Economic Cooperation and Development (OECD), ed.,.&amp;amp;nbsp;&#039;&#039;Infrastructure to 2030 Volume 1: Telecom, Land Transport, Water and Electricity&#039;&#039;. Infrastructure to 2030. Paris: Organisation for Economic Cooperation and Development (OECD), 185–239.&lt;br /&gt;
&lt;br /&gt;
World Health Organization, and UNICEF. 2013.&amp;amp;nbsp;&#039;&#039;Progress on Sanitation and Drinking-Water - 2013 Update&#039;&#039;. Geneva: World Health Organization.&lt;br /&gt;
&lt;br /&gt;
Yepes, Tito. 2008. “Investment Needs for Infrastructure in Developing Countries 2008-15”. Draft. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Yepes, Tito. 2005.&amp;amp;nbsp;&#039;&#039;Expenditure on Infrastructure in East Asia Region, 2006–2010&#039;&#039;. East Asia Pacific Infrastructure Flagship Study. Manila: Asian Development Bank (ADB), Japan Bank for International Cooperation (JBIC), World Bank.&lt;br /&gt;
&lt;br /&gt;
You, Liangzhi, Claudia Ringler, Ulrike Wood-Sichra, Richard Robertson, Stanley Wood, Tingju Zhu, Gerald Nelson, Zhe Guo, and Yan Sun. 2011. “What Is the Irrigation Potential for Africa? A Combined Biophysical and Socioeconomic Approach.”&amp;amp;nbsp;&#039;&#039;Food Policy&#039;&#039;&amp;amp;nbsp;36(6): 770–782. doi:10.1016/j.foodpol.2011.09.001.&amp;amp;nbsp;[http://www.sciencedirect.com/science/article/pii/S030691921100114X http://www.sciencedirect.com/science/article/pii/S030691921100114X].&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Infrastructure&amp;diff=8320</id>
		<title>Infrastructure</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Infrastructure&amp;diff=8320"/>
		<updated>2017-09-07T22:11:11Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
The most recent and complete infrastructure model documentation is available on Pardee&#039;s [http://pardee.du.edu/ifs-infrastructure-model-documentation website]. Although the text in this interactive system is, for some IFs models, often significantly out of date, you may still find the basic description useful to you.&lt;br /&gt;
&lt;br /&gt;
The current version of the infrastructure model within IFs was developed in concert with the production of &#039;&#039;Building Global Infrastructure&#039;&#039;, the fourth volume in the Patterns of Potential Human Progress series (Rothman et al 2013). Further details on the model and analyses can be found in that volume.&lt;br /&gt;
&lt;br /&gt;
The purpose of the infrastructure model is to forecast the following:&lt;br /&gt;
&lt;br /&gt;
#the amount of particular forms of infrastructure;&lt;br /&gt;
#the level of access to these particular forms of infrastructure;&lt;br /&gt;
#the level of spending on infrastructure; and&lt;br /&gt;
#the effect of infrastructure development on other socio-economic and environmental systems&lt;br /&gt;
&lt;br /&gt;
The infrastructure model includes parameters that allow users to explore a range of alternative scenarios around infrastructure. These can be used to ask questions such as:&lt;br /&gt;
&lt;br /&gt;
#What would be the costs and benefits if countries were to accelerate infrastructure development above that seen in the Base Case?&lt;br /&gt;
#What if the unit costs of infrastructure development or infrastructure lifetimes were to differ from the assumptions used in the Base Case?&lt;br /&gt;
#What if the impacts of infrastructure development on economic productivity and health were to differ from the assumptions used in the Base Case?&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Unlike many previous studies, which estimate only the demand for infrastructure, IFs forecasts a path jointly determined by both the demand for infrastructure and the funding available to meet that demand. Therefore, the amount of infrastructure forecasted in IFs in each year explicitly accounts for expected fiscal constraints. Furthermore, the socio-economic and environmental effects of infrastructure feed forward to the drivers of infrastructure demand and supply in future years.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
The figure below provides an overview of the infrastructure model within IFs. In brief, the infrastructure modeling in IFs involves moving through the following sequence for each forecast year:&lt;br /&gt;
&lt;br /&gt;
#Estimating the expected levels of infrastructure&lt;br /&gt;
#Translating the expected levels of infrastructure into financial requirements&lt;br /&gt;
#Balancing the financial requirements with available resources&lt;br /&gt;
#Forecasting the actual levels of attained infrastructure&lt;br /&gt;
#Estimating the social, economic, and environmental impacts of the attained infrastructure&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Each of these steps are described in more detail below. [[File:Health16.png|frame|center|Visual representation of the infrastructure model]]&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Structure and Agent System: Infrastructure&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot; border=&amp;quot;0&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width: 50%&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;System/Subsystem&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Infrastructure&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| &amp;lt;div&amp;gt;&#039;&#039;&#039;Organizing Structure&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;amp;nbsp;&lt;br /&gt;
|-&lt;br /&gt;
| &amp;lt;div&amp;gt;&#039;&#039;&#039;Stocks&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Physical infrastructure, Access rates&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| &amp;lt;div&amp;gt;&#039;&#039;&#039;Flows&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Spending (public and private on ‘core’ infrastructure; public on ‘other’ infrastructure)&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| &amp;lt;div&amp;gt;&#039;&#039;&#039;Key Aggregate&amp;amp;nbsp;&#039;&#039;&#039; &#039;&#039;&#039;Relationships&amp;amp;nbsp;&#039;&#039;&#039;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;(illustrative, not comprehensive)&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Demand for physical infrastructure and access changes with population, income, and other societal changes&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;More infrastructure helps economic growth and reduces health effects from specific diseases&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Public spending available for infrastructure rises with income level&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Public spending leverages private spending&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Lack (surplus) of public spending on ‘core’ infrastructure hurts (helps) infrastructure development&amp;amp;nbsp;&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| &amp;lt;div&amp;gt;&#039;&#039;&#039;Key Agent-Class Behavior&amp;amp;nbsp;&#039;&#039;&#039; &#039;&#039;&#039;Relationships&#039;&#039;&#039;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;(illustrative, not comprehensive)&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &lt;br /&gt;
Government revenue and expenditure on infrastructure&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Infrastructure Types&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
IFs distinguishes between ‘core’ and ‘other’ infrastructure. Core infrastructure refers to those types of infrastructure that we represent explicitly in IFs—roads, electricity generation, improved water and sanitation, and ICT. Other infrastructure refers to those types that we do not represent explicitly—e.g., railroads, ports, airports, and types of infrastructure yet to be envisioned. The choice of what to include as core infrastructure reflects the availability of historical data and understanding of what can be modelled.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Infrastructure Access and Stocks&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The table below summarizes the primary variables in IFs related to infrastructure stocks and access. From these and other variables forecasted by IFs, we are able to calculate numerous other indicators—for example, the number of persons with access to electricity.&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%; border: 1px solid #cccccc&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; | &amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; | &#039;&#039;&#039;Variable Name in IFs (dimensions)&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; | &#039;&#039;&#039;Description&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; | &#039;&#039;&#039;Units&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; rowspan=&amp;quot;11&amp;quot; | &#039;&#039;&#039;Access&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | &#039;&#039;&#039;INFRAROADRAI*&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Access to rural roads&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | percentage of rural population living within 2 kilometers of an all-season road&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | INFRAELECACC* (rural, urban, total)&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Access to electricity&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | percentage of population with access&amp;amp;nbsp;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | ENSOLFUEL&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Solid fuel use&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | percentage of population using solid fuels as their main household energy source&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | WATSAFE* (none, other improved, piped)&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Access to improved water&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | percentage of population with access by type&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | SANITATION* (other unimproved, shared, improved)&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Access to improved sanitation&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | percentage of population with access by type&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | WATWASTE&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Access to wastewater collection connection&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | percentage of population with wastewater collection&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | WATWASTETREAT*&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Access to wastewater treatment&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | percentage of population with wastewater treatment&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | INFRATELE*&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Fixed telephone lines&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | lines per 100 persons&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | ICTBROAD*&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Fixed broadband subscriptions&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | subscriptions per 100 persons&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | ICTMOBIL*&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Mobile telephone subscriptions&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | subscriptions per 100 persons&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | ICTBROADMOBIL*&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Mobile broadband subscriptions&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | subscriptions per 100 persons&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; rowspan=&amp;quot;4&amp;quot; | &#039;&#039;&#039;Physical Stocks&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | &#039;&#039;&#039;INFRAROAD*&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Total road density&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | kilometers per 1000 hectares&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | INFRAROADPAVEDPCNT*&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Percentage of roads paved&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | percentage&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | INFRAELECGENCAP*&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Electricity generation capacity per capita&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | kilowatts per person&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | LANDIRAREAEQUIP&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | Area equipped with irrigation&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; | 1000 hectares&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; colspan=&amp;quot;4&amp;quot; | &#039;&#039;&#039;*Note: Each of these variables has a companion variable with the extension DEM; for example, the variable INFRAROADRAI has a companion variable named INFRAROADRAIDEM. These companion variables indicate the amount of the infrastructure stock or access that would be expected to exist in the absence of financial constraints.&#039;&#039;&#039;&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Infrastructure Spending&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The following table summarizes the primary variables in IFs related to infrastructure spending. As with the access and stock variables, from these and other variables forecasted in IFs, we are able to calculate numerous other indicators—for example, the ratio of total public to private spending on infrastructure. Please note that although we do not represent these other forms of infrastructure explicitly, we do estimate spending on them in order to avoid almost certainly underrepresenting the total demand for infrastructure. This is given by the variable GDS(InfraOther).&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%; border: 1px solid #cccccc&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; | &#039;&#039;&#039;Variable Name in IFs&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; | &#039;&#039;&#039;Description&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; | &#039;&#039;&#039;Units&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | GDS (infrastructure, infraother)&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Government consumption, by category&amp;lt;sup&amp;gt;[1]&amp;lt;/sup&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | billion dollars&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | INFRAINVESTMAINT&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Total (public plus private) investment for infrastructure maintenance, by type of infrastructure&amp;lt;sup&amp;gt;[2]&amp;lt;/sup&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | billion dollars&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | INFRAINVESTMAINTPUB&amp;lt;sup&amp;gt;[3]&amp;lt;/sup&amp;gt;&amp;lt;br/&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Public investment for infrastructure maintenance, by type of infrastructure&amp;lt;sup&amp;gt;[2]&amp;lt;/sup&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | billion dollars&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | INFRAINVESTNEW&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Total (public plus private) investment for construction of new infrastructure, by type of infrastructure&amp;lt;sup&amp;gt;[2]&amp;lt;/sup&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | billion dollars&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | INFRAINVESTNEWPUB&amp;lt;sup&amp;gt;[3]&amp;lt;/sup&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Public investment for construction of new infrastructure, by type of infrastructure&amp;lt;sup&amp;gt;[2]&amp;lt;/sup&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | billion dollars&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; colspan=&amp;quot;3&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;&amp;lt;sup&amp;gt;[1]&amp;lt;/sup&amp;gt; The categories are military, health, education, R&amp;amp;D, Infrastructure, InfraOther, Other, and Total.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&amp;lt;sup&amp;gt;[2]&amp;lt;/sup&amp;gt; The types of infrastructure included are RoadPaved, RoadUnPaved, ElectricityGen, ElectricityAccRural, ElectricityAccUrban, Irrigation, SafeWaterHH, SafeWaterImproved, SanitationHH, SanitationImproved, WasteWater, Telephone, Mobile, Broadband, BroadbandMobile, and Total. Currently, no cost is assumed for access to Unimproved water, Other unimproved sanitation, solid fuel use, or a wastewater collection connection. &amp;lt;sup&amp;gt;[3]&amp;lt;/sup&amp;gt; Each of these variables has a companion variable, which indicates the amount of public investment that is desired based upon the expected levels of infrastructure. &amp;amp;nbsp;For INFRAINVESTMAINTPUB, the companion variable is named INFRABUDDEMMNT and for INFRAINVESTNEWPUB, the companion variable is named INFRABUDDEMNEW. The differences between the desired and actual amounts of public investment result from the budgeting process described below.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&amp;lt;sup&amp;gt;[3]&amp;lt;/sup&amp;gt;&amp;amp;nbsp;Each of these variables has a companion variable, which indicates the amount of public investment that is desired based upon the expected levels of infrastructure. &amp;amp;nbsp;For INFRAINVESTMAINTPUB, the companion variable is named INFRABUDDEMMNT and for INFRAINVESTNEWPUB, the companion variable is named INFRABUDDEMNEW. The differences between the desired and actual amounts of public investment result from the budgeting process described below.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Forward Links from Infrastructure&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Although there are a wide range of potential social, economic, and environmental impacts of infrastructure, we limit our modeling of the direct effects of infrastructure to its effects on economic productivity and a small set of health impacts. Currently, the empirical research on these effects are more advanced—and the effects themselves more amenable to modeling—than the direct effects of infrastructure on factors such as income inequality, educational attainment, or governance. To the extent direct effects and other aspects, such as spending on infrastructure that reduces spending on other categories, affect other systems included in IFs, infrastructure will have a number of indirect effects.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Sources of Infrastructure Data&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Infrastructure Stocks and Access&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
In terms of historical data on infrastructure stocks and access, we can turn to various international organizations with specific emphases. These include the International Road Federation (IRF) for transportation, the International Energy Agency (IEA) for energy, and the International Telecommunication Union (ITU) for telecommunications. No one organization focuses on water and sanitation systems, but a number of different organizations, such as the Joint Monitoring Programme (JMP) of WHO and the United Nations Children’s Fund (UNICEF), the United Nations Statistics Division, and the United Nations Food and Agriculture Organization (FAO), maintain global data related to certain aspects of water infrastructure. The table below summarizes a number of the datasets these groups maintain.&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%; border: 1px solid #cccccc&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Infrastructure Type&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Organization&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Spatial Coverage&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Temporal Coverage&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Infrastructure Coverage&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; rowspan=&amp;quot;2&amp;quot; valign=&amp;quot;middle&amp;quot; | Transportation&amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;International Road Federation&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Global&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data: 1968–2009&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Total road network length, percent of road network paved, and road density&amp;amp;nbsp;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | World Bank&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Global&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Data for most recent year only&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Percentage of rural population with access to an all-season road&amp;amp;nbsp;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; rowspan=&amp;quot;2&amp;quot; valign=&amp;quot;middle&amp;quot; | Electricity and Energy&amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;United States Energy Information Administration&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Global&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data: 1980–2010&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Total installed electricity generation capacity and generation capacity by energy type&amp;amp;nbsp;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | International Energy Agency&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Global&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data: 1960–2009&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Electricity production by source type; total electricity production; percent of total, urban, and rural population with access to electricity&amp;amp;nbsp;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; rowspan=&amp;quot;3&amp;quot; valign=&amp;quot;middle&amp;quot; | Water and Sanitation&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;WHO and UNICEF Joint Monitoring Programme for Water Supply and Sanitation&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Global&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data: 1990−2010&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Percent of population with access to improved, piped, other improved, and unimproved water, and to sanitation facilities&amp;amp;nbsp;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Food and Agriculture Organization AQUASTAT database&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Global&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data: 1960–2010&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Percent of arable land equipped for irrigation and water use/withdrawals by sector&amp;amp;nbsp;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | United Nations Statistics Division&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Global&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Data for most recent year available only&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Percent of population with wastewater connection and percent with connection to wastewater treatment&amp;amp;nbsp;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Information and Communication Technologies&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;International Telecommunication Union&#039;&#039;&#039;&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Global&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data: 1960–2011&amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Number of telephone mainlines, cell phone subscriptions, broadband subscriptions, mobile broadband subscriptions, and number of computer/internet users&amp;amp;nbsp;&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
In addition to these primary data sources, the World Bank’s World Development Indicators (WDI) and the World Resources Institute’s Earth Trends databases act as clearinghouses for much of the same data. We can turn also to Canning (1998), Canning and Farahani (2007), and Estache and Goicoechea (2005),who have drawn on these and other sources in attempts to create global databases of infrastructure stocks and access, increase the number of years covered for certain time-series while maintaining consistent definitions, and correct errors. Further, as part of the Africa Infrastructure Country Diagnostic (AICD), the World Bank and the African Development Bank developed an extensive database on infrastructure in Africa. Finally, G. Hughes, Chinowsky, and Strzepek (2009) and Calderón and Servén (2010a; 2010b), among others, have used and modified a number of these databases in their own studies.&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Infrastructure Spending&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
There exist relative little organized historical data on infrastructure spending. In considering public investment in infrastructure (PII), some researchers have used other measures in the Systems of National Accounts, usually fixed capital formation or government outlays by economic sector, as proxies (Agénor, Nabli, and Yousef 2007; Cavallo and Daude 2008; Organisation for Economic Co-operation and Development 2009a; Ter-Minassian and Allen 2004). Lora (2007: 7), however, strongly argued against this practice&lt;br /&gt;
&lt;br /&gt;
:because capital expenditures by the central or the consolidated government as measured by the International Monetary Fund’s Government Financial Statistics . . . are a very poor measure of actual PII, which in many countries is mostly undertaken by state-owned enterprises or local governments whose operations are not well captured by this source.&lt;br /&gt;
&lt;br /&gt;
Estache (2010: 67) adds:&lt;br /&gt;
&lt;br /&gt;
:Neither the national accounts nor the IMF [International Monetary Fund] Government Finance Statistics (GFS) report a disaggregation of total and public investment data detailed enough to allow identifying every infrastructure sub-sector. In national accounts, energy data cover both electricity and gas but also all primary-energy related products such as petroleum. Similarly, the data do not really distinguish between transport and communication. Water expenditures can be hidden in public works or even in health expenditures.&lt;br /&gt;
&lt;br /&gt;
The World Bank does collect data on private investment in infrastructure in its Private Participation in Infrastructure Project Database. Unfortunately, limitations to this database make us hesitant to rely on it as a primary source of data on infrastructure investment. First, it provides data only on projects in low and middle-income countries in which there is private participation. Second, the amounts in the database primarily reflect commitments, not actual investments. Third, it relies exclusively on information that is made publicly available. Finally, the Bank itself states that it “should not be seen as a fully comprehensive resource.”&lt;br /&gt;
&lt;br /&gt;
This leaves us needing to rely on national, regional, and global studies and reports that provide estimates of infrastructure spending. Given their varied purposes, these studies and reports tend to differ in a number of significant dimensions: temporal coverage; types of infrastructure included; sources of funding (e.g., public versus private); and purpose of expenditure (e.g., new construction versus maintenance). Therefore, we need to be careful in comparing data across studies and in drawing conclusions from them. Even so, they provide a starting point for our exploration. The following table lists a number of these studies and summarizes some of the major elements in their approaches.&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 750px; height: 1011px; border: 1px solid #cccccc&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; height: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Study&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; width: 500px&amp;quot; scope=&amp;quot;colgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Spatial Coverage&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; height: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Temporal Coverage&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; height: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Infrastructure Coverage&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; height: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Source of Funds&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; height: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Purpose of Expenditure&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Trends in Transport Infrastructure Investment 1995–2009 (International Transport Forum and Organisation for Economic Co-operation and Development 2011)&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; width: 500px&amp;quot; scope=&amp;quot;colgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Albania, Australia, Austria, Azerbaijan, Belgium, Bosnia, Bulgaria, Canada, Croatia, Czech Republic, Denmark, Estonia, Finland, , France, Georgia, Germany, Greece, Hungary, Iceland, India, Ireland, Italy, Japan, Korea, Latvia, Liechtenstein, Lithuania, Luxembourg, Macedonia, Malta, Mexico, Moldova, Montenegro, Netherlands, New Zealand, Norway, Poland, Portugal, Romania, Russia, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data: 1992–2009&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Separate data for rail, road, inland waterways, maritime ports, and airports&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Combined public and private sources for investment; only spending by public authorities for maintenance&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Separate data for investment and maintenance&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Africa Infrastructure Country Diagnostic ([http://www.-infrastructureafrica.-org/aicd/tools/data) http://www.-infrastructureafrica.-org/aicd/tools/data]);&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; width: 500px&amp;quot; scope=&amp;quot;colgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Benin, Botswana, Burkina Faso, Cameroon, Cape Verde, Chad, Congo, Côte d’Ivoire, Democratic Republic of the Congo, Ethiopia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mozambique, Namibia, Nigeria, Rwanda, Senegal, South Africa, Tanzania, Uganda, Zambia&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual average for one period: 2001–2006&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Separate data for electricity, ICT, irrigation, transportation, and water supply and sanitation&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Public and private&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Separate data for new construction and for operation and maintenance&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Infrastructure in Latin America (Calderón and Servén 2010b)&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; width: 500px&amp;quot; scope=&amp;quot;colgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Argentina, Brazil, Chile, Colombia, Mexico, Peru&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data: 1980–2006&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Separate data for telecommunications, power generation, land transportation (roads and railways), and water and sanitation&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Separate data for public and private&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Total spending (construction, operations, and maintenance)&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Public Spending on Transportation and Water Infrastructure (Congressional Budget Office 2010)&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; width: 500px&amp;quot; scope=&amp;quot;colgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | United States&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data: 1956–2007&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Separate data for highways, mass transit, rail, aviation, water transportation, water resources, and water supply and wastewater treatment&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Public only, broken down by (1) federal, and (2) state and local&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Separate data for capital expenditures and for operation and maintenance&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Infrastructure Development in India and China—A Comparative Analysis (Kim and Nangia 2010)&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; width: 500px&amp;quot; scope=&amp;quot;colgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | China, India&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data: 1985–2006&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Combined data for electricity, water, gas, transport, and communications&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Combined public and private&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Not stated&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Going for Growth: Economic Policy Reforms (Organisation for Economic Co-operation and Development 2009a)&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; width: 500px&amp;quot; scope=&amp;quot;colgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Australia, Austria, Belgium, Canada, Finland, France, Iceland, Ireland, Italy, Netherlands, New Zealand, Norway, South Korea, Spain, Sweden, United Kingdom, United States&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual averages for four periods: 1970–1979, 1980–1989, 1990–1999, 2000–2006&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Aggregate data provided separately for (1) electricity, gas, and water, and (2) transport and communications&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Combined public and private&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Aggregate investment (from national accounts)&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Connecting East Asia: A New Framework for Infrastructure (Asian Development Bank, Japan Bank for International Cooperation, and World Bank 2005)&#039;&#039;&#039;&amp;lt;br/&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px; width: 500px&amp;quot; scope=&amp;quot;colgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Cambodia, China, Indonesia, Laos, Mongolia, Philippines, Thailand, Vietnam&amp;lt;br/&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Annual data for select years: 1998, 2003&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Separate data for transportation, telecommunications, water and sanitation, other urban infrastructure, and power&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Separate data for national government, local government, state owned enterprises, and private&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; scope=&amp;quot;rowgroup&amp;quot; valign=&amp;quot;middle&amp;quot; | Not stated&amp;lt;br/&amp;gt;&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Dominant Relations: Infrastructure&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The dominant relations in the Infrastructure model are those that determine the expected levels of infrastructure stocks and access, spending on infrastructure, and the impacts of infrastructure on health and productivity. The expected levels of infrastructure stocks and access are influenced by socio-economic factors related to population, economic activity, governance, and educational attainment. In almost every case there are also path dependencies that supplement the basic relationships, reflecting the considerable inertia in infrastructure development.&lt;br /&gt;
&lt;br /&gt;
Spending on infrastructure is divided into private and public spending, with the latter further divided into ‘core’ and ‘other’ infrastructure. ‘Core’ infrastructure refers to those types of infrastructure that are explicitly represented in the model; ‘other’ infrastructure refers to those types of infrastructure that are not explicitly represented in the model (see [[Infrastructure#Structure_and_Agent_System:_Infrastructure|Infrastructure Types]]). Public spending on core infrastructure, GDS(Infra), is driven by the required spending to meet the expected levels of infrastructure (INFRABUDDEMMNT and INFRABUDDEMNEW), total government consumption (GOVCON), and the demands on government consumption from other categories. Public spending on other infrastructure, GDS(InfraOther), is driven by average GDP per capita (GDPPCP), total government consumption (GOVCON), and the demands on government consumption from other categories. Deficits and surpluses of government funds will affect the actual levels of funds allocated for both core and other infrastructure. The public spending on core infrastructure leverages a certain amount of private spending on core infrastructure, with the amount leveraged depending upon historical relationships found in the literature, which nominally reflect the variation in public and private returns between particular types of infrastructure. Finally, in recognition of the incremental approaches that public budgeting decisions usually follow, our model avoids unusually sharp increases in public spending on infrastructure by smoothing it out over time.&lt;br /&gt;
&lt;br /&gt;
Infrastructure development directly affects multifactor productivity, with this effect being treated separately for non-ICT and ICT related infrastructure. The use of solid fuels in the home and access to improved water and sanitation directly affect human health through their effects on the mortality and morbidity rates of specific diseases—diarrheal diseases, acute respiratory infections, and respiratory diseases.&lt;br /&gt;
&lt;br /&gt;
For detailed discussion of the model&#039;s causal dynamics, see the discussions of [[Infrastructure#Infrastructure_Flow_Charts|flow charts]] (block diagrams) and [[Infrastructure#Infrastructure_Equations|equations]].&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Initializing the Infrastructure Data&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The IFs preprocessor uses historical data to prepare data for the base year of the model, currently 2010. We describe the general workings of the IFs preprocessor [http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf here]. However, there are some peculiarities in the infrastructure model, specifically related to the initialization of the variables related to spending on infrastructure.&lt;br /&gt;
&lt;br /&gt;
Because of the paucity and inconsistency of the historical data on infrastructure spending discussed above, IFs does not use actual historical data on spending, but rather estimates spending in the first year of the model based upon data on the stocks of and access to infrastructure after the pre-processor has filled any gaps in the historical data. The procedure is as follows:&lt;br /&gt;
&lt;br /&gt;
*We assume that: 1) the amount of infrastructure requiring maintenance in the base year is given by the level of infrastructure in the previous year (2009) times a factor based on the lifetime of the infrastructure (see table 5 below), and 2) the amount of newly constructed infrastructure is the difference between the amount of infrastructure in the base year (2010) and the previous year (2009).&lt;br /&gt;
*Total spending on maintenance, &#039;&#039;INFRAINVESTMAINT&#039;&#039;, is estimated as the amount of infrastructure requiring maintenance times the unit cost for each type of infrastructure (see Table 6 below).&lt;br /&gt;
*Total spending on new construction, &#039;&#039;INFRAINVESTNEW&#039;&#039;, is estimated as the amount of new construction times the unit cost for each type of infrastructure. If the amount of newly constructed infrastructure is less than or equal to zero, spending on that type of infrastructure is set to zero.&lt;br /&gt;
*For each type of infrastructure, public spending on maintenance, &#039;&#039;INFRAINVESTMAINTPUB&#039;&#039;, and new construction, &#039;&#039;INFRAINVESTNEWPUB&#039;&#039;, are estimated by multiplying the total spending by infrastructure specific parameters, &#039;&#039;&#039;&#039;&#039;infrainvmaintpubshrm&#039;&#039; &#039;&#039;&#039; and &#039;&#039;&#039;&#039;&#039;infrainvnewpubshrm&#039;&#039; &#039;&#039;&#039;, indicating the share of total spending that is assumed to be public.&lt;br /&gt;
*The sum of estimated public spending on maintenance and new construction, across all types of core infrastructure, provides an initial estimate of government consumption for core infrastructure, &#039;&#039;GDS(Infrastructure)&#039;&#039;.&lt;br /&gt;
*If, in the first year budgeting process, total estimated government consumption on core infrastructure is reduced, an infrastructure cost adjustment factor, &#039;&#039;INFRACOSTADJFAC&#039;&#039;, is calculated as the ratio of the final to the initial value of &#039;&#039;GDS(Infrastructure)&#039;&#039;. The value of &#039;&#039;INFRACOSTADJFAC&#039;&#039; is also used to adjust infrastructure spending in future years. It gradually converges to 1 over the time period given by the parameter &#039;&#039;&#039;&#039;&#039;infracostadjfacconvtime&#039;&#039; &#039;&#039;&#039;.&lt;br /&gt;
*The initial estimates of &#039;&#039;INFRAINVESTMAINT&#039;&#039;, &#039;&#039;INFRAINVESTNEW&#039;&#039;, &#039;&#039;INFRAINVESTMAINTPUB&#039;&#039;, and &#039;&#039;INFRAINVESTNEWPUB&#039;&#039; are each multiplied by &#039;&#039;INFRACOSTADJFAC&#039;&#039; to calculate their final values.&lt;br /&gt;
*The initial value of public spending on other infrastructure, &#039;&#039;GDS(InfraOther&#039;&#039; &#039;&#039;&#039;)&#039;&#039;&#039;, is calculated as a function of average income, &#039;&#039;GDPPCP&#039;&#039;, multiplied by &#039;&#039;INFRACOSTADJFAC&#039;&#039;. This function is:&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GDS(InfraOther)_{r,t}=GDP_{r,t}*(1.8162+0.061*ln(GDPPCP_{r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:GDS(InfraOther) = government spending on other infrastructure in billion constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:GDP = gross domestic product at market exchange rates in billion constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Infrastructure Flow Charts&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt;&amp;amp;nbsp; ===&lt;br /&gt;
&lt;br /&gt;
The introduction provided an overview of the infrastructure model within IFs, noting that this involves moving through the following sequence for each forecast year. This section describes each of these five steps:&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;1. Estimating the Expected Levels of Infrastructure&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
At the core of our forecasts of the expected levels of infrastructure is a set of estimated equations embedded within a set of accounting relationships. The equations are presented [[Infrastructure#Infrastructure_Equations|here]].&lt;br /&gt;
&lt;br /&gt;
Additional elements beyond the estimated equations are involved in specifying the expected values of infrastructure, and we handle some of these elements algorithmically. For instance, the base year calculated estimations will most often not match exactly the historical data for countries in the base year.&amp;lt;sup&amp;gt;[1]&amp;lt;/sup&amp;gt; Each country has peculiarities that differentiate it from the “typical pattern”; among the factors not captured by our equations for estimating the base year country values are many aspects of geography, culture, and unique historical development paths. And sometimes, of course, data errors account for such differences.&lt;br /&gt;
&lt;br /&gt;
To deal with this issue of differences between our estimated values and reported data in the base year, the model calculates an additive or a multiplicative country and variable specific shift factor representing that difference; we allow those shift factors to gradually diminish over time, thereby causing countries to approach the expected value function. Among the reasons for allowing convergence is that we quite consistently see that the patterns of higher-income countries are more similar and more like those of our general equations than are those of lower-income countries. On the assumption that countries will seldom abandon infrastructure they have already developed, however, our downward convergence is extremely slow relative to our upward convergence.&lt;br /&gt;
&lt;br /&gt;
A second instance in which we make adjustments to our core estimated equations is when the dynamic trajectory of demand/supply growth in a country in recent years is inconsistent with the forecasts produced by the equations. For instance, a policy-based surge of infrastructure development like that seen recently in China may result in a historical growth rate well above the one that our functions produce in the first years of our forecasting. Making a simplifying assumption that these growth rates will change only gradually, we estimate the growth rate of physical infrastructure stock using the historical data over three to five recent years and incorporate that growth rate in the demand estimation through a moving average-based extrapolative formulation.&lt;br /&gt;
&lt;br /&gt;
We make a final adjustment in those cases where we wish to modify the estimates of expected infrastructure for scenario analysis. This can be accomplished in several ways. First, most of the estimates can be adjusted with the use of a simple multiplier. Second, we can stipulate specific levels for specific types of infrastructure in a specific future year; in this case, the model will automatically forecast a linear approach to the targeted level from the base year. Third, we can modify both the rates at which the country shift factors converge and the levels, in relation to the expected values, to which the shift factors converge. For example, we can drive the shift factors to those of the best performing countries, i.e., those that perform better than expected, by a certain date. This will, in turn, affect the levels to which the physical infrastructures themselves converge (see [[Understand_IFs#Standard_Error_Targeting|Standard Error Targeting]]).&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Transportation&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
The primary indicators of transportation infrastructure included in IFs are: 1) the total road density in kilometers per 1000 hectares, &#039;&#039;INFRAROAD&#039;&#039;, 2) the percentage of roads that are paved, &#039;&#039;INFRAROADPAVEDPCNT&#039;&#039;, and 3) the Rural Access Index, &#039;&#039;INFRAROADRAI&#039;&#039;, the percentage of the rural population living within two kilometers of an all-season road. From these, we can calculate additional indicators, such as the expected lengths of paved and unpaved roads.&lt;br /&gt;
&lt;br /&gt;
The general sequence of calculations for estimating the expected values of these variables is shown in the figure below. We begin by estimating road density (&#039;&#039;INFRAROAD&#039;&#039;) as a function of income density, population density, and land area. The percentage of roads that are paved (&#039;&#039;INFRAROADPAVEDPCNT&#039;&#039;) is then calculated as a function of the estimated road density, GDP per capita (&#039;&#039;GDPPCP&#039;&#039;), population (&#039;&#039;POP&#039;&#039;), and land area (&#039;&#039;LANDAREA&#039;&#039;). In parallel, the Rural Access Index(&#039;&#039;INFRAROADRAI&#039;&#039;) is calculated as a function of the estimated road density (kilometers per person) and income density (dollars per hectare).&lt;br /&gt;
&lt;br /&gt;
[[File:Infrastructure2.png|frame|center|Visual representation of transportation infrastructure]]&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Electricity&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Our focus in the energy sector is on the generation and use of electricity. In terms of physical infrastructure, the key indicator we forecast is the level of electricity generation capacity, &#039;&#039;INFRAELECGENCAP&#039;&#039;. From the user perspective, we forecast the percentage of the rural and urban populations that have access to electricity, &#039;&#039;INFRAELECACC(rural)&#039;&#039; and &#039;&#039;INFRAELECACC(urban)&#039;&#039;. These access rates, in combination with the forecasts for population and average household size, are used to calculate the number of household connections, which drive the cost calculations described below. Finally, given its connection to electricity access, we also forecast the percentage of the population that uses solid fuels as the main source of energy, &#039;&#039;ENSOLFUEL&#039;&#039;. At the moment, no physical infrastructure is associated with solid fuels, so this value does not enter into the cost calculations.&lt;br /&gt;
&lt;br /&gt;
The following figure presents an overview of the submodel that forecasts access to electricity and electricity generation capacity in IFs. It is fully integrated with the larger IFs system, which provides forecasts of critical variables such as energy demand, energy production by primary type, poverty, and governance character. The electricity submodel contains three components—estimating consumption, estimating production, and sending a signal for additional generation capacity in the case of a gap between production and consumption.&lt;br /&gt;
&lt;br /&gt;
Beginning with consumption, we first estimate the percentage of the population with access to electricity (&#039;&#039;INFRAELECACC&#039;&#039;). This is forecast as a function of poverty levels (&#039;&#039;INCOMELT1CS/POP)&#039;&#039; and a measure of government effectiveness (&#039;&#039;GOVEFFECT&#039;&#039;). The levels of access, along with average income (&#039;&#039;GDPPCP&#039;&#039;) determine the share of the population using Solid Fuel for heating and cooking (&#039;&#039;ENSOLFUEL&#039;&#039;). Next, the levels of access and average income (&#039;&#039;GDPPCP&#039;&#039;), along with the historic ratios of fossil fuel and non-fossil fuel production to total primary energy use (&#039;&#039;FossilFuelShare &#039;&#039;and &#039;&#039;NonFossilFuelShare&#039;&#039;), are used to forecast the expected ratio of electricity use to total primary energy use (&#039;&#039;INFRAELECSHRENDEM&#039;&#039;). With this ratio and the level of total primary energy use (&#039;&#039;ENDEMSH&#039;&#039;)&amp;lt;sup&amp;gt;[2]&amp;lt;/sup&amp;gt;, forecast elsewhere in IFs, we then calculate the desired electricity use (&#039;&#039;INFRAELEC * POP&#039;&#039;).&lt;br /&gt;
&lt;br /&gt;
The amount of domestically produced electricity (&#039;&#039;INFRAELECPROD&#039;&#039;) is determined by the existing generation capacity (&#039;&#039;INFRAELECGENCAP&#039;&#039;), adjusted by a capacity utilization factor (&#039;&#039;INFRAELECTADJFACT&#039;&#039;). We estimate the initial capacity utilization factor for each country based on historical data related to generating capacity and electricity production. Over the forecast horizon, the capacity utilization factor is assumed to converge, over a 50 year period, to a global average value, 0.55, which we derived from current data on generation capacity and production in high-income countries. We also account for transmission and distribution loss (&#039;&#039;INFRAELECTRANLOSS&#039;&#039;), which we forecast as a function of average income (&#039;&#039;GDPPCP&#039;&#039;) and a measure of governance regulatory quality (&#039;&#039;GOVREGQUAL&#039;&#039;). This allows us to calculate post-loss production of electricity.&lt;br /&gt;
&lt;br /&gt;
The desired electricity use can be met by either the domestic post-loss production or imports. Similarly, the post-loss production can be used for either domestic use or exports. At the moment, we assume that the imports are available, when necessary, and that any excess post-loss production can be exported; i.e., we do not attempt to balance the trade in electricity. In parallel, we use the ratio of desired electricity use to post-loss production (&#039;&#039;INFRAELECCONSPRODRATIO&#039;&#039;) as a driver of future levels of generating capacity. Each year the computed ratio is compared to a historical value calculated in the pre-processor. We make the simplifying assumption that countries wish to keep this ratio constant over time. A growing ratio implies that domestic consumption is increasing at a faster rate than domestic production, which sends a signal indicating a desire to build additional capacity. A declining ratio implies that domestic consumption is increasing at a slower rate than domestic production. While this could send a signal to remove existing capacity, the model does not do so; rather it calls for no new construction and less than full replacement of depreciated capacity. Over time, this should bring the production and use back into historical balance.&lt;br /&gt;
&lt;br /&gt;
[[File:Infra3.png|frame|center|964x672px|Visual representation of electricity infrastructure]]&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Water and Sanitation&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
==== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Access to Water, Access to Sanitation, and Wastewater Treatment&amp;lt;/span&amp;gt; ====&lt;br /&gt;
&lt;br /&gt;
The key access indicators we include for water and sanitation infrastructure are the percentages of the population with access to different levels of improved drinking water and sanitation and whose wastewater is collected and subsequently treated. The physical quantities include the number of connections providing these services and the amount of land that is equipped for irrigation.[[File:Infra4.png|frame|right|Visual representation of water and sanitation infrastructure]]&lt;br /&gt;
&lt;br /&gt;
We originally introduced forecasts of access to improved sources of drinking water and sanitation into IFs in support of the third volume in the PPHP series, &#039;&#039;Improving Global Health&#039;&#039; (Hughes, Kuhn, et al. 2011), because of the health risks associated with a lack of clean water and/or improved sanitation. We have extended this portion of the model to include forecasts of the share of wastewater that is collected and then treated prior to being returned to the environment. In addition, we have added a component to forecast the area equipped for irrigation.&lt;br /&gt;
&lt;br /&gt;
The WHO and UNICEF (2013) use the concept of “ladders” for drinking water sources and sanitation systems. They currently include four steps for both drinking water (surface water, unimproved, other improved, and piped on premises) and sanitation (open defecation, unimproved, shared, and improved). As countries develop, more of their citizens ascend these ladders. We have combined these into three categories each; for drinking water these are unimproved, other improved, and piped; for sanitation, these are other unimproved, shared, and improved. Notably, using international standards, estimates of the total population with access to improved sanitation does not include the shared category.&lt;br /&gt;
&lt;br /&gt;
We forecast the shares of the population in each of the water and sanitation ladder categories using average income, poverty levels (measured as the percentage of the population living on less than $1.25 per day), educational attainment (measured as the average number of years of formal education for adults over 25), and public health expenditures as explanatory variables (see the next figure). These results then feed into the forecasts of the percentage of population with wastewater collection and wastewater treatment.&lt;br /&gt;
&lt;br /&gt;
Finally, these access rates, in combination with the forecasts for population and average household size, are used to calculate the number of safe water, sanitation, and wastewater treatment connections, which drive the cost calculations described below.&lt;br /&gt;
&lt;br /&gt;
==== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;Area Equipped for Irrigation&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt; ====&lt;br /&gt;
&lt;br /&gt;
There have been few forecasts of the area equipped for irrigation, and those that do exist tend to be based on very detailed analyses of specific situations. In a recent report from the United Nations Food and Agriculture Organization (FAO) looking out to the year 2050, Bruinsma (2011: 251) stated that the “projections of irrigation presented in this section are based on scattered information about existing irrigation expansion plans in different countries, potentials for expansion (including water availability) and the need to increase crop production.” Another report looking at global agriculture over the next half century (Nelson et al. 2010), this one from the International Food Policy Research Institute, relies on exogenous assumptions of the growth in irrigated area. The authors do not specify the source of these assumptions, but some of the same authors (You et al. 2011) have reported on the irrigation potential for Africa, basing their conclusions on agronomic, hydrological, and economic factors.&lt;br /&gt;
&lt;br /&gt;
Rather than attempt to replicate the level of detailed analysis of most previous studies, we forecast the area equipped for irrigation based on data from the FAO’s FAOSTAT and AQUASTAT databases on historical irrigation patterns and the area that could potentially be equipped for irrigation. These data are incomplete; for area equipped for irrigation, data are provided for 168 of the 186 countries included in IFs, and for the potentially irrigable area, data are provided for 117 of 186 countries. In our examination of these historical data, we found that a number of countries had already reached an apparent plateau in the amount of area equipped for irrigation that was often well below the potential indicated. For example, Argentina’s equipped area has stayed at a bit over 1.5 million hectares since the late 1970s, even though its potential is given as more than 6 million hectares. Why a country saturates below its ultimate potential is often unclear, but one obvious reason for some countries is that they receive enough rainfall to not warrant further irrigation.&lt;br /&gt;
&lt;br /&gt;
In any case, once we have determined an appropriate saturation level for each country and a recent historical growth rate, we assume that the expected area equipped for irrigation gradually approaches the saturation level. The rate of growth starts at the historical growth rate, with the growth rate slowing as the saturation level is approached. The user can modify this path using the parameter&amp;amp;nbsp;&#039;&#039;&#039;&#039;&#039;ladirareaequipm&#039;&#039; &#039;&#039;&#039;, which acts as a multiplier. Still, the amount of area equipped for irrigation cannot exceed the specified saturation level for the country.&lt;br /&gt;
&lt;br /&gt;
==== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;ICT&amp;lt;/span&amp;gt; ====&lt;br /&gt;
&lt;br /&gt;
We forecast four basic indicators of ICT infrastructure: fixed telephone lines, fixed broadband subscriptions, mobile telephone subscriptions, and mobile broadband subscriptions, all per 100 persons. Our forecasts for the expected levels of these different forms of ICT infrastructure are driven in part by cross-sectional relationships with average income and government regulatory quality. As the next figure illustrates, however, there are also interactions among the different forms of ICT.[[File:Infra5.png|frame|right|Visual representation of ICT infrastructure]]&lt;br /&gt;
&lt;br /&gt;
For each technology, we found strong relationships indicating that usage levels (our proxies in this case for access) increase with rises in average income and governance regulatory quality; in the case of fixed broadband, we also found urbanization to be important, as one might expect for a technology whose installation is supported by population density.&lt;br /&gt;
&lt;br /&gt;
As for the interactions between the different forms of ICT, we start with fixed telephone lines. Given the potential for substitution by mobile telephone lines, we assume that the demand for fixed telephone lines will decline as mobile usage increases. Already we see this happening in the data, especially, but not exclusively, in high-income countries. Our analysis of the historical data indicates a level of approximately 30 mobile telephone subscriptions per 100 persons as the point at which fixed-line telephone decline begins, so we build this into our forecasts algorithmically. We do not expect that fixed telephone line usage will completely disappear. Rather, we assume arbitrarily that it will settle at a low level; this is set by default to 2.5 lines per 100 persons. Furthermore, we also assume that: (1) mobile broadband subscriptions will never exceed mobile telephone subscriptions; and (2) any decline in fixed telephone lines will boost the growth in fixed broadband because countries that have existing investments in fixed-line infrastructure are able to leverage these networks to provide broadband access with rather modest investments.&lt;br /&gt;
&lt;br /&gt;
The cross-sectional relationships with income do not remain static across time for mobile phones, fixed broadband, and mobile broadband. The last figure shows this for mobile telephone subscriptions. The individual points reflect historical data for country access rates for the years 2000, 2005, and 2010. The lines are logarithmic curves fit through these data. The upward shift over time reflects advances in information and communication technologies that are making ICT cheaper and more accessible around the world. These advances are, in turn, driven by various systemic factors ranging from product and process innovation to network effects.[[File:Infra6.png|frame|right|Example of saturation levels]]&lt;br /&gt;
&lt;br /&gt;
In order to capture the effect of this rapid change in our forecasts of future access, we combine the use of the cross-sectional function with an algorithmic approach that simulates the upward shift of the curves for mobile phones, fixed broadband, and mobile broadband. The algorithmic element assumes a standard technology diffusion process in which the growth in penetration rate associated with the technological shift rises from a low annual percentage point increase at low levels of penetration to a maximum at the middle of the range (the inflection point) and falls again as saturation is approached. For each of the three technologies, we have looked at historical patterns to estimate the minimum and maximum growth rates, expressed as annual percentage points of absolute change.&lt;br /&gt;
&lt;br /&gt;
The choice of saturation levels is obviously quite important. Data from the International Telecommunications Union show penetration rates for mobile phones that exceed 100 subscriptions per 100 persons (e.g., approaching 200 in Hong Kong). At the same time, some countries (e.g., Denmark) seem to be reaching a saturation level for fixed broadband well below 100 subscriptions per 100 persons. Uncertainty remains over the proper level of saturation to assume for these subscriptions, and therefore, different researchers use different values. Specifically, we define saturation as 50 subscriptions per 100 persons for fixed broadband and 150 subscriptions per 100 persons for both mobile technologies. In addition, we assume that mobile broadband penetration cannot exceed mobile phone penetration.&lt;br /&gt;
&lt;br /&gt;
Similarly to the other access rates, the numbers of lines and subscriptions per 100 persons, in combination with the forecasts for population, are used to calculate the absolute number of lines and subscriptions, which drive the cost calculations described in the diagram below.&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
[1]&amp;amp;nbsp;Not all countries have data for all indicators included in the model in the base year. IFs includes a preprocessor that uses a series of algorithms that draw on historical data for previous years, the estimated equations, and other factors to initialize these missing data.&lt;br /&gt;
&lt;br /&gt;
[2]&amp;amp;nbsp;&#039;&#039;ENDEMSH&#039;&#039; is an adjusted value of &#039;&#039;ENDEM&#039;&#039;, which takes into account the differences between the base year values for total primary energy use from historic data and the base year values calculated in the pre-processor, which adjusts for differences between the physical and financial data on energy trade. The ratio of &#039;&#039;ENDEMSH&#039;&#039; to &#039;&#039;ENDEM&#039;&#039; gradually converges to 1 over a number of years given by the parameter &#039;&#039;&#039;&#039;&#039;enconv&#039;&#039; &#039;&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;2. Translating the Expected Levels of Infrastructure into Financial Requirements&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
In estimating the financial requirements to achieve the expected levels of infrastructure, we adopt the approach introduced by Fay (2001) and Fay and Yepes (2003) described earlier. In this approach, there are two components to the financial requirements for each type of infrastructure each year. First there is the cost of maintenance/renewal of existing infrastructure. Second, there is the cost of new construction. These then need to be separated into public and private shares. The following figure shows the general process for each type of infrastructure.&lt;br /&gt;
&lt;br /&gt;
[[File:Infra7.png|frame|center|Visual representation of financial requirement estimation]]&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Estimating the financial requirements for new construction&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
For each type of infrastructure, the existing level of physical infrastructure is subtracted from the forecasted level and the difference is multiplied by the unit cost (see the table below for the list of the parameters that store the information on the unit costs). The results are then summed across the different types of infrastructure to calculate the total demand for funding for new construction. In a slight variation, rather than calculate the growth of the physical stock, Stambrook (2006) first calculated the asset value of the existing road stock by multiplying the level of the physical stock by a unit cost. He then directly forecasted the growth of this asset value, which was assumed to be equal to the investment requirements.&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Estimating the financial requirements for maintenance/renewal&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Although we use the term “maintenance” for this second set of infrastructure funding requirements, different studies use different nomenclature. Bhattacharyay (2010), Fay and Yepes (2003), Kohli and Basil (2011), and Yepes (2005), all use “maintenance”; Chatterton and Puerto (2006) refer to “rehabilitation.” Yepes (2008) refers to “maintenance and rehabilitation.” Finally, G. Hughes, Chinowsky, and Strzepek (2009) provide separate estimates for replacement and for maintenance. In general, however, the methodology for the estimation of the funding requirements is the same across all studies. For each type of infrastructure, the funding is determined as a percentage of the dollar value of the existing infrastructure. The dollar value is given as the amount of infrastructure in physical units multiplied by the same unit cost used for estimating the funding for new construction. The percentage is based on the average lifetime of the particular infrastructure (see Table 6 for the list of the parameters that store the infrastructure lifetimes in IFs). Fay and Yepes (2003: 10) referred to this as “the minimum annual average expenditure on maintenance, below which the network’s functionality will be threatened.” Later authors have more specifically related the percentage to the depreciation rate or average expected lifetime of each type of infrastructure (Chatterton and Puerto 2006; Yepes 2005, 2008).&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Separating the financial requirements into public and private shares&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
In the real world funding for infrastructure comes from both public and private sources, so we separate the funding requirements into public and private components. We assume a specific share of public and private funding for each type of infrastructure. This, in effect, implies that public spending on infrastructure leverages a certain amount of private spending. These shares differ by type of infrastructure, but are constant across countries and time. The share parameters are &#039;&#039;&#039;&#039;&#039;infrainvmaintpubshrm&#039;&#039; &#039;&#039;&#039; and &#039;&#039;&#039;&#039;&#039;infrainvnewpubshrm&#039;&#039; &#039;&#039;&#039;, each of which is a vector, with the dimension representing the type of infrastructure. The balancing of the financial requirements with the available resources included in IFs and described in the next section only considers the public sector.&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%; border: 1px solid #cccccc&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Infrastructure Type (unit)&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Unit Cost Parameters&amp;lt;sup&amp;gt;[1]&amp;lt;/sup&amp;gt;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Lifetime Parameter&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Paved road (kilometer)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infraroadpavedcostlower, infraroadpavedcostm, infraroadpavedcostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infraroadpavedlife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Unpaved road (kilometer)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infraroadunpavedcostlower, infraroadunpavedcostm, infraroadunpavedcostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infraroadunpavedlife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Electricity generation (megawatt)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infraelecgencostlower, infraelecgencostm, infraelecgencostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infraelecgenlife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Rural electricity (connection)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infraelecaccruralcostlower, infraelecaccruralcostm, infraelecaccruralcostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infraelecaccrurallife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Urban electricity (connection)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infraelecaccurbancostlower, infraelecaccurbancostm, infraelecaccurbancostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infraelecaccurbanlife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Irrigation equipment (hectare)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;landircostlower, landircostm, landircostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;landirlife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Improved water (connection)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;watsafeimpcostlower, watsafeimpcostm, watsafeimpcostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;watsafeimplife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Piped water (connection)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;watsafecostlower, watsafecostm, watsafecostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;watsafelife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Shared sanitation (connection)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;sanitationimpcostlower, sanitationimpcostm, sanitationimpcostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;sanitationimplife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Improved sanitation (connection)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;sanitationcostlower, sanitationcostm, sanitationcostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;sanitationlife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Wastewater treatment (connection)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;watwastetreatcostlower, watwastetreatcostm, watwastetreatcostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;watwastetreatlife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Fixed telephone (line)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infratelecostlower, infratelecostm, infratelecostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;infratelelife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Fixed broadband (subscription)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;ictbroadcostlower, ictbroadcostm, ictbroadcostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;ictbroadlife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Mobile phone (subscription)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;ictmobilcostlower, ictmobilcostm, ictmobilcostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;ictmobillife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Mobile broadband (subscription)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;ictbroadmobilcostlower, ictbroadmobilcostm, ictbroadmobilcostupper&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;ictbroadmobillife&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; colspan=&amp;quot;3&amp;quot; valign=&amp;quot;middle&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;&amp;lt;span style=&amp;quot;font-size:small;&amp;quot;&amp;gt;[1] The actual unit costs can change as a function of GDP per capita (&#039;&#039;GDPPCP&#039;&#039;). For a given type of infrastructure, below a given level of &#039;&#039;GDPPCP&#039;&#039;, the unit cost takes on the value specified by the parameter ending with ‘lower’. Above a given level of &#039;&#039;GDPPCP&#039;&#039;, the unit cost takes on the value specified by the parameter ending with ‘upper’. Between these two values of &#039;&#039;GDPPCP&#039;&#039;, the unit cost changes in a linear fashion between the ‘lower’ and ‘upper’ value as a function of &#039;&#039;GDPPCP&#039;&#039;. Currently, the lower and upper thresholds for &#039;&#039;GDPPCP&#039;&#039; are hard coded in the model and vary by type of infrastructure.&amp;lt;/span&amp;gt;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&amp;lt;span style=&amp;quot;font-size:small;&amp;quot;&amp;gt;[2] The unit cost parameters ending in ‘m’ are multipliers that can be used to change the unit cost directly.&amp;lt;/span&amp;gt;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&amp;lt;span style=&amp;quot;font-size:small;&amp;quot;&amp;gt;[3] As described in the discussion on initializing the infrastructure data for IFs, the unit costs are also multiplied by the variable &#039;&#039;INFRACOSTADJFAC&#039;&#039;, which is calculated in the first year of the model as part of balancing the government spending in that year. This variable always has a value between 0 and 1, and gradually converges to 1 over the time period given by the parameter &#039;&#039;infracostadjfacconvtime&#039;&#039; .&amp;lt;/span&amp;gt;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&amp;lt;/div&amp;gt;&amp;lt;br/&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;3. Determining the Actual Funds for Infrastructure Spending&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
There is no guarantee that the requirements for infrastructure funds will match those made available. In determining whether this is the case, we focus on the public spending for infrastructure. In IFs, government domestic revenues and net foreign aid are summed into government expenditure (GOVEXP), which is then allocated between transfers, (GOVHHTRN - pensions and other social payments) and direct government spending (GOVCON). The latter is divided among broad categories— defense, education, health, research and development, core infrastructure, other infrastructure, and a residual category of other government spending. It is through this process of allocating government revenues that the amount of public funding for infrastructure ultimately is determined. IFs allows some imbalance between revenues and total expenditures year to year, but neither debt nor surpluses can accumulate indefinitely; as their percentages of GDP change, signals adjust revenues and expenditures over time.&lt;br /&gt;
&lt;br /&gt;
The figure below illustrates how the actual public funds available for core infrastructure are determined starting from the public funds required for core infrastructure estimated in the previous step. During this step, the amount of public funds available for other infrastructure is also determined.&lt;br /&gt;
&lt;br /&gt;
[[File:Infra8.png|frame|right|Visual representation of public spending for infrastructure]]&lt;br /&gt;
&lt;br /&gt;
Prior to the budget algorithm, the public funds required for core infrastructure can be modified by a spending multiplier, &#039;&#039;&#039;&#039;&#039;gdsm(Infrastructure)&#039;&#039; &#039;&#039;&#039;, to determine the public funds desired for core infrastructure. Similarly, the public funds desired for other infrastructure, which are initially estimated as function of GDP per capita, can be modified by a spending multiplier, &#039;&#039;&#039;&#039;&#039;gdsm(InfraOther&#039;&#039; &#039;&#039;&#039; &#039;&#039;)&#039;&#039;. Finally, the parameter &#039;&#039;&#039;&#039;&#039;infrabudsdrat&#039;&#039; &#039;&#039;&#039; can be used to indicate the priority that should be given to core and other infrastructure in the budget allocation process (it affects both categories equally).&lt;br /&gt;
&lt;br /&gt;
The budget algorithm takes this information, along with the public funds desired for other categories and government consumption to determine the public funds available for core and other infrastructure. First, a fraction, defined by &#039;&#039;&#039;&#039;&#039;infrabudsdrat&#039;&#039; &#039;&#039;&#039; divided by 1, of the public funds desired for core and other infrastructure, up to the level of total government consumption, is allocated to these categories and removed from total government consumption ([[Education#Education_Financial_Flow|there is a similar parameter, &#039;&#039;&#039;&#039;&#039;edbudgon&#039;&#039; &#039;&#039;&#039;, discussed in the Education section of the Help system]]). The remaining government consumption is allocated to the various categories based upon their desired levels of funding (at this point, the amounts of desired funding for core and other infrastructure does not include the amounts already set aside). In the case of demand-supply mismatches, the subtractions or additions are allocated to each category based on their relative shares of the total desired funding. There is also a minimal level of funds allocated to each category; i.e., each category will receive at least some funds. (Note, [[Governance#Equations:_Broader_Regime_Capacity|the budget allocation process is described in more detail in the governance section of this Help system.]])&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;4. Determining the Forecasted Levels of Infrastructure Spending and Attainment&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Once the level of public funds available for core infrastructure is determined, we can forecast the levels of infrastructure that will be attained. If there is a match between the estimated funding requirements and the estimated funding available, the process is fairly straightforward. In the case where there is a demand-supply mismatch, the forecasting becomes more complicated. The following figure presents this process.&lt;br /&gt;
&lt;br /&gt;
[[File:Infra9.png|frame|right|Visual representation of forecasted levels of infrastructure spending and attainment]]&lt;br /&gt;
&lt;br /&gt;
Recognizing that the infrastructure sector may not be able to manage rapid increases in public funding, we first smooth the actual provision of the public funds. Specifically, if the public funds available in the current year dramatically exceed the amount spent in the previous year, a portion of the available funds are held in reserve (in a lockbox). The threshold for this increase is half a percent of GDP. The funds in the lockbox are gradually released over time. The amount released from the lockbox depends on the amount in the lockbox and a fixed coefficient, InfraSpndBoxUnloadFactor, indicating the fraction that can be released in any year. This value is currently hard coded at 0.2. The amount of public funds available in the present year for core infrastructure is, therefore, the sum of the public funds coming out of the budget process for the current year not put in reserve plus the funds released from the lockbox in the current year. This amount is then compared to the public funds required for core infrastructure determined previously.&lt;br /&gt;
&lt;br /&gt;
In the case of an exact match between the public funds available in the present year for core infrastructure exactly matches the public funds required, the amounts of public and private spending on new construction and maintenance/renewal are exactly the amounts required. Similarly, the levels of infrastructure attained exactly match those expected (see again earlier figures on expected levels of infrastructure).&lt;br /&gt;
&lt;br /&gt;
In the case of a budget shortfall, we make three simplifying assumptions. First, we assume that all forms of infrastructure are affected equally; specifically, each receives the same proportionate cut in the amount of public funding received. Second, with the exception of ICT infrastructure (fixed and mobile telephones and broadband), we assume that the amount of private funding is reduced by the same proportion. This is based on our premise, stated earlier, that public funding for infrastructure leverages private spending, so less public funding also means less private spending. We make the exception for ICT because this is a less-tenable assumption for that sector given the degree to which private spending historically has driven ICT development. Specifically, private funding for ICT is not reduced even in the case of a reduction of public funding. Third, we assume that the reductions in funding affect spending on both maintenance and new construction equally. The net result is that there will be less new construction of infrastructure than desired, as well as less maintenance of existing infrastructure. This can lead to an absolute decline in some forms of infrastructure when the new construction is not enough to make up for the amount of infrastructure lost due to inadequate maintenance.&lt;br /&gt;
&lt;br /&gt;
This is slightly altered when targets are set for infrastructure. Here, an algorithm is used that first tries to ensure that the funds are used to provide the levels of construction and maintenance implied by the expected values estimated in the absence of a target. In this way, infrastructures with high targets are not favored over other forms of infrastructure. Any remaining funds are then distributed among all other infrastructure types, with their shares being proportional to the funds required to achieve the expected levels of construction and maintenance implied by the target.&lt;br /&gt;
&lt;br /&gt;
A further effect of a budget shortfall is that when infrastructure stocks do not achieve their expected levels, there is a feedback to our access measures.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;When there is a budget surplus, the extra funds go to additional new construction because the maintenance/renewal requirements are already covered. The surplus is spread across the different forms of infrastructure using the following logic. First, r&amp;lt;/span&amp;gt; &amp;lt;span&amp;gt;oads and electricity generation are allocated shares of the excess funds determined by their historical shares in total infrastructure spending. Second, the remainder of the excess funds is disbursed among the infrastructures that involve access. They are used to meet the gap to universal (stipulated) access rate with a cap on how much of the gap can be met each year.&amp;lt;/span&amp;gt; &amp;lt;span&amp;gt;Private funding is not affected by increases in public funding from “surplus funds.”&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;5. Estimating the Social, Economic, and Environmental Impacts of the Attainable Infrastructure&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
There a number of possible social, economic, and environmental impacts of infrastructure. We divided these into impacts on economic growth, income distribution, health, education, governance, and the environment. Given the limited empirical support for many of these linkages and, thus, a high level of uncertainty about whether and how to represent them, we have limited our inclusion of direct links from infrastructure to the links from infrastructure to economic growth and health. Important indirect linkages supplement the direct linkages that we describe here. For example, the forward linkages from economic growth to environmental impact (via paths such as increased energy use and food demand) and from improved health to demographic change are present in the current model. In fact, the indirect linkages via both of these paths are pervasive across the model.&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Impacts on productivity and economic growth&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
We estimate the impact of infrastructure on economic growth through its effect on multifactor productivity. Most economic models relate aggregate growth to changes in factors of production, typically capital (K) and labor (L), and an additional component, which is variously called the Solow residual, the technological change parameter, total factor productivity (TFP) or multifactor productivity (MFP); here we use the MFP label. Analyses have long shown that MFP can be quite large (Solow 1956; 1957). Within IFs, we treat MFP as an endogenous variable that human capital, social capital, physical capital, and knowledge capital influence (Hughes 2007). Infrastructure is a key component of physical capital, along with natural resources. The impact of the latter is represented through the effect of energy prices on [[Economics#Multifactor_Productivity|MFP]].&lt;br /&gt;
&lt;br /&gt;
In estimating the impact of infrastructure on MFP, we relate the impact to measures of physical infrastructure and not to measures of infrastructure spending. Because of the interaction effects across infrastructure types, we do not attempt to estimate the impact of individual forms of infrastructure but rather estimate the impact as a function of a composite index of infrastructure. Due to the very different historical and expected growth patterns of more traditional infrastructure—transportation, energy and water—vis-à-vis ICT, we create a separate index for ICT and link it to the physical capital component of MFP (MFPPC) in a different way.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Traditional Infrastructure – Transportation, Electricity, and Water and Sanitation&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
For the more traditional forms of infrastructure—transportation, electricity, and water and sanitation, we first construct a set of component indices—&#039;&#039;INFRAINDTRAN&#039;&#039;, &#039;&#039;INFRAINDELEC&#039;&#039;, and &#039;&#039;INFRAINDWATSAN&#039;&#039; (see the figure below). These are then aggregated into an overall index, &#039;&#039;INFRAINDTRAD&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
In order to construct these indices, we followed the approach presented in Calderón and Servén (2010a). This begins with basic measures of infrastructure, e.g., the number of telephone lines, the amount of electricity generating capacity, and the length of the road network. These measures are ‘standardized’, as follows:&lt;br /&gt;
&lt;br /&gt;
#If the indicator is not already normalized by a meaningful scaling factor, e.g., land area or total population, calculate an appropriate normalized value. This is based on the notion that, for example, it makes more sense to compare countries based on the number of telephones per person rather than the total number of telephones. The following figure shows the normalized indicators used for each of the component indices.&lt;br /&gt;
#The logarithms of the normalized indicators are calculated.&lt;br /&gt;
#The mean and standard deviation for each of the normalized and logged indicators in the year 2010 are calculated in the pre-processor. These are stored in the vectors &#039;&#039;INFRAINDTRANCOMPMEANI&#039;&#039;, &#039;&#039;INFRAINDTRANCOMPSDI&#039;&#039;, &#039;&#039;INFRAINDELECCOMPMEANI&#039;&#039;, &#039;&#039;INFRAINDELECCOMPSDI&#039;&#039;, &#039;&#039;INFRAINDWATSANCOMPMEANI&#039;&#039;, and &#039;&#039;INFRAINDWATSANCOMPSDI&#039;&#039;, each of which has an entry for each indicator included in the component index.&lt;br /&gt;
#In each forecast year, a z-value for each of the normalized indicators is calculated by subtracting the mean value for the year 2010 and then dividing by the standard deviation for the year. This provides a more standardized measure of the difference across countries and is independent of the original units of measure. If a country has negative (positive) z-value for a particular indicator, this indicates that its level of that indicator is smaller (greater) than it was for the average country in 2010. By definition, the aggregated z-value for the world for each indicator in 2010 is equal to 0.[[File:Infra10.png|frame|right|Visual representation of the basic measures of infrastructure]]&lt;br /&gt;
&lt;br /&gt;
The component indices are then calculated as a weighted sum of the z-values for the normalized indicators used for each of the component indices. The weights are given by the parameters &#039;&#039;&#039;&#039;&#039;infraindtrancompwt&#039;&#039; &#039;&#039;&#039;, &#039;&#039;&#039;&#039;&#039;infraindeleccompwt&#039;&#039; &#039;&#039;&#039;, and &#039;&#039;&#039;&#039;&#039;infraindwatsancompwt&#039;&#039; &#039;&#039;&#039;, where, once again each of these is a vector with an entry for each indicator included in the component index. Finally, the overall traditional infrastructure index, &#039;&#039;INFRAINDTRAD&#039;&#039;, is calculated as a weighted sum of the component indices. The weights are given by the parameter &#039;&#039;&#039;&#039;&#039;infraindtradcompwt&#039;&#039; &#039;&#039;&#039;, which is a vector with three entries, one for each of the component indices.&lt;br /&gt;
&lt;br /&gt;
As with the z-values for the individual indicators, a negative (positive) value of for one the component indices or the overall index implies that a country ranks below (above) the average country in 2010 for that indicator. Furthermore, by definition, the aggregated value for the world for each index in 2010 is equal to 0.&lt;br /&gt;
&lt;br /&gt;
We use the overall Traditional Infrastructure Index to calculate the impact of traditional infrastructure on MFP in the same way as we do for most factors that influence MFP. As described by Hughes (2007: 15–16), we do this by comparing the value of &#039;&#039;INFRAINDTRAD&#039;&#039; to the value of &#039;&#039;INFRAINDTRADEXP&#039;&#039;, which is calculated using a benchmark function&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt;&amp;amp;nbsp;that indicates what value we would expect to see for a country given its current level of GDP per capita (see the figure below). A country whose index falls above (below) the benchmark value receives a boost to (reduction from) its MFP. For example, Gabon and Latvia have similar levels of GDP per capita in 2010, but Latvia’s Traditional Infrastructure Index falls well above the benchmark line, while Gabon’s falls well below. Thus, the former will receive a boost to its MFP due to traditional infrastructure, while the latter will receive a reduction.&lt;br /&gt;
&lt;br /&gt;
The size of the boost or reduction depends on the distance from the benchmark value, &#039;&#039;INFRAINDTRAD&#039;&#039; – &#039;&#039;INFRAINDTRADEXP&#039;&#039;, and a factor relating this distance to productivity, which is given by the parameter &#039;&#039;&#039;&#039;&#039;mfpinfrindtrad&#039;&#039; &#039;&#039;&#039;. Calderón and Servén (2010a: i35) presented a value of 2.193 as their estimate of the increase in annual average growth rate of GDP per capita for an increase in 1 unit of their index. Based on this, we use a default value of 2 for the effect of traditional infrastructure on MFP. Specifically, if the value of the Traditional Infrastructure Index for a country is a full point above its expected value in a given year, it would receive a 2 percentage point boost to its MFP, which roughly translates into the same increase in growth in GDP per capita, over the coming year. The model user can change this value, allowing for exploration of the sensitivity of model results to the traditional infrastructure parameter.&lt;br /&gt;
&lt;br /&gt;
[[File:Infra11.png|frame|center|Traditional infrastructure index]]ICT&lt;br /&gt;
&lt;br /&gt;
The ICT Index, &#039;&#039;INFRAINDICT&#039;&#039; &amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[2]&amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt;, is calculated as a weighted average of the subscription rates for three of the four different kinds of ICT – mobile phones, fixed broadband, and mobile broadband. Since the subscription rates for mobile phones and mobile broadband saturate at 150 per 100 persons, their values are first multiplied by 2/3 so that they range from 0 to 100. The weights are given by the parameter &#039;&#039;&#039;&#039;&#039;infraindictcompwt&#039;&#039; &#039;&#039;&#039;, which is a vector with three entries, one for each of the component indices. By default, these values are set to 1, indicating equal weighting.&lt;br /&gt;
&lt;br /&gt;
When considering the impact of ICT infrastructure on MFP, using the same approach as for traditional infrastructure would be problematic. Our formulation for forecasting ICT infrastructure includes a technology shift factor. Therefore, any relationship between GDP per capita and the expected level of ICT would not remain stable over time; for example, a country with a GDP per capita of $5,000 in 2015 would be expected to have more ICT infrastructure than a country with a GDP per capita of $5,000 in 2010.&lt;br /&gt;
&lt;br /&gt;
We therefore associate the growth contribution from ICT advances with annual changes in the ICT Index, rather than with the level of the index as we do for traditional infrastructure. We multiply the annual unit change in the ICT Index by the parameter &#039;&#039;&#039;&#039;&#039;mfpinfrindict&#039;&#039; &#039;&#039;&#039;. Qiang, Rossotto and Kimura (2009: 45) estimated that each 10 percent increase in broadband penetration in developing countries increased the growth rate of per capita GDP by 1.38 percentage points (by 1.21 percentage points for developed countries) during the 1980 to 2006 period. We arbitrarily reduced the impact by using a default value of 0.8 because our index is a mixture of several types of ICT infrastructures, not all of which might have as strong an impact on economic productivity as does broadband. Thus, a 10 point increase in the value of the ICT index would result in a 0.8 addition to MFP, or an approximate increase of 0.8 percent in GDP per capita.&lt;br /&gt;
&lt;br /&gt;
There is one obviously questionable implication of this approach. When a country reaches saturation in the ICT Index, it will no longer receive a productivity boost from ICT. Given the current rapid increase in mobile telephones and mobile broadband that together make up two-thirds of the ICT Index, we see in most scenarios a near-term boost to MFP from ICT in much of the world, followed by little or no contribution later in the horizon. Our uncertainty with respect to appropriate treatment of the longer-term contribution of ICT points to one of the limitations of trying to forecast rapidly changing technologies.&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Impacts on health&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
There are many ways in which infrastructure can affect human health. We have chosen to limit our inclusion of these effects to a small set, specifically the impact of (1) unsafe water, sanitation, and hygiene directly on diarrheal diseases, and indirectly on diseases related to undernutrition; and (2) indoor air pollution on respiratory infections, such as pneumonia, and respiratory diseases, such as chronic obstructive pulmonary disease. These health outcomes are influenced directly by infrastructure via our measures of access to improved sources of drinking water and sanitation and the use of solid fuels in the home. These measures serve as proxies for the environmental health risks linked to infrastructure in IFs. We explored these effects in a previous volume in this series, &#039;&#039;Improving Global Health &#039;&#039;(B.Hughes, Kuhn, et al. 2011: 95–100), and have some confidence in the reasonableness of our results.&lt;br /&gt;
&lt;br /&gt;
Our approach for estimating the impact of these health risks is described in the [[Health#Proximate_Drivers_and_Risk-Specific_Population_Attributable_Fractions|health documentation]]. Therefore, we provide only a brief overview here. In general, we compare the forecasted values of these infrastructure indicators to values that we would anticipate based only on income and educational attainment (distal drivers). If the estimated and expected values differ, we adjust the levels of mortality and morbidity for the associated diseases forecasted based only on the distal drivers. For example, if the levels of access to improved sources of water and sanitation are higher than expected, we reduce the mortality rate from diarrheal diseases. The amount by which the mortality rate is reduced is based on the analysis presented in the Comparative Risk Analysis work of the World Health Organization (Ezzati et al. 2004). This general approach, comparing forecasted values with expected ones and translating the difference into impact in a forward linkage, is fundamentally similar to the method described above for linking infrastructure development and economic productivity.&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt;&amp;amp;nbsp;&amp;lt;span&amp;gt;This benchmark function is actually the combination of two functions: 1) INFRAINDTRADEXP = -0.881 + 0.519 * GDPPCPPP at levels of GDPPCPPP below $5000 and 2) INFRAINDTRADEXP = 2.767 + 0.225 * GDPPCPPP at levels of GDPPCPPP above $40000, with blending between these two thresholds.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[2]&amp;lt;/span&amp;gt;&amp;amp;nbsp;&amp;lt;span&amp;gt;A separate index, &amp;lt;/span&amp;gt; &#039;&#039;&amp;lt;span&amp;gt;INFRAINDICTZ&amp;lt;/span&amp;gt; &#039;&#039; &amp;lt;span&amp;gt;, is also calculated following the same approach as for the component indices of traditional infrastructure. This is only used for display purposes.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Infrastructure Equations&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
The primary equations in the infrastructure model in IFs are those for estimating the expected levels of infrastructure stocks or access. Each of the estimated equations relates one aspect of physical infrastructure to specific economic, structural, and demographic drivers; in some cases these equations also include other types of infrastructure, creating explicit linkages across those infrastructures. While a number of earlier studies did provide equations for forecasting future levels of some of the types of physical infrastructure we include, we chose to undertake our own analyses for the purposes of this volume. This allowed us to use more recent data to drive the relationships than earlier studies and to better integrate the resulting relationships within the broader IFs system.&lt;br /&gt;
&lt;br /&gt;
Our choices of the driving variables ultimately included in the equations were influenced by theoretical considerations, previous efforts, the availability of data, and, of course, the analytical results themselves. These factors also influenced our choices of functional forms. In particular, for variables that have natural minimums and maximums, such as the percentage of population with access to electricity, we use functional forms that guarantee that the forecasted values fall in this range.&lt;br /&gt;
&lt;br /&gt;
The basic equations shown below provide only the initial estimates of the expected levels of the specific infrastructure stock or access.&amp;amp;nbsp;[[Understand_IFs#Specialized_Functions|The final values are adjusted based upon a number of algorithmic and scenario-specific processes, including the use of shift factors, multipliers, extrapolative formulations, targeting processes.]]&amp;amp;nbsp;Some key aspects of these algorithmic processes, including key parameters available to the user for scenario development, are provided below the definitions of the variables used in the basic equations. Finally, the nature of the data used for estimation, the model fitted, and the R-squared values for a fit of the predicted against the actual historical values used for our estimations are also provided.&lt;br /&gt;
&lt;br /&gt;
As with the flow charts, this section presents the equations grouped by the four categories of infrastructure: transportation, electricity, water and sanitation, and ICT. Unless specified otherwise, in all of the following equations, the subscripts r and t refer to region/country and time/year, respectively.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
For help understanding the equations see [[Understand_IFs#Equation_Notation|Notation]].&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Equations: Transportation Infrastructure&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The estimated equations for transportation infrastructure in IFs are: 1) the total road density in kilometers per 1000 hectares, &#039;&#039;INFRAROAD&#039;&#039;, 2) the percentage of roads that are paved, &#039;&#039;INFRAROADPAVEDPCNT&#039;&#039;, and 3) the Rural Access Index, &#039;&#039;INFRAROADRAI&#039;&#039;, the percentage of the rural population living within two kilometers of an all-season road. From these we can calculate other transportation indicators.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Total road density&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ln(INFRAROAD_{r,t})=-2.539+0.483*ln(\frac{GDPP_{r,t}}{LANDAREA_{r,t}})+0.183*ln(\frac{POP_{r,t}}{LANDAREA_{r,t}}-0.102*ln(LANDAREA_{r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:INFRAROAD = road network density in kilometers per 1,000 hectares&lt;br /&gt;
&lt;br /&gt;
:GDPP = gross domestic product at purchasing power parity in billion constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:LANDAREA = land area in 10,000 square kilometers (million hectares)&lt;br /&gt;
&lt;br /&gt;
:POP = total population in million persons&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;uses extrapolative formulation: &#039;&#039;&#039;extmafuncroad, extmaposnconvtimeroad, extmaposnroad&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;additive shift factor: RoadDensShift, downward shift over 300 years, upward shift over 40 years&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;infraroadm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline in the absence of a target or multiplier or lack of finance for maintenance&#039;&#039;&lt;br /&gt;
*&#039;&#039;pooled cross-sectional data, OLS regression, R-squared = 0.79&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Percentage of total roads that are paved&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;INFRAROADPAVEDPCNT_{r,t}=\frac{100}{1+e^{-(-1.022+0.833*GDPPCP_{r,t}+0.756*POP_{r,t}-0.726*LANDAREA_{r,t}-0.267*INFRAROAD_{r,t}})}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:INFRAROADPAVEDPCNT = road network, paved percent in percentage&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:LANDAREA = land area in 10,000 square kilometers (million square hectares)&lt;br /&gt;
&lt;br /&gt;
:POP = total population in million persons&lt;br /&gt;
&lt;br /&gt;
:INFRAROAD = road network density in kilometers per 1,000 hectares&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;uses extrapolative formulation: &#039;&#039;&#039;extmafuncroadpaved, extmaposnconvtimeroadpaved, extmaposnroadpaved&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;additive shift factor: INFRARoadPavedPcntShift, downward shift over 500 years, upward shift over 50 years&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;infraroadpavedpcntm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline in the absence of a target or multiplier or lack of finance for maintenance&#039;&#039;&lt;br /&gt;
*&#039;&#039;&amp;lt;span&amp;gt;pooled cross-sectional data, OLS regression, R-squared = 0.45&amp;lt;/span&amp;gt;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Rural Access Index&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;INFRAROADRAI_{r,t}=100*e^{-3.558+1.328*ln(\frac{GDPPC_{r,t}*1000}{LANDAREA_{r,t}})+0.239*ln(INFRAROAD_{r,t}*\frac{LANDAREA_{r,t}*1000}{POP_{r,t}})}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:INFRAROADRAI = Rural Access Index, percent of rural population living within 2 kilometers of an all-weather road in percentage&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:LANDAREA = land area in 10,000 square kilometers (million square hectares)&lt;br /&gt;
&lt;br /&gt;
:POP = total population in million persons&lt;br /&gt;
&lt;br /&gt;
:INFRAROAD = road network density in kilometers per 1,000 hectares&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;additive shift factor: INFRAROADRAIShift, downward shift over 500 years, upward shift over 50 years&#039;&#039;&lt;br /&gt;
*&#039;&#039;there is currently no multiplier for INFRAROADRAI&#039;&#039;&lt;br /&gt;
*&#039;&#039;targeting parameters: &#039;&#039;&#039;infraroadraitrgtval, infraroadraitrgtyr, infraroadraisetar, infraroadraiseyrtar&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline unless lack of finance for maintenance&#039;&#039;&lt;br /&gt;
*&#039;&#039;cross-sectional data, OLS regression, R-squared = 0.51&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Equations: Energy Infrastructure&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Percentage of urban population with access to electricity&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;INFRAELECACC(urban)_{r,t}=\frac{100}{1+e^{-(1.144-4.858*\frac{INCOMELT1CS_{r,t-1}}{POP_{r,t-1}}+0.837*GOVEFFECT_{r,t})}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:INFRAELECACC(urban) = percent of urban population with access to electricity in percentage&lt;br /&gt;
&lt;br /&gt;
:INCOMELT1CS = population with income less than $1.25 per day, cross sectional computation in millions&lt;br /&gt;
&lt;br /&gt;
:POP = total population in million persons&lt;br /&gt;
&lt;br /&gt;
:GOVEFFECT = government effectiveness using the World Bank WGI scale, shifting it 2.5 points so that it runs from 0-5 instead of from -2.5 to 2.5&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;additive shift factor: INFRAELECACCShift(R%, Urban), downward shift over 500 years, upward shift over 50 years&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;infraelecaccm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;targeting parameters: &#039;&#039;&#039;infraelecacctrgtval, infraelecacctrgtyr, infraelecaccsetar, infraelecaccseyrtar&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline in the absence of a target or multiplier or lack of finance for maintenance&#039;&#039;&lt;br /&gt;
*&#039;&#039;cross-sectional data, GLM regression, R-squared = 0.68&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Percentage of rural population with access to electricity&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;INFRAELECACC(rural)_{r,t}=\frac{100}{1+e^{-(-0.500-6.925*\frac{INCOMELT1CS_{r,t-1}}{POP_{r,t-1}}+0.858*GOVEFFECT_{r,t})}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:INFRAELECACC(rural) = percent of urban population with access to electricity in percentage&lt;br /&gt;
&lt;br /&gt;
:INCOMELT1CS = population with income less than $1.25 per day, cross sectional computation in millions&lt;br /&gt;
&lt;br /&gt;
:POP = total population in million persons&lt;br /&gt;
&lt;br /&gt;
:GOVEFFECT = government effectiveness using the World Bank WGI scale, shifting it 2.5 points so that it runs from 0-5 instead of from -2.5 to 2.5&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;shift factor: INFRAELECACCShift(R%, Urban), downward shift over 500 years, upward shift over 50 years&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;infraelecaccm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;targeting parameters: &#039;&#039;&#039;infraelecacctrgtval, infraelecacctrgtyr, infraelecaccsetar, infraelecaccseyrtar&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline in the absence of a target or multiplier or lack of finance for maintenance&#039;&#039;&lt;br /&gt;
*&#039;&#039;cross-sectional data, GLM regression, R-squared = 0.77&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Ratio of electricity use to total primary energy demand&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENELECSHRENDEM_{r,t}=0.979*GDPPCP_{r,t}^{0.275}*INFRAELECACC(national)_{r,t}^{0.492}*FossilShare_{r,t=1}^{-0.077}*NonFossilShare_{r,t=1}^{0.123}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;FossilShare_{r,t=1}=\frac{ENP(oil)_{r,t=1}+ENP(gas)_{r,t=1}+ENP(coal)_{r,t=1}}{ENDEMSH_{r,t=1}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;NonFossilShare_{r,t=1}=\frac{ENP(hydro)_{r,t=1}+ENP(renew)_{r,t=1}}{ENDEMSH_{r,t=1}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:ENELECSHRENDEM = ratio of electricity use to total primary energy demand, in percentage&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:INFRAELECACC(national) = percent of total population with access to electricity in percentage&lt;br /&gt;
&lt;br /&gt;
:FossilShare = ratio of fossil fuel production to total primary energy demand in base year, as a fraction&lt;br /&gt;
&lt;br /&gt;
:NonFossilShare= ratio of hydroelectric and renewable energy production to total primary energy demand in base year, as a fraction&lt;br /&gt;
&lt;br /&gt;
:ENP = energy production for oil, gas, coal, hydro, and other renewables in billion barrels of oil equivalent&lt;br /&gt;
&lt;br /&gt;
:ENDEM = total primary energy use in billion barrels of oil equivalent&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;uses an extrapolative formulation:&#039;&#039; &#039;&#039;&#039;&#039;&#039;extmafuncenelecshr, extmaposnconvtimeenelecshr, extmaposnenelecshr&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;no shift factor&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;enelecshrendemm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline in the absence of a target or multiplier or lack of finance for maintenance&#039;&#039;&lt;br /&gt;
*&#039;&#039;cross-sectional data, OLS regression, R-squared = 0.65&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
As described in the flowchart for electricity the value of ENELECSHRENDEM is used to calculate the value of desired electricity use, given by INFRAELEC * POP, where INFRAELEC is electricity consumption per capita in kilowatt-hours and POP is total population in million persons. INFRAELEC is in initially calculated as:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;INFRAELEC_{r,t}=ENELECSHRENDEM_{r,t}*\frac{ENDEM_{r,t}*EnDemDFRIVal_{r,t}*17,000}{POP_{r,t}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:INFRAELEC = electricity consumption per capita in kilowatt-hours&lt;br /&gt;
&lt;br /&gt;
:ENDEM = total primary energy use in billion barrels of oil equivalent&lt;br /&gt;
&lt;br /&gt;
:EnDemDFRI = a multiplicative shift factor based on the ratio of the actual energy consumption in physical units in the historical data to the apparent energy consumption calculated in the pre-processor as part of adjusting the physical data to match the financial data on energy imports and exports; this converges to a value of 1 over a number of years given by the parameter enconv&lt;br /&gt;
&lt;br /&gt;
:17,000 = the conversion factor from barrels of oil equivalent to kilowatt-hours&lt;br /&gt;
&lt;br /&gt;
:POP = total population in million persons&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;&amp;lt;span&amp;gt;an additional multiplicative shift factor, InfraElecRI, which converges over 40 years to a value of 1, is used to further adjust the estimate of INFRAELEC&amp;lt;/span&amp;gt;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Percentage of electricity lost in transmission and distribution&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;INFRAELECTRANLOSS_{r,t}=e^{(3.125-0.026*GDPPCP_{r,t}-0.125*GOVREGQUAL_{r,t})}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:INFRAELECTRANLOSS = transmission and distribution loss as a percentage of total electricity production, in percentage&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:GOVEREGQUAL = government regulatory quality using the World Bank WGI scale, shifting it 2.5 points so that it runs from 0-5 instead of from -2.5 to 2.5&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;uses extrapolative formulation: &#039;&#039;&#039;extmafuncelectran, extmaposnconvtimeelectran, extmaposnelectran&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;additive shift factor: INFRAELECTRANLOSSShift, converges downward over 50 years, upward over 500 years&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;infraelectranlossm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;bound between 3 and 90&#039;&#039;&lt;br /&gt;
*&#039;&#039;&#039;&#039;&amp;lt;span&amp;gt;pooled cross-sectional data, OLS regression, R-squared = 0.85&amp;lt;/span&amp;gt; &#039;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Percentage of population primarily using solid fuels for heating and cooking&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENSOLFUEL_{r,t}=\frac{100}{1+e^{-(2.823+0.166*GDPPCP_{r,t}+0.032*INFRAELECACC(national)_{r,t})}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:ENSOLFUEL = ratio of electricity use to total primary energy demand, in percentage&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:INFRAELECACC(national) = percent of total population with access to electricity in percentage&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;multiplicative shift factor: ENSOLFUELShift; never converges&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;ensolfuelm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;targeting parameters: &#039;&#039;&#039;ensolfuelsetar, ensolfueltrgtyr, ensolfuelsetar, ensolfuelseyrtar&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;hold switch: &#039;&#039;&#039;ensolflhldsw&#039;&#039;&#039;, fixes value of ENSOLFUEL at initial year value&#039;&#039;&lt;br /&gt;
*&#039;&#039;cross-sectional data, GLM regression, R-squared = 0.81&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Equations: Water and Sanitation Infrastructure&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Percentage of population with access to improved drinking water and sanitation&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
For access to water and sanitation, we use a nominal logistic model to determine the share of the population in each category of access. For both water and sanitation, the number of categories is 3. For water these are no improved access, other improved access, and piped; for sanitation they are other unimproved access, shared access, and improved access.&lt;br /&gt;
&lt;br /&gt;
The values p&amp;lt;sub&amp;gt;i&amp;lt;/sub&amp;gt; shown below represent the share of population with access to each of these categories. The resulting values of p&amp;lt;sub&amp;gt;i&amp;lt;/sub&amp;gt;will all fall between 0 and 1 and sum to 1. These are then multiplied by100 in order to obtain values that range between 0 and 100 and sum to 100.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;P_i=\frac{S_i}{1+\sum^2_{i=1^{S_i}}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:for i = 1 to 2&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;and&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;P_3=1-\sum^2_{i=1^{P_i}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;S_i=e^{(a_i+\sum^n_{j=1}b_{i,j}*x_j)}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:for i = 1 to 2&lt;br /&gt;
&lt;br /&gt;
:n is the number of explanatory variables&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%; border: 1px solid #cccccc&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; colspan=&amp;quot;6&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Estimated coefficients&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &amp;amp;nbsp;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Intercept&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;EDYRSAGE25&#039;&#039; &amp;lt;sub&amp;gt;r,t&amp;lt;/sub&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;GDPPCP&#039;&#039; &amp;lt;sub&amp;gt;r,t&amp;lt;/sub&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;INCOMELT1CS&#039;&#039; &amp;lt;sub&amp;gt;r,t&amp;lt;/sub&amp;gt; &#039;&#039;/ POP&#039;&#039; &amp;lt;sub&amp;gt;r,t&amp;lt;/sub&amp;gt; * 100&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;GDS(health)&#039;&#039; &amp;lt;sub&amp;gt;r,t&amp;lt;/sub&amp;gt; &#039;&#039;/ GDP&#039;&#039; &amp;lt;sub&amp;gt;r,t&amp;lt;/sub&amp;gt; * 100&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; colspan=&amp;quot;6&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Water&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | s0&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 0.47200933&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | -0.4414453&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | -0.7033376&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; | 0.0253734&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; | -0.1616335&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | s1&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 1.17414971&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 0.13867779&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | -1.1508133&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; | 0.01181508&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; | -0.2769033&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; colspan=&amp;quot;6&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;Sanitation&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | s0&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 0.73081107&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | -0.6420051&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | -0.4497351&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; | 0.02170283&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; | -0.1562885&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | s1&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | -2.1593291&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 0.22539909&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | -0.3555466&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; | 0.02823687&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; | -0.1579957&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
:EDYEARSAGE25 = mean years of education for adults over the age of 25, in years&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:INCOMELT1CS = population with income less than $1.25 per day, cross sectional computation in millions&lt;br /&gt;
&lt;br /&gt;
:POP = total population in million persons&lt;br /&gt;
&lt;br /&gt;
:GDS(health) = government expenditure on health in billion constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:GDP = gross domestic product at market exchange rates in billion constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;additive shift factors: WATSAFEshift and SANITATIONShift; converge over &#039;&#039;&#039;watsanconv&#039;&#039;&#039; years for high and low categories; for intermediate categories, convergence time is 20 years for positive shift factors and 50 years for negative shift factors&#039;&#039;&lt;br /&gt;
*&#039;&#039;multipliers; &#039;&#039;&#039;watsafem&#039;&#039;&#039; and &#039;&#039;&#039;sanitationm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;targeting parameters: &#039;&#039;&#039;sanitationtrgtval, sanitationtrgtyr,&#039;&#039;&#039; &#039;&#039;&#039;sanitnoconsetar, sanitnoconseyrtar, sanitimpconsetar, sanitnoconsetar, sanitnoconseyrtar, watsafetrgtval, watsafetrgtyr, watsafehhconsetar, watsafeimpconsetar, watsafenoconsetar, watsafenoconseyrtar,&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;hold switches: &#039;&#039;&#039;watsafhldsw&#039;&#039;&#039; and &#039;&#039;&#039;sanithldsw,&#039;&#039;&#039; , fixes value of WATSAFE and SANITATION at initial year value&#039;&#039;&lt;br /&gt;
*&#039;&#039;values are normalized so that the three categories for water and sanitation each sum to 100&#039;&#039;&lt;br /&gt;
*&#039;&#039;pooled cross-sectional data, nominal logistic regression, R-squared = 0.85 for safe water, 0.87 for sanitation&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Percentage of population with wastewater collection&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WATWASTE_{r,t}=\frac{SANITATION(improved)_{r,t}}{1+e^{-(-2.4+0.043*GDPPCP_{r,t}+0.042*\frac{POPURBAN_{r,t}}{POP_{r,t}})}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:WATWASTE = percent of population with wastewater collection, in percentage&lt;br /&gt;
&lt;br /&gt;
:SANITATION(improved) = percent of population with access to improved sanitation, in percentage&lt;br /&gt;
&lt;br /&gt;
:POPURBAN = urban population in million persons&lt;br /&gt;
&lt;br /&gt;
:POP = total population in million persons&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;uses extrapolative formulation – coefficients are hard coded&#039;&#039;&lt;br /&gt;
*&#039;&#039;additive shift factor: WatWasteColShift; converge upward over 25 years, downward over 250 years&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;watwastem&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;no targeting parameters&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to exceed SANITATION(improved)&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline in the absence of a target or multiplier or lack of finance for maintenanc&#039;&#039;&lt;br /&gt;
*&#039;&#039;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;pooled cross-sectional data, OLS regression with country random effect, R-squared = 0.34&amp;lt;/span&amp;gt;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Percentage of population with wastewater treatment&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WATWASTETREAT_{r,t}=\frac{100}{1+e^{-(-2.482+0.038*GDPPCP_{r,t}+0.029*WATWASTE_{r,t})}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:WATWASTETREAT = percent of population with wastewater treatment, in percentage&lt;br /&gt;
&lt;br /&gt;
:WATWASTE = percent of population with wastewater collection, in percentage&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;additive shift factor: WatWasteTreatShift; converge upward over 25 years, downward over 250 years&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;watwastetreatm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;targeting parameters: &#039;&#039;&#039;watwastetreatsetar&#039;&#039;&#039;. &#039;&#039;&#039;watwastetreatseyrtar&#039;&#039;&#039; (no targeting parameters for absolute targets)&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to exceed WATWASTETREAT&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline in the absence of a target or multiplier or lack of finance for maintenance&#039;&#039;&lt;br /&gt;
*&#039;&#039;pooled cross-sectional data, GLM regression, R-squared = 0.59&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Equations: ICT Infrastructure&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Fixed telephone lines per 100 persons&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;INFRATELE_{r,t}=1.030+2.554*GDPPCP_{r,t}-0.033*GDPPCP^2_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:INFRATELE = fixed telephone lines per 100 persons&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;uses extrapolative formulation – parameters are hard coded&#039;&#039;&lt;br /&gt;
*&#039;&#039;no shift factor&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;infratelem&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;no targeting parameters&#039;&#039;&lt;br /&gt;
*&#039;&#039;when ICTMOBIL reaches 30, if INFRATELE &amp;gt; 2.5 value will fall to level of 2.5 over time period given by &#039;&#039;&#039;infrateledtfp&#039;&#039;&#039;: if INFRATELE &amp;lt; 2.5, then can continue to grow&#039;&#039;&lt;br /&gt;
*&#039;&#039;cross-sectional data, OLS regression, R-squared = 0.70&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Mobile telephone subscriptions per 100 persons&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ICTMOBIL_{r,t}=43.938+23.919*ln(GDPPCP_{r,t})+1.405*GOVREGQUAL_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:ICTMOBIL = mobile phone subscriptions per 100 persons&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:GOVEREGQUAL = government regulatory quality using the World Bank WGI scale, shifting it 2.5 points so that it runs from 0-5 instead of from -2.5 to 2.5&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;additive shift factor: MOBILshift; converge upward over 100 years, no convergence downward&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;ictmobilm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;targeting parameters: &#039;&#039;&#039;ictmobilsetar&#039;&#039;&#039;. &#039;&#039;&#039;ictmobilseyrtar&#039;&#039;&#039; (no targeting parameters for absolute targets)&#039;&#039;&lt;br /&gt;
*&#039;&#039;tech shift parameters: &#039;&#039;&#039;ictmobiltecinflection, ictmobiltechighrt, ictmobilteclowrt&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;saturation level: &#039;&#039;&#039;ictmobilsaturation&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline in the absence of a target or multiplier or lack of finance for maintenance&#039;&#039;&lt;br /&gt;
*&#039;&#039;cross-sectional data, OLS regression, R-squared = 0.53&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Fixed broadband subscriptions per 100 persons&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ICTBROAD_{r,t}=-12.581+2.534*ln(GDPPCP_{r,t})+6.496*GOVREGQUAL_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:ICTBROAD = fixed broadband subscriptions per 100 persons&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:GOVEREGQUAL = government regulatory quality using the World Bank WGI scale, shifting it 2.5 points so that it runs from 0-5 instead of from -2.5 to 2.5&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;additive shift factor: BROADshift; converges over 100 years (both upwards and downwards)&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;ictbroadm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;targeting parameters: &#039;&#039;&#039;ictbroadsetar, ictbroadseyrtar&#039;&#039;&#039; (no targeting parameters for absolute targets)&#039;&#039;&lt;br /&gt;
*&#039;&#039;urbanization increases growth using parameters &#039;&#039;&#039;ictbroadurimpmin&#039;&#039;&#039; and &#039;&#039;&#039;ictbroadurimpmax&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;as INFRATELE falls, this boosts growth of fixed broadband using the parameter &#039;&#039;&#039;ictbroadfromtelem&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;tech shift parameters: &#039;&#039;&#039;ictbroadtecinflection, ictbroadtechighrt, ictbroadteclowrt&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;saturation level: given by ictbroadcap (not in common block, currently hard coded as 50)&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline in the absence of a target or multiplier or lack of finance for maintenance&#039;&#039;&lt;br /&gt;
*&#039;&#039;cross-sectional data, OLS regression, R-squared = 0.74&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Mobile broadband subscriptions per 100 persons&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ICTBROADMOBIL_{r,t}=-21.827+9.139*ln(GDPPCP_{r,t})+9.357*GOVREGQUAL_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:ICTBROADMOBIL = mobile broadband subscriptions per 100 persons&lt;br /&gt;
&lt;br /&gt;
:GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
:GOVEREGQUAL = government regulatory quality using the World Bank WGI scale, shifting it 2.5 points so that it runs from 0-5 instead of from -2.5 to 2.5&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;additive shift factor: BroadMOBILshift; converge upward over 100 years, no convergence downward&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier: &#039;&#039;&#039;ictbroadmobilm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;targeting parameters: &#039;&#039;&#039;ictbroadmobiltrgtval, ictbroadmobiltrgtyr, ictbroadmobilsetar, ictbroadmobilseyrtar&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;tech shift parameters: &#039;&#039;&#039;ictbroadmobiltecinflection, ictbroadmobiltechighrt, ictbroadmobilteclowrt&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;saturation level: &#039;&#039;&#039;ictmobilsaturation&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to exceed ICTMOBIL&#039;&#039;&lt;br /&gt;
*&#039;&#039;value is not allowed to decline in the absence of a target or multiplier or lack of finance for maintenance&#039;&#039;&lt;br /&gt;
*&#039;&#039;cross-sectional data, OLS regression, R-squared = 0.70&#039;&#039;&lt;br /&gt;
&amp;lt;/div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Infrastructure Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Agénor, Pierre-Richard, Mustapha Kamel Nabli, and Tarik M. Yousef. 2007. “Public Infrastructure and Private Investment in the Middle East and North Africa.” In Mustapha Kamel Nabli, ed.,. Breaking the Barriers to Higher Economic Growth: Better Governance and Deeper Reforms in the Middle East and North Africa. Washington, DC: World Bank Publications, 399–422.&lt;br /&gt;
&lt;br /&gt;
Asian Development Bank, Japan Bank for International Cooperation, and World Bank. 2005.&amp;amp;nbsp;&#039;&#039;Connecting East Asia: A New Framework for Infrastructure&#039;&#039;. Tokyo: Asian Development Bank, Japan Bank for International Cooperation, and World Bank.&amp;amp;nbsp;[http://siteresources.worldbank.org/INTEASTASIAPACIFIC/Resources/Connecting-East-Asia.pdf http://siteresources.worldbank.org/INTEASTASIAPACIFIC/Resources/Connecting-East-Asia.pdf].&lt;br /&gt;
&lt;br /&gt;
Bhattacharyay, Biswa Nath. 2010. “Estimating Demand for Infrastructure in Energy, Transport, Telecommunications, Water and Sanitation in Asia and the Pacific: 2010-2020”. Working Paper no. 248. Asian Development Bank Institute, Tokyo.&amp;amp;nbsp;[http://www.adbi.org/working-paper/2010/09/09/4062.infrastructure.demand.asia.pacific/ http://www.adbi.org/working-paper/2010/09/09/4062.infrastructure.demand.asia.pacific/].&lt;br /&gt;
&lt;br /&gt;
Bruinsma, Jelle. 2011. “The Resources Outlook: By How Much Do Land, Water and Crop Yields Need to Increase by 2050?” In Piero Conforti, ed.,.&amp;amp;nbsp;&#039;&#039;Looking Ahead in World Food and Agriculture: Perspectives to 2050&#039;&#039;. Rome: Food and Agriculture Organization of the United Nations (FAO), 233–275.&amp;amp;nbsp;[http://www.fao.org/docrep/014/i2280e/i2280e.pdf http://www.fao.org/docrep/014/i2280e/i2280e.pdf].&lt;br /&gt;
&lt;br /&gt;
Calderón, César, and Luis Servén. 2010a. “Infrastructure and Economic Development in Sub-Saharan Africa.”&amp;amp;nbsp;&#039;&#039;Journal of African Economies&#039;&#039;&amp;amp;nbsp;19(Supplement 1): i13–i87. doi:10.1093/jae/ejp022.&amp;amp;nbsp;[http://jae.oxfordjournals.org/cgi/content/abstract/19/suppl_1/i13 http://jae.oxfordjournals.org/cgi/content/abstract/19/suppl_1/i13].&lt;br /&gt;
&lt;br /&gt;
Calderón, César, and Luis Servén. 2010b. “Infrastructure in Latin America”. World Bank Policy Research Working Paper. Report Number 5317. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Canning, David. 1998. “A Database of World Stocks of Infrastructure, 1950-1995.”&amp;amp;nbsp;&#039;&#039;The World Bank Economic Review&#039;&#039;&amp;amp;nbsp;12(3): 529–548.&lt;br /&gt;
&lt;br /&gt;
Canning, David, and Mansour Farahani. 2007. “A Database of World Stocks of Infrastructure: Update 1950-2005”. Harvard School of Public Health, Boston, MA.&amp;amp;nbsp;[http://www.hsph.harvard.edu/faculty/david-canning/data-sets/ http://www.hsph.harvard.edu/faculty/david-canning/data-sets/].&lt;br /&gt;
&lt;br /&gt;
Cavallo, Eduardo Alfredo, and Christian Daude. 2008. “Public Investment in Developing Countries: A Blessing or a Curse?” RES Working Paper #4597. Inter-American Development Bank (IADB) - Research Department, OECD, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Chatterton, Isabe, and Olga S. Puerto. 2006.&amp;amp;nbsp;&#039;&#039;Estimation of Infrastructure Investment Needs in the South Asia Region: Executive Summary&#039;&#039;. Washington, DC: World Bank.&amp;amp;nbsp;[http://siteresources.worldbank.org/INTSARREGTOPTRANSPORT/Resources/Inf_Investment_Needs_IC_version4.pdf http://siteresources.worldbank.org/INTSARREGTOPTRANSPORT/Resources/Inf_Investment_Needs_IC_version4.pdf].&lt;br /&gt;
&lt;br /&gt;
Congressional Budget Office. 2010.&amp;amp;nbsp;&#039;&#039;Public Spending on Transportation and Water Infrastructure&#039;&#039;. Washington, DC: Congressional Budget Office.&amp;amp;nbsp;[http://www.cbo.gov/publication/21902 http://www.cbo.gov/publication/21902].&lt;br /&gt;
&lt;br /&gt;
Estache, Antonio, and Ana Goicoechea. 2005. “A Research Database on Infrastructure Economic Performance”. Policy Research Working Paper no. 3643. World Bank, Washington, DC.&amp;amp;nbsp;[http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2005/06/16/000016406_20050616100502/Rendered/PDF/wps3643.pdf http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2005/06/16/000016406_20050616100502/Rendered/PDF/wps3643.pdf].&lt;br /&gt;
&lt;br /&gt;
Ezzati, Majid, Alan D. Lopez, Anthony Rodgers, and Christopher J. L. Murray, eds. 2004.&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors&#039;&#039;. Geneva, Switzerland: World Health Organization (WHO).&lt;br /&gt;
&lt;br /&gt;
Fay, Marianne. 2001. “Financing the Future: Infrastructure Needs in Latin America, 2000-05”. Policy Research Working Paper no. 2545. World Bank, Washington, DC.&amp;amp;nbsp;[http://elibrary.worldbank.org/docserver/download/2545.pdf?expires=1375200693&amp;amp;id=id&amp;amp;accname=guest&amp;amp;checksum=DB7E5FB146EE28C93511B5ADAB2FD3CB http://elibrary.worldbank.org/docserver/download/2545.pdf?expires=1375200693&amp;amp;id=id&amp;amp;accname=guest&amp;amp;checksum=DB7E5FB146EE28C93511B5ADAB2FD3CB].&lt;br /&gt;
&lt;br /&gt;
Fay, Marianne, and Tito Yepes. 2003. “Investing in Infrastructure: What Is Needed from 2000 to 2010?” Policy Research Working Paper no. 3102. World Bank, Washington, DC. RePEc.&amp;amp;nbsp;[http://ideas.repec.org/p/wbk/wbrwps/3102.html http://ideas.repec.org/p/wbk/wbrwps/3102.html].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2007. “Forecasting Global Economic Growth with Endogenous Multifactor Productivity: The International Futures (IFs) Approach”. Pardee Center for International Futures Working Paper, University of Denver. Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., Devin Joshi, Jonathan Moyer, Timothy Sisk and José Roberto Solórzano. 2014. Strengthening Governance Globally. vol. 5, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Hughes, Gordon, Paul Chinowsky, and Ken Strzepek. 2009. “The Costs of Adapting to Climate Change for Infrastructure”. Economics of Adaptation to Climate Change Discussion Paper no. 2. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
International Transport Forum, and Organisation for Economic Cooperation and Development (OECD). 2011. “Trends in Transport Infrastructure Investment 1995-2009”. Paris.&lt;br /&gt;
&lt;br /&gt;
Kohli, Harpaul Alberto, and Phillip Basil. 2011. “Requirements for Infrastructure Investment in Latin America Under Alternate Growth Scenarios.”&amp;amp;nbsp;&#039;&#039;Global Journal of Emerging Market Economies&#039;&#039;&amp;amp;nbsp;3(1): 59 –110. doi:10.1177/097491011000300103.&amp;amp;nbsp;[http://eme.sagepub.com/content/3/1/59.abstract http://eme.sagepub.com/content/3/1/59.abstract].&lt;br /&gt;
&lt;br /&gt;
Kim, M. Julie, and Rita Nangia. 2010. “Infrastructure Development in India and China—A Comparative Analysis.” In William Ascher and Corinne Krupp, eds.,.&amp;amp;nbsp;&#039;&#039;Physical Infrastructure Development: Balancing The Growth, Equity, and Environmental Imperatives&#039;&#039;. New York, NY: Palgrave Macmillan, 97–140.&lt;br /&gt;
&lt;br /&gt;
Lora, Eduardo A. 2007.&amp;amp;nbsp;&#039;&#039;Public Investment in Infrastructure in Latin America: Is Debt the Culprit?&#039;&#039;&amp;amp;nbsp;Inter-American Development Bank Working Paper. Washington, DC: Inter-American Development Bank (IADB) - Research Department.&lt;br /&gt;
&lt;br /&gt;
Nelson, Gerald C., Mark W. Rosegrant, Amanda Palazzo, Ian Gray, Christina Ingersoll, Richard Robertson, Simla Tokgoz, Tingju Zhu, Timothy B. Sulser, Claudia Ringler, Siwa Msangi, and Liangzhi You. 2010.&amp;amp;nbsp;&#039;&#039;Food Security, Farming, and Climate Change to 2050: Scenarios, Results, Policy Options&#039;&#039;. Washington, DC: International Food Policy Research Institute.&amp;amp;nbsp;[http://www.ifpri.org/publication/food-security-farming-and-climate-change-2050 http://www.ifpri.org/publication/food-security-farming-and-climate-change-2050].&lt;br /&gt;
&lt;br /&gt;
Organisation for Economic Co-operation and Development. 2006.&amp;amp;nbsp;&#039;&#039;Infrastructure to 2030 Volume 1: Telecom, Land Transport, Water and Electricity&#039;&#039;. Infrastructure to 2030. Paris: Organisation for Economic Cooperation and Development.&lt;br /&gt;
&lt;br /&gt;
Organisation for Economic Co-operation and Development. 2009.&amp;amp;nbsp;&#039;&#039;Going for Growth: Economic Policy Reforms&#039;&#039;. Paris: Organisation for Economic Cooperation and Development (OECD).&lt;br /&gt;
&lt;br /&gt;
Qiang, Christine Zhen-Wei, Carlo M. Rossotto, and Kaoru Kimura. 2009. “Economic Impacts of Broadband.” In World Bank, ed.,.&amp;amp;nbsp;&#039;&#039;2009 Information and Communications for Development: Extending Reach and Increasing Impact&#039;&#039;. Washington, DC: World Bank, 35–50.&lt;br /&gt;
&lt;br /&gt;
Rothman, Dale S. Mohammod T. Irfan, Eli Margolese-Malin, Barry B. Hughes, Jonathan Moyer, and Janet Dickson. 2013.&amp;amp;nbsp;&#039;&#039;Building Global Infrastructure.&amp;amp;nbsp;&#039;&#039;vol. 4, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press. Stambrook, David. 2006. “Key Factors Driving the Future Demand for Surface Transport Infrastructure and Services.” In Organisation for Economic Cooperation and Development (OECD), ed.,.&amp;amp;nbsp;&#039;&#039;Infrastructure to 2030 Volume 1: Telecom, Land Transport, Water and Electricity&#039;&#039;. Infrastructure to 2030. Paris: Organisation for Economic Cooperation and Development (OECD), 185–239.&lt;br /&gt;
&lt;br /&gt;
World Health Organization, and UNICEF. 2013.&amp;amp;nbsp;&#039;&#039;Progress on Sanitation and Drinking-Water - 2013 Update&#039;&#039;. Geneva: World Health Organization.&lt;br /&gt;
&lt;br /&gt;
Yepes, Tito. 2008. “Investment Needs for Infrastructure in Developing Countries 2008-15”. Draft. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Yepes, Tito. 2005.&amp;amp;nbsp;&#039;&#039;Expenditure on Infrastructure in East Asia Region, 2006–2010&#039;&#039;. East Asia Pacific Infrastructure Flagship Study. Manila: Asian Development Bank (ADB), Japan Bank for International Cooperation (JBIC), World Bank.&lt;br /&gt;
&lt;br /&gt;
You, Liangzhi, Claudia Ringler, Ulrike Wood-Sichra, Richard Robertson, Stanley Wood, Tingju Zhu, Gerald Nelson, Zhe Guo, and Yan Sun. 2011. “What Is the Irrigation Potential for Africa? A Combined Biophysical and Socioeconomic Approach.”&amp;amp;nbsp;&#039;&#039;Food Policy&#039;&#039;&amp;amp;nbsp;36(6): 770–782. doi:10.1016/j.foodpol.2011.09.001.&amp;amp;nbsp;[http://www.sciencedirect.com/science/article/pii/S030691921100114X http://www.sciencedirect.com/science/article/pii/S030691921100114X].&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8319</id>
		<title>Introduction to IFs</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8319"/>
		<updated>2017-09-07T22:10:10Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Purposes&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;International Futures (IFs) is a tool for thinking about long-term global trends and planning more strategically for the&amp;amp;nbsp;future.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
IFs can help you:&lt;br /&gt;
&lt;br /&gt;
*Understand&amp;amp;nbsp;the state of major&amp;amp;nbsp;global systems&lt;br /&gt;
*Explore&amp;amp;nbsp;long-term trends and consider where they might take&amp;amp;nbsp;us&lt;br /&gt;
*Learn about the dynamic&amp;amp;nbsp;interactions between global systems&lt;br /&gt;
*Clarify long-term organizational goals/priorities&lt;br /&gt;
*Develop alternative scenarios (if-then statements) about the future&lt;br /&gt;
*Investigate how different groups (households, firms or governments) can shape the future&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;IFs development and analysis depend&amp;amp;nbsp;on core, underlying assumptions, including the following.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*Global issues are becoming&amp;amp;nbsp;more significant as the scope of human interaction and human impact on the broader environment grow.&lt;br /&gt;
*Goals and priorities for human systems are becoming clearer and are more frequently and consistently communicated.&lt;br /&gt;
*Understanding of the dynamics of human systems is improving&amp;amp;nbsp;rapidly.&lt;br /&gt;
*The domain of human choice and action is broadening.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;What issues can you&amp;amp;nbsp;investigate with IFs?&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*[[Environment]]: Atmospheric carbon dioxide levels, world forest area, fossil fuel usage&lt;br /&gt;
*[[Socio-Political|Socio-Political Change]]: Life expectancy, literacy rate, democracy level, status of women, value change&lt;br /&gt;
*[[Population|Demographics]]: Population levels and growth, fertility, mortality, migration&lt;br /&gt;
*[[Agriculture|Food and Agriculture]]: Land use and production levels, calorie availability, malnutrition rates&lt;br /&gt;
*[[Energy]]: Resource and production levels, demand patterns, renewable energy share&lt;br /&gt;
*[[Economics]]: Sectoral production, consumption, and trade patterns and structural change&lt;br /&gt;
*[[Interstate_Politics_(IP)|Geopolitics]]: Country and regional power levels&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Visual Representation&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
[[File:IFsOverviewChart.jpg|frame|center|Visual representation of IFs structure]]&lt;br /&gt;
&lt;br /&gt;
Among the philosophical premises of the International Futures (IFs) project is that the model cannot be a &amp;quot;black box&amp;quot; to users and be truly useful. Model users must be able to examine the structures of IFs in order (1) to have confidence in them, and (2) learn from them.&lt;br /&gt;
&lt;br /&gt;
There is available (see topics under Understanding the Mode in the contents of this help system):&lt;br /&gt;
&lt;br /&gt;
*[[Understand_IFs#Dominant_Relations|Dominant Relations]] of the model structure&lt;br /&gt;
*[[Understand_IFs#2.2|Structure-Based and Agent-Class Driven Modeling]]&lt;br /&gt;
*[[Understand_IFs#Equation_Notation|Equation Notation]]&lt;br /&gt;
*[[Introduction_to_IFs#IFs_Bibliography|IFs Bibliography]] of data and data sources&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Quick Survey&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;population&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents 22 age-sex cohorts to age 100+&lt;br /&gt;
*calculates change in fertility and mortality rates in response to income, income distribution, and analysis multipliers&lt;br /&gt;
*computes average life expectancy at birth, literacy rate, and overall measures of human development (HDI) and physical quality of life&lt;br /&gt;
*represents migration and HIV/AIDS&lt;br /&gt;
*includes a newly developing submodel of formal education across primary, secondary, and tertiary levels&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;economic&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents the economy in six sectors: agriculture, materials, energy, industry, services, and ICT (other sectors could be configured, using raw data from the GTAP project)&lt;br /&gt;
*computes and uses input-output matrices that change dynamically with development level&lt;br /&gt;
*is a general equilibrium-seeking model that does not assume exact equilibrium will exist in any given year; rather it uses inventories as buffer stocks and to provide price signals so that the model chases equilibrium over time&lt;br /&gt;
*contains an endogenous production function that represents contributions to growth in multifactor productivity from R&amp;amp;D, education, worker health, economic policies (&amp;quot;freedom&amp;quot;), and energy prices (the &amp;quot;quality&amp;quot; of capital)&lt;br /&gt;
*uses a Linear Expenditure System to represent changing consumption patterns&lt;br /&gt;
*utilizes a &amp;quot;pooled&amp;quot; rather than the bilateral trade approach for international trade&lt;br /&gt;
*is being imbedded during 2002 in a social accounting matrix (SAM) envelope that will tie economic production and consumption to intra-actor financial flows&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;agricultural&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents production, consumption and trade of crops and meat; it also carries ocean fish catch and aquaculture in less detail&lt;br /&gt;
*maintains land use in crop, grazing, forest, urban, and &amp;quot;other&amp;quot; categories&lt;br /&gt;
*represents demand for food, for livestock feed, and for industrial use of agricultural products&lt;br /&gt;
*is a partial equilibrium model in which food stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the agricultural sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;energy&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*portrays production of six energy types: oil, gas, coal, nuclear, hydroelectric, and other renewable&lt;br /&gt;
*represents consumption and trade of energy in the aggregate&lt;br /&gt;
*represents known reserves and ultimate resources of the fossil fuels&lt;br /&gt;
*portrays changing capital costs of each energy type with technological change as well as with draw-downs of resources&lt;br /&gt;
*is a partial equilibrium model in which energy stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the energy sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The two &#039;&#039;&#039;socio-political&#039;&#039;&#039; sub-modules:&lt;br /&gt;
&lt;br /&gt;
Within countries or geographic groupings&lt;br /&gt;
&lt;br /&gt;
*represents fiscal policy through taxing and spending decisions&lt;br /&gt;
*shows six categories of government spending: military, health, education, R&amp;amp;D, foreign aid, and a residual category&lt;br /&gt;
*represents changes in social conditions of individuals (like fertility rates or literacy levels), attitudes of individuals (such as the level of materialism/postmaterialism of a society from the World Value Survey), and the social organization of people (such as the status of women)&lt;br /&gt;
*represents the evolution of democracy&lt;br /&gt;
*represents the prospects for state instability or failure&lt;br /&gt;
&lt;br /&gt;
Between countries or groupings of countries&lt;br /&gt;
&lt;br /&gt;
*traces changes in power balances across states and regions&lt;br /&gt;
*allows exploration of changes in the level of interstate threat&lt;br /&gt;
*represents possible action-reaction processes and arms races with associated potential for conflict among countries&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;environmental&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows tracking of remaining resources of fossil fuels, of the area of forested land, of water usage, and of atmospheric carbon dioxide emissions&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;technology&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows changes in assumptions about rates of technological advance in agriculture, energy, and the broader economy&lt;br /&gt;
*explicitly represents the extent of electronic networking of individuals in societies&lt;br /&gt;
*is tied to the governmental spending model with respect to R&amp;amp;D spending&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Background&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
International Futures (IFs) has evolved since 1980 through three &amp;quot;generations,&amp;quot; with a fourth generation now taking form.&lt;br /&gt;
&lt;br /&gt;
The first generation had deep roots in the world models of the 1970s, including those of the Club of Rome. In particular, IFs drew on the Mesarovic-Pestel or World Integrated Model (Mesarovic and Pestel 1974). The author of IFs had contributed to that project, including the construction of the energy submodel. IFs consciously also drew on the Leontief World Model (Leontief et al. 1977), the Bariloche Foundation’s world model (Herrera et al. 1976), and Systems Analysis Research Unit Model (SARU 1977), following comparative analysis of those models by Hughes (1980). That generation was written in FORTRAN and available for use on main-frame computers through CONDUIT, an educational software distribution center at the University of Iowa. Although the primary use of that and subsequent generations was by students, IFs has always had some policy analysis capability that has appealed to specialists. For example, the U.S. Foreign Service Institute used the first generation of IFs in a mid-career training program.&lt;br /&gt;
&lt;br /&gt;
The second generation of International Futures moved to early microcomputers in 1985, using the DOS platform. It was a very simplified version of the original IFs without regional or country differentiation.&lt;br /&gt;
&lt;br /&gt;
The third generation, first available in 1993, became a full-scale microcomputer model. The third generation improved earlier representations of demographic, energy, and food systems, but added new environmental and socio-political content. It built upon the collaboration of the author with the GLOBUS project, and it adopted the economic submodel of GLOBUS (developed by the author). GLOBUS had been created with the inspiration of Karl Deutsch and under the leadership of Stuart Bremer (1987) at the Wissenschaftszentrum in Berlin.&lt;br /&gt;
&lt;br /&gt;
The third generation has produced three editions/major releases of IFs, each accompanied by a book also called International Futures (Hughes 1993, 1996, 1999). The second edition moved to a Visual Basic platform that allowed a much improved menu-driven interface, running under Windows. The third edition incorporated an early global mapping capability and an initial ability to do cross-sectional and longitudinal data analysis.&lt;br /&gt;
&lt;br /&gt;
The fourth generation has been taking shape since early 2000. It has been heavily influenced by the usage of the model in an increasingly policy-analysis mode by several important organizations. First, General Motors commissioned a specialized version of IFs named CoVaTrA (Consumer Values Trends Analysis) with a need for updated and extended demographic modeling and representation of value change. An alliance was established with the World Values Survey, directed by Ronald Inglehart, to create that version. Second, the Strategic Assessments Group of the Central Intelligence Agency commissioned a specialized version named IFs for SAG. The work involved in preparing that greatly extended and enhanced the socio-political representations of the model, both domestic and international. Third, the European Commission sponsored a project named TERRA which has led to a specialized version named IFs for TERRA. IFs for TERRA work led to enhancements across the model, including improved representation of economic sectors, updated IO matrices and a basic Social Accounting Matrix, GINI and Lorenz curves, and preparing for extended environmental impact representation (drawing upon the Advanced Sustainability Analysis framework of the Finland Futures Research Center).&lt;br /&gt;
&lt;br /&gt;
Throughout this emergence of a fourth generation IFs (incorporating all of the above elements for additional users) there has been also a heavy emphasis on enhanced usability. Ideas from Robert Pestel in the TERRA project led to the creation of a new tree-structure for scenario creation and management. Ideas from Ronald Inglehart led to the development of the Guided Use structure and a somewhat more game-like character within that structure. Inglehart also help arrange funding to support the programming of Guided Use through the European Union Center of the University of Michigan.&lt;br /&gt;
&lt;br /&gt;
The fifth version of IFs is currently in use and represents broad strides to improving the model and its usability. It is the first version of this software to be placed online due to the help of the National Intelligence Council ([http://www.ifs.du.edu http://www.ifs.du.edu]). Also, usability has been increased as Packaged Displays and Flex Packaged Displays were introduced that allowed for the creation of very specific lists of countries/regions, groups or Glists. A new education model has also been incorporated into the broader IFs model. New scenarios were created for UNEP (focusing on environmental change) and Pardee (focusing on poverty). Finally, one of the largest changes made was incorporating 182 countries into the Base-Case scenario used by IFs. Previous versions of IFs used broader regions to forecast global trends. This change also did away with the Student and Professional versions.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Geographic Representation of the World&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
186 countries underpin the functioning of IFs and these countries can be displayed separately or as parts of larger groups that users can determine.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;Below is a visual representation of how different entities are organized into Countries/Regions, Groups or Glists:&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
[[File:Geo 186.png|frame|right|Visual representation of IF&#039;s definition of regions/countries/groups/glists]]&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;*Note: In older versions of IFs, Regions were used as intermediaries between Countries and Groups. In the future, they, or some similarly named unit, will be a sub-unit of Countries. Regions, acting as a sub-unit of Countries, are currently not a feature of IFs. See the image located at the bottom of this Help topic.&#039;&#039;&lt;br /&gt;
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When using IFs, there are many occasions where the user is asked whether or not they would like to display their results as a product of single countries, or larger groups. This is typically a toggle switch that moves between Country/Region and Groups, however, it might be a three-way-toggle that includes Country/Region, Group and Glist.&lt;br /&gt;
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Countries/Regions are currently the smallest geographical unit that users can represent. The ability to split countries down into smaller regions, or states, is under development. There are 186 different countries/regions that users can display.&lt;br /&gt;
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Groups are variably organized geographically or by memberships in international institutions/regimes. You can find out who is represented in each group and add or delete members by exploring the [[Extended_Features#Manage_Groups/Regions|Managing Regionalization]] function.&lt;br /&gt;
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Glists merge both Groups and Countries/Regions. These lists are mostly geographically bound. In the future, the Glist distinction will become more important as some users may want to place, for example, both the Indian state of Kerala in a Glist with Sri Lanka and Nepal.&lt;br /&gt;
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Users may also want to [[Extended_Features#Change_Grouping/Regionalization|create]] their own groups or [[Extended_Features#Identify_Groups_or_Country/Region_Members|explore]] what countries are members of what groups.&lt;br /&gt;
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= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Time Horizon&amp;lt;/span&amp;gt; =&lt;br /&gt;
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&#039;&#039;&#039;Future Forecasts.&#039;&#039;&#039; IFs begins computation with data from 2000 and can dynamically calculate values for all variables annually through 2100.&lt;br /&gt;
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&#039;&#039;&#039;Historical Analysis and &amp;quot;Forecasts.&amp;quot;&#039;&#039;&#039; IFs also includes an extensive and growing historical data base starting in 1960. The data basis allows analysis of relationships among variables across countries and across time.&amp;amp;nbsp;&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Instructional Use&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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The standard modes for using IFs in a classroom are:&lt;br /&gt;
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1. Assigning class members to an issue area or topic. Consider identifying specific questions for them to address.&lt;br /&gt;
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2. Assigning class members to a country/geographic region. Again, specificity helps.&lt;br /&gt;
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Most often, students will work independently or in groups on projects and share information after completing them. It is possible, however, to have students work interactively, by assigning them topics or regions, letting them begin work, and then have the interacting groups (or individuals) create a collective model run with the changes that each group proposes by topic or region. That process, although more difficult to organize, allows the class as whole to investigate the interaction of their topics or regions (and to share learning about model use).&lt;br /&gt;
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There is a&amp;amp;nbsp;[http://portfolio.du.edu/bhughes web site]&amp;amp;nbsp;available in support of the educational use of IFs. You will find syllabi at that site. There are several [[Introduction_to_IFs#Publications_on_IFs|publications]] on IFs, including a book structured specifically for educational use.&lt;br /&gt;
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Donald Borock has described his classroom use of IFs in print. Borock, Donald. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot; Advances in Social Science and Computers 4, 103-127.&lt;br /&gt;
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= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Acknowledgements&amp;lt;/span&amp;gt; =&lt;br /&gt;
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The author gratefully recognizes critical contributions in the forms of:&lt;br /&gt;
&lt;br /&gt;
:1. Testing and suggestions for development of IFs in one or more of multiple generations. By Donald Borock, Richard Chadwick, William Dixon, Dale Rothman, Phil Schrodt, Douglas Stuart, Donald Sylvan, Jonathan Wilkenfeld, and Ronald Inglehart.&lt;br /&gt;
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:2. Computer assistance across many releases. By Michael Niemann, Terrance Peet-Lukes, Douglas McClure, Mohammod Irfan, and Jose Solorzano.&lt;br /&gt;
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:3. Data gathering and general assistance. By James Chung, Padma Padula, Shannon Brady, David Horan, Michael Ferrier, Kay Drucker, Warren Christopher, and Anwar Hossain.&lt;br /&gt;
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:4. Long-term encouragement and support. By Harold Guetzkow, Karl Deutsch, Richard Chadwick, Gerald Barney, and Ronald Inglehart.&lt;br /&gt;
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:5. Association in related world modeling projects and projects building upon IFs. By Mihajlo Mesarovic, Aldo Barsotti, Juan Huerta, John Richardson, Thomas Shook, Patricia Strauch, and other members of the World Integrated Model (WIM) team. By Stuart Bremer, Peter Brecke, Thomas Cusack, Wolf Dieter-Eberwein, Brian Pollins, and Dale Smith of the GLOBUS modeling project. By Evan Hillebrand, Paul Herman, and others of the IFs for SAG project. By Rob Lempert and Steve Bankes at RAND, Santa Monica. By Robert Pestel, Jonathan Cave, Ronald Inglehart, Sergei Parinov, Pentti Malaska, and many others in the IFs for TERRA project.&lt;br /&gt;
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:6. Financial assistance (without responsibility for the form of the evolving product). By the National Science Foundation, the Cleveland Foundation, the Exxon Education Foundation, the Kettering Family Foundation, the Pacific Cultural Foundation, the United States Institute of Peace, General Motors, the Strategic Assessments Group of the Central Intelligence Agency, the European Commission (Information Society Technology) Programme, the European Union Center of the University of Michigan, the National Intelligence Council (for web conversion), and Frederick S. Pardee. &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
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= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Feedback&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
Feedback on how to improve IFs is always appreciated, especially if you find something that is not working. Compliments are also accepted. Please contact. To send the IFs team an e-mail, click on&amp;amp;nbsp;[mailto:pardee.center@du.edu Pardee Center]&amp;amp;nbsp;in stand-alone versions or on the web.&lt;br /&gt;
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= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Support for IFs Use&amp;lt;/span&amp;gt; =&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Publications on IFs&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
To obtain additional information about IFs and its use, consult:&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes and Evan E. Hillebrand, &#039;&#039;&#039;Exploring and Shaping International Futures.&#039;&#039;&#039; Boulder, CO: Paradigm Publishers, 2006. Specifically, see chapter 4.&lt;br /&gt;
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Barry B. Hughes, &#039;&#039;&#039;International Futures: Choices in the Face of Uncertainty,&#039;&#039;&#039; 3rd ed. Boulder, CO: Westview Press, 1999. This volume is built around IFs and contains detailed suggestions for its use. Version 3.17 of IFs, which runs under Windows 95, is distributed with the third edition of the book. The second edition contained a version for Windows 3.1, and the first edition ran under DOS. Chapter 4 of the 2nd edition of IFs included Flow Charts of Worldviews , reproduced now in this Help system.&lt;br /&gt;
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Barry B. Hughes, &#039;&#039;&#039;Continuity and Change in World Politics,&#039;&#039;&#039; 4th ed. Englewood Cliffs, N.J.: Prentice Hall, 2000. IFs can also usefully supplement this textbook on global politics.&lt;br /&gt;
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Barry B. Hughes, &amp;quot;The International Futures (IFs) Modeling Project. 1999. &#039;&#039;&#039;Simulation and Gaming&#039;&#039;&#039; 30, No. 3 (September): 304-326.&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;IFs Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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Alcamo, Joseph, Rik Leemans and Eric Kreileman, eds. 1998.&amp;amp;nbsp;&#039;&#039;Global Change Scenarios of the 21st Century: Results from the IMAGE 2.1 Model&#039;&#039;. The Netherlands: Pergamon.&lt;br /&gt;
&lt;br /&gt;
Alcamo, Joseph. 1994.&amp;amp;nbsp;&#039;&#039;IMAGE 2.0: Integrated Modeling of Global Climate Change&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
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Alexandratos, Nikos, ed. 1995.&amp;amp;nbsp;&#039;&#039;World Agriculture: Towards 2010&#039;&#039;&amp;amp;nbsp;(An FAO Study). New York: FAO and John Wiley and Sons.&lt;br /&gt;
&lt;br /&gt;
Allen, R. G. D. 1968.&amp;amp;nbsp;&#039;&#039;Macro-Economic Theory: A Mathematical Treatment&#039;&#039;. New York: St. Martin&#039;s Press.&lt;br /&gt;
&lt;br /&gt;
Avery, Dennis. 1995. &amp;quot;Saving the Planet with Pesticides,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 50-82.&lt;br /&gt;
&lt;br /&gt;
Bailey, Ronald, ed. 1995.&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;. New York: The Free Press.&lt;br /&gt;
&lt;br /&gt;
Barbieri, Kathleen. 1996. &amp;quot;Economic Interdependence: A Path to Peace or a Source of Interstate Conflict?&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Peace Research&#039;&#039;&amp;amp;nbsp;33: 29-50.&lt;br /&gt;
&lt;br /&gt;
Barker, T.S. and A.W.A. Peterson, eds. 1987.&amp;amp;nbsp;&#039;&#039;The Cambridge Multisectoral Dynamic Model of the British Economy&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Barney, Gerald O., W. Brian Kreutzer, and Martha J. Garrett, eds. 1991.&amp;amp;nbsp;&#039;&#039;Managing a Nation&#039;&#039;, 2nd ed. Boulder: Westview Press.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. 1997.&amp;amp;nbsp;&#039;&#039;Determinants of Economic Growth: A Cross-Country Empirical Study&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. and Xavier Sala-i-Martin. 1999.&amp;amp;nbsp;&#039;&#039;Economic Growth&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
&lt;br /&gt;
Bennett, D. Scott, and Allan Stam. 2003.&amp;amp;nbsp;&#039;&#039;The Behavioral Origins of War: Cumulation and Limits to Knowledge in Understanding International Conflict&#039;&#039;. Ann Arbor: University of Michigan Press&lt;br /&gt;
&lt;br /&gt;
Birg, Herwig. 1995.&amp;amp;nbsp;&#039;&#039;World Population Projections for the 21st Century&#039;&#039;. Frankfurt: Campus Verlag.&lt;br /&gt;
&lt;br /&gt;
Borock, Donald M. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot;&amp;amp;nbsp;&#039;&#039;Advances in Social Science and Computers&#039;&#039;&amp;amp;nbsp;4, 103-127.&lt;br /&gt;
&lt;br /&gt;
Bos, Eduard, My T. Vu, Ernest Massiah, and Rodolfo A. Bulatao. 1994.&amp;amp;nbsp;&#039;&#039;World Population Projections 1994-95 Edition&#039;&#039;&amp;amp;nbsp;[editions are biannual] Baltimore: Johns Hopkins Press.&lt;br /&gt;
&lt;br /&gt;
Boulding, Elise and Kenneth E. Boulding. 1995.&amp;amp;nbsp;&#039;&#039;The Future: Images and Processes&#039;&#039;. Thousand Oaks, CA: Sage Publications.&lt;br /&gt;
&lt;br /&gt;
Brecke, Peter. 1993. &amp;quot;Integrated Global Models that Run on Personal Computers,&amp;quot;&amp;amp;nbsp;&#039;&#039;Simulation&#039;&#039;60 (2).&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. 1977.&amp;amp;nbsp;&#039;&#039;Simulated Worlds: A Computer Model of National Decision-Making&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A., ed. 1987.&amp;amp;nbsp;&#039;&#039;The GLOBUS Model: Computer Simulation of World-wide Political and Economic Developments&#039;&#039;. Boulder, CO: Westview.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. and Walter Gruhn. 1988.&amp;amp;nbsp;&#039;&#039;Micro GLOBUS: A Computer Model of Long-Term Global Political and Economic Processes&#039;&#039;. Berlin: edition sigma.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. and Barry B. Hughes. 1990.&amp;amp;nbsp;&#039;&#039;Disarmament and Development: A Design for the Future?&#039;&#039;&amp;amp;nbsp;Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Brockmeier, Martina and Channing Arndt (presentor). 2002. Social Accounting Matrices. Powerpoint presentation on GTAP and SAMs (June 21). Found on the web.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1981.&amp;amp;nbsp;&#039;&#039;Building a Sustainable Society&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1988. &amp;quot;Analyzing the Demographic Trap,&amp;quot; in&amp;amp;nbsp;&#039;&#039;State of the World 1987&#039;&#039;, eds. Lester R. Brown and others. New York: W.W. Norton, pp. 20-37.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1995.&amp;amp;nbsp;&#039;&#039;Who Will Feed China?&#039;&#039;&amp;amp;nbsp;New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1996.&amp;amp;nbsp;&#039;&#039;Tough Choices: Facing the Challenge of Food Scarcity&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., et al. 1996&amp;amp;nbsp;&#039;&#039;State of the World 1996&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., Nicholas Lenssen, and Hal Kane. 1995.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1995. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., Christopher Flavin, and Hal Kane. 1996.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1996. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Burkhardt, Helmut. 1995. &amp;quot;Priorities for a Sustainable Civilization,&amp;quot; unpublished conference paper. Department of Physics, Ryerson Polytechnic University, Toronto, Canada.&lt;br /&gt;
&lt;br /&gt;
Bussolo, Maurizio, Mohamed Chemingui and David O’Connor. 2002. A Multi-Region Social Accounting Matrix (1995) and Regional Environmental General Equilibrium Model for India (REGEMI). Paris: OECD Development Centre (February). Available at&amp;amp;nbsp;[http://www.oecd.org/dev/technics www.oecd.org/dev/technics].&lt;br /&gt;
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British Petroleum Company. 1995.&amp;amp;nbsp;&#039;&#039;BP Statistical Review of World Energy&#039;&#039;. London: British Petroleum Company.&lt;br /&gt;
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Central Intelligence Agency (CIA). 1991.&amp;amp;nbsp;&#039;&#039;Handbook of Economic Statistics, 1991&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
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Central Intelligence Agency (CIA). 1994.&#039;&#039;&amp;amp;nbsp;The World Factbook 1994&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
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Chang, Sheldon S. L. 1961.&amp;amp;nbsp;&#039;&#039;Synthesis of Optimum Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
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Chenery, Hollis and Moises Syrquin. 1975.&amp;amp;nbsp;&#039;&#039;Patterns of Development 1950-1970&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Cipolla, Carlo M. 1962.&amp;amp;nbsp;&#039;&#039;The Economic History of World Population&#039;&#039;. Baltimore: Penguin.&lt;br /&gt;
&lt;br /&gt;
Cook, Earl. 1976.&amp;amp;nbsp;&#039;&#039;Man, Energy, Society&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
&lt;br /&gt;
Committee on the Strategic Assessment of the U.S. Department of Energy’s Coal Program. 1995.&amp;amp;nbsp;&#039;&#039;Coal: Energy for the Future&#039;&#039;. Washington, D.C.: National Academy Press.&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1981.&amp;amp;nbsp;&#039;&#039;The Global 2000 Report to the President&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1981b.&amp;amp;nbsp;&#039;&#039;Environmental Trends&#039;&#039;. Washington, D.C. (July).&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1991.&amp;amp;nbsp;&#039;&#039;21st Annual Report&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Crescenzi, Mark J.C. and Andrew J. Enterline. 2001. &amp;quot;Time Remembered: A Dynamic Model of Interstate Interaction,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;45, no. 3 (September): 409-431.&lt;br /&gt;
&lt;br /&gt;
Crosson, Pierre, and Jock R. Anderson. 1992.&amp;amp;nbsp;&#039;&#039;Resources and Global Food Prospects&#039;&#039;. Washington, D.C.: The World Bank. World Bank Technical Paper Number 184.&lt;br /&gt;
&lt;br /&gt;
Cusack, Thomas R. and Richard J. Stoll. 1990.&amp;amp;nbsp;&#039;&#039;Exploring Realpolitik: Probing International Relations with Computer Simulatio&#039;&#039;n. Boulder: Lynne Rienner Publishers.&lt;br /&gt;
&lt;br /&gt;
Dargay, Joyce and Dermot Gately. 1999. &amp;quot;Income’s Effect on Car and Vehicle Ownership, Worldwide: 1960-2015,&amp;quot;&amp;amp;nbsp;&#039;&#039;Transportation Research Part A&#039;&#039;&amp;amp;nbsp;33: 101-138.&lt;br /&gt;
&lt;br /&gt;
Dall, P., Kaspar, F. and Alcamo, J. 1998. &amp;quot;Modeling World-wide Water Availability and Water Use Under the Influence of Climate Change,&amp;quot;&amp;amp;nbsp;&#039;&#039;Proceedings of the Second International Conference on Climate and Water&#039;&#039;, July 17-20, Espoo, Finland.&lt;br /&gt;
&lt;br /&gt;
Dimaranan, Betina V. and Robert A. McDougall, eds. 2002.&amp;amp;nbsp;&#039;&#039;Global Trade, Assistance, and Production: The GTAP 5 Data Base&#039;&#039;. Center for Global Trade Analysis, Purdue University. Available at [http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp].&lt;br /&gt;
&lt;br /&gt;
Dowlatabadi, H., and Morgan, M.G. 1993. &amp;quot;A Model Framework for Integrated Studies of the Climate Problem,&amp;quot;&amp;amp;nbsp;&#039;&#039;Energy Policy&#039;&#039;&amp;amp;nbsp;(March): 209-221.&lt;br /&gt;
&lt;br /&gt;
Duchin, Faye. 1998.&amp;amp;nbsp;&#039;&#039;Structural Economics: Measuring Change in Technology, Lifestyles, and the Environment&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
&lt;br /&gt;
Edwards, Stephen R. 1995. &amp;quot;Conserving Biodiversity,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 212-265.&lt;br /&gt;
&lt;br /&gt;
Edmonds, J., and Reilly, J.M. 1985.&amp;amp;nbsp;&#039;&#039;Global Energy: Assessing the Future&#039;&#039;. Oxford, UK: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Edmonds, J., Pitcher, H. Rosenberg, N., and Wigley, T. &amp;quot;Design for the Global Change Assessment Model.&amp;quot;&amp;amp;nbsp;&#039;&#039;Integrative Assessment of Mitigation, Impacts and Adaptation to Climate Change&#039;&#039;. Laxenburg, Austria.&lt;br /&gt;
&lt;br /&gt;
Ehrlich, Paul R. and Anne H. Ehrlich. 1972.&amp;amp;nbsp;&#039;&#039;Population, Resources, Environment&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
&lt;br /&gt;
Eicher, Carl. 1982. &amp;quot;Facing up to Africa&#039;s Food Crisis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Foreign Affairs&#039;&#039;&amp;amp;nbsp;61, no. 1 (Fall): 151-74.&lt;br /&gt;
&lt;br /&gt;
Eberstadt, Nicholas. 1995. &amp;quot;Population, Food, and Income,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 8-47.&lt;br /&gt;
&lt;br /&gt;
Esty, Daniel C., Jack A. Goldstone, Ted Robert Gurr, Barbara Harff, Marc Levy, Geoffrey D. Dabelko, Pamela T. Surko, and Alan N. Unger. 1998. State Failure Task Force Report: Phase II Findings. Volume provided courtesy of Ted Robert Gurr.&lt;br /&gt;
&lt;br /&gt;
Flavin, Christopher. 1996. &amp;quot;Facing Up to the Risks of Climate Change,&amp;quot; in Lester R. Brown and others, eds., State of the World 1996 (New York: W.W. Norton), pp. 21-39.&lt;br /&gt;
&lt;br /&gt;
Forrester, Jay W. 1968.&amp;amp;nbsp;&#039;&#039;Principles of Systems&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Gilpin, Robert. 1981.&amp;amp;nbsp;&#039;&#039;War and Change in World Politics&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Globerman, Steven. 2000 (May). Linkages Between Technological Change and Productivity Growth. Industry Canada Research Publications Program: Occasional Paper 23.&lt;br /&gt;
&lt;br /&gt;
Grant, Lindsey. 1982.&amp;amp;nbsp;&#039;&#039;The Cornucopian Fallacies&#039;&#039;. Washington, D.C.: Environmental Fund.&lt;br /&gt;
&lt;br /&gt;
Griffith, Rachel, Stephen Redding, and John Van Reenen. 2000.&amp;amp;nbsp;&#039;&#039;Mapping the Two Faces of R&amp;amp;D: Productivity Growth in a Panel of OECD Industries&#039;&#039;. Institute for Fiscal Studies (January)&lt;br /&gt;
&lt;br /&gt;
Gwartney, James and Robert Lawson with Dexter Samida. 2000.&amp;amp;nbsp;&#039;&#039;Economic Freedom of the World: 2000 Annual Report&#039;&#039;. Vancouver, B.C.: the Fraser Institute.&lt;br /&gt;
&lt;br /&gt;
Hammond, Allen. 1998.&amp;amp;nbsp;&#039;&#039;Which World? Scenarios for the 21st Century&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
&lt;br /&gt;
Harff, Barbara, with Ted Robert Gurr and Alan Unger. 1999. Preconditions of Genocide and Politicide: 1955-1998. Paper prepared for the State Failure Task Force and provided courtesy of Barbara Harff and Ted Gurr.&lt;br /&gt;
&lt;br /&gt;
Henderson, Hazel. 1996. &amp;quot;Changing Paradigms and Indicators: Implementing Equitable, Sustainable and Participatory Development,&amp;quot; in Jo Marie Griesgraber and Bernhard G. Gunter,&amp;amp;nbsp;&#039;&#039;Development: New Paradigms and Principles for the 21st Century&#039;&#039;. East Haven, CT: Pluto Press, pp. 103-136.&lt;br /&gt;
&lt;br /&gt;
Herrera, Amilcar O., et al. 1976.&#039;&#039;&amp;amp;nbsp;Catastrophe or New Society? A Latin American World Model&#039;&#039;. Ottawa: International Development Research Centre.&lt;br /&gt;
&lt;br /&gt;
Hoekstra, A.Y. 1998.&amp;amp;nbsp;&#039;&#039;Perspectives on Water: An Integrated Model-Based Exploration of the Future&#039;&#039;. Utrecht, the Netherlands: International Books.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1980.&amp;amp;nbsp;&#039;&#039;World Modeling&#039;&#039;. Lexington, Mass: Lexington Books.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1982.&amp;amp;nbsp;&#039;&#039;International Futures Simulation: User&#039;s Manual&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985a.&amp;amp;nbsp;&#039;&#039;International Futures Simulation&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985b. &amp;quot;World Models: The Bases of Difference,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;29, 77-101.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985c.&amp;amp;nbsp;&#039;&#039;World Futures: A Critical Analysis of Alternatives&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1987. &amp;quot;Domestic Economic Processes,&amp;quot; in Stuart A. Bremer, ed.,&amp;amp;nbsp;&#039;&#039;The Globus Model: Computer Simulation of Worldwide Political Economic Development&#039;&#039;&amp;amp;nbsp;(Frankfurt and Boulder: Campus and Westview), pp. 39-158.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1988. &amp;quot;International Futures: History and Status,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Science Microcomputer Review&#039;&#039;&amp;amp;nbsp;6, 43-48.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1999. &amp;quot;The International Futures (IFs) Modeling Project.&#039;&#039;&amp;amp;nbsp;Simulation and Gaming&#039;&#039;&amp;amp;nbsp;Vol 30, No. 3 (September): 304-326.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1999.&amp;amp;nbsp;&#039;&#039;International Futures&#039;&#039;, 3rd edition Boulder: Westview Press, 1999.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2000.&amp;amp;nbsp;&#039;&#039;Continuity and Change in World Politics&#039;&#039;. Englewood Cliffs, N.J.: Prentice-Hall, fourth edition.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2001. &amp;quot;Global Social Transformation: The Sweet Spot, the Steady Slog, and the Systemic Shift,&amp;quot;&amp;amp;nbsp;&#039;&#039;Economic Development and Cultural Change&#039;&#039;&amp;amp;nbsp;49, No. 2 (January): 423-458.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2002.&amp;amp;nbsp;&#039;&#039;Theats and Opportunities Analysis&#039;&#039;. Living document prepared for the Strategic Assessments Group, Office of Transnational Issues, Central Intelligence Agency, August 2002.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. and Anwar Hossain. 2003. Long-Term Socio-Economic Modeling: With Universal, Globally-Integrated Social Accounting Matrices (SAMs) in a General Equilibrium Model Structure. IFs Project Living Document, University of Denver.&lt;br /&gt;
&lt;br /&gt;
Huth, Paul. 1996.&amp;amp;nbsp;&#039;&#039;Standing Your Ground: Territorial Disputes and International Conflict&#039;&#039;. Ann Arbor, MI: University of Michigan Press.&lt;br /&gt;
&lt;br /&gt;
Inglehart, Ronald. 1997.&amp;amp;nbsp;&#039;&#039;Modernization and Postmodernization: Cultural, Economic, and Political Change in 43 Societies&#039;&#039;. Ewing, NJ: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1995.&amp;amp;nbsp;&#039;&#039;Oil, Gas, and Coal Supply Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996.&amp;amp;nbsp;&#039;&#039;World Energy Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996b.&amp;amp;nbsp;&#039;&#039;The Strategic Value of Fossil Fuels: Challenges and Responses&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;International Financial Statistics&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;World Economic Outlook&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
Intergovernmental Panel on Climate Change (IPCC). 1995. Several volumes by various working groups. Published by Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Jansen, Karel and Rob Vos, eds. 1997.&amp;amp;nbsp;&#039;&#039;External Finance and Adjustment: Failure and Success in the Developing World&#039;&#039;. London: Macmillan Press Ltd.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1998.&amp;amp;nbsp;&#039;&#039;Modeling Global Change: The Art of Integrated Assessment Modelling&#039;&#039;. Cheltenham, UK: Edward Elgar.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1996.&amp;amp;nbsp;&#039;&#039;Meeting Targets: Tools to Support Integrated Modelling of Global Change&#039;&#039;. Den Haag: CIP-Gegevens Koninklijke Bibliotheek.&lt;br /&gt;
&lt;br /&gt;
Jansson, Kurt, Michael Harris, Angela Penrose. 1987.&amp;amp;nbsp;&#039;&#039;The Ethiopian Famine&#039;&#039;. London: Zed Books Ltd.&lt;br /&gt;
&lt;br /&gt;
Jeffreys, Kent. 1995. &amp;quot;Rescuing the Oceans,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 296-338.&lt;br /&gt;
&lt;br /&gt;
Jones, Daniel M., Stuart A. Bremer, and J. David Singer. 1996. &amp;quot;Militarized Interstate Disputes, 1816-1992: Rationale, Coding Rules, and Empirical Patterns,&amp;quot;&amp;amp;nbsp;&#039;&#039;Conflict Management and Peace Science&#039;&#039;&amp;amp;nbsp;XV, No. 2: 163-215.&lt;br /&gt;
&lt;br /&gt;
Khan, Haider A. 1998.&amp;amp;nbsp;&#039;&#039;Technology, Development and Democracy&#039;&#039;. Northhampton, Mass: Edward Elgar Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Kahn, Herman, William Brown, and Leon Martel. 1976.&amp;amp;nbsp;&#039;&#039;The Next 200 Years&#039;&#039;. New York: William Morrow.&lt;br /&gt;
&lt;br /&gt;
Kalymon, Basil A. 1975. &amp;quot;Economic Incentives in OPEC Oil Pricing Policy.&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Development Economics&#039;&#039;&amp;amp;nbsp;2: 337-362.&lt;br /&gt;
&lt;br /&gt;
Kaplan, Robert. 1994. &amp;quot;The Coming Anarchy,&amp;quot;&amp;amp;nbsp;&#039;&#039;The Atlantic Monthly&#039;&#039;&amp;amp;nbsp;273 (February): .&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999a. &amp;quot;Aggregating Governance Indicators&amp;quot;. World Bank Policy Research Department Working Paper No. 2195.&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999b. &amp;quot;Governance Matters&amp;quot;. World Bank Policy Research Department Working Paper No. 2196.&lt;br /&gt;
&lt;br /&gt;
Keepin, B. and B. Wynne. 1984. &amp;quot;Technical Analysis of the IIASA Energy Scenarios,&amp;quot;&amp;amp;nbsp;&#039;&#039;Nature&#039;&#039;312: 691-695.&lt;br /&gt;
&lt;br /&gt;
Kehoe, Timothy J. 1996. Social Accounting Matrices and Applied General Equilibrium Models. Federal Reserve Bank of Minneapolis, Working Paper 563.&lt;br /&gt;
&lt;br /&gt;
Kennedy, Paul. 1993.&amp;amp;nbsp;&#039;&#039;Preparing for the Twenty-First Century&#039;&#039;. New York: Random House.&lt;br /&gt;
&lt;br /&gt;
Klein, Lawrence R. and Fu-chen Lo, eds. 1995.&amp;amp;nbsp;&#039;&#039;Modeling Global Change&#039;&#039;. Tokyo: United Nations University Press.&lt;br /&gt;
&lt;br /&gt;
Kornai, J. 1971.&amp;amp;nbsp;&#039;&#039;Anti-Equilibrium&#039;&#039;. Amsterdam: North Holland.&lt;br /&gt;
&lt;br /&gt;
Kwasnicki, Witold and Halina Kwasnicka. 1996. &amp;quot;Long-Term Diffusion Factors of Technological Development: An Evolutionary Model and Case Study,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 31-57.&lt;br /&gt;
&lt;br /&gt;
Leontief, Wassily, Anne Carter and Peter Petri. 1977.&amp;amp;nbsp;&#039;&#039;The Future of the World Economy&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander H., and Elizabeth R. Ducot. 1976. &amp;quot;AGRIMOD: A Simulation Model for the Analysis of U.S. Food Policies.&amp;quot; Paper delivered at Conference on Systems Analysis of Grain Reserves, Joint Annual Meeting of GRSA and TIMS, Philadelphia, Pa., March 31-April 2.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander, H., et al. 1977. Energy in Agriculture: On Modeling Inputs in AGRIMOD. Final Report to U.S. Department of Energy. Palo Alto: Systems Control, Inc., August, available through NTIS.&lt;br /&gt;
&lt;br /&gt;
Lichbach, Mark Irving. 1989. &amp;quot;An Evaluation of ‘Does Economic Inequality Breed Political Conflict?,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;, Vol 41 , No. 4 (July 1989): 431-470.&lt;br /&gt;
&lt;br /&gt;
Liverman, Dianne. 1983.&amp;amp;nbsp;&#039;&#039;The Use of Global Simulation Models in Assessing Climate Impacts on the World Food System&#039;&#039;. Dissertation, University of California, Los Angeles.&lt;br /&gt;
&lt;br /&gt;
Londregan, John B. and Keith T. Poole. 1996. &amp;quot;Does High Income Promote Democrary?&amp;quot;,&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 1 (October): 1-30.&lt;br /&gt;
&lt;br /&gt;
MacKenzie, James J. 1996. &amp;quot;Oil as a Finite Resource: When is Global Production Likely to Peak?&amp;quot; Paper of the World Resources Institute. Washington, D.C.: WRI.&lt;br /&gt;
&lt;br /&gt;
Maddison, Angus. 1995.&amp;amp;nbsp;&#039;&#039;Monitoring the World Economy 1820-1992&#039;&#039;. Paris: OECD.&lt;br /&gt;
&lt;br /&gt;
Malthus, Thomas. 1798.&amp;amp;nbsp;&#039;&#039;An Essay on the Principle of Population as It Affects the Future Improvement of Society&#039;&#039;. London (reprinted many times).&lt;br /&gt;
&lt;br /&gt;
Mansfield, Edward D. 1994.&amp;amp;nbsp;&#039;&#039;Power, Trade, and War&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Marchetti, Cesare, Perrin S. Meyer, and Jesse H. Ausubel. 1996. &amp;quot;Human Population Dynamics Revisited with the Logistic Model: How Much Can be Modeled and Predicted?,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 1-30.&lt;br /&gt;
&lt;br /&gt;
Martens, Pim and Jan Rotmans, eds. 1999.&amp;amp;nbsp;&#039;&#039;Climate Change: An Integrated Perspective&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
&lt;br /&gt;
Martens, W.J.M. 1997. &amp;quot;Health Impacts of Climate Change and Ozone Depletion: An Eco-Epidemiological Approach,&amp;quot; Maastricht, the Netherlands: Maastricht University.&lt;br /&gt;
&lt;br /&gt;
Mason, Andrew. 1997. &amp;quot;The Role of Population Change in the Asian Economic Miracle,&amp;quot; Honolulu, Hawaii: East-West Center, AsiaPacific Issues, No. 33 (October), 8 pages.&lt;br /&gt;
&lt;br /&gt;
McMahon, Walter W. 1997.&amp;amp;nbsp;&#039;&#039;Education and Development: Measuring the Social Benefits&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, Jorgen Randers, and William K. Behrens, III. 1972.&amp;amp;nbsp;&#039;&#039;Limits to Growth&#039;&#039;. New York: Universe Books.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, and Jorgen Randers. 1992.&amp;amp;nbsp;&#039;&#039;Beyond the Limits&#039;&#039;. Post Mills, Vermont: Chelsea Green Publishing Company.&lt;br /&gt;
&lt;br /&gt;
Meadows, Dennis L. et al. 1974.&amp;amp;nbsp;&#039;&#039;Dynamics of Growth in a Finite World&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Mesarovic, Mihajlo D. and Eduard Pestel. 1974.&amp;amp;nbsp;&#039;&#039;Mankind at the Turning Point&#039;&#039;. New York: E.P. Dutton &amp;amp; Co.&lt;br /&gt;
&lt;br /&gt;
Mishkin, Eli. And Ludwig Braun, ed. 1961.&amp;amp;nbsp;&#039;&#039;Adaptive Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Moore, Will H., Ronny Lindstrom, and Valerie O’Regan. 1996. &amp;quot;Land Reform, Political Violence and the Economic Inequality-Political Conflict Nexus: A Longitudinal Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Interactions&#039;&#039;&amp;amp;nbsp;21, No. 4: 335-363.&lt;br /&gt;
&lt;br /&gt;
Mori, Shunsuke and Masato Takahaashi, 1997. An Integrated Assessment Model for the Evaluation of New Energy Technologies and Food Production, accepted by&amp;amp;nbsp;&#039;&#039;International Journal of Global Energy Issues&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
Naill, Roger F. 1977.&amp;amp;nbsp;&#039;&#039;Managing the Energy Transition&#039;&#039;. Vols. 1 and 2. Cambridge, Mass: Ballinger Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1992. &amp;quot;The DICE Model: Background and Structure of a Dynamic Integrated Climate Economy,&amp;quot; New Haven: Yale University.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1979.&amp;amp;nbsp;&#039;&#039;The Efficient Use of Energy Resources&#039;&#039;. New Haven, CT: Yale University Press.&lt;br /&gt;
&lt;br /&gt;
Oneal, John R. and Bruce M. Russett. 1997. The Classical Liberals were Right: Democracy, Interdependence, and Conflict, 1950-1985.&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;41, no. 2 (June): 267-294.&lt;br /&gt;
&lt;br /&gt;
Pan, Xiaoming. 2000 (January). &amp;quot;Social and Ecological Accounting Matrix: an Empirical Study for China,&amp;quot; paper submitted for the Thirteenth International Conference on Input-Output Techniques, Macerata, Italy, August 21-25, 2000.&lt;br /&gt;
&lt;br /&gt;
Pesaran, M. Hashem and G. C. Harcourt. 1999. Life and Work of John Richard Nicholas Stone.&lt;br /&gt;
&lt;br /&gt;
Pirages, Dennis. 1989.&amp;amp;nbsp;&#039;&#039;Global Technopolitics&#039;&#039;. Pacific Grove, Calif: Brooks/Cole Publishing.&lt;br /&gt;
&lt;br /&gt;
Prinn, R. H.J., A. Sokolov, C. Wand, X. Xiao, Z. Yang, R. Eckhaus, P. Stone, D. Ellerman, J Melilo, J. Fitzmaurice, D. Kicklighter, and Y. Liu. 1996. &amp;quot;Integrated Global System Model for Climate Policy Analysis: Model Framework and Sensitivity Analysis.&amp;quot; Cambridge, Mass: Global Change Center, Massachusetts Institute of Technology.&lt;br /&gt;
&lt;br /&gt;
Przeworski, Adam and Fernando Limongi. 1997. &amp;quot;Modernization: Theories and Facts,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 2 (January): 155-183.&lt;br /&gt;
&lt;br /&gt;
Population Reference Bureau. 1996. World Population Data Sheet 1996. Washington, D.C.: Population Reference Bureau.&lt;br /&gt;
&lt;br /&gt;
Postel, Sandra. 1996.&amp;amp;nbsp;&#039;&#039;Dividing the Waters: Food Security, Ecosystem Health, and the New Politics of Scarcity&#039;&#039;. Worldwatch Paper 132. Washington, D.C.: Worldwatch Institute, September.&lt;br /&gt;
&lt;br /&gt;
Pyatt, G. and J.I. Round, eds. 1985.&amp;amp;nbsp;&#039;&#039;Social Accounting Matrices: A Basis for Planning&#039;&#039;. Washington, D.C.: The World Bank.&lt;br /&gt;
&lt;br /&gt;
Raskin, P., T. Banuri, G. Gallopín, P. Gutman, A. Hammond, R. Kates, and R. Swart. 2001. Great Transition:&amp;amp;nbsp;&#039;&#039;The Promise and Lure of the Times Ahead&#039;&#039;. Forthcoming.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1990.&amp;amp;nbsp;&#039;&#039;Global Politics&#039;&#039;, 4th edition. Boston: Houghton Mifflin.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1995.&amp;amp;nbsp;&#039;&#039;Democracy and International Conflict&#039;&#039;. Columbia: University of South Carolina Press.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee and J. David Singer. 1973. &amp;quot; Measuring the Concentration of Power in the International System,&amp;quot;&#039;&#039;&amp;amp;nbsp;Sociological Methods and Research&#039;&#039;&amp;amp;nbsp;1, no. 4: 403-436. Reprinted in&amp;amp;nbsp;&#039;&#039;Measuring the Correlates of War&#039;&#039;, edited by J. David Singer and Paul Diehl. Ann Arbor: University of Michigan Press, 1990.&lt;br /&gt;
&lt;br /&gt;
Rayner. S. 1992. &amp;quot;Cultural Theory and Risk Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Theory of Risk&#039;&#039;, ed. G. D. Preagor. Westport, USA.&lt;br /&gt;
&lt;br /&gt;
Repetto, Robert and Duncan Austin. 1997.&amp;amp;nbsp;&#039;&#039;The Costs of Climate Protection&#039;&#039;. Washington, D.C.: World Resources Institute.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis Fry. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Chicago: Quadrangle Books.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis F. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Pittsburgh: Boxwood Press.&lt;br /&gt;
&lt;br /&gt;
Romer, Paul M. 1994. &amp;quot;The Origins of Endogenous Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Economic Perspectives&#039;&#039;Vol 8, No. 1 (Winter): 3-22.&lt;br /&gt;
&lt;br /&gt;
Root T. and Stephen Schneider. 1995. &amp;quot;Ecology and Climate: Research Strategies and Implications,&amp;quot; Science 269 (52): 334-341.&lt;br /&gt;
&lt;br /&gt;
Rosegrant, Mark W., Mercedita Agcaoili-Sombilla, and Nicostrato D. Perez. 1995. &amp;quot;Global Food Projections to 2020: Implications for Investment.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 5.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1999. Integrated Assessment Models: Uncertainty, Quality and Use. Maastricht, the Netherlands: Maastricht University, International Centre for Integrative Studies (ICIS), Working Paper 199-E005.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and Burt de Vries, eds. 1997.&amp;amp;nbsp;&#039;&#039;Perspectives on Global Change: The Targets Approach&#039;&#039;. Cambridge, UK: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and M.B.A. van Asselt. 1996. &amp;quot;Integrated Assessment: A Growing Child on its Way to Maturity,&amp;quot;&amp;amp;nbsp;&#039;&#039;Climatic Change&#039;&#039;&amp;amp;nbsp;34 (3-4): 327-336.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1990.&amp;amp;nbsp;&#039;&#039;IMAGE: An Integrated Model to Assess the Greenhouse Effect&#039;&#039;. Dordrecht, the Netherlands: Kluwer Academics.&lt;br /&gt;
&lt;br /&gt;
Saaty, Thomas L. 1996. The Analytic Network Process: Decision Making with Dependence and Feedback. Pittsburgh: RWS Publications.&lt;br /&gt;
&lt;br /&gt;
Schafer, Andreas and David G. Victor. 1997. The Future Mobility of the World Population. Massachusetts Institute of Technology and International Institute for Applied Systems Analysis, Discussion Paper 97-6-4 (revision 2, September).&lt;br /&gt;
&lt;br /&gt;
Scheer, Sara J. and Satya Yadav. 1996. &amp;quot;Land Degradation in the Developing World: Implications for Food, Agriculture, and the Environment to 2020.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 14.&lt;br /&gt;
&lt;br /&gt;
Schneider, Stephen. 1997. &amp;quot;Integrated Assessment Modeling of Climate Change: Transparent Rational Tool for Policy Making or Opaque Screen Hiding Value-Laden Assumptions?&amp;quot;&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(4): 229-250.&lt;br /&gt;
&lt;br /&gt;
Schwartz, Peter. 1996.&#039;&#039;&amp;amp;nbsp;The Art of the Long View.&#039;&#039;&amp;amp;nbsp;New York: Doubleday.&lt;br /&gt;
&lt;br /&gt;
Sedjo, Roger A. 1995. &amp;quot;Forests: Conflicting Signals,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 178-209.&lt;br /&gt;
&lt;br /&gt;
Shane, Harold G. and Gary A. Sojka. 1990. &amp;quot;John Elfreth Watkins, Jr.: Forgotten Genius of Forecasting,&amp;quot; in Edward Cornish, ed.,&#039;&#039;&amp;amp;nbsp;The 1990s and Beyond&#039;&#039;. Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Shaw, Timothy W. and Clement E. Adibe. 1995-96. &amp;quot;Africa and Global Developments in the Twenty-First Century,&amp;quot; International Journal 51 (Winter): 1-26.&lt;br /&gt;
&lt;br /&gt;
Siegmann, Heinrich. 1985.&amp;amp;nbsp;&#039;&#039;Recent Developments in World Modeling&#039;&#039;. Berlin: Science Center.&lt;br /&gt;
&lt;br /&gt;
Simon, Julian. 1981.&amp;amp;nbsp;&#039;&#039;The Ultimate Resource&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Singer, J. David, Stuart Bremer, and John Stuckey. 1972. &amp;quot;Capability Distribution, Uncertainty, and Major Power Wars, 1820-1965.&amp;quot; In Bruce Russett, ed.,&amp;amp;nbsp;&#039;&#039;Peace, War, and Numbers.&#039;&#039;&amp;amp;nbsp;Beverly Hills: Sage.&lt;br /&gt;
&lt;br /&gt;
Sivard, Ruth Leger. 1993.&amp;amp;nbsp;&#039;&#039;World Military and Social Expenditures 1993.&#039;&#039;&amp;amp;nbsp;Washington, D.C. 20007: World Priorities, Box 25140.&lt;br /&gt;
&lt;br /&gt;
Solow, Robert M. 1956. &amp;quot;A Contribution to the Theory of Economic Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Quarterly Journal of Economics&#039;&#039;&amp;amp;nbsp;70, 1 (February): 65-94.&lt;br /&gt;
&lt;br /&gt;
Stanford University. 1978.&amp;amp;nbsp;&#039;&#039;Stanford Pilot Energy/Economic Model&#039;&#039;. Stanford: Department of Research, Interim Report, Vol. 1.&lt;br /&gt;
&lt;br /&gt;
Stockholm International Peace Research Institute (SIPRI). 1994.&amp;amp;nbsp;&#039;&#039;SIPRI Yearbook&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Stone, Richard. 1986. &amp;quot;The Accounts of Society,&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Applied Econometrics&#039;&#039;&amp;amp;nbsp;1, no. 1 (January): 5-28.&lt;br /&gt;
&lt;br /&gt;
Strategic Assessments Group (SAG), Office of Transnational Issues, Directorate of Intelligence. 2001 (February). The Global Economy in the Long Term. OTI IR 2001-013.&lt;br /&gt;
&lt;br /&gt;
Systems Analysis Research Unit (SARU). 1977.&amp;amp;nbsp;&#039;&#039;SARUM 76 Global Modeling Project&#039;&#039;. Departments of the Environment and Transport, 2 Marsham Street, London, 3WIP 3EB.&lt;br /&gt;
&lt;br /&gt;
Tammen, Ronald L, Jacek Kugler, Douglas Lemke, Allan C. Stam III, Carole Alsharabati, Mark Andrew Abdollahian, Brian Efird, and A.F.K. Organski. 2000. Power Transitions: Strategies for the 21st Century. New York: Chatham House Publishers.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1975. &amp;quot;Theoretical Foundations and Technical Implications.&amp;quot; in Charles Blitzer, Peter Clark and Lance Taylor, eds.,&amp;amp;nbsp;&#039;&#039;Economy-Wide Models and Development Planning.&#039;&#039;&amp;amp;nbsp;Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1979.&amp;amp;nbsp;&#039;&#039;Macro Models for Developing Countries&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Thirlwall, A. P. 1977.&amp;amp;nbsp;&#039;&#039;Growth and Development&#039;&#039;. New York: John Wiley and Sons.&lt;br /&gt;
&lt;br /&gt;
Thompson, M. 1997. Cultural Theory and Integrated Assessment.&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(3): 139-150.&lt;br /&gt;
&lt;br /&gt;
Thompson, M., R. Ellis and A. Wildavsky. 1990.&amp;amp;nbsp;&#039;&#039;Cultural Theory&#039;&#039;. Boulder, Co: Westview Press.&lt;br /&gt;
&lt;br /&gt;
Thorbecke, Erik. 2001. &amp;quot;The Social Accounting Matrix: Deterministic or Stochastic Concept?&amp;quot;, paper prepared for a conference in honor of Graham Pyatt&#039;s retirement, at the Institute of Social Studies, The Hague, Netherlands (November 29 and 30). Available at [http://people.cornell.edu/pages/et17/etpapers.html http://people.cornell.edu/pages/et17/etpapers.html].&lt;br /&gt;
&lt;br /&gt;
United Nations, Department of Economic and Social Affairs. 1956.&amp;amp;nbsp;&#039;&#039;Methods of Population Projections by Sex and Age&#039;&#039;. New York: United Nations, ST/SOA Series A.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1992.&amp;amp;nbsp;&#039;&#039;Long-Range World Population Projections. Two Centuries of Population Growth: 1950-2150&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1993.&amp;amp;nbsp;&#039;&#039;World Population Prospects - the 1992 Revision&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations Development Program (UNDP). 1995.&amp;amp;nbsp;&#039;&#039;Human Development Report&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1992.&amp;amp;nbsp;&#039;&#039;Production Yearbook.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1995.&#039;&#039;&amp;amp;nbsp;World Agriculture: Towards 2010.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 1999. The World at Six Billion New York: UN.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2000. Replacement Migration: Is it a Solution to Declining and Ageing Populations? New York: UN.&lt;br /&gt;
&lt;br /&gt;
United States Arms Control and Disarmament Agency (ACDA). 1995.&amp;amp;nbsp;&#039;&#039;World Military Expenditures and Arms Transfers 1995&#039;&#039;. Washington, D.C.: Arms Control and Disarmament Agency.&lt;br /&gt;
&lt;br /&gt;
United States Bureau of the Census. 1991.&amp;amp;nbsp;&#039;&#039;World Population Profile: 1991&#039;&#039;. Report WP/91 Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Walters, Robert S. and David H. Blake. 1992.&amp;amp;nbsp;&#039;&#039;The Politics of Global Economic Relations&#039;&#039;, 4th edition. Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Waltz, Kenneth N. 1959. Man, the State, and War: A Theoretical Analysis. New York: Columbia University Press.&lt;br /&gt;
&lt;br /&gt;
Watkins, John Elfreth, Jr. 1990. &amp;quot;What May Happen in the Next Hundred Years,&amp;quot; in Edward Cornish, ed.,&amp;amp;nbsp;&#039;&#039;The 1990s and Beyond.&#039;&#039;&amp;amp;nbsp;Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Wildavsky, Aaron, and Ellen Tenenbaum. 1981.&amp;amp;nbsp;&#039;&#039;The Politics of Mistrust&#039;&#039;. Beverly Hills: Sage Publications.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1991b.&amp;amp;nbsp;&#039;&#039;World Tables 1991&#039;&#039;. New York: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1995&amp;amp;nbsp;&#039;&#039;World Development Report 1995&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
World Energy Council (WEC) Commission. 1993.&amp;amp;nbsp;&#039;&#039;Energy for Tomorrow’s World.&#039;&#039;&amp;amp;nbsp;New York: St. Martin’s Press.&lt;br /&gt;
&lt;br /&gt;
World Resources Institute (WRI). 1994.&amp;amp;nbsp;&#039;&#039;World Resources 1994-95.&#039;&#039;&amp;amp;nbsp;New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Wortman, Sterling and Ralph W. Cummings, Jr. 1978.&#039;&#039;&amp;amp;nbsp;To Feed This World&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Zinnes, Dina A. and John W. Gillespie, eds. 1976.&amp;amp;nbsp;&#039;&#039;Mathematical Models in International Relations&#039;&#039;&amp;amp;nbsp;(New York: Preaeger).&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Infrastructure Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Agénor, Pierre-Richard, Mustapha Kamel Nabli, and Tarik M. Yousef. 2007. “Public Infrastructure and Private Investment in the Middle East and North Africa.” In Mustapha Kamel Nabli, ed.,. Breaking the Barriers to Higher Economic Growth: Better Governance and Deeper Reforms in the Middle East and North Africa. Washington, DC: World Bank Publications, 399–422.&lt;br /&gt;
&lt;br /&gt;
Asian Development Bank, Japan Bank for International Cooperation, and World Bank. 2005.&amp;amp;nbsp;&#039;&#039;Connecting East Asia: A New Framework for Infrastructure&#039;&#039;. Tokyo: Asian Development Bank, Japan Bank for International Cooperation, and World Bank.&amp;amp;nbsp;[http://siteresources.worldbank.org/INTEASTASIAPACIFIC/Resources/Connecting-East-Asia.pdf http://siteresources.worldbank.org/INTEASTASIAPACIFIC/Resources/Connecting-East-Asia.pdf].&lt;br /&gt;
&lt;br /&gt;
Bhattacharyay, Biswa Nath. 2010. “Estimating Demand for Infrastructure in Energy, Transport, Telecommunications, Water and Sanitation in Asia and the Pacific: 2010-2020”. Working Paper no. 248. Asian Development Bank Institute, Tokyo.&amp;amp;nbsp;[http://www.adbi.org/working-paper/2010/09/09/4062.infrastructure.demand.asia.pacific/ http://www.adbi.org/working-paper/2010/09/09/4062.infrastructure.demand.asia.pacific/].&lt;br /&gt;
&lt;br /&gt;
Bruinsma, Jelle. 2011. “The Resources Outlook: By How Much Do Land, Water and Crop Yields Need to Increase by 2050?” In Piero Conforti, ed.,.&amp;amp;nbsp;&#039;&#039;Looking Ahead in World Food and Agriculture: Perspectives to 2050&#039;&#039;. Rome: Food and Agriculture Organization of the United Nations (FAO), 233–275.&amp;amp;nbsp;[http://www.fao.org/docrep/014/i2280e/i2280e.pdf http://www.fao.org/docrep/014/i2280e/i2280e.pdf].&lt;br /&gt;
&lt;br /&gt;
Calderón, César, and Luis Servén. 2010a. “Infrastructure and Economic Development in Sub-Saharan Africa.”&amp;amp;nbsp;&#039;&#039;Journal of African Economies&#039;&#039;&amp;amp;nbsp;19(Supplement 1): i13–i87. doi:10.1093/jae/ejp022.&amp;amp;nbsp;[http://jae.oxfordjournals.org/cgi/content/abstract/19/suppl_1/i13 http://jae.oxfordjournals.org/cgi/content/abstract/19/suppl_1/i13].&lt;br /&gt;
&lt;br /&gt;
Calderón, César, and Luis Servén. 2010b. “Infrastructure in Latin America”. World Bank Policy Research Working Paper. Report Number 5317. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Canning, David. 1998. “A Database of World Stocks of Infrastructure, 1950-1995.”&amp;amp;nbsp;&#039;&#039;The World Bank Economic Review&#039;&#039;&amp;amp;nbsp;12(3): 529–548.&lt;br /&gt;
&lt;br /&gt;
Canning, David, and Mansour Farahani. 2007. “A Database of World Stocks of Infrastructure: Update 1950-2005”. Harvard School of Public Health, Boston, MA.&amp;amp;nbsp;[http://www.hsph.harvard.edu/faculty/david-canning/data-sets/ http://www.hsph.harvard.edu/faculty/david-canning/data-sets/].&lt;br /&gt;
&lt;br /&gt;
Cavallo, Eduardo Alfredo, and Christian Daude. 2008. “Public Investment in Developing Countries: A Blessing or a Curse?” RES Working Paper #4597. Inter-American Development Bank (IADB) - Research Department, OECD, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Chatterton, Isabe, and Olga S. Puerto. 2006.&amp;amp;nbsp;&#039;&#039;Estimation of Infrastructure Investment Needs in the South Asia Region: Executive Summary&#039;&#039;. Washington, DC: World Bank.&amp;amp;nbsp;[http://siteresources.worldbank.org/INTSARREGTOPTRANSPORT/Resources/Inf_Investment_Needs_IC_version4.pdf http://siteresources.worldbank.org/INTSARREGTOPTRANSPORT/Resources/Inf_Investment_Needs_IC_version4.pdf].&lt;br /&gt;
&lt;br /&gt;
Congressional Budget Office. 2010.&amp;amp;nbsp;&#039;&#039;Public Spending on Transportation and Water Infrastructure&#039;&#039;. Washington, DC: Congressional Budget Office.&amp;amp;nbsp;[http://www.cbo.gov/publication/21902 http://www.cbo.gov/publication/21902].&lt;br /&gt;
&lt;br /&gt;
Estache, Antonio, and Ana Goicoechea. 2005. “A Research Database on Infrastructure Economic Performance”. Policy Research Working Paper no. 3643. World Bank, Washington, DC.&amp;amp;nbsp;[http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2005/06/16/000016406_20050616100502/Rendered/PDF/wps3643.pdf http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2005/06/16/000016406_20050616100502/Rendered/PDF/wps3643.pdf].&lt;br /&gt;
&lt;br /&gt;
Ezzati, Majid, Alan D. Lopez, Anthony Rodgers, and Christopher J. L. Murray, eds. 2004.&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors&#039;&#039;. Geneva, Switzerland: World Health Organization (WHO).&lt;br /&gt;
&lt;br /&gt;
Fay, Marianne. 2001. “Financing the Future: Infrastructure Needs in Latin America, 2000-05”. Policy Research Working Paper no. 2545. World Bank, Washington, DC.&amp;amp;nbsp;[http://elibrary.worldbank.org/docserver/download/2545.pdf?expires=1375200693&amp;amp;id=id&amp;amp;accname=guest&amp;amp;checksum=DB7E5FB146EE28C93511B5ADAB2FD3CB http://elibrary.worldbank.org/docserver/download/2545.pdf?expires=1375200693&amp;amp;id=id&amp;amp;accname=guest&amp;amp;checksum=DB7E5FB146EE28C93511B5ADAB2FD3CB].&lt;br /&gt;
&lt;br /&gt;
Fay, Marianne, and Tito Yepes. 2003. “Investing in Infrastructure: What Is Needed from 2000 to 2010?” Policy Research Working Paper no. 3102. World Bank, Washington, DC. RePEc.&amp;amp;nbsp;[http://ideas.repec.org/p/wbk/wbrwps/3102.html http://ideas.repec.org/p/wbk/wbrwps/3102.html].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2007. “Forecasting Global Economic Growth with Endogenous Multifactor Productivity: The International Futures (IFs) Approach”. Pardee Center for International Futures Working Paper, University of Denver. Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., Devin Joshi, Jonathan Moyer, Timothy Sisk and José Roberto Solórzano. 2014. Strengthening Governance Globally. vol. 5, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Hughes, Gordon, Paul Chinowsky, and Ken Strzepek. 2009. “The Costs of Adapting to Climate Change for Infrastructure”. Economics of Adaptation to Climate Change Discussion Paper no. 2. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
International Transport Forum, and Organisation for Economic Cooperation and Development (OECD). 2011. “Trends in Transport Infrastructure Investment 1995-2009”. Paris.&lt;br /&gt;
&lt;br /&gt;
Kohli, Harpaul Alberto, and Phillip Basil. 2011. “Requirements for Infrastructure Investment in Latin America Under Alternate Growth Scenarios.”&amp;amp;nbsp;&#039;&#039;Global Journal of Emerging Market Economies&#039;&#039;&amp;amp;nbsp;3(1): 59 –110. doi:10.1177/097491011000300103.&amp;amp;nbsp;[http://eme.sagepub.com/content/3/1/59.abstract http://eme.sagepub.com/content/3/1/59.abstract].&lt;br /&gt;
&lt;br /&gt;
Kim, M. Julie, and Rita Nangia. 2010. “Infrastructure Development in India and China—A Comparative Analysis.” In William Ascher and Corinne Krupp, eds.,.&amp;amp;nbsp;&#039;&#039;Physical Infrastructure Development: Balancing The Growth, Equity, and Environmental Imperatives&#039;&#039;. New York, NY: Palgrave Macmillan, 97–140.&lt;br /&gt;
&lt;br /&gt;
Lora, Eduardo A. 2007.&amp;amp;nbsp;&#039;&#039;Public Investment in Infrastructure in Latin America: Is Debt the Culprit?&#039;&#039;&amp;amp;nbsp;Inter-American Development Bank Working Paper. Washington, DC: Inter-American Development Bank (IADB) - Research Department.&lt;br /&gt;
&lt;br /&gt;
Nelson, Gerald C., Mark W. Rosegrant, Amanda Palazzo, Ian Gray, Christina Ingersoll, Richard Robertson, Simla Tokgoz, Tingju Zhu, Timothy B. Sulser, Claudia Ringler, Siwa Msangi, and Liangzhi You. 2010.&amp;amp;nbsp;&#039;&#039;Food Security, Farming, and Climate Change to 2050: Scenarios, Results, Policy Options&#039;&#039;. Washington, DC: International Food Policy Research Institute.&amp;amp;nbsp;[http://www.ifpri.org/publication/food-security-farming-and-climate-change-2050 http://www.ifpri.org/publication/food-security-farming-and-climate-change-2050].&lt;br /&gt;
&lt;br /&gt;
Organisation for Economic Co-operation and Development. 2006.&amp;amp;nbsp;&#039;&#039;Infrastructure to 2030 Volume 1: Telecom, Land Transport, Water and Electricity&#039;&#039;. Infrastructure to 2030. Paris: Organisation for Economic Cooperation and Development.&lt;br /&gt;
&lt;br /&gt;
Organisation for Economic Co-operation and Development. 2009.&amp;amp;nbsp;&#039;&#039;Going for Growth: Economic Policy Reforms&#039;&#039;. Paris: Organisation for Economic Cooperation and Development (OECD).&lt;br /&gt;
&lt;br /&gt;
Qiang, Christine Zhen-Wei, Carlo M. Rossotto, and Kaoru Kimura. 2009. “Economic Impacts of Broadband.” In World Bank, ed.,.&amp;amp;nbsp;&#039;&#039;2009 Information and Communications for Development: Extending Reach and Increasing Impact&#039;&#039;. Washington, DC: World Bank, 35–50.&lt;br /&gt;
&lt;br /&gt;
Rothman, Dale S. Mohammod T. Irfan, Eli Margolese-Malin, Barry B. Hughes, Jonathan Moyer, and Janet Dickson. 2013.&amp;amp;nbsp;&#039;&#039;Building Global Infrastructure.&amp;amp;nbsp;&#039;&#039;vol. 4, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press. Stambrook, David. 2006. “Key Factors Driving the Future Demand for Surface Transport Infrastructure and Services.” In Organisation for Economic Cooperation and Development (OECD), ed.,.&amp;amp;nbsp;&#039;&#039;Infrastructure to 2030 Volume 1: Telecom, Land Transport, Water and Electricity&#039;&#039;. Infrastructure to 2030. Paris: Organisation for Economic Cooperation and Development (OECD), 185–239.&lt;br /&gt;
&lt;br /&gt;
World Health Organization, and UNICEF. 2013.&amp;amp;nbsp;&#039;&#039;Progress on Sanitation and Drinking-Water - 2013 Update&#039;&#039;. Geneva: World Health Organization.&lt;br /&gt;
&lt;br /&gt;
Yepes, Tito. 2008. “Investment Needs for Infrastructure in Developing Countries 2008-15”. Draft. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Yepes, Tito. 2005.&amp;amp;nbsp;&#039;&#039;Expenditure on Infrastructure in East Asia Region, 2006–2010&#039;&#039;. East Asia Pacific Infrastructure Flagship Study. Manila: Asian Development Bank (ADB), Japan Bank for International Cooperation (JBIC), World Bank.&lt;br /&gt;
&lt;br /&gt;
You, Liangzhi, Claudia Ringler, Ulrike Wood-Sichra, Richard Robertson, Stanley Wood, Tingju Zhu, Gerald Nelson, Zhe Guo, and Yan Sun. 2011. “What Is the Irrigation Potential for Africa? A Combined Biophysical and Socioeconomic Approach.”&amp;amp;nbsp;&#039;&#039;Food Policy&#039;&#039;&amp;amp;nbsp;36(6): 770–782. doi:10.1016/j.foodpol.2011.09.001.&amp;amp;nbsp;[http://www.sciencedirect.com/science/article/pii/S030691921100114X http://www.sciencedirect.com/science/article/pii/S030691921100114X].&lt;br /&gt;
&lt;br /&gt;
== [[Development_Mode_Features|Development Mode Features]] ==&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Health&amp;diff=8318</id>
		<title>Health</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Health&amp;diff=8318"/>
		<updated>2017-09-07T21:46:33Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;The most recent and complete health model documentation is available on Pardee&#039;s [http://pardee.du.edu/ifs-health-model-documentation website]. Although the text in this interactive system is, for some IFs models, often significantly out of date, you may still find the basic description useful to you.&lt;br /&gt;
&lt;br /&gt;
The IFs health model allows users to forecast age, sex, and country specific health outcomes related to 15 cause categories (see table) out to the year 2100.&amp;amp;nbsp; Based on previous work done by the World Health Organization’s (WHO) Global Burden of Disease (GBD) project&amp;lt;span style=&amp;quot;color: #990000&amp;quot; data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;sup&amp;gt;[1]&amp;lt;/sup&amp;gt;&amp;lt;/span&amp;gt;, formulations based on three distal drivers – income, education, and technology – comprise the core of the IFs health model.&amp;amp;nbsp; However, the IFs model goes beyond the distal drivers, including both richer structural formulations and proximate health drivers (e.g. nutrition and environmental variables).&amp;amp;nbsp; Integration into the IFs system also allows us to incorporate forward linkages from health to other systems, such as the economic and population modules.&amp;amp;nbsp; Importantly, IFs provides the user the ability to vary model assumptions and create customized scenarios; as such, IFs is a tool exploring how policy choices might result in alternative health futures.&lt;br /&gt;
&lt;br /&gt;
This documentation supplements the third volume of the PPHP series, “Improving Global Health,” (Hughes et al, 2011) by providing technical details of health model integration into the IFs system.&amp;amp;nbsp; It includes the specific equations used to forecast outcomes and drivers, relative risk values for proximate drivers, and data manipulations related to model initialization and projection.&amp;amp;nbsp; We intend the IFs model to be fully transparent to all users, and invite comments and questions at [http://www.ifs.du.edu/contact/index.aspx http://www.ifs.du.edu/contact/index.aspx].&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Cause groups in IFs&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&amp;amp;nbsp;Group I – Communicable, Maternal, Perinatal, and Nutritional Conditions&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*Diarrheal diseases&lt;br /&gt;
*Malaria&lt;br /&gt;
*Respiratory infections&lt;br /&gt;
*HIV/AIDS&lt;br /&gt;
*Other Group I causes&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&amp;amp;nbsp;Group II – Noncommunicable Diseases&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*Malignant neoplasms&lt;br /&gt;
*Cardiovascular diseases&lt;br /&gt;
*Digestive diseases&lt;br /&gt;
*Chronic respiratory diseases&lt;br /&gt;
*Diabetes&lt;br /&gt;
*Mental health&lt;br /&gt;
*Other Group II causes&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&amp;amp;nbsp;Group III – Injuries&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*Road traffic accidents&lt;br /&gt;
*Other unintentional injuries&lt;br /&gt;
*Intentional injuries&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Structure and Agent System: Health&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot; border=&amp;quot;0&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width: 50%&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;System/Subsystem&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Health&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| &amp;lt;div&amp;gt;&#039;&#039;&#039;Organizing Structure&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Hybrid structure using distal driver formulations supplemented by proximate drivers; integrated with larger IFs systems such as population and governance&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| &amp;lt;div&amp;gt;&#039;&#039;&#039;Stocks&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Population by age-sex; stunted population; HIV prevalence&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| &amp;lt;div&amp;gt;&#039;&#039;&#039;Flows&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Births, mortality and morbidity&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| &amp;lt;div&amp;gt;&#039;&#039;&#039;Key Aggregate&amp;amp;nbsp;&#039;&#039;&#039; &#039;&#039;&#039;Relationships&amp;amp;nbsp;&#039;&#039;&#039;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;(illustrative, not comprehensive)&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Distal driver formulations driven by income, education, and time as a proxy for technological advance&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Proximate driver formulations driven by various social patterns and behaviors&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| &amp;lt;div&amp;gt;&#039;&#039;&#039;Key Agent-Class Behavior&amp;amp;nbsp;&#039;&#039;&#039; &#039;&#039;&#039;Relationships&#039;&#039;&#039;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;(illustrative, not comprehensive)&amp;lt;br/&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Behavior related to proximate drivers such as smoking, indoor solid fuel use, obesity&amp;lt;br/&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Dominant Relations: Health&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
Health forecasting systems typically can help us either (1) to understand better where patterns of human development appear to be taking us with respect to global health, giving attention to the distribution of disease burden and the patterns of change in it; or (2) to consider opportunities for intervention and achievement of alternative health futures, enhancing the foundation for decisions and actions that improve health.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Broad structural models (e.g., that of the Global Burden of Disease or GBD) assist in the first purpose by relating deep or distal development drivers to outcomes.&amp;amp;nbsp; More specialized structural formulations and the inclusion of proximate drivers open the door to the second, allowing for consideration of interventions in the pursuit of alternate health futures.&amp;amp;nbsp; A more hybrid and integrated model form like that of IFs can help with both purposes and provide a richer overall picture of alternative health futures.[[File:Health1.png|frame|right|Visual representation of health module]]&lt;br /&gt;
&lt;br /&gt;
The figure shows the general structure.&amp;amp;nbsp; Formulations based on distal drivers (the GBD methodology) sit at its core.&amp;amp;nbsp; There is no inherent reason, however, that income, education and time (the distal drivers of the GBD approach) should be equally capable of helping us forecast disease in each of the major categories (let alone each of the specific diseases) that the GBD models examine.&amp;amp;nbsp; For example, distal driver formulations tend to produce forecasts of constantly decreasing death rates.&amp;amp;nbsp; Yet we know, for instance, that smoking, obesity, road traffic accidents, and their related toll on health tend to increase in developing societies among those who first obtain higher levels of income and education; with further societal spread of income and education, at least smoking and road traffic deaths (and perhaps also obesity) typically decline.&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;sup&amp;gt;[http://www.du.edu/ifs/help/understand/health/dominant.html#footnote &amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1&amp;lt;/span&amp;gt;] ]&amp;lt;/sup&amp;gt;&amp;lt;/span&amp;gt; A hybrid model can therefore help us identify opportunities for interventions to improve health futures. These interventions might also occur in the form of super-distal drivers (for example, policy-driven human action with respect to health systems).&amp;amp;nbsp; The sociopolitical and environmental modules in IFs act in part as super-distal foundations for variables such as undernutrition&amp;amp;nbsp;and indoor air pollution which, in turn, facilitate analyses of proximate risk factors and human action around them.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
The integrated nature of the IFs modeling system further allows us to think about feedback loops between health outcomes and larger development variables such as economic progress and population structure.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; It is partly for this reason that the creators of the GBD models added exogenous specification of smoking impact to the otherwise mostly monotonically (one-direction only) changing specifications.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Health Flow Charts&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt;&amp;lt;span style=&amp;quot;font-size:large;&amp;quot;&amp;gt;&amp;amp;nbsp;&amp;lt;/span&amp;gt; ===&lt;br /&gt;
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Mortality from most causes of death is a function of a small number of distal or deep drivers and a larger number of proximate or more immediate drivers.&amp;amp;nbsp; For two specific mortality types, however, specifically deaths from AIDS and vehicle accidents, there are more specialized representations that rely on a number of more cause-related drivers.&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Distal Drivers and Basic Indicators&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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To forecast mortality related to most of the major cause clusters we use the regression models and associated beta coefficients prepared for the GBD project (Mathers and Loncar 2006).&amp;amp;nbsp; Age, sex, cause, and country-specific mortality rate is a function of income (using GDP per capita as a proxy), adult education, technological progress. For specific death causes, smoking impact (for malignant neoplasms, cardiovascular disease, and respiratory disease) or body mass index (for diabetes only) add to the causality; see the discussion of flow charts and equations for information on the determination within IFs of smoking and smoking impact and of body mass index and obesity.&lt;br /&gt;
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[[File:Health2.png|frame|right|Visual representation of distal drivers and basic indicators]]A number of parameters [[Health#Forecasting_Technology_for_the_Distal_Driver_Formulation_of_Mortality|control technology in the distal functions]].&amp;amp;nbsp; In the default mode &#039;&#039;(&#039;&#039;&#039;hlmortmodsw&#039;&#039;&#039; &#039;&#039; = 1), IFs modifies the technology (time) coefficient in recognition of slower than expected historical progress in many countries, an approach developed in the Global Burden of Disease (GBD project). &amp;amp;nbsp;Those country differences are controlled by &#039;&#039;&#039;&#039;&#039;hltechbase&#039;&#039; &#039;&#039;&#039;,&#039;&#039;&#039;&#039;&#039;hltechlinc,&#039;&#039; &#039;&#039;&#039; and &#039;&#039;&#039;&#039;&#039;hltechssa.&#039;&#039; &#039;&#039;&#039; &amp;amp;nbsp;Setting the switch value to 0 activates an alternative IFs project approach to the impact of those parameters.&amp;amp;nbsp;&lt;br /&gt;
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The user can also affect the mortality patterns directly with several parameters, including &#039;&#039;&#039;&#039;&#039;mortm&#039;&#039; &#039;&#039;&#039;, which allows simultaneous manipulation of all causes of death and &#039;&#039;&#039;&#039;&#039;hlmortm&#039;&#039; &#039;&#039;&#039;, which facilitates manipulation of each cause of death separately.&amp;amp;nbsp;&amp;amp;nbsp; &#039;&#039;&#039;&#039;&#039;Hlmortcdchldm&#039;&#039; &#039;&#039;&#039; changes the rates of all communicable diseases for children aged 5 and younger, while &#039;&#039;&#039;&#039;&#039;hlmortcdadltm&#039;&#039; &#039;&#039;&#039; affects rates of death from communicable diseases for adults aged 15-49.&amp;amp;nbsp;&lt;br /&gt;
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Based on the mortality level, it is possible to compute the years of life lost to each cause of death (HLYLL).&amp;amp;nbsp; Using WHO-based estimates, IFs links mortality also to years of living with disability (HLYLD).&amp;amp;nbsp; The sum of the two is disability-adjusted life years lost (HLDALYS).&amp;amp;nbsp;&lt;br /&gt;
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The forecast of mortality in this figure, dependent almost entirely on distal factors, is not actually the final calculation in the model.&amp;amp;nbsp; See the discussion of the entry of proximate drivers into the discussion of [[Health#Proximate_Drivers_and_Risk-Specific_Population_Attributable_Fractions|population attributable mortality fractions]]&amp;amp;nbsp;(PAFs), in interaction with distal-based mortality, for the rest of the story.&amp;amp;nbsp;&lt;br /&gt;
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Because of the importance of [[Health#Smoking_and_Smoking_Impact|smoking impact]]&amp;amp;nbsp;in the distal driver formulation, it is important that we elaborate that term. &amp;amp;nbsp;[[Health#Body_Mass_Index_and_Obesity|Body mass index]] is, at this point, only linked to diabetes and we discuss that in the context of the PAFs.&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Smoking and Smoking Impact&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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Of the various specific health risks that the model treats, smoking has a special place because its impact is in the distal driver formulation of the IFs health model.&amp;amp;nbsp; The figure shows that the impact is driven by the rate of smoking (differentiated by males and females) 25 years earlier, with the relationship controlled by an impact elasticity (&#039;&#039;&#039;&#039;&#039;hlsmkel&#039;&#039; &#039;&#039;&#039;).&amp;amp;nbsp; The user can also posit as a nearer term (in the model immediate) impact by setting a switch for that (&#039;&#039;&#039;&#039;&#039;hlsmkimeff&#039;&#039; &#039;&#039;&#039;) at some fractional value of the full delayed impact–the value in the base case is 0.1 or 10 percent. &amp;amp;nbsp;For analysis purposes, another switch (&#039;&#039;&#039;&#039;&#039;hlsmimpsw&#039;&#039; &#039;&#039;&#039;) can turn off the endogenous computation of smoking impact and leave it constant at the initial year value.[[File:Health3.png|frame|right|Visual representation of smoking impact]]&lt;br /&gt;
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Smoking rate itself is computed in two different ways.&amp;amp;nbsp; The basic formulation uses only the initial condition and a function linked to the simple and squared values of GDP per capita at PPP.&amp;amp;nbsp; The more extended formulation is an algorithmic one based on the same general concept of a pattern that initially rises with GDP per capita, peaks, and then falls, but with a series of parameters that allow much more control over the stages.&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1&amp;lt;/span&amp;gt; ] &amp;lt;/span&amp;gt;&amp;amp;nbsp; This staged algorithmic approach (see Lopez et al. 1994; Shibuya et al. 2005; Ploeg et al. 2009) is turned on with a switch (&#039;&#039;&#039;&#039;&#039;hlsmokingstsw&#039;&#039; &#039;&#039;&#039;).&lt;br /&gt;
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Because control of tobacco is a major policy objective in many countries, there is also a representation of a tobacco control score on a 100-point scale (&#039;&#039;&#039;&#039;&#039;hlsmokingtcs&#039;&#039; &#039;&#039;&#039;) with an associated parameter to control the elasticity of smoking with that score (&#039;&#039;&#039;&#039;&#039;hlsmokingtcsel&#039;&#039; &#039;&#039;&#039;), as well as a multiplier on the score (&#039;&#039;&#039;&#039;&#039;hlsmokingtcsm&#039;&#039; &#039;&#039;&#039;).&lt;br /&gt;
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Finally, there is a multiplier that allows direct manipulation of the smoking rate, again by sex (&#039;&#039;&#039;&#039;&#039;hlsmokingm&#039;&#039;&#039;&#039;&#039;).&lt;br /&gt;
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&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; Cecilia Peterson developed this approach for IFs. [[File:Health4|border|right|Health4]]&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Proximate Drivers and Risk-Specific Population Attributable Fractions&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt;&amp;amp;nbsp; ===&lt;br /&gt;
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Although mortality can be calculated solely from distal drivers such as income and education, it is better to calculate it from proximate or more immediate factors, such as undernutrition or exposure to pollutants. But IFs, and perhaps any model, will never be able to represent all such proximate drivers.&amp;amp;nbsp; Hence there is value in having an approach that combines the use of distal and proximate drivers, supplementing and adjusting the distal-driver based approach whenever possible.&amp;amp;nbsp;[[File:Health4.png|frame|right|Visual representation of proximate drivers and PAFs]]&lt;br /&gt;
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The figure below shows such a combination. Each proximate driver (and there are many different ones in IFs, in spite of the generalized representation in the figure) can be associated with a fraction of the mortality of a society.&amp;amp;nbsp; That population attributable fraction or PAF (derivative from the risk exposure level relative to a theoretical risk minimum) can be used to adjust the mortality associated with any cause that would have an implicit risk-related mortality built into the distal driver formulation.&amp;amp;nbsp; IFs makes those implicit distal-driver associated risk levels explicit by using the distal drivers to identify a risk level that would be expected based on cross-sectional analysis using the distal drivers.&amp;amp;nbsp; That allows the computation of a distal-driver based PAF. In similar fashion a PAF can be calculated that relates an exposure level to the risk, calculated mostly elsewhere in IFs (such as in the food and agriculture model for undernutrition of children) to a PAF.&lt;br /&gt;
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The complication mathematically lies in the interaction of (1) the distal-driver and proximate risk-based PAFs and (2) the multiple specific-risk PAFs, because avoidance of death from one will generally increase the risk of death from others.&amp;amp;nbsp; See the [[Health#Incorporating_Proximate_Drivers|equations associated with PAFs]] for details.&lt;br /&gt;
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Among the specific risk factors treated in IFs are overly high body mass indices and associated obesity,&amp;amp;nbsp; undernutrition of children, access to unsafe water and sanitation, indoor use of solid fuels, and levels or urban air particulates.&lt;br /&gt;
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=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Child Undernutrition&amp;lt;/span&amp;gt; ===&lt;br /&gt;
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Although [[Health#Body_Mass_Index_and_Obesity|obesity]]&amp;amp;nbsp;is a growing problem and killer around the world, the most important risk factor for children in particular has traditionally been undernutrition (often simply referred to as malnutrition).&amp;amp;nbsp; The percentage of children undernourished (MALNCHP) affects mortality rates from communicable diseases in particular via the mechanism that the model uses to modify cause-specific mortality from the [[Health#Distal_Driver_Formulation|distal driver formulation]]&amp;amp;nbsp;by actual risk level in a country.&amp;amp;nbsp; The core of that approach is to compare the risk-specific population attributable fraction (PAF) of total morality as calculated from the distal drivers with the PAF calculated from the actual level of the risk in the country.&amp;amp;nbsp;[[File:Health5.png|frame|right|Visual representation of child undernutrition]]&lt;br /&gt;
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The figure below shows the approach for childhood undernutrition.&amp;amp;nbsp; The two key variables in the distal driver formulation at any point in time (ignoring the technology factor that adds dynamics over time) are GDP per capita at purchasing power parity and years of adult education.&amp;amp;nbsp; They are used in a cross-sectionally estimated function to calculate an implicit body mass index that then produces the associated implicit PAF.&amp;amp;nbsp; IFs uses an alternative and more risk-factor specific formulation to forecast values of child undernutrition over time.&amp;amp;nbsp; The PAF associated with this explicit representation of MALNCHP is compared with the PAF from the implicit calculation and the comparison alters the actual mortality pattern.&amp;amp;nbsp;&lt;br /&gt;
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To calculate MALNCHP the explicit formulation also uses GDP per capita, as in the distal formulation, but augments it with calories per capita and with access to safe water and sanitation (unsafe water can cause diarrheal disease and undernutrition even with caloric intake would be adequate).&amp;amp;nbsp; A multiplicative parameter (&#039;&#039;&#039;&#039;&#039;malnchpm&#039;&#039; &#039;&#039;&#039;) can be used to change child undernutrition in scenario analysis.&amp;amp;nbsp; Another parameter (&#039;&#039;&#039;&#039;&#039;malnchpsw&#039;&#039; &#039;&#039;&#039;) can be used to hold the level of undernutrition at the level of the first year, an approach useful for counterfactual scenario analysis.&lt;br /&gt;
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Although not used in the health model, IFs contains two other measure of undernutrition.&amp;amp;nbsp; The first is an alternative measure of child undernutrition developed by Smith and Haddad (2000); MALNCHPSH is computed as a function of the ratio of female and male life expectancy, of female secondary school gross enrolment rate, and of access to safe water. The second is a measure of rate of undernutrition for the entire population (MALNPOPP), computed as a function only of calories per capita.&lt;br /&gt;
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=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Body Mass Index and Obesity&amp;lt;/span&amp;gt; ===&lt;br /&gt;
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The [[Health#Distal_Driver_Formulation|distal driver formulation]] used for forecasting mortality in IFs contains a country’s average body mass index (HLBMI) for diabetes. HLBMI also affects mortality from cardiovascular disease in IFs via the mechanism that the model uses to modify cause-specific mortality from the distal driver formulation by actual risk level in a country.&amp;amp;nbsp; [[File:Health6.png|frame|right|Visual representation of BMI and obesity]]The core of that approach is to compare the risk-specific population attributable fraction (PAF) of total morality as calculated from the distal drivers with the PAF calculated from the actual level of the risk in the country.&amp;amp;nbsp;&lt;br /&gt;
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The figure below shows the approach for body mass index.&amp;amp;nbsp; The two key variables in the distal driver formulation at any point in time (ignoring the technology factor that adds dynamics over time) are GDP per capita at purchasing power parity and years of adult education.&amp;amp;nbsp; They are used in a cross-sectionally estimated function to calculate an implicit body mass index that then produces the associated implicit PAF.&amp;amp;nbsp; IFs uses an alternative and more risk-factor specific formulation to forecast values of body mass index over time.&amp;amp;nbsp; The PAF associated with this explicit representation of HLBMI is compared with the PAF from the implicit calculation and the comparison alters the actual mortality pattern.&amp;amp;nbsp;&lt;br /&gt;
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To calculate HLBMI the explicit formulation uses calories per capita as the sole driving variable.&amp;amp;nbsp; A multiplicative parameter (&#039;&#039;&#039;&#039;&#039;hlbmim&#039;&#039; &#039;&#039;&#039;) can be used to change HLBMI in scenario analysis.&amp;amp;nbsp; A forecast of the obese population as a percent of the total population (HLOBESITY) is driven by the body mass index. A separate multiplicative parameter can modify it (&#039;&#039;&#039;&#039;&#039;hlobesitym&#039;&#039; &#039;&#039;&#039;).&lt;br /&gt;
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=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Indoor Use of Solid Fuels&amp;lt;/span&amp;gt; ===&lt;br /&gt;
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One of the most important health risk factors in the developing world, especially for women and children under 5 is the use of solid fuels for cooking (and heating) indoors (ENSOLFUEL).&amp;amp;nbsp; It is a major cause of respiratory diseases.&amp;amp;nbsp; In IFs it affects mortality rates via the mechanism that the model uses to modify cause-specific mortality from the [[Health#Distal_Driver_Formulation|distal driver formulation]] by using information concerning actual risk level in a country.&amp;amp;nbsp; The core of that approach is to compare the risk-specific population attributable fraction (PAF) of total morality as calculated from the distal drivers with the PAF calculated from the actual level of the risk in the country.&amp;amp;nbsp;&lt;br /&gt;
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The figure below shows the approach for indoor air pollution from the use of solid fuels.&amp;amp;nbsp; The two key variables in the distal driver formulation at any point in time (ignoring the technology factor that adds dynamics over time) are GDP per capita at purchasing power parity and years of adult education.&amp;amp;nbsp; They are used in a cross-sectionally estimated function to calculate indoor air pollution (linked to solid fuel use) that then produces the associated implicit PAF.&amp;amp;nbsp; IFs uses an alternative and more risk-factor specific formulation to forecast values of solid fuel use over time.&amp;amp;nbsp; The PAF associated with this explicit representation of ENSOLFUEL is compared with the PAF from the implicit calculation and the comparison alters the actual mortality pattern.&amp;amp;nbsp;&lt;br /&gt;
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To calculate ENSOLFUEL the explicit formulation also uses GDP per capita, as in the distal formulation, but augments it with access to electricity. &amp;amp;nbsp;For the actual equation, see the topic on equations for solid fuel use in the [[Infrastructure#Equations:_Energy_Infrastructure|infrastructure documentation]]. &amp;amp;nbsp;A multiplicative parameter (&#039;&#039;&#039;&#039;&#039;ensolfuelm&#039;&#039; &#039;&#039;&#039;) can be used to change solid fuel use in scenario analysis.&amp;amp;nbsp; Another parameter (&#039;&#039;&#039;&#039;&#039;ensolhldsw&#039;&#039; &#039;&#039;&#039;) can be used to hold the rate of solid fuel use at the level of the first year, an approach useful for counterfactual scenario analysis.[[File:Health7.png|frame|right|Visual representation of indoor air pollution from the use of solid fuels]]&lt;br /&gt;
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Major factors affecting the health impact of indoor solid fuel use are the efficiency and ventilation of the stoves.&amp;amp;nbsp; The model provides a coefficient (&#039;&#039;&#039;&#039;&#039;ensfvent&#039;&#039; &#039;&#039;&#039;) for scenario analysis concerning those factors.&lt;br /&gt;
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Much analysis on this health issue will want to use control of solid fuel use, partly through the use of a multiplier (&#039;&#039;&#039;&#039;&#039;ensolfuelm&#039;&#039; &#039;&#039;&#039;). &amp;amp;nbsp;There is also targeting of solid fuel use and the model provides two different kinds of targeting parameters, absolute and relative.&amp;amp;nbsp; The absolute (or universal) targeting allows the setting of a year (&#039;&#039;&#039;&#039;&#039;ensolfueltrgtyr&#039;&#039; &#039;&#039;&#039;) by which solid fuel use would be eliminated; it is available country by country.&amp;amp;nbsp; The relative targeting approach, available only globally across all countries, allows the setting of a value based on the typical rate of solid fuel use at different levels of GDP per capita (estimated cross-sectionally).&amp;amp;nbsp; A target rate (&#039;&#039;&#039;&#039;&#039;ensolfuelsetar&#039;&#039; &#039;&#039;&#039;) would normally be no higher than the typical rate at the country’s level of GDP per capita and could be, for instance, one standard error lower than the typical rate.&amp;amp;nbsp; An associated parameter (&#039;&#039;&#039;&#039;&#039;ensolfuelseyrtar&#039;&#039; &#039;&#039;&#039;) identifies the number of years over which a country would move to the target level.&amp;amp;nbsp; If a country already meets or exceeds a relative target, it will not move (adversely) toward it.&amp;amp;nbsp; Moreover, only the absolute or relative target should be used in analysis, not both together–an attempt to use both together will result in neither being used.&lt;br /&gt;
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=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Outdoor Urban Air Pollution&amp;lt;/span&amp;gt; ===&lt;br /&gt;
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One of the more important health risk factors in the developing and developed world alike, especially for middle-income industrializing countries, is the concentration of particulate matter of diameter 2.5 micrometers or less per cubic centimeter in urban air (ENVPM2PT5).&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1&amp;lt;/span&amp;gt; ] &amp;lt;/span&amp;gt;&amp;amp;nbsp; It is a major cause of respiratory infections, respiratory diseases, and cardiovascular disease in adults 30 and older.&amp;amp;nbsp; In IFs it affects mortality rates via the mechanism that the model uses to modify cause-specific mortality from the &amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[[File:Health8.png|frame|right|Visual representation of outdoor urban air pollution]]&amp;lt;/span&amp;gt;[[Health#Distal_Driver_Formulation|distal driver formulation]]&amp;amp;nbsp;by using information concerning actual risk level in a country.&amp;amp;nbsp; The core of that approach is to compare the risk-specific population attributable fraction (PAF) of total morality as calculated from the distal drivers with the PAF calculated from the actual level of the risk in the country. &amp;amp;nbsp;&lt;br /&gt;
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The figure below shows the approach for outdoor urban air pollution, focusing on the measure of ENVPM2PT5.&amp;amp;nbsp; The two key variables in the distal driver formulation at any point in time (ignoring the technology factor that adds dynamics over time) are GDP per capita at purchasing power parity and years of adult education.&amp;amp;nbsp; They are used in a cross-sectionally estimated function to calculate outdoor air pollution that then produces the associated implicit PAF.&amp;amp;nbsp; IFs uses an alternative and more risk-factor specific formulation to forecast values of outdoor urban air pollution use over time.&amp;amp;nbsp; The PAF associated with this explicit representation of ENVPM2PT5 is compared with the PAF from the implicit calculation and the comparison alters the actual mortality pattern.&amp;amp;nbsp;&lt;br /&gt;
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To calculate ENVPM2PT5 the explicit formulation also uses GDP per capita, as in the distal formulation, but augments the spending of a country on health as a portion of GDP (which appears to serve reasonably well as a proxy for more general attention to the environment).&amp;amp;nbsp; For the actual equation, see the topic on&amp;amp;nbsp;[[Health#Specific_Risk_Factors|outdoor urban air pollution equations]]&amp;amp;nbsp;in the health documentation&amp;amp;nbsp;.&amp;amp;nbsp; A multiplicative parameter (&#039;&#039;&#039;&amp;amp;nbsp;&#039;&#039;envpm2pt5m&#039;&#039;&amp;amp;nbsp;&#039;&#039;&#039;) can be used to change urban air pollution in scenario analysis.&amp;amp;nbsp; Another parameter (&#039;&#039;&#039;&#039;&#039;envpm2hldsw&#039;&#039;&amp;amp;nbsp;&#039;&#039;&#039;) can be used to hold the level of urban air pollution at the level of the first year, an approach useful for counterfactual scenario analysis.&lt;br /&gt;
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----&lt;br /&gt;
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&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; Initialized in IFs by converting World Bank data on PM10 concentrations.&lt;br /&gt;
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=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Water and Sanitation&amp;lt;/span&amp;gt; ===&lt;br /&gt;
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Although unsafe water and sanitation is a killer via its contribution to undernutrition of children in particular, it creates its own mortality risk especially via diarrheal disease. The variables of importance in IFs are access to safe water (WATSAFE) and safe sanitation (SANITATION).&amp;amp;nbsp; In IFs they affect mortality rates via the mechanism that the model uses to modify cause-specific mortality from the [Health#Distal Driver Formulation|distal driver formulation]] by using information concerning actual risk level in a country.&amp;amp;nbsp; The core of that approach is to compare the risk-specific population attributable fraction (PAF) of total morality as calculated from the distal drivers with the PAF calculated from the actual level of the risk in the country.&amp;amp;nbsp;&lt;br /&gt;
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The figure below shows the approach for safe water and sanitation.&amp;amp;nbsp; The two key variables in the distal driver formulation at any point in time (ignoring the technology factor that adds dynamics over time) are GDP per capita at purchasing power parity and years of adult education.&amp;amp;nbsp; They are used in a cross-sectionally estimated function to calculate unsafe water and sanitation that then produces the associated implicit PAF.&amp;amp;nbsp; IFs uses alternative and more risk-factor specific formulations to forecast values of safe access to water and sanitation over time.&amp;amp;nbsp; The PAF associated with this explicit representation of WATSAFE and SANITATION (in combination) is compared with the PAF from the implicit calculation and the comparison alters the actual mortality pattern.&amp;amp;nbsp;&lt;br /&gt;
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To calculate WATSAFE and SANITATION (separately) the explicit formulation also uses both average years of adult education and GDP per capita, as in the distal formulation, but augments those with the spending of a country on health as a portion of GDP (which appears to serve reasonably well as a proxy for more general attention to the environment) and portion of the citizenry living on less than $1.25 per day.&amp;amp;nbsp; For the actual equations, see the topic on outdoor urban air pollution equations in the [[Infrastructure#Equations:_Water_and_Sanitation_Infrastructure|infrastructure documentation]].[[File:Health9.png|frame|right|Visual representation of water and sanitation]]&lt;br /&gt;
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Both access to safe water and to safe sanitation have ladders of access quality ranging from none to household connections.&amp;amp;nbsp; Parameters affecting them must thus take into account those ladders and the specific level(s) the parameter affects. Multiplicative parameters (&#039;&#039;&#039;&#039;&#039;watsafem &#039;&#039; &#039;&#039;&#039;and &#039;&#039;&#039;&#039;&#039;sanitationm&#039;&#039; &#039;&#039;&#039;) can be used to change access at any level on the two ladders (the model normalizes access across levels to assure summation to 100 percent.&amp;amp;nbsp; &amp;amp;nbsp;Another parameter pair (&#039;&#039;&#039;&#039;&#039;watsafehldsw&#039;&#039; &#039;&#039;&#039; and&#039;&#039;&#039;&#039;&#039;sanithldsw&#039;&#039; &#039;&#039;&#039;) can be used to hold the level of access at that of the first year, an approach useful for counterfactual scenario analysis.&lt;br /&gt;
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Other parameters control targeting, both universal and relative. &amp;amp;nbsp;With respect to absolute targeting, &#039;&#039;&#039;&#039;&#039;watsafetrgtval&#039;&#039; &#039;&#039;&#039; and &#039;&#039;&#039;&#039;&#039;watersafetrgtyr&#039;&#039; &#039;&#039;&#039; control those with no access to safe water (the proportion and the number of years to reach the target, respectively).&amp;amp;nbsp; Similarly, &#039;&#039;&#039;&#039;&#039;sanitationtrgtval&#039;&#039; &#039;&#039;&#039; and &#039;&#039;&#039;&#039;&#039;sanitationtrgtyr&#039;&#039; &#039;&#039;&#039; control those with access to household connections.&amp;amp;nbsp; The relative targeting approach, available only globally across all countries, allows the setting of a value based on the typical rate of access at different levels of GDP per capita (estimated cross-sectionally).&amp;amp;nbsp; A target level (&#039;&#039;&#039;&#039;&#039;watsafenoconsetar, sanithhconsetar, sanitnoconsetar&#039;&#039; &#039;&#039;&#039;) would normally be no better (which could mean no higher or no lower) than the typical level at the country’s level of GDP per capita and could be, for instance, one standard error better (higher or lower depending on the variable being targeted)&amp;amp;nbsp; than the typical level.&amp;amp;nbsp; An associated parameter (&#039;&#039;&#039;&#039;&#039;watsafenoconseyrtar, sanithhconseyrtar, sanitnoconseyrtar&#039;&#039; &#039;&#039;&#039;) identifies the number of years over which a country would move to the target level.&amp;amp;nbsp; If a country already meets or exceeds a relative target, it will not move (adversely) toward it.&amp;amp;nbsp; Only the absolute or relative target should be used in analysis, not both together–an attempt to use both together will result in neither being used.&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Specialized Models: Deaths from AIDS and Vehicle Accidents&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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AIDS deaths depend very directly on the number (or stock) of HIV-infected individuals, which depends in turn on the HIV-infection rate (HIVRATE). Data on HIV infection rates were used to compute a basic, country/region-specific rate of HIV infection increase (hivincrate), which the user can alter, as they can exogenous assumptions about the peak year of the epidemic (hivpeakyr) and the infection rate in that year (hivpeakr). If a country is beyond the peak year of the epidemic, control will be bringing the rate down over time (HIVTECCNTL).&amp;amp;nbsp; The user may also rely upon a country/region-specific multiplier to move rates up or down (hivm).&lt;br /&gt;
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[[File:Health10.png|frame|center|Visual representation of deaths from AIDS]]&lt;br /&gt;
&lt;br /&gt;
There is both a policy and medical effort underway to reduce the growth in infections. An HIV technical advance rate (hivtadvr) represents the success of that in rate of reduction in annual infection growth, and a variable (HIVTECCNTL) shows the cumulative impact of changes past a peak rate and year. Although highly speculative, the user will recognize the long-term importance of such assumptions.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Turning from the infection rate to the death rate, the user can make changes in the initial AIDS death rate (aidsdrate) to reflect possible progress or lack of it in reducing the deaths from HIV (using aidsdrtadvr).&amp;amp;nbsp; When the deaths from AIDS are computed, they are used to compute an incremental number of deaths (since some are already in the mortality of the base year); and an exogenous vector spreads them by age and sex.&lt;br /&gt;
&lt;br /&gt;
The computation of deaths from vehicle accidents starts with computing the number of vehicles per capita (VEHICFLPC) as a function of population density and GDP per capita.&amp;amp;nbsp; That allows computation of the total number of vehicles (VEHICLESTOT).&amp;amp;nbsp; The function used by IFs to compute the total number of road deaths uses the number of vehicles and the population.&lt;br /&gt;
&lt;br /&gt;
[[File:He|border|right|He]][[File:Health11.png|frame|center|Visual representation of deaths from vehicle accidents]]&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Forward Linkages from Health&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt;&amp;amp;nbsp; ===&lt;br /&gt;
&lt;br /&gt;
[[File:Health12.png|frame|right|Visual representation of major pathways between health/demography and GDP]]Chapter 7 of Hughes, Kuhn, Peterson, Rothman, and Solorzano (2011) elaborated the forward linkages of the health model to other parts of the IFs system at the time of that volume&#039;s completion.&amp;amp;nbsp; It begins by discussing a controversy in the literature about whether the effects on economic well-being (as indicated by GDP per capita) of improvements in life expectancy are positive or negative.&amp;amp;nbsp; It goes on to devote much attention to three major and general pathways of impact between health and GDP, each of which corresponds to an element in standard production functions and that in IFs.&amp;amp;nbsp; The diagram below shows the major pathways between health/demography and GDP, each of which requires elaboration by showing the variables and logic of the IFs system; those three are [[Health#Forward_Linkages_of_Health_to_Population_and_Labor_Supply|labor]], [[Health#Forward_Linkages_of_Health_to_Capital_Stock|capital]], and [[Health#Forward_Linkages_of_Health_to_Economic_Productivity|multifactor productivity]].&lt;br /&gt;
&lt;br /&gt;
There are other potential forward linkages of health in the IFs system, many of which would have additional implications for economic production. Those other possible forward linkages include linkages of health (or lack of it) to public spending on health and to education years and quality of it. Potentially there could also be a linkage in the system from health to economic inequality.&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Forward Linkages of Health to Population and Labor Supply&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
The IFs demographic model captures the mechanical or accounting effects of mortality on population (see the solid paths in the figure below).&amp;amp;nbsp; A key pathway passes from mortality through adult age population to labor supply (including aging-related lags).&amp;amp;nbsp;&amp;lt;sup&amp;gt;[1]&amp;lt;/sup&amp;gt;&amp;amp;nbsp;&amp;amp;nbsp; Similarly, IFs captures the mechanical effect of mortality on fertility through the death of women of childbearing age.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[[File:Health13.png|frame|center|Visual representation of mechanical or accounting effects of mortality on population]]&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The most important non-mechanical linkage is almost certainly the relationship between child mortality and fertility. IFs forecasts fertility as a relationship with infant mortality, the log of educational level of those aged 15 and older (neither the education of women alone nor the education of those 15-24 work as well), and the percentage use of modern contraception.&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; IFs also includes income-based formulations for changing the female participation rate.&lt;br /&gt;
&amp;lt;/div&amp;gt;&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Forward Linkages of Health to Capital Stock&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
The figure below sketches the primary paths between health (morbidity and mortality) and capital stock.&amp;amp;nbsp; Most capital stock consists of buildings and machinery for producing goods and services; some representations may include land also, but most treat land separately and largely as a constant (although land developed for crop production or grazing can, in fact, be highly variable, as it is in the IFs agricultural model).&amp;amp;nbsp; Most immediately, investment increases capital stock and depreciation reduces it.&amp;amp;nbsp; Although there is certainly some impact of morbidity and mortality on the rate of depreciation of both built physical and natural capital, the relationship may not be substantial and we do not understand it well enough to model it.&amp;amp;nbsp; Investment is responsive to both domestic savings and foreign flows.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Turning our gaze to the paths by which health affects investment, the three major ones run though health spending, which can crowd out savings and investment, through the age-structure of societies, which affects the savings rate, and through investment from abroad, which can augment that generated domestically.[[File:Health14.png|frame|right|Visual representation of primary paths between health (morbidity and mortality) and capital stock]]&lt;br /&gt;
&lt;br /&gt;
With respect to health spending, to which we return later, the IFs model uses a social accounting matrix (SAM) structure.&amp;amp;nbsp; Thus the flow of funds into health spending automatically competes with other consumption uses and with savings and investment.&amp;amp;nbsp; The major current weakness of the model with respect to this path is that there is no linkage from morbidity (associated in IFs with mortality) and health expenditures.&amp;amp;nbsp; (There is a linkage in IFs back from health spending to mortality –all categories except for AIDS).&lt;br /&gt;
&lt;br /&gt;
The paths in IFs that link age structure most directly to domestic savings have two important elements.&amp;amp;nbsp; The most fundamental one represents the understanding of life-cycle dynamics in income, consumption and savings.&amp;amp;nbsp; The cycle for income is fairly clear-cut with a peak in the middle to latter periods of the working years.&amp;amp;nbsp; Workers set aside some portion of income as savings and that portion, too, tends to peak in the middle and late period of working years.&amp;amp;nbsp; Society-wide savings themselves become negative after retirement age (65 in the Base Case scenario but variable in scenarios) even though some portion of the population will continue to work. The second fundamental element is that both the horizon of life expectancy and the average income level of a society can have an impact on the portion set aside for savings and the degree to which it rises and then falls.&amp;amp;nbsp; Thus, for example, the life-cycle “bulge” of savings may be earlier and flatter in developing countries.&lt;br /&gt;
&lt;br /&gt;
We implemented the representation of savings and investment in accord with that understanding.&amp;amp;nbsp; Relying upon analyses of selected countries that Fernández-Villaverde and Kruegger (2004 and 2005) and Deaton&amp;amp;nbsp; and Paxson (2000) undertook, we extracted general stylized patterns of the savings life cycle to represent more and less developed (and lower life expectancy) countries. In forecasting we use the pattern for less developed countries when life expectancy falls below 40 years, use that for more developed countries when life expectancy exceeds 80 years, and interpolate in between for all other countries.&amp;amp;nbsp; The result of this largely algorithmic approach[http://www.du.edu/ifs/help/understand/health/flowcharts/forward/capital.html#footnote &amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;sup&amp;gt;[1&amp;lt;/sup&amp;gt;&amp;lt;/span&amp;gt;] ] is an adjustment factor (SavingsAgeAdj) that augments or reduces investment.&lt;br /&gt;
&lt;br /&gt;
In addition, investment is somewhat augmented or reduced as a direct result of changing life expectancy.&amp;amp;nbsp; Life expectancy is compared over time with an expected value (tied to cross-sectional estimation with income).&amp;amp;nbsp; That difference is compared to the difference in the initial year and, if it rises, augments investment.&lt;br /&gt;
&lt;br /&gt;
Although conceptually tied to savings rates, neither the life-cycle analysis nor the life-expectancy term directly affect savings in IFs.&amp;amp;nbsp; Instead, they affect investment directly and savings indirectly via the dynamics in IFs that balance savings and investment over time.&lt;br /&gt;
&lt;br /&gt;
The path linking health to foreign direct investment is potentially quite important.&amp;amp;nbsp; Alsan, Bloom and Canning (2006: 613) reported that one additional year of life expectancy boosts FDI inflows by 9 percent, controlling for other variables. &amp;amp;nbsp;We have implemented that relationship in IFs.&amp;amp;nbsp; The representation of FDI in IFs captures the accumulation over time of FDI inflows in stocks of FDI, as well as the accumulation of FDI outflows in stocks.&amp;amp;nbsp; In addition, the stocks set up their own dynamics, including the tendency for stocks to reinforce flows.&amp;amp;nbsp; For that reason, we have set the base case parameter for the impact of each year of life expectancy on FDI flows to 0.05 (5 percent), lower than the estimate of Alsan, Bloom and Canning (2006).&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; See the subroutine SavingsDemogAdj in routine Populat.bas, which draws upon table IncConSav in IFs.mdb&amp;amp;nbsp; with different patterns of income, consumption, and savings for more developed countries (MDCs) and&amp;amp;nbsp; less developed countries (LDCs) across age categories; in general, peaks of income, consumption, savings occur the in late 40s and savings turn negative at 65.&amp;lt;/div&amp;gt;&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Forward Linkages of Health to Economic Productivity&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Health outcomes impact productivity through a variety of pathways (see the figure below).&amp;amp;nbsp; Overall the function for multifactor productivity from human capital (MFPHC) is a sum of terms linked to educational expenditures (GDS(EDUC)) as a portion of GDP and to educational attainment of adults in society (EDYRSAG15) with two more directly health-related terms of interest to us here, respectively from adult stunting (STUNTCONTRIB) and disability of those in their working years (HLYLDWORK).&amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
In the IFs health module, the prevalence of adult stunting (HLSTUNT) relates negatively to overall productivity via an elasticity (&#039;&#039;&#039;&#039;&#039;mfpstunt&#039;&#039; &#039;&#039;&#039;).&amp;amp;nbsp; In extreme cases, stunting could cost as much as 1 percent of economic growth.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
We compute HLSTUNT in the health model itself.&amp;amp;nbsp; We initialize adult stunting in a long-term lagged relationship (using a moving average of 25 years) with child malnutrition (MALNCPH) and forecast it as a function of both malnutrition and child mortality as a proxy for morbidity.[[File:Health15.png|frame|right|Visual representation of how health outcomes impact productivity]]&lt;br /&gt;
&lt;br /&gt;
Turning to disability (which is driven by mortality rates), childhood malnutrition and morbidity do not give rise to all of disability in working years; much also comes from disabilities arising during the working years. IFs therefore also calculates millions of years of living with disability related to mortality rates specific to the working aged-population (HLYLDWORK). &amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Turning to the forecasting relationship between disability and productivity, the IFs approach drives changes in the growth of productivity from the changing difference between computed and expected values of disability.&amp;amp;nbsp; We used the world average disability rate as an “expected” value.&amp;amp;nbsp; Because we have replicated the practice of the GBD project and kept mental health disability rates constant over time, and because mental health generally dominates disability, forecasts of this disability term are relatively stable over time. Thus analysis with respect to this variable will depend on scenarios that increase or decrease those disability rates.&amp;amp;nbsp; &amp;amp;nbsp;Changes in disability levels (relative to expected ones) relate to change in productivity via a parameter,&#039;&#039;&#039;&#039;&#039;mfphlyld&#039;&#039; &#039;&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Health Equations&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
[[Health#Smoking_and_Smoking_Impact|The hybrid IFs model for forecasting health (using distal and proximate drivers)]] provides forecasts of age, sex and country-specific mortality rates for most of the 15 cause clusters it represents. The model within IFs for mortality in those cause clusters builds on the Global Burden of Disease (GBD) methodology, which uses mainly [[Health#Distal_Drivers_and_Basic_Indicators|distal (more distant) drivers]] to project mortality.&amp;lt;span style=&amp;quot;color: #990000&amp;quot; data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;sup&amp;gt;[1]&amp;lt;/sup&amp;gt; &amp;lt;/span&amp;gt; IFs then extends that methodology in many cases by adding attention to a selected set of [[Health#Proximate_Drivers_and_Risk-Specific_Population_Attributable_Fractions|proximate drivers]].&lt;br /&gt;
&lt;br /&gt;
For several other causes or cause clusters (*), most of those related to communicable disease, the IFs system uses a variant of the distal driver approach, one that looks to a forecast of all communicable disease except HIV/AIDS and then subdivides that total by more specific disease type.&lt;br /&gt;
&lt;br /&gt;
For still other causes of mortality (**), it uses more specialized models totally unrelated to the distal driver approach.&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
&#039;&#039;&#039;Cause Clusters in IFs&#039;&#039;&#039; &amp;lt;sup&amp;gt;[2]&amp;lt;/sup&amp;gt;&lt;br /&gt;
&lt;br /&gt;
#Other Group I diseases (excludes AIDS, diarrhea, malaria and respiratory infections)*&lt;br /&gt;
#Malignant neoplasms&lt;br /&gt;
#Cardiovascular diseases&lt;br /&gt;
#Digestive diseases&lt;br /&gt;
#Diabetes&lt;br /&gt;
#Chronic respiratory diseases&lt;br /&gt;
#Other Group II diseases (excludes malignant neoplasms, cardiovascular diseases, digestive diseases, diabetes, chronic respiratory diseases, and mental health)&lt;br /&gt;
#Road traffic accidents**&lt;br /&gt;
#Other unintentional injuries (excludes road traffic accidents)&lt;br /&gt;
#Intentional injuries&lt;br /&gt;
#HIV/AIDS**&lt;br /&gt;
#Diarrhea*&lt;br /&gt;
#Malaria*&lt;br /&gt;
#Respiratory Infection *&lt;br /&gt;
#Mental Health**&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;For help understanding the equations see&amp;lt;/span&amp;gt;&amp;amp;nbsp;[[Understand_IFs#Equation_Notation|Notation]] &amp;lt;span&amp;gt;.&amp;lt;/span&amp;gt;&lt;br /&gt;
&amp;lt;/div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Distal Driver Formulation&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
For the basic forecast of mortality related to most of the major cause clusters (exceptions are [[Health#Specialized_Models:_Deaths_from_AIDS_and_Vehicle_Accidents|deaths from HIV/AIDS and traffic accidents]]) we use the regression models and associated beta coefficients prepared for the Global Burden of Disease project (Mathers and Loncar 2006).&amp;amp;nbsp; Age, sex, cause, and country-specific mortality rate is a function of income, adult education, technological progress, and (in specific cases) smoking impact:&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ln(M_{c,p,d,r})=C_{c,p,d}+\beta_1*ln(Y_{\gamma}+\beta_2*ln(HC_{\gamma})+\beta_3*(ln(Y_{\gamma})))^2+\beta_4*T+\beta_5*ln(SI_{c,k,p,r})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:M is mortality rate in deaths per 100,000 for a given age category c, sex p, cause of death d and country or region r.&lt;br /&gt;
&lt;br /&gt;
:Y is GDP per capita at PPP&lt;br /&gt;
&lt;br /&gt;
:HC (human capital) is Years of Adult Education over 25&lt;br /&gt;
&lt;br /&gt;
:T is time&lt;br /&gt;
&lt;br /&gt;
:SI is Smoking Impact&amp;amp;nbsp;&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
Income and education (IFs variables GDPPCP and EDYRSAG25, respectively) are forecast endogenously in IFs.&amp;amp;nbsp; Time, a proxy for technological progress, is calculated as calendar year minus 1900 (for example, T for the year 2001 equals 101).&amp;amp;nbsp; Smoking impact, a variable meant to capture historical smoking patterns, is included only in the forecasts of mortality related to malignant neoplasms, cardiovascular disease, and respiratory disease.&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1&amp;lt;/span&amp;gt; ] &amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt;&amp;amp;nbsp; As described in [[Health#Indoor_Air_Pollution|another section]] of this document, IFs uses both historical smoking rate estimates and SI projections to 2030 (as provided by GBD authors) to forecast the SI variable.&lt;br /&gt;
&lt;br /&gt;
Using an historical database representing mortality data from 106 countries for the years 1950-2002, the GBD calculated sex-specific regression coefficients for seven age groups (&amp;lt;5, 5-14, 15-29, 30-44, 45-59, 60-69, and 70+) and ten major cause clusters–the first ten in the list above (Protocol S1, 1-3).&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[2&amp;lt;/span&amp;gt; ] &amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt;&amp;amp;nbsp; GBD estimations using the data from the 106 countries created separate low- and high-income regression models (not coefficients for each country separately), with low income defined as GDPPCP &amp;lt; $3,000 in the initial year.&amp;amp;nbsp; Both sets of coefficients are publicly available online.&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[3&amp;lt;/span&amp;gt; ] &amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt;&amp;amp;nbsp; In IFs we spread the coefficients for the seven age groups across 5-year subcategories; that is, we use the same coefficients for each subcategory within the larger GBD ones–normalization of mortality within each 5-year subcategory across causes and to total mortality rates for each subcategory (taken from UN Population Division data) does, however, create differences in mortality rates across those 5-year groupings.&lt;br /&gt;
&lt;br /&gt;
We generally use the beta coefficients provided by GBD authors to forecast mortality related to six cause groups: Group I excluding detailed communicable causes, malignant neoplasms, digestive diseases, Group II excluding diabetes and mental health, other intentional injuries, and intentional injuries.&amp;amp;nbsp; However, for a few age and Group III cause groups where regression models provided low predictive value, we also follow the GBD in keeping mortality rates constant over time instead of using the regression equations.&amp;amp;nbsp; Affected groups include: unintentional injuries for males older than 70; unintentional injuries for females older than 60; intentional injuries for males and females under 5; intentional injuries for males older than 60; and intentional injuries for females older than 45. &amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Although we forecast mortality by age, sex, cause, and country as in the general GBD equation above (and the details can be seen in the specialized displays of the model on mortality by age, sex, and cause and the mortality J-curve), the major model variable for display is DEATHCAT, which is total deaths by country/region, cause, and sex.&amp;amp;nbsp; The equation for it, using the IFs variables for GDP per capita at PPP (GDPPCP), average years of education for adults aged 25 and older (EDYRSAG25), time (IY%), and smoking impact (HLSMOKINGIMP) is&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;DEATHCAT_{r,d,p}=F((Constant_{r,d,p,c}+\beta_1*ln(GDPPCP_r)+\beta_2*ln(EDYSAG25_r)+\beta_3*ln(GDPPCP_r)^2+\beta_4*IY%+\beta_5*ln(HLSMOKINGIMP_{r,p})*mortm_r*hlmortm_{r,d})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The betas in the equation, as indicated earlier, are from the GBD work and are dimensioned also by country/region r (only as high income or low income), cause of death d, sex p, and age category c.&amp;amp;nbsp; The entire equation for mortality is adjusted in an algorithmic process so that the total across all causes of death equal the mortality rates from the UN Population Division’s data (using a normalization factor), while the relative weights for each disease match WHO data (using a scaling factor).&amp;amp;nbsp; The normalization and scaling factors are multiplicative, affecting everything in the equation.&amp;amp;nbsp; In the Base Case scenario we keep those factors constant, but we can control convergence of them (see the [[Health#Normalization_and_Scaling_Factors|Normalization and Scaling Factors]] section).&lt;br /&gt;
&lt;br /&gt;
The equation allows scenario modification with multiplicative parameters that change mortality overall (&#039;&#039;&#039;&#039;&#039;mortm&#039;&#039; &#039;&#039;&#039;) or by cause of death (&#039;&#039;&#039;&#039;&#039;hlmortm&#039;&#039; &#039;&#039;&#039;).&amp;amp;nbsp; Not shown in the equation,&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp; &#039;&#039;&#039;&#039;&#039;hlmortcdchldm&#039;&#039; &#039;&#039;&#039; changes the rates of all communicable diseases for children aged 5 and younger, while &#039;&#039;&#039;&#039;&#039;hlmortcdadltm&#039;&#039; &#039;&#039;&#039; affects rates of death from communicable diseases for adults aged 15-49.&lt;br /&gt;
&amp;lt;/div&amp;gt;&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Forecasting Income and Education for the Distal Driver Formulation&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
For the basic distal driver formulation, GDP per capita at PPP (GDPPCP) and years of adult education (EdYrsAg25) are forecast endogenously within IFs.&amp;amp;nbsp; GDP per capita is computed in the Economic Module as an annual flow variable (that is, it is generated anew each year), driven in part by underlying stocks such as capital supply and, in fact, years of adult education (both of which accrete or deplete very slowly over time). Adult education is computed in the Education Module using government spending on education as the main driver.&amp;amp;nbsp;For a more complete description of driver forecasting in IFs, please see [http://www.ifs.du.edu/ www.ifs.du.edu].&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Forecasting Technology for the Distal Driver Formulation of Mortality&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Although two IFs variables, namely GDP per capita at purchasing power parity (GDPPCP) and years of adult education (EDYRSAG25) drive the distal formulation in most of our forecasting and scenario analysis, the technology parameter (the beta on time) in the distal equation is very powerful.&amp;amp;nbsp; We therefore want some control over it, ideally with ability to differentiate that control with respect to level of income of countries and with respect to the age structure of mortality. For a basic approach to providing such control, we follow the Global Burden of Disease (GBD) project in modifying the regression models for child mortality low-income countries.&amp;amp;nbsp; But we have extended that GBD approach to allow some additional parametric control.&lt;br /&gt;
&lt;br /&gt;
The control system in IFs uses a switching parameter (&#039;&#039;&#039;&#039;&#039;hlmortmodsw&#039;&#039; &#039;&#039;&#039;) in interaction with three other parameters (with their default values those are &#039;&#039;&#039;&#039;&#039;hltechbase&#039;&#039; &#039;&#039;&#039;=1,&#039;&#039;&#039;&#039;&#039;hltechlinc&#039;&#039; &#039;&#039;&#039;=0.25, and &#039;&#039;&#039;&#039;&#039;hltechssa&#039;&#039; &#039;&#039;&#039;=0); see the table below for a summary of the application of those parameters.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
In the default mode (&#039;&#039;&#039;&#039;&#039;hlmortmodsw&#039;&#039; &#039;&#039;&#039; = 1), IFs uses the GBD approach to modifying the technology (time) coefficients for children under 5 in recognition of slower than expected historical progress in many countries.&amp;lt;span style=&amp;quot;color: #990000&amp;quot; data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;sup&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;&amp;lt;/span&amp;gt; &amp;amp;nbsp;Specifically, for children under 5 in low-income countries in four regions (Africa, Europe, SE Asia and West Pacific) the time variable is held constant (zero, or no technological advance, using &#039;&#039;&#039;&#039;&#039;hltechssa&#039;&#039; &#039;&#039;&#039;); in low-income countries in the Middle East and North Africa the coefficient on time is reduced to 25 percent of its original value using the parameter &#039;&#039;&#039;&#039;&#039;hltechlinc&#039;&#039; &#039;&#039;&#039;.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;In the IFs implementation of the GBD approach to treatment of technology, we wished to change the patterns not just for children under 5, but also for older children and adults.&amp;amp;nbsp; We decided, however, to regularize the somewhat ad hoc assignment of countries by the GBD to the low-income category by defining low-income as being less than $3,000 per capita and high-income as being above that level. &amp;amp;nbsp;We use the parameter &#039;&#039;&#039;&#039;&#039;hltechlinc&#039;&#039; &#039;&#039;&#039; to control technological change for older children and adults in low-income countries regardless of geographical region; at its default setting that parameter reduces the coefficient on time to 25 percent of its original value.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%; border: 1px solid #cccccc&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; | &#039;&#039;&#039;&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; colspan=&amp;quot;2&amp;quot; | &amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;Age and Geographic Impact of the Parameters&amp;lt;/span&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Base or default values&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | For children under 5 (GBD Geographic Classification)&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | For older children and adults (IFs Geographic Classification)&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | &#039;&#039;&#039;&#039;&#039;hltechssa&#039;&#039; &#039;&#039;&#039;=0&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Low Income Countries in mostly 4 regions (Africa, Europe, SE Asia and West Pac; also selected countries such as Haiti)&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Not used&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | &#039;&#039;&#039;&#039;&#039;hltechlinc&#039;&#039;&#039; &#039;&#039;=0.25&amp;lt;br/&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Low Income Countries in the Middle East and North Africa&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Low Income Countries (GDPPCP &amp;lt; $3k in 2010)&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | &#039;&#039;&#039;&#039;&#039;hltechbase&#039;&#039;&#039; &#039;&#039;=1&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | All other countries, mostly High Income and including most of Latin America&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | High Income Countries (GDPPCP &amp;gt;= $3k in 2010)&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
For children older than 5 and adults in what IFs classifies as high-income countries (countries with GDP per capita at PPP in 2010 above $3,000), IFs uses the parameter &#039;&#039;&#039;&#039;&#039;hltechbase&#039;&#039; &#039;&#039;&#039;.&amp;amp;nbsp; Thus in the default situation, technological change is unchanged from the basic value.&amp;amp;nbsp; &amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;These GBD technology factor modifications (and their extensions by IFs to adults and older children in our definition of low- and high-income countries) can be turned off in the model (&#039;&#039;&#039;&#039;&#039;hlmortmodsw&#039;&#039; &#039;&#039;&#039; = 0).&amp;lt;span style=&amp;quot;color: #990000&amp;quot; data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;sup&amp;gt;[2]&amp;lt;/sup&amp;gt;&amp;lt;/span&amp;gt;&amp;amp;nbsp;When the switch is turned on, adjustments can also be made to &#039;&#039;&#039;&#039;&#039;hltechbase&#039;&#039; &#039;&#039;&#039;, &#039;&#039;&#039;&#039;&#039;hltechlinc&#039;&#039; &#039;&#039;&#039;, and &#039;&#039;&#039;&#039;&#039;hltechssa&#039;&#039; &#039;&#039;&#039; to build new scenarios.&amp;lt;span style=&amp;quot;color: #990000&amp;quot; data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;sup&amp;gt;[3]&amp;lt;/sup&amp;gt;&amp;lt;/span&amp;gt; &amp;amp;nbsp;It is important for the user to know, however, that regardless of age or income level of countries, the model uses &#039;&#039;&#039;&#039;&#039;hltechbase&#039;&#039; &#039;&#039;&#039; for mortality from cardiovascular causes (which uses a different regression model for forecasting).&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Changes to &#039;&#039;&#039;&#039;&#039;hltechbase&#039;&#039; &#039;&#039;&#039; can also be adjusted by using a shift parameter called &#039;&#039;&#039;&#039;&#039;hltechshift&#039;&#039; &#039;&#039;&#039; (0 by default), which adjusts the technology factor depending on the level of initial GDP per capita at PPP.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;hltechbaseadj=\mathbf{hltechbase}+Min(40,GDPPPCi_r*&lt;br /&gt;
\mathbf{hltecshift})/40&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
This adjustment increases the technology factor for high income countries more quickly than for middle or low-income countries.&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; &amp;lt;/span&amp;gt; The Global&amp;amp;nbsp;&amp;amp;nbsp; Burden of Disease (GBD) project&amp;amp;nbsp; made low-income modifications after recognizing that historical child mortality data did not match back projections of the model (Mathers and Loncar 2006b: 9).&amp;amp;nbsp; Note that the GBD approach to these modifications changed from the 2002 revision to the 2004 revision of the project. In the 2002 revision, the human capital (education) beta was reduced to half of its magnitude for sub-Saharan countries, and to 75 percent of its original magnitude for other low-income countries. This was done only if the beta on the human capital (education) term in the distal model formulation was negative (reducing mortality with increases in education).&amp;amp;nbsp; Technological advance factor (time) was left constant (no advance) for sub-Saharan Africa and reduced to 25 percent for other low income countries.&amp;amp;nbsp; The 2004 revision dropped the human capital modifications, but continued to reduce the coefficient on time.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;[2]&amp;lt;/span&amp;gt; &amp;lt;/span&amp;gt;&amp;amp;nbsp;Although not recommended, IFs also allows the user to use the original GBD 2002 modifications (as described in a previous footnote) by specifying&amp;amp;nbsp;&#039;&#039;&#039;&#039;&#039;hlmortmodsw&#039;&#039; &#039;&#039;&#039;&amp;amp;nbsp;= 2.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;[3]&amp;lt;/span&amp;gt; &amp;lt;/span&amp;gt;&amp;amp;nbsp;Given the results found for Intentional Injuries, where mortality was reaching unrealistic levels, we have limited the changes to&amp;amp;nbsp;&#039;&#039;&#039;&#039;&#039;hltechbase&amp;amp;nbsp;&#039;&#039; &#039;&#039;&#039;and&amp;amp;nbsp;&#039;&#039;&#039;&#039;&#039;hltechlinc&#039;&#039; &#039;&#039;&#039;&amp;amp;nbsp;to at most 1.5 for this particular cause of death in the 2004 revision (&#039;&#039;&#039;&#039;&#039;hlmortmodsw&amp;amp;nbsp;&#039;&#039; &#039;&#039;&#039;= 1).&lt;br /&gt;
&amp;lt;/div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Diabetes and Chronic Respiratory Diseases&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Two chronic cause groups, diabetes and respiratory, are so strongly influenced by specific risk factors that estimates based on&amp;amp;nbsp;[[Health#Distal_Driver_Formulation|distal drivers]]&amp;amp;nbsp;alone fail to accurately represent expected mortality rate trajectories.&amp;amp;nbsp; In the case of diabetes, rising population levels of overweight and obesity contradict suggestions that diabetes-related mortality will fall over time in line with other Group II causes.&amp;amp;nbsp; Conversely, declining smoking rates in many high income countries may temper projections of increasing chronic respiratory-related mortality (Protocol S1, 5-6).&amp;amp;nbsp; Therefore, IFs follows the GBD methods in modifying the distal driver formulation by adding proximate risk factors (BMI and SI, respectively) to forecast base diabetes- and chronic respiratory-related mortality rates.&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Equations: Diabetes&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
To forecast diabetes, IFs uses the following formula:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;M_{r,d=Diabetes,c,p}=HLDIABETESRR_{r,c,p}*ONCD^{0.75}_{r,c,p}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span style=&amp;quot;font-size:small;&amp;quot;&amp;gt;M&amp;lt;sub&amp;gt;r,c,d=Diabetes,p,r&amp;lt;/sub&amp;gt; is diabetes-related mortality by country/region r, age category c, and sex p.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
ONCD&amp;lt;sub&amp;gt;r,c,p&amp;lt;/sub&amp;gt;&amp;amp;nbsp; is other Group II (non-communicable disease) mortality, derived using the basic distal driver equation . HLDIABETESRR&amp;lt;sub&amp;gt;r,c,p&amp;lt;/sub&amp;gt; is a “Diabetes Relative Risk” factor, explained below.&lt;br /&gt;
&lt;br /&gt;
In a population at the “theoretical minimum” level of body mass index (BMI), where BMI is 21, diabetes-related mortality is expected to fall at 75 percent of other Group II mortality.&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt;&amp;amp;nbsp;&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;&amp;amp;nbsp; The diabetes relative risk factor (HLDIABETESRR) captures the increased risk represented by a population above the theoretical BMI minimum level.&amp;amp;nbsp; For example, the factor is about 1 for young females in Vietnam (where BMI is close to the theoretical minimum level of 21).&amp;amp;nbsp; Comparatively, the RR is approximately 28 for middle-aged women in the United Kingdom where population BMI is much higher.&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[2]&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The GBD project projected the RR variable for diabetes out to 2030 using fairly involved estimates of age and sex-specific levels (plus standard deviations) of population BMI.&amp;amp;nbsp; Our estimates in IFs of future BMI (HLBMI) are less sophisticated, and we only forecast country/region (r) and sex-specific (p) mean BMI (see [[Health#Adult_Body_Mass_Index_(BMI)_and_Obesity|this section]] for a description of our forecasts of BMI).&amp;amp;nbsp; As such, while we endogenize the RR variable by tying it to our&amp;amp;nbsp;forecasts of BMI, we also adjust our forecast by initializing RR using the GBD estimates for the year 2010 and computing an age-category specific shift factor (HLDIABSHIFT) in order to tie our forecast of expected RR with GBD estimates.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
The RR for diabetes forecast in IFs (HLDIABETESRR) assumes that country-specific BMI is distributed normally, and also assumes a standard deviation of 10% of the mean:&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[3]&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;HLDIABETESRR_{r,c,p}=HLDIABSHIFT_{r,c,p}*\int{e^{((HLBMI_{r,p}-(\frac{HLBMI_{r,p}-avgBMI}{StdDev}+21))*Log(RR_{c,p}))}}*P(HLBMI)*dBMI&amp;lt;/math&amp;gt;&lt;br /&gt;
&amp;lt;span&amp;gt;LogRR is the change in log of RR per 1 unit change in BMI.&amp;lt;/span&amp;gt; &amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[4]&amp;lt;/span&amp;gt;&amp;amp;nbsp;&amp;lt;/sup&amp;gt;&amp;lt;span&amp;gt;&amp;amp;nbsp; These values are age category (c) and sex (p) specific; the absolute relative risk of diabetes-related mortality in relation to a unit increase in BMI varies from between 1.47 (females under 45) and 1.2 (females over 80).&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[5&amp;lt;/span&amp;gt;] &amp;lt;/sup&amp;gt; P(HLBMI) is a normal distribution function with mean of avgBMI; StdDev is a fixed 10% of avgBMI.&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[6]&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;&amp;lt;/span&amp;gt;&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; &amp;lt;/span&amp;gt; The slower decrease in diabetes-related mortality reflects assumptions that risk factors for diabetes will improve more slowly that risk factors for other Group II diseases (Protocol S1, 6).&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[2]&amp;lt;/span&amp;gt; All RRs available in the IFs system, variable name HLDIABETESRR.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[3]&amp;lt;/span&amp;gt;&amp;amp;nbsp;We recognize, of course, that BMI is most likely not distributed normally in a population.&amp;amp;nbsp; However, we follow CRA authors in assuming normality in order to compare a given population with an ideal counterfactual population (James et al 2004). &amp;amp;nbsp;&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[4]&amp;lt;/span&amp;gt;&amp;amp;nbsp;WHO Comparative Risk Assessment Methodology, Kelly et al, 2009&lt;br /&gt;
&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[5]&amp;lt;/span&amp;gt;&amp;amp;nbsp;See associated data table, Kelly et al 2009.&lt;br /&gt;
&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[6]&amp;lt;/span&amp;gt;&amp;amp;nbsp;avgBMI is our forecast of BMI, while BMI are the values from -3 standard deviations to +3 standard deviations away from that avgBMI. Cecilia Peterson determined the fixed 10 percent rate for StdDev from the literature.&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Equations: Chronic Respiratory Disease&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Again following GBD authors, IFs separately computes the two components of the chronic respiratory disease category–chronic obstructive pulmonary disease (COPD) (where smoking is the overwhelming related risk factor) and “other” respiratory disease (where smoking is somewhat less determinative). Both elements follow the same formulation:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;M_{r,d=chronic respiratory disease,c,p}=ln((SIR_{r,c,p}*CRDRR_{c,p,d}+1-SIR_{r,c,p})*(e^{ONCDMORT_{d=OtherGroupII}})^{0.75})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;SIR_{r,c,p}=\frac{HLSMOKINGIMP_{r,c,p}}{SMOKIMPADJ_{r=three regions,c,p}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
SIR is the “smoking impact ratio, ” &amp;amp;nbsp;that is smoking impact in IFs (HLSMOKINGIMP) divided by an adjustment factor that is specific to three big regions (1. China, 2. World, and 3. SearD (Bangladesh, Bhutan, India, North Korea, Maldives, Myanmar, Nepal, Afghanistan, Pakistan)),&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;sup&amp;gt;&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt; &amp;lt;/span&amp;gt;&amp;amp;nbsp; age category c, gender p. CRDRR is the relative risk for chronic respiratory disease specific to age category c, gender p, and disease/death d type (COPD or other respiratory disease).&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;sup&amp;gt;&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[2]&amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt; &amp;lt;/span&amp;gt;&amp;amp;nbsp; Chronic respiratory disease is assumed to be declining at 75 percent of ONCD_Mort, which is other Group II related mortality.&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1] &amp;lt;/span&amp;gt;GBD authors provided the adjustment factor for SIR, and it is constant over the length of the IFs forecast. The computation is done with the hard-code value in a procedure called UpdateRespDisease.&amp;amp;nbsp; The name for SmokingImpactAdj in the model is sird.&amp;amp;nbsp; CRDRR is hard-code in the same procedure.&lt;br /&gt;
&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[2]&amp;lt;/span&amp;gt; RR ranges from approximately 10 for COPD to about 2 for other chronic causes.&amp;amp;nbsp; Again, GBD authors provided the relative risk estimates used in IFs.&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Cardiovascular Disease&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
The regression models used in the GBD project did not differentiate between-subject from within-subject variation.&amp;amp;nbsp; Particularly for cardiovascular-related outcomes in some age/sex groups, this model produced a perverse finding: a negative relationship between cardiovascular-related mortality and smoking impact (HLSMOKINGIMP).&amp;amp;nbsp; However our further statistical investigation showed, as expected, a positive relationship between cardiovascular-related mortality and HLSMOKINGIMP within a given country over time.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
As such, we completed a more sophisticated mixed model regression analysis (using SAS, version 9.1) to capture both within and between-subject effects.&amp;amp;nbsp; We used the GBD mortality database described [[Health#Data_Initialization|here]], supplemented by our historical series of income per capita&amp;lt;sup&amp;gt;[1]&amp;lt;/sup&amp;gt;.&amp;amp;nbsp; All distal drivers were included as fixed effects, with random effects included for subject (country) and time (T).&amp;amp;nbsp; The revised coefficients (see Appendix Table 1) were used to forecast cardiovascular disease-related mortality.&amp;amp;nbsp; We created only one model for all countries (no separate low-income model) due to lack of data.&amp;amp;nbsp; Comparison with the original GBD models reveals fairly similar forecast outcomes overall.&amp;amp;nbsp; However, the positive change in the smoking/cardiovascular mortality relationship allows us to better examine how smoking intervention scenarios might impact cardiovascular-related mortality.&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;div&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; Note that we did use historical estimates of education provided by the GBD project, instead of using the less complete historical series available through IFs.&amp;amp;nbsp; Future distal driver analysis may explore using alternate sets of education data, including those included in the IFs system.&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Diarrhea, Malaria, and Respiratory Infections&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
IFs added three additional communicable diseases, namely diarrhea, malaria, and respiratory infection, after it had developed the modelling approach for distal drivers discussed above.&amp;amp;nbsp; The model uses the more general Group I (communicable disease and maternal mortality excluding AIDS) forecast to project mortality related to all three additions:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ln(M_{r,c,p,dg-1,d})=C_{r,c,p,dg-1,d}+\beta_{r,c,p,dg-1,d}*ln(M_{r,c,p,dg-1})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
M is mortality rate in deaths per 100,000 for a given region r, age category c, sex p, general (Group 1) cause dg=1 and specific disease d within general cause group dg=1. Here dg=1 refers to all communicable diseases (other than HIV/AIDS because Mathers and Loncar 2006 did not use the same approach to that particular communicable disease) and d refers to diarrhea, malaria or respiratory infections.&lt;br /&gt;
&lt;br /&gt;
The constants and beta coefficients for the above equation for the three diseases come from Mathers and Loncar (2005 and 2006c).&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;sup&amp;gt;&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt; &amp;lt;/span&amp;gt; For diarrhea and malaria we used their coefficients for infectious and parasitic diseases (Mathers and Loncar 2005: Table A-6 on page 115); for respiratory infections we used their coefficients for respiratory infections specifically (Mathers and Loncar 2006c, Table S5).&lt;br /&gt;
&lt;br /&gt;
The results for these three subtypes are then subtracted from the mortality for the total Group I category (except HIV/AIDS). The reason for this is to make sure that the sum of all them does not exceed the total of Group 1 (excluding HIV/AIDS), which is a result the equation could theoretically produce.&amp;amp;nbsp; In fact, we want to be sure that there is room for the Other Group 1 cause of death, so IFs limits the sum for diarrhea, malaria, and&amp;amp;nbsp;&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;respiratory infection to 95 percent of the total of Group 1 (excluding HIV/AIDS).&amp;amp;nbsp;&amp;amp;nbsp; If necessary, all three subcategories are reduced proportionally by a factor of 0.95/(SUM(3 subtypes)/Tot(big type)). Note that, if this restraint needs to be imposed, the denominator will always be higher than 0.95 and then the multiplicative adjustment factor will always be lower than 1.&amp;lt;/span&amp;gt;&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;div&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; The extended process for using those is described in 2 working notes for the IFs project by Dale Rothman, Dealing with Diarrhoeal Diseases Including the Effects of Unsafe Water &amp;amp; Sanitation and Undernutrition (March 25, 2009) and Dealing with Effects of Indoor Air Pollution (October 8 2009).&amp;amp;nbsp; Titles of the files are Incorporating Diarrhoea 25 March 2009 and Incorporating Indoor Air Pollution 9 October 2009, respectively.&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;HIV/AIDS&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The ultimate objective of the calculations around HIV infections and AIDS is to forecast annual deaths from AIDS (AIDSDTHS) by age category and sex.&amp;amp;nbsp; We did not look to the forecast methodology of Mathers and Loncar (2006) for their approach on this particular communicable disease; in fact, they also used an approach that did not rely upon the general distal driver formulation.&lt;br /&gt;
&lt;br /&gt;
The IFs approach begins by forecasting country-specific values for the HIV prevalence rate (HIVRATE).&amp;amp;nbsp; For the period from 1990-2007 we have reasonably good data and estimates from UNAIDS (2008) on prevalence rates and have used values from 2004 and 2006 to calculate an initial rate of increase (hivincr) in the prevalence rate across the population (which for most countries is now negative).&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; data-mce-mark=&amp;quot;1&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;&lt;br /&gt;
&lt;br /&gt;
There will be an ultimate peak to the epidemic in all countries, so we need to deal with multiple phases of changing prevalence:&amp;amp;nbsp; continued rise where rates are still growing steadily, slowing rise as rates peak, decline (accelerating) as rates pass the peak, and slowing rates of decline as prevalence approaches zero in the longer term.&amp;amp;nbsp; In general, we need to represent something of a bell-shaped pattern, but one with a long tail because prevalence will persist for the increasingly long lifetimes of those infected and if pockets of transmission linger in selected population sub-groups.&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; data-mce-mark=&amp;quot;1&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[2]&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;&amp;amp;nbsp; As a first level of user-control over the pattern, we add scenario specification via an exogenous multiplier on the prevalence rate (&#039;&#039;&#039;&#039;&#039;hivm&#039;&#039; &#039;&#039;&#039;).&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;The movement up to the peak involves annual compounding of the initial growth rate in prevalence (&#039;&#039;&#039;&#039;&#039;hivincr&#039;&#039; &#039;&#039;&#039;), dampened as a country approaches the peak year.&amp;amp;nbsp; Thus we can further control the growth pattern via specification of peak years (&#039;&#039;&#039;&#039;&#039;hivpeakyr&#039;&#039; &#039;&#039;&#039;) and prevalence rate in those peak years (&#039;&#039;&#039;&#039;&#039;hivpeakr&#039;&#039; &#039;&#039;&#039;), with an algorithmic logic that gradually dampens growth rate to the peak year:&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[3]&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;HIVRATE_{r,t}=HIVRATE_{r,t-1}*(1+hivincr_{r,t})*\mathbf{hivm}_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;where&amp;lt;/span&amp;gt;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;hivincr_{r,t}=F(\mathbf{hivincr}_{r,t=1},\mathbf{hivpeakyr_r,hivpeakr_r})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
t is time, r is country or region.&amp;amp;nbsp; Names in bold are exogenously specified parameters.&lt;br /&gt;
&lt;br /&gt;
As countries pass the peak, we posit that advances are being made against the epidemic, both in terms of social policy and technologies of control, at a speed that reduces the total prevalence rate a certain percent annually (&#039;&#039;&#039;&#039;&#039;hivtadvr&#039;&#039; &#039;&#039;&#039;).&amp;amp;nbsp; To do this, we apply to the prevalence rate an accumulation of the advances (or lack of them) in a technology/social control factor (HIVTECCNTL).&amp;amp;nbsp; In addition, if decline is already underway in the data for recent years, we add a term based on the initial rate of that decline (&#039;&#039;&#039;&#039;&#039;hivincr&#039;&#039; &#039;&#039;&#039;), in order to match the historical pattern; that initial rate of decline decays over time and shifts the dominance of the decline rate to the exogenously specified rate (&#039;&#039;&#039;&#039;&#039;hivtadvr&#039;&#039; &#039;&#039;&#039;).&amp;amp;nbsp; This algorithmic formulation generates the slowly accelerating decline and then slowing decline of a reverse S-shaped pattern with a long tail:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;HIVRATE_{r,t}=HIVRATE_{r,t-1}*(1-HIVTECCNTL_{r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;HIVTECCNTL_{r,t}=HIVTDCCNTL_{r,t-1}*(1+\mathbf{hivtadvr}*\frac{t}{100})+F(\mathbf{hivincr}_{r,t=1})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Finally, calculation of country and region-specific numbers of HIV prevalence is simply a matter of applying the rates to the size of the population number.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;HIVCASES_{r,t}=Pop_{r,t}*HIVRATE_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The rate of death to those with HIV would benefit from a complex model in itself, because it varies by the medical technology available, such as antiretroviral therapy (ART) and the age structure of prevalence.&amp;amp;nbsp; We have simplified such complexities because of data constraints, while maintaining basic representation of the various elements.&amp;amp;nbsp; Because the manifestation of AIDS and deaths from it both lag considerably behind the incidence of HIV, we link the death rate of AIDS (HIVAIDSR) to a 10-year moving average of the HIV prevalence (HIVRateMAvg).&amp;amp;nbsp; We also posit an exogenously specified technological advance factor (&#039;&#039;&#039;&#039;&#039;aidsdrtadvr&#039;&#039; &#039;&#039;&#039;) that gradually reduces the death rate of infected individuals (or inversely increases their life span), as ART is doing.&amp;amp;nbsp; And we allow the user to apply an exogenous multiplier (&#039;&#039;&#039;&#039;&#039;aidsratem&#039;&#039; &#039;&#039;&#039;) for further scenario analysis:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;AIDSDRATE_{r,t}=HIVRateMAvg_{r,t}*HIVAIDSR_{r,t=1}*(1-\frac{\mathbf{aidsdrtadvr}_{r,t}}{100})*\mathbf{aidsratem}_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;HIVRateMAvg_{r,t}=F(HIVRATE_{r,t},last 10 years)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
We spread this death rate across sex and age categories. We apply a user-changeable table function to determine the male portion as a function of GDP per capita (at PPP), estimating that the male portion rises to 0.9 with higher GDP per capita.&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[4]&amp;lt;/span&amp;gt;&amp;amp;nbsp;&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;&amp;amp;nbsp; To specify the age structure of deaths, we examined data from large numbers of studies on infections by cohort in Brazil and Botswana (in a U.S. Census Bureau database) and extracted a rough cohort pattern (&#039;&#039;&#039;&#039;&#039;aidsdeathsbyage&#039;&#039; &#039;&#039;&#039;) from those data.&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot; data-mce-mark=&amp;quot;1&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; The HIV/AIDS data were being update in October, 2013. The IFs pre-processor calculates initial rates of HIV prevalence and annual changes in it using the middle estimates of the UNAIDS 2008 data.&amp;amp;nbsp; When middle estimates do not exist, as in the case of the Democratic Republic of Congo, it uses an average of high and low estimates.&amp;amp;nbsp; The system uses data for total population prevalence, but also includes HIV prevalence for those 15-49.&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[2]&amp;lt;/span&amp;gt; A more satisfactory approach would use stocks and flows and have a more strongly systems dynamics’ character.&amp;amp;nbsp; It would track infected individuals, presumably by age cohorts, but at least in the aggregate.&amp;amp;nbsp; It would compute new infections (incidence) annually, adding those to existing prevalence numbers, transitioning those already infected into some combination of those manifesting AIDS, those dying, and those advancing in age with HIV.&amp;amp;nbsp; But the data do not seem widely available to parameterize such transition rates, especially at the age-category level.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[3]&amp;lt;/span&amp;gt;&amp;amp;nbsp;Table 17 (pp 77-78) of the Annex to World Population Prospects: the 2002 Revision (UNPD 2003) provided such estimates for 38 African countries and selected others outside of Africa; the IFs project has revised and calibrated many of the estimates over time as more data have become available.&amp;amp;nbsp; By 2004-2006, however, quite a number of countries had begun to experience reductions, and this logic has become less important except in scenario analysis for countries where prevalence is still rising.&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[4]&amp;lt;/span&amp;gt;&amp;amp;nbsp;Early epidemic data from sub-Saharan Africa and the United States supported this assumption.&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Road Traffic Accidents&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
In forecasting mortality related to road traffic accidents, IFs replaces the GBD regression model with a structural formulation designed to better capture relevant drivers for this cause group.&amp;amp;nbsp; Specifically, IFs projects deaths due to traffic accidents (DEATHCAT, Traffic) as a function of deaths in traffic per vehicle (DEATHTRPV) and vehicle numbers (VEHICLESTOT), both computed in the automobile module of IFs. &amp;amp;nbsp;&amp;amp;nbsp;We first need to compute the total size of the vehicle fleet.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;Total vehicles per capita (VEHICFLPC) is based on a formula proposed in a paper by Dargay et al (2007) in which fleet size per capita is a function of GDP per capita at PPP (GDPPCP).&amp;amp;nbsp; Translating the Dargay et al (2007) equation into one using IFs variable names yields:&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1&amp;lt;/span&amp;gt;]&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;VEHICFLPC_r=(852-RF)*e^{-5.987*e^{(-0.2*GDPPCP_r)}}*\mathbf{vehicfpcm}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;The parameter &#039;&#039;&#039;&#039;&#039;vehicfpcm&#039;&#039; &#039;&#039;&#039; allows scenario intervention. RF is an adjustment factor that compensates for different land densities, that is the ratio of population (POP) to land area (LANDAREA), taking the U.S. as the base:&amp;amp;nbsp;&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;RF=38.8*(\frac{POP_r}{LANDAREA_r}-\frac{POP_{r=USA}}{LANDAREA_{r=USA}})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The computation was only used when country R had higher density than the US. The paper also describes another adjustment factor related to urbanization as percentage of total population, but we did not use this additional adjustment factor in our model.&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
Given fleet size per capita and the population, we compute the total size of the fleet.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;VEHICLESTOT_r=VEHICLEFLPC_r*POP_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;The number of deaths per vehicle is based on Smeed’s Law&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[2]&amp;lt;/span&amp;gt;&amp;amp;nbsp;&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;, an empirical rule originally proposed by R.J. Smeed, which relates deaths to vehicle ownership.&amp;amp;nbsp; In the original conceptual form Smeed’s Law is:&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;D=0.0003(np^2)^{1/3}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:D is annual road deaths&lt;br /&gt;
&lt;br /&gt;
:n is number of vehicles&lt;br /&gt;
&lt;br /&gt;
:p is population&lt;br /&gt;
&lt;br /&gt;
In terms of IFs variable names this would translate literally (ignoring some unit issues) as:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;DEATHCAT_{r,d=TrafficAcc}=0.0003*(VEHICLESTOT_r*POP^2_r)^{1/3}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The actual representation in IFs involves two steps.&amp;amp;nbsp; First we calculate the death rates per vehicle, adding a division by a multiplicative term that is equivalent to total vehicle numbers VEHICLESTOT.&amp;amp;nbsp; One of the virtues of this first step is that we can add an exogenous multiplier for death rates per vehicle, &#039;&#039;&#039;&#039;&#039;deathtrpvm&#039;&#039; &#039;&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;DEATHTRPV_r=\frac{0.0003*(VEHICLFLPC_r*1E+15*POP^3_r)^{1/3}}{VEHICFLPC_r*POP_r}*\mathbf{deathtrpvm}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The second step is to use the death rate per vehicle, the vehicle fleet size per capita, and information on the age and sex distribution of deaths from vehicles to compute the mortality rate from vehicle accidents by age and sex, putting the results into a variable internal to model named &#039;&#039;&#039;modmordstdet&#039;&#039;&#039;.&amp;amp;nbsp; In a third step that variable is used with population by age and sex to compute the total deaths from vehicle accidents (DEATHCAT).&amp;amp;nbsp; These second and third steps stylistically yield&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;DEATHCAT_{r,d=TrafficAcc}=DEATHTRPV_r*VEHICLESTOT_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
After initialization in the base year (using GBD estimates of road traffic-related mortality and total vehicles from the automobile module in IFs), IFs calculates a multiplicative shift factor that is kept constant for the entire forecast horizon. If this initialization value is greater than 40 deaths per 1000 vehicles, we adjust the number of vehicles per capita to set 40 as our initialization value. We started using this limit after finding inconsistencies between estimates derived from Smeed’s Law and those from initial estimates.&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; data-mce-mark=&amp;quot;1&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[3&amp;lt;/span&amp;gt;]&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;&lt;br /&gt;
&lt;br /&gt;
IFs also computes a ratio (in a variable internal to the model) of traffic accident mortality for males compared to females.&amp;amp;nbsp;&amp;amp;nbsp; The model converges that ratio to 1.5 over 100 years by preserving the total mortality for each age category but adjusting the distribution between males and females.&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; Dargay, Gately, and Sommer 2007. “Vehicle Ownership and Income Growth, Worldwide: 1960-2030”. Joyce Dargay, Dermot Gately and Martin Sommer, January 2007.&amp;lt;br/&amp;gt;&amp;lt;div&amp;gt;&amp;lt;div&amp;gt;&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[2]&amp;lt;/span&amp;gt;&amp;amp;nbsp;&amp;lt;/span&amp;gt; [http://en.wikipedia.org/wiki/Smeed&#039;s_law http://en.wikipedia.org/wiki/Smeed%27s_law]&lt;br /&gt;
&lt;br /&gt;
Smeed, RJ 1949. &amp;quot;Some statistical aspects of road safety research&amp;quot;.&amp;amp;nbsp;[http://en.wikipedia.org/wiki/Royal_Statistical_Society &#039;&#039;Royal Statistical Society&#039;&#039; ], Journal (A) CXII (Part I, series 4). 1-24.&lt;br /&gt;
&lt;br /&gt;
Adams 1987.&amp;amp;nbsp;[http://www.geog.ucl.ac.uk/~jadams/PDFs/smeed&#039;s%20law.pdf &amp;quot;Smeed&#039;s Law: some further thoughts.&amp;quot;]&amp;amp;nbsp;&#039;&#039;Traffic Engineering and Control&#039;&#039;&amp;amp;nbsp;(Feb) 70-73&lt;br /&gt;
[3]&amp;amp;nbsp;The case of Bangladesh is illustrative, where the forecast calculation of 141 deaths/thousand vehicles contrasts with an expectation of 30 deaths/thousand vehicles&amp;amp;nbsp; using Smeed’s Law.&amp;amp;nbsp; We concluded that our mortality figures were consistent with WHO estimates, but sometimes the total number of vehicles was too low.&amp;amp;nbsp; For example, for Bangladesh our data showed 1 vehicle per thousand people, which meant about 141,000 vehicles, when several reports indicate the real number is much higher (850,000) ([http://www.brta.gov.bd http://www.brta.gov.bd]).&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Mental Health&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The IFs model assumes that the initial rate of mortality related to mental health remains constant across our forecast horizon.&amp;amp;nbsp; That rate is subtracted from the other Group II category.&amp;amp;nbsp; The scenario parameter &#039;&#039;&#039;&#039;&#039;hlmortm&#039;&#039; &#039;&#039;&#039; allows the user easy control over mortality from the cause.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Modifications to the Basic Health Model&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
After examining in IFs the long-term behavior of the regression model forecasts using the Global Burden of Disease (GBD) distal driver approach and coefficients, we made a limited number of modifications.&amp;amp;nbsp; One set of modifications was made to the treatment of the technological change term in the distal formulation (see [[Health#Forecasting_Technology_for_the_Distal_Driver_Formulation_of_Mortality|this section]]). We also allow countries to transition from low-income status, given expected improvements in development status over the long-term.&amp;amp;nbsp; An adjustment for monotonicity ensures that the forecast population comports with well-known patterns of rising chronic-cause mortality rates with age.&amp;amp;nbsp; Finally, we include health spending in our model in order to better forecast potential outcomes.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Mortality Transition for Low-Income Countries&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
As described in the discussion of [[Health#Distal_Driver_Formulation|distal driver coefficients for low-income countries]]&amp;amp;nbsp;we use Global Burden of Disease (GBD) regression coefficients developed separately for low and high income countries.&amp;amp;nbsp; However, given the long forecast horizon of IFs, we recognize that many low-income countries eventually will reach high levels of income and thus should follow a similar pattern of mortality.&amp;amp;nbsp; Therefore, we allow low-income countries to transition gradually by computing two mortality rates for low-income countries˗one using the low-income beta coefficients and the other using the high-income model. We start the transition when countries reach GDPPCP of $3,000, and finish the transition when countries reach $15,000. The transition is computed finding target mortality in between the two, interpolating depending on the current level of GDPPCP.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Given the target mortality, we compute how much change we need from current mortality (low-income based), and slowly adjust using a moving average of 20 percent of current required change and 80 percent of change used in previous years:&lt;br /&gt;
&lt;br /&gt;
:Change = Target Mortality – Low Income Mortality&lt;br /&gt;
&lt;br /&gt;
:Smooth Change = 0.2 * Change + 0.8 * Last Year Change&lt;br /&gt;
&lt;br /&gt;
:Final Mortality = Low Income Mortality + Maximum(Smooth Change, Change)&lt;br /&gt;
&lt;br /&gt;
:where Last Year Change = Maximum(Smooth Change(yr-1), Change(yr-1)).&lt;br /&gt;
&lt;br /&gt;
Note that most of the time target mortality is lower than low income mortality, and thus change is negative.&amp;amp;nbsp; Thus, when we find the maximum we are finding the smaller absolute number and smoothing change.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Maintaining Increase with Age in Non-Communicable Death Rates&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
In general, both in our initial conditions and forecasts, we try to maintain monotonicity in growth of death rates from chronic causes with increasing age (above 45) by adjusting deaths from a particular cause when initial computations do not illustrate increases with age and compensating in death rates from an alternative cause for which initial computations indicate room for mortality reduction while (a) maintaining monotonicity for that cause also and (b) not changing total mortality in the pair of causes.&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1&amp;lt;/span&amp;gt; ] &amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt; If necessary we also work to make acceptable adjustments in one age category by readjusting the next age category for the same cause of death, decreasing deaths in the younger age category and increasing deaths in the older one, while doing the opposite for the compensating cause of death. Finally, in this overall and quite complicated algorithmic process we try to minimize the adjustments made to the initial calculations of mortality.&lt;br /&gt;
&lt;br /&gt;
To elaborate this process further, when we find for a chronic cause of death a monotonicity problem for a country and a given age category relative to the next younger category, we find the type (H1) with the highest mortality rate (in the 100+ category among non communicable disease),&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[2&amp;lt;/span&amp;gt; ] &amp;lt;/sup&amp;gt; &amp;lt;/span&amp;gt; then we try to use H1, which often turns out to be cardiovascular disease, to compensate adjustments in other types in order to keep total mortality constant for the same age category.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
For each 5-year age category starting at 45 to 49, we compute total mortality as the sum of all types, then for each non-communicable type with non-zero mortality we compute its growth G from the current age category j to the next j+1, for example in the first step from 45-49 to 50-54. Although our emphasis is on avoiding non-monotonicity, we also would like to see some regularity of progression of mortality increase with age, as we find in the quite high-quality data of Sweden. Thus we also look to that progression in Swedish data for a rate of increase of across age categories that we can use as a minimum. Specifically, &amp;lt;del cite=&amp;quot;mailto:Jose&amp;quot; datetime=&amp;quot;2013-09-26T17:36&amp;quot;&amp;gt;&amp;amp;nbsp;&amp;lt;/del&amp;gt;across two adjacent age categories &amp;amp;nbsp;we find a Proxy growth P, where we use Sweden’s mortality for each type, but we do not allow this P to be higher than 1/4&amp;lt;sup&amp;gt;th&amp;lt;/sup&amp;gt; of Total Mortality Growth. If G is smaller than P then we start the procedure for the given age category j and type of mortality d.&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[3&amp;lt;/span&amp;gt; ]&amp;lt;/sup&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Once we start the adjustment procedure we check if there is room to reduce mortality in the current age and type, so we check growth from the previous age category to avoid breaking monotonicity. First we compute Proxy Growth P1 from the previous age category j-1 (40-44 for our example) to the current one j (45-49).&lt;br /&gt;
&lt;br /&gt;
Second we compute the minimum acceptable value for current mortality:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;Min=Mort_{r,a=j-1,d}*(1+P1)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where &amp;lt;math&amp;gt;Mort_{r,a=j-1,d}&amp;lt;/math&amp;gt; is the mortality for country r, type d in age category j-1.&lt;br /&gt;
&lt;br /&gt;
Third we compute maximum acceptable value for current mortality, we start with:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;Max=\frac{Mort_{r,a=j+1,d}}{1+P}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
But we know that &amp;lt;math&amp;gt;Mort_{r,a=j-1,d}&amp;lt;/math&amp;gt; is also going to change to keep the number of deaths constant, so we also consider this adjustment:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;Max=\frac{Mort_{r,a=j+1,d}+Adj*\frac{Pop_{r,a=j}}{Pop_{r,a=j+1}}}{(1+P)}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
And we know that:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;Adj=Mort_{r,a=j,d}-Max&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Solving for max, we have:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;Max=\frac{(Mort_{r,a=j+1,d}+Mort_{r,a=j,d}*\frac{Pop_{r,a=j}}{Pop_{r,a=j+1}})}{(1+P+\frac{Pop_{r,a=j}}{Pop_{r,a=j}})}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Where Mort is the original mortality for age category j and j+1, country r, and type d. Pop is population for age j and country r and P is the Proxy Growth computed as explained above.&lt;br /&gt;
&lt;br /&gt;
If Min is smaller than Max, then we use Max as the new mortality in age j, in order to keep the adjustment as small as possible, if not that means that Max wouldn’t keep monotonicity from age j-1, so we start trying to adjust going backwards, given that frequently there’s more room in previous age categories. In order to start going backwards we keep track of the first age category that it’s already saturated, i.e. that its growth is already the minimum possible without breaking monotonicity. If we find that the first saturated category is higher than the 45-49 that we started with, that means we have some room going backwards, so we take Max, otherwise we use Min as the new mortality in age j, and keep adjusting forward. The adjustment A is just the difference between original mortality in age j and the new chosen mortality.&lt;br /&gt;
&lt;br /&gt;
Adjust backwards means that we will adjust mortality in age category j2 and j2-1, where j2 goes from j-1 to 10 (which corresponds to 45-49). While going backwards the formulas for min and max change a little bit, given that the adjustment is done in the previous age category.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;Min=(Mort_{r,a=j2-1,d}+Adj*Pop_{a=j2})*(1+P1)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
And substituting the adjustment computed earlier (&amp;lt;math&amp;gt;Adj=Mort_{r,a=j,d}-Max&amp;lt;/math&amp;gt;),&lt;br /&gt;
&lt;br /&gt;
we end up with:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;Min=\frac{(Mort_{r,a=j2-1,d}+Mort_{r,a=j2,d}*\frac{Pop_{a=j2}}{Pop_{a=j2-1}})}{\frac{1}{1+P1}+\frac{Pop_{a=j2}}{Pop_{a=j2-1}}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;Max gets simplified to:&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;Max=(\frac{Mort_{r,a=j2+1,d}}{(1+P)})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
We then check for room in type H1, and if there’s enough room we adjust mortality for j2. If Min &amp;lt;= Max then we can stop, otherwise we keep going back until we reach the 45-49 category.&lt;br /&gt;
&lt;br /&gt;
Fourth, we verify that, in doing compensation for type H, monotonicity is preserved too. In order to make this verification first we find the potential growth rate GH after applying adjustment A to type H. Then we compute the Proxy Growth PH for type H. If GH is greater or equal than PH then we can apply the adjustments if not we just leave mortality unchanged.&lt;br /&gt;
&lt;br /&gt;
Fifth, applying the adjustments to type d by subtracting the adjustment from the original mortality in age j for type d, and adding it up adjusted for deaths to the original mortality in age j+1 for type d:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;Mort_{r,a=j,d}=Mort_{r,a=j,d}-Adj&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;Mort_{r,a=j+1,d}=Mort_{r,a=j+1,d}+Adj*\frac{Pop_{r,a=j}}{Pop_{r,a=j+1}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Sixth, applying the adjustments to type H by adding the adjustment to original mortality in age j for type H, and subtracting it adjusted for deaths from the original mortality in age j+1 for type H:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;Mort_{r,a=j,d=H}=Mort_{r,a=j,d=H}+Adj&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;Mort_{r,a=j+1,d=H}=Mort_{r,a=j+1,d=H}-Adj*\frac{Pop_{r,a=j}}{Pop_{r,a=j+1}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Seventh, if Min is greater than Max and we couldn’t go backwards means that we took Min as the new mortality for age k, and it means that we still don’t have monotonicity because we haven’t changed age j+1 yet.&amp;amp;nbsp; Then we need to find the new mortality value for j+1 using Proxy Growth:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;Newmort=Mort_{r,a=j,d}*(1+P)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;Adj=Newmort-Mort_{r,a=j+1,d}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Eighth, we check that this new adjustment doesn’t break monotonicity in death type H, if it doesn’t we apply it as we did for age j, if does break it, we just leave mortality unchanged.&lt;br /&gt;
&lt;br /&gt;
Ninth, applying this adjustment is the same as step 5 and 6, but using ages j+1 and j+2 instead of j and j+1. The only difference here is that when we get to the second to last age category (j = 20, 95-99),&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[4&amp;lt;/span&amp;gt; ] &amp;lt;/sup&amp;gt; &amp;lt;/span&amp;gt; then the compensatory adjustment for deaths is done in the first age category of the loop (j=10, 45 to 49), and we restart the process for a second and final check of monotonicity.&lt;br /&gt;
&lt;br /&gt;
We have added check limits along the process to avoid mortality going above 1000 per 1000 and below 0 per 1000 at all times, and if the limits are reached then mortality is left unchanged.&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; The changes described here for monotonicity with age do not guarantee monotonicity of changes in rates over time within an age category.&amp;amp;nbsp; In fact, they could contribute to some small transients or irregularities in rates over time.&amp;amp;nbsp; In general, however, we believe that they will make such behavior less likely. For such irregularities, see the longitudinal curve in Afghanistan for cancer at 80-84 males; these are most likely to appear, as they would in the real world, when total mortality is not changing much over time.&amp;amp;nbsp; &amp;amp;nbsp;&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[2]&amp;lt;/span&amp;gt; In specialized work looking at low senescent aging the last category is 200+.&lt;br /&gt;
&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&lt;br /&gt;
&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[3]&amp;lt;/span&amp;gt;&amp;amp;nbsp;&amp;lt;/span&amp;gt;An example can illustrate. Say male mortality is 0.5 for Cancer at 45-49, and 1.1 for 50-54, then P is 140% (these are numbers from Sweden). Say total male mortality is 10.1 for 45-49 and 14.5 for 50-54, so total growth is 43%. (These are numbers for any country, say Afghanistan). We can’t use P of 140% on Afghanistan for male cancer, given that for those ages Total Mortality grows only 43%, so we use a Proxy (P) of 11% (43*.25), for male cancer in Afghanistan between ages 45-49 and 50-59. G is the actual growth in mortality for male cancer in Afghanistan between ages of 45-49 and 50-54; say for example: 0.7664, 1.2876 respectively, so G is 68%.&amp;amp;nbsp; In this example, there is no need to change anything (68 &amp;gt; 11). Only if G is smaller than P does the adjustment process begin.&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[4]&amp;lt;/span&amp;gt;&amp;amp;nbsp;In specialized work looking at low senescent aging the last category is 195-199.&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Elasticity of Child Mortality with Health Spending&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
For countries that have a GDP per capita in the initial year of less than $15,000 an elasticity factor with health spending (&#039;&#039;&#039;&#039;&#039;elhlmortspn&#039;&#039; &#039;&#039;&#039;) of -0.06 will affect mortality of children under 5.&amp;amp;nbsp; That is, each 1 percent change in health spending as a percentage of GDP will lower mortality by 0.06 percent; an increase of 100 percent (doubling) would produce an automatic reduction of 6 percent in mortality. We have implemented a limit on the reductions to be at most 80 percent of mortality.&lt;br /&gt;
&lt;br /&gt;
The GBD project’s distal driver formulation does not take public health spending into account.&amp;amp;nbsp; However, we add a term to the basic GBD distal driver formulation to incorporate public health spending as a proximate driver to account for the relatively consistent inverse relationship between total public health expenditures and child mortality rates in poor countries (Anand and Ravallion 1993; Bidani and Ravallion 1997; Jamison et al. 1996; Nixon and Ulmann 2006; Wagstaff 2002). For countries having a GDP per capita (at PPP) of $15,000 or less, our model applies a simple elasticity for the effects of government health expenditure as a percentage of GDP on all-cause mortality (except HIV/AIDS) for the age 0-4 group from the distal driver formulation (the base calculation that health expenditures adjust):&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ln(_5q^{adj}_0)=ln(_5q^{base}_0)-0.06*HealthExp%&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where &amp;lt;math&amp;gt;_5q_0&amp;lt;/math&amp;gt; is the mortality rate for age 0-4.&lt;br /&gt;
&lt;br /&gt;
In IFs this formalized version becomes&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;MortAdj_{r,j=0-4,d=1,t}=Mort_{r,j=0-4,d=1,t}*(1+HlExpFct_{r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;HlExpFct_{r,t}=\mathbf{elhlmortspn}*\frac{(100*\frac{GDS_{r,g=health,t}}{GDP_{r,t}})-GDSHI_{r,t=1}}{GDSHI_{r,t=1}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GDSHI_{r,t=1}=\frac{GDS_{r,g=health,t=1}}{GDP_{r,t=1}}*100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
GDS is government expenditure; elhlmortspn is the elasticity of mortality with health spending, &#039;&#039;j &#039;&#039;is age category; r is country/region; d is cause (1 is other communicable); t is time step.&lt;br /&gt;
&lt;br /&gt;
In this calculation we use health expenditure as a percentage of GDP, rather than health expenditure per capita, to avoid any confounding with the distal driver for GDP per capita. We established this coefficient for all-cause mortality in the 0-4 age category on the basis of multivariate regressions using the GBD distal driver specifications as a base.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Data Initialization&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Several important differences in our approach to forecasting health relative to that of the Global Burden of Disease (GBD) project required development of algorithms for computation of initial conditions and small multiplicative adjustments to formulations.&amp;amp;nbsp; Specifically, we forecast by country, we begin forecasts in the base year of 2010, and we maintain 5-year age categories.&amp;amp;nbsp; Moreover, we have our own sources of data for GDP per capita and education attainment level, which we forecast using our own models.&amp;amp;nbsp; The initial data we obtained from the GBD project provided country, sex, and cause specific mortality, but from the year 2008 (subsequently updated to 2010) and in slightly different age categories.&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; &amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt;&amp;amp;nbsp; This section details our approach to reconciling differences in initial data.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Normalization and Scaling Factors&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
IFs initializes the base year (2010) data using age, sex, cause, and country-specific mortality data for 2010 provided by the Global Burden of Disease (GBD), courtesy of Colin Mathers at the World Health Organization.&amp;amp;nbsp; Those data are for infants and then for 5-year age categories up to 85+.&amp;amp;nbsp; To fill holes we used the same base rates for 1-4 as for infants and for all 5-year age categories above 85, subject to normalization to total mortality for the age category.&amp;amp;nbsp; The model then computes normalization and scaling factors which reconcile the results of the forecast regression models with these initial data.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
The normalization factor helps match the sum of all mortalities in the health module to the mortality computed in the population module in the base year (2010).&amp;amp;nbsp; This process assures that we have initial conditions consistent with UNPD mortality data in our base year (i.e., the sum of all deaths will be the same as the UNPD mortality data for each 5-year age and sex category for the year 2010).&amp;amp;nbsp; The normalization factor uses all types of mortality except for AIDS in the numerator and denominator:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;NormalizationFactor=\frac{TotalMortalitydata-Aidsmortalitydata}{\sum{Mortalitydata_i}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The scaling factor sets the historic proportions across the different causes of mortality, assuring consistency of total deaths forecast using the GBD formulations and our 2010 values of driving variables with the cause-specific mortality data in the GBD’s detailed death file.&amp;amp;nbsp; The scaling factor uses distal driver regression results for the denominator and GBD 2010 data for the numerator:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ScalingFactor=\frac{Mortalitydata_i}{Mortalitycalculation_i}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
These adjustments mean that, except for the total mortality by age and sex of the UN, our numbers in the 2010 base year will not match other data precisely, but that the overall pattern of deaths by cause should be quite close to the GBD data.&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;sup&amp;gt;&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1&amp;lt;/span&amp;gt; ] &amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt; &amp;lt;/span&amp;gt;&amp;amp;nbsp; In the forecasts themselves, we keep the multiplicative scaling and normalization parameters constant over time because there is no clear reason for changing them in the base, but have added parameters to control convergence in scenarios: &#039;&#039;&#039;&#039;&#039;hlgbdconvdown&#039;&#039; &#039;&#039;&#039;, &#039;&#039;&#039;&#039;&#039;hlgbdconvup&#039;&#039; &#039;&#039;&#039;, &#039;&#039;&#039;&#039;&#039;hlscaleconvdown&#039;&#039; &#039;&#039;&#039;, where the first parameter controls the normalization factor when is greater than 1, the second one controls the normalization factor when is smaller than 1, and the last one controls the scaling factor when is greater than 1.&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;&amp;amp;nbsp;[1]&amp;lt;/span&amp;gt; &amp;lt;/span&amp;gt; Complicating initialization further, the UNPD presents its data in 5-year ranges, including 2005-2010 and 2010-2015.&amp;amp;nbsp; The age- and sex-specific survivor-table values in those ranges therefore do not correspond to specific years like our base of 2010.&amp;amp;nbsp; After correspondence with Kirill Andreev of the UNPD, which we acknowledge appreciatively, we decided to average the mortality values in the two 5-year ranges ending and beginning with 2010.&amp;lt;/div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Cause-specific Mortality: Infants and the Elderly&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The detailed deaths data file (by cause, sex, and age) that we have obtained through the generosity of Colin Mathers at the World Health Organization does not include cause-specific infant or old-age (85+) mortality (greater than 85).&amp;amp;nbsp; Because IFs forecasts both infant mortality and 5-year age categories to 100 years, we incorporate detailed mortality data from Sweden (as a proxy, thanks mainly to availability of data)&amp;amp;nbsp; in order to initialize Group II (excluding mental health) cause-specific mortality&amp;amp;nbsp; for these missing populations.&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1&amp;lt;/span&amp;gt; ] &amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
The first step is to find the weights per age category for Sweden as follows:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;Weight_{a=j,d,p}=\frac{Deaths_{r=Sweden,a=j,p}}{Deaths_{r=Sweden,a=JJ,p}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Where j is the smaller IFs age category a (for example infants), JJ is the bigger corresponding GBD age category (for example children &amp;lt; 5, which implies the addition of infants plus children 1-4), p is gender, and d is mortality type.&lt;br /&gt;
&lt;br /&gt;
The second step is to check the monotonicity of growth in the existing mortality data for each country and type of mortality (from age 45 forward).&amp;amp;nbsp; If monotonicity is not found&amp;amp;nbsp; (i.e., mortality rates do not rise in step with increasing age groups) then the initialization data is left unchanged for this country and mortality type combination.&amp;amp;nbsp; If initial mortality does increase monotonically, we further adjust mortality:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;Mortality_{r,a=j,d,p}=Mortality_{r,a=j,d,p}*Weight_{a=j,d,p}*\frac{Pop_{r,a=JJ,p}}{Pop_{r,a=JJ,p}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Where j, JJ, p and d are the same as in the previous equation and Pop is the population vector.&lt;br /&gt;
&lt;br /&gt;
This option has been currently disabled because it was producing too much NCDs compared to CDs for countries like Mali for people over 90 years of age.&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[2&amp;lt;/span&amp;gt;]&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot; data-mce-mark=&amp;quot;1&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; This may be an issue also for Groups 1 and 3, but we have used the procedure with Sweden only for Group 2.&amp;lt;br/&amp;gt;&amp;lt;div&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; data-mce-mark=&amp;quot;1&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[2]&amp;lt;/span&amp;gt;&amp;amp;nbsp;Beginning with discovery of a problem for Mali, the use of this distribution procedure in IFs ran into some logic problems with poor behavior.&amp;amp;nbsp; As of September, 2013, the spread of initialization data for infants 1-4 and for ages 85+ is disabled.&amp;amp;nbsp; That spread, although desirable, is not necessary and at this point the code generates more problems than it solves.&amp;amp;nbsp; This section of the documentation is maintained should we want to revisit the issue.&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Incorporating Proximate Drivers&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Although the [[Health#Distal_Drivers_and_Basic_Indicators|distal driver approach]]&amp;amp;nbsp;serves us well in forecasting health, thinking about intervention and leverage points in order to achieve alternate health futures necessarily involves the inclusion of proximate health drivers into the model.&amp;amp;nbsp; Therefore we need to have an approach that can layer specific risk analysis onto the underlying distal driver specifications.&amp;amp;nbsp; We need to discuss our general approach to doing that, which uses population attributable fraction or PAF formulations, and we need to provide equations of the models used to forecast proximate drivers in IFs and to build the relative risk estimates we used in the model.&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt;&amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Adjusting Mortality Due to Changes in a Single Risk Factor&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
We build our approach on an understanding of two basic concepts used in the Comparative Risk Assessment (CRA) project (Ezzati et al. 2004), specifically &#039;&#039;relative risk &#039;&#039;(RR) and &#039;&#039;population attributable fraction &#039;&#039;(PAF). An RR is a “measure of the risk of a certain event happening in one group compared to the risk of the same event happening in another group”.&amp;lt;sup&amp;gt;[1] &amp;lt;/sup&amp;gt;&amp;amp;nbsp; We follow the approach taken by the CRA study, comparing our forecast population at risk to an “ideal” population with a “theoretical minimum” level of risk. For example, the WHO estimates that children under five who are moderately or severely underweight are almost nine times more likely to die of communicable causes than a population of “normal-weight” children (Blössner and de Onis 2005).&lt;br /&gt;
&lt;br /&gt;
As its name suggests, a PAF or population attributable fraction reflects the degree to which a specific risk factor is associated with the occurrence of a specific health outcome. &amp;amp;nbsp;Formally, it is the proportional reduction in disease or death rates for the total population (including those with and without the risk factor) that we would expect if we reduced a particular risk factor to a theoretically minimum level (Ezzati et al. 2004). &amp;amp;nbsp;The further the current situation is from the ideal, the closer the value of the PAF will be to 1.&lt;br /&gt;
&lt;br /&gt;
A PAF is calculated as:&lt;br /&gt;
&lt;br /&gt;
:&#039;&#039;&#039;(sumRR(x)P(x)-sumRR(x)P’(x)/sumRR(x)P(x)) = 1 - sumRR(x)P’(x)/sumRR(x)P(x)&amp;amp;nbsp;&amp;amp;nbsp;&#039;&#039;&#039;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:RR(x) is relative risk at exposure level x; and&lt;br /&gt;
&lt;br /&gt;
:P(x) is the population distribution in terms of exposure level, i.e. the shares of the population exposed to each level of exposure;&lt;br /&gt;
&lt;br /&gt;
:P’(x) is the theoretical minimum population distribution in terms of exposure level; for certain risks this is defined as no exposure; where this is not realistic, the WHO defines an international reference population&lt;br /&gt;
&lt;br /&gt;
Following this definition, multiplying the mortality from a particular disease by the PAF yields an estimate of the number of people who would not have died had the risk factor been at its theoretical minimum level.&#039;&#039;&#039;&amp;amp;nbsp; &#039;&#039;&#039;If we assume that the values of RR(x) and P’(x) for particular risk factors and diseases do not differ across countries or change over time,&amp;lt;sup&amp;gt;[2] &amp;lt;/sup&amp;gt; then changes in the PAF are solely a function of changes in P(x), the exposure of the population to the particular risk factor. Thus, it is necessary to be able to forecast the future levels of the risk factors. Other Help topics describe how this is done for specific risk factors such as [[Health#Childhood_Undernutrition|undernutrition]], [[Health#Adult_Body_Mass_Index_(BMI)_and_Obesity|obesity]], [[Health#Smoking_Rate_and_Smoking_Impact|smoking]], and [[Health#Indoor_Air_Pollution|indoor air pollution]]. Since our forecast of health outcomes from distal drivers implicitly suggests certain proximate driver levels, we are really interested in the effect of a difference in (1) estimates of the future levels of a risk factor based only on distal drivers (representing an “expected” value for a country given those distal drivers), and (2) estimates based upon a more complete set of drivers (representing our best forecast for a country using initial conditions and therefore path dependency, the additional and/or alternative drivers, and potential scenario interventions) .&amp;amp;nbsp; We therefore calculate two versions of the PAF, namely PAF&amp;lt;sub&amp;gt;Distal&amp;lt;/sub&amp;gt; and PAF&amp;lt;sub&amp;gt;Full.&amp;lt;/sub&amp;gt; Defining Mortality&amp;lt;sub&amp;gt;Distal&amp;lt;/sub&amp;gt; as the mortality calculated using only the distal drivers and Mortality&amp;lt;sub&amp;gt;Final&amp;lt;/sub&amp;gt; as the mortality after accounting explicitly for the risk factor, we can state that:&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;&#039;Mortality&amp;lt;sub&amp;gt;Distal&amp;lt;/sub&amp;gt; * PAF&amp;lt;sub&amp;gt;Distal&amp;lt;/sub&amp;gt; &#039;&#039;&#039; represents the number of people who would not have died had the risk factor been at its theoretical minimum level using the distal formulations for mortality and the proximate risk factor; and&lt;br /&gt;
*&#039;&#039;&#039;Mortality&amp;lt;sub&amp;gt;Final&amp;lt;/sub&amp;gt; * PAF&amp;lt;sub&amp;gt;Full&amp;lt;/sub&amp;gt; &#039;&#039;&#039; represents the number of people who would not have died had the risk factor been at its theoretical minimum level using a more complete formulation for mortality and the proximate risk factor&lt;br /&gt;
&lt;br /&gt;
If we assume that no other factors influence the difference in total mortality between the distal formulation and that using the full model, then:&lt;br /&gt;
&lt;br /&gt;
:&#039;&#039;&#039;Mortality&amp;lt;sub&amp;gt;Final&amp;lt;/sub&amp;gt; - Mortality&amp;lt;sub&amp;gt;Distal&amp;lt;/sub&amp;gt; = Mortality&amp;lt;sub&amp;gt;Final&amp;lt;/sub&amp;gt; * PAF&amp;lt;sub&amp;gt;Full&amp;lt;/sub&amp;gt; - Mortality&amp;lt;sub&amp;gt;Distal&amp;lt;/sub&amp;gt; * PAF&amp;lt;sub&amp;gt;Distal&amp;lt;/sub&amp;gt;&amp;amp;nbsp;&#039;&#039;&#039;&amp;amp;nbsp; &amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Yields:&lt;br /&gt;
&lt;br /&gt;
:&#039;&#039;&#039;Mortality&amp;lt;sub&amp;gt;Final &amp;amp;nbsp;&amp;amp;nbsp;&amp;lt;/sub&amp;gt;= Mortality&amp;lt;sub&amp;gt;Distal&amp;lt;/sub&amp;gt; * ((1-PAF&amp;lt;sub&amp;gt;Distal&amp;lt;/sub&amp;gt;) / (1-PAF&amp;lt;sub&amp;gt;Full&amp;lt;/sub&amp;gt;))&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&#039;&#039;&#039;&amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp;= Mortality&amp;lt;sub&amp;gt;Distal&amp;lt;/sub&amp;gt; * sumRR(x)P&amp;lt;sub&amp;gt;Full&amp;lt;/sub&amp;gt;(x)/sumRR(x)P&amp;lt;sub&amp;gt;Distal&amp;lt;/sub&amp;gt;(x)&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&#039;&#039;&#039;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
The adjustment factor is the ratio of the weighted average relative risks based on the distributions using the distal-only versus the full formulations for estimating the value of the risk factor. A higher weighted-average relative risk based on the full formulation implies that the distal drivers overestimate our anticipated improvement (or underestimate the deterioration) in the risk factor. Thus, the mortality forecast needs to be adjusted upwards.&amp;amp;nbsp; Alternatively, if the weighted-average relative risk is lower based on the full formulation than on the distal formulation, the mortality forecast will be adjusted downwards. Note that this property of the calculation actually obviates the need to know the theoretical minimum population.&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
[1] Dictionary of Cancer terms, National Cancer Institute; accessed online, January 2010.&amp;amp;nbsp; [http://www.cancer.gov/dictionary/ http://www.cancer.gov/dictionary/].&amp;lt;br/&amp;gt;&amp;lt;div&amp;gt;[2]&amp;amp;nbsp;This is very reasonable for P’(x) by its definition. With respect to RR(x), we assume these to be the same for all countries unless otherwise specified in the CRA reports. Any change over time is likely to be picked up in other parts of our model dealing with changes in technology and the efficiency of health care systems.&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Multiple Risk Factors&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Sometimes more than one risk factor will be linked to a particular disease. In theory, this requires estimating joint relative risks and exposure distributions. Under certain circumstances, however, a simple method can be used to calculate a combined PAF that involves multiple risk factors (Ezzati and others 2004):&lt;br /&gt;
&lt;br /&gt;
:&#039;&#039;&#039;PAF&amp;lt;sup&amp;gt;combined&amp;lt;/sup&amp;gt; = 1 - ∏(1-PAF&amp;lt;sup&amp;gt;i&amp;lt;/sup&amp;gt;) &amp;amp;nbsp;&#039;&#039;&#039; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:PAF&amp;lt;sup&amp;gt;i&amp;lt;/sup&amp;gt; is the PAF for risk factor i&lt;br /&gt;
&lt;br /&gt;
The logic here is as follows. 1-PAF&amp;lt;sup&amp;gt;i&amp;lt;/sup&amp;gt; represents the proportion of the disease that is not attributable to risk factor i. Multiplying these risks yields the share of the disease that is not attributable to any of the risk factors, and subtracting this from 1 leaves the share of the disease that is attributable to the set of risk factors considered.&lt;br /&gt;
&lt;br /&gt;
Say that we have 2 risk factors:&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1&amp;lt;/span&amp;gt; ]&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&#039;&#039;&#039;PAF&amp;lt;sup&amp;gt;combined&amp;lt;/sup&amp;gt; = 1 - (1-PAF&amp;lt;sup&amp;gt;1&amp;lt;/sup&amp;gt;)(1-PAF&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;)&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;Following from the discussion above, the combined adjustment factor can be calculated as:&lt;br /&gt;
&lt;br /&gt;
:&#039;&#039;&#039;&amp;amp;nbsp;((1-PAF&amp;lt;sup&amp;gt;combined&amp;lt;/sup&amp;gt; &amp;lt;sub&amp;gt;Distal&amp;lt;/sub&amp;gt;) / (1-PAF&amp;lt;sup&amp;gt;combined&amp;lt;/sup&amp;gt; &amp;lt;sub&amp;gt;Full&amp;lt;/sub&amp;gt;)) = [(1-PAF&amp;lt;sup&amp;gt;1&amp;lt;/sup&amp;gt; &amp;lt;sub&amp;gt;Distal&amp;lt;/sub&amp;gt;)(1-PAF&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; &amp;lt;sub&amp;gt;Distal&amp;lt;/sub&amp;gt;)] / [(1-PAF&amp;lt;sup&amp;gt;1&amp;lt;/sup&amp;gt; &amp;lt;sub&amp;gt;Full&amp;lt;/sub&amp;gt;)(1-PAF&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; &amp;lt;sub&amp;gt;Full&amp;lt;/sub&amp;gt;)]&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&#039;&#039;&#039;&amp;amp;nbsp;= [(1-PAF&amp;lt;sup&amp;gt;1&amp;lt;/sup&amp;gt; &amp;lt;sub&amp;gt;Distal&amp;lt;/sub&amp;gt;)/(1-PAF&amp;lt;sup&amp;gt;1&amp;lt;/sup&amp;gt; &amp;lt;sub&amp;gt;Full&amp;lt;/sub&amp;gt;)] * [(1-PAF&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; &amp;lt;sub&amp;gt;Distal&amp;lt;/sub&amp;gt;)/(1-PAF&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; &amp;lt;sub&amp;gt;Full&amp;lt;/sub&amp;gt;)]&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&#039;&#039;&#039;&amp;amp;nbsp;= [sumRR&amp;lt;sup&amp;gt;1&amp;lt;/sup&amp;gt;(x)P&amp;lt;sup&amp;gt;1&amp;lt;/sup&amp;gt; &amp;lt;sub&amp;gt;Full&amp;lt;/sub&amp;gt;(x)/sumRR&amp;lt;sup&amp;gt;1&amp;lt;/sup&amp;gt;(x)P&amp;lt;sup&amp;gt;1&amp;lt;/sup&amp;gt; &amp;lt;sub&amp;gt;Distal&amp;lt;/sub&amp;gt;(x)] * [sumRR&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;(x)P&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; &amp;lt;sub&amp;gt;Full&amp;lt;/sub&amp;gt;(x)/sumRR&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;(x)P&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; &amp;lt;sub&amp;gt;Distal&amp;lt;/sub&amp;gt;(x)]&#039;&#039;&#039; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;In other words, the combined adjustment factor is a simple multiplication of the individual adjustment factors.&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; &amp;lt;/span&amp;gt; In the sequence of our calculations we decompose this equation in practice by finding the individual PAFs, computing their individual independent effects with (1-PAF&amp;lt;sub&amp;gt;Distal&amp;lt;/sub&amp;gt;)/(1-PAF&amp;lt;sub&amp;gt;Full&amp;lt;/sub&amp;gt;), and multiplying mortality independently and cumulatively.&amp;amp;nbsp;&amp;lt;/div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Specific Risk Factors&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
For each of the proximate risk factors used in the IFs model, we develop “full” model formulations (our best forecasts using IFs variables) and “distal” models (using just income and education) in order to compute the two PAFs described above.&amp;amp;nbsp; The IFs system includes full formulations and proximate risk analysis for selected risk categories: childhood undernutrition, adult body mass index and related obesity, unsafe water and sanitation, indoor air pollution associated with solid fuel use, outdoor urban air pollution, and smoking.&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Childhood Undernutrition&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
The population level of childhood undernutrition impacts IFs forecasts of under-5 mortality related to communicable causes.&amp;amp;nbsp; The “full” IFs forecast is based on estimated calories per capita&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1&amp;lt;/span&amp;gt; ] &amp;lt;/sup&amp;gt; &amp;lt;/span&amp;gt; and access to safe water/sanitation:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;z=1.9212672-0.00069*CLPC_r-0.011589*(WATSAFE_{r,ss=2}+WATSAFE_{r,ss=3})-0.020278*(SANITATION_{r,ss=2}+SANITATION_{r,ss=3})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;MALNCHP_r=100*\frac{e^z}{1+e^z}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;Where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
CLPC is calories per capita&lt;br /&gt;
&lt;br /&gt;
WATSAFE(R, 2) is improved access to water&amp;lt;br/&amp;gt;WATSAFE(R, 3) is piped access to water&amp;lt;br/&amp;gt;SANITATION(R, 2) is shared access to sanitation&amp;lt;br/&amp;gt;SANITATION(R, 3) is improved access to sanitation&amp;amp;nbsp;&amp;lt;br/&amp;gt;MALNCHP is percent of children malnourished.&amp;lt;br/&amp;gt;&amp;lt;br/&amp;gt;For each country/region r&lt;br /&gt;
&lt;br /&gt;
Parameters for the distal regression were estimated in a mixed model regression analysis (using Proc Mixed in SAS Version 9.1) from historical data (1960-2005):&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;z=-0.714297-0.58979*LN(GDPPCP_r)-0.544938*LN(EDYRSAG25_r)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;PMN=100*\frac{e^z}{1+e^z}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
In the above equation GDPPCP is GDP per capita at purchasing power parity and EDYRSAG25 is average years of formal education for adults over 25.&amp;amp;nbsp; PMN is the percentage of children categorized as “moderately” or “severely” undernourished (&amp;lt;=-2 standard deviations below the international standard of weight for age).&amp;amp;nbsp; Both the distal and full models incorporate an additive shift factor to match initial year (2010) model estimates to historical data.&amp;amp;nbsp; This additive shift factor converges to 0 in 100 years.&lt;br /&gt;
&lt;br /&gt;
Assuming a normal distribution, we further categorize the under-5 population into four categories: severe (&amp;amp;lt;-3 standard deviations below normal weight for age); moderate (-3 &amp;lt;= -2 standard deviations below normal weight for age); mild (-2 &amp;lt;= -1 standard deviations below normal weight for age); and baseline (&amp;amp;gt;-1 standard deviations below normal weight for age).&amp;amp;nbsp; The relative risks of mortality related to communicable disease category (compared to a baseline risk of 1) are listed in the table below&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[2&amp;lt;/span&amp;gt; ] &amp;lt;/sup&amp;gt; &amp;lt;/span&amp;gt;.&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%; border: 1px solid #cccccc&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Cause&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Mild&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Moderate&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Severe&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Other Group I&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 2.06&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 4.24&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 8.72&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Diarrheal Disease&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 2.32&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 5.39&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 12.5&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Malaria&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 2.12&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 4.48&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 9.49&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Respiratory Infection&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 2.01&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 4.03&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 8.09&amp;lt;br/&amp;gt;&lt;br /&gt;
|}&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; Calories per capita are calculated through the agricultural module in IFs.&amp;amp;nbsp; The number of available calories depends strongly on the interaction of two factors: income (including its distribution) and food price.&amp;amp;nbsp; Long-term trends in caloric availability reflect fairly rapidly-rising incomes in most parts of the world.&amp;amp;nbsp;&amp;amp;nbsp;&amp;lt;br/&amp;gt;&amp;lt;div&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[2]&amp;lt;/span&amp;gt;&amp;amp;nbsp;Relative risk estimates from Gakidou et al. 2007, Table 3: 1880.&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:large;&amp;quot;&amp;gt;Adult Body Mass Index (BMI) and Obesity&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Population levels of BMI (HLBMI) impact IFs forecasts of adult (over 30 years) mortality related to cardiovascular disease and diabetes.&amp;amp;nbsp; Both the distal driver and full model formulations are initialized using a multiplicative shift factor to match historic data; these shift factors are kept constant over time.&amp;amp;nbsp; Given the lack of historical data, all regressions were created using 2005 estimates.&amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Full model formulations for females and males, respectively, use calories per capita (CLPC) as the driver. The parameter &#039;&#039;&#039;&#039;&#039;hlbmim&#039;&#039; &#039;&#039;&#039; can be used to modify the result:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;HLBMI_{r,p=2}=(18.73+0.00265*CLPC_r)*\mathbf{hlbmim}_{r,p=2}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;HLBMI_{r,p=1}=(16.54+0.00305*CLPC_r)*\mathbf{hlbmim}_{r,p=1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Distal driver formulations are based on GDP per capita at PPP (GDPPCP) and years of formal education for adults 25 and older (EDYRSAG25):&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;HLBMI_{r,p=2}=22.7+0.46*ln(GDPPCP_r)+1.36*ln(EDYRSAG25_r)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;HLBMI_{r,p=1}=21.3+0.95*ln(GDPPCP_r)+1.17*ln(EDYRSAG25_r)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
In order to use the PAF methodology, we assume a normal distribution of BMI around the mean with a standard deviation of 10% of the mean.&amp;amp;nbsp; BMI has a normal distribution where we forecast the mean, and assume a standard deviation of 10% of the mean.&lt;br /&gt;
&lt;br /&gt;
Another Help topic describes the use of BMI in forecasting diabetes-related mortality.&amp;amp;nbsp; For cardiovascular disease, the relative risk of mortality increases with every unit of BMI.&amp;amp;nbsp; The calculation of relative risk uses a continuous formulation based on BMI level:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;RR(HLBMI_r)=CONSTANT^{HLBMI_r-21}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The constant (CONSTANT) depends on age category: 1.14 for 30-44 year olds, 1.09 for 45-59 year olds, 1.09 for 60-69 year olds, and 1.05 for 70-79 year olds.&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1&amp;lt;/span&amp;gt; ]&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;&lt;br /&gt;
&lt;br /&gt;
From BMI it is possible also to compute the portion of a population that is obese (HLOBESITY).&amp;amp;nbsp; The model does that as a function of HLBMI by using separate table functions for females (BMI Versus Female Obesity&amp;amp;nbsp;% (CRA) Quad) and males (BMI Versus Male) Obesity&amp;amp;nbsp;% (CRA) Quad).&amp;amp;nbsp; The parameter &#039;&#039;&#039;&#039;&#039;hlobesitym&#039;&#039; &#039;&#039;&#039; can be used for modification in scenario analysis.&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;div&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; &amp;lt;/span&amp;gt; Estimates derived from Kelly et al. 2009.&amp;amp;nbsp;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Water and Sanitation&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Forecasts of mortality related to diarrheal disease (all ages) depend on access to safe water and improved sanitation.&amp;amp;nbsp; The regression models for each were estimated using the most recent year of data.&lt;br /&gt;
&lt;br /&gt;
Full model formulations:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;SANITATION_{r,ss=otherimproved}=0.3809-0.0579*\frac{GDS_{r,g=health}}{GDP_r}*100-0.3309*ln(GDPPCP_r)-0.7968*ln(EDYRSAG25_r)+0.0233*\frac{INCOMELT1CS_r}{POP_r}*100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;SANITATION_{r,ss=shared}=-1.7235-0.0298*\frac{GDS_{r,g=health}}{GDP_r}*100-0.7834*ln(GDPPCP_r)+0.2591*ln(EDYRSAG25_r)+0.0195*\frac{INCOMELT1CS_r}{POP_r}*100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WATSAFE_{r,ss=unimproved}=-0.936+0.02*POPRURAL_r-0.0891*\frac{GDS_{r,g=health}}{GDP_r}*100-0.8896*ln(GDPPCP_r)-0.2384*ln(EDYRSAG25_r)+0.0254*\frac{INCOMELT1CS_r}{POP_r}*100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WATSAFE_{r,ss=otherimproved}=-1.0409+0.0293*POPRURAL_r-0.1129*\frac{GDS_{r,g=health}}{GDP_r}*100-0.7521*ln(GDPPCP_r)+0.082*ln(EDYRSAG25_r)+0.018*\frac{INCOMELT1CS_r}{POP_r}*100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Distal Driver formulation:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;SANITATION_{r,ss=otherunimproved}=0.9875-0.8841*ln(GDPPCP_r)-0.6483*ln(EDYRSAG25_r)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;SANITATION_{r,ss=shared}=-1.1191-0.9798*ln(GDPPCP_r)-0.1388*ln(EDYRSAG25_r)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WATSAFE_{r,ss=unimproved}=1.4998-1.4532*ln(GDPPCP_r)-0.5593*ln(EDYRSAG25_r)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WATSAFE_{r,ss=otherimproved}=1.6681-1.3199*ln(GDPPCP_r)-0.2643*ln(EDYRSAG25_r)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:INCOMELT1CS/POP*100 is the percentage of people living with less than $1.25 per day&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GDS_{r,g=health}&amp;lt;/math&amp;gt; is health expenditures as a percentage of GDP&lt;br /&gt;
&lt;br /&gt;
:POPRURAL is the percentage of the total population living in rural areas&lt;br /&gt;
&lt;br /&gt;
We use a logit formulation to manage the saturation of the 3 levels of access to either of these 2 services, so that the sum of the 3 levels never goes above 100 percent. In this logit formulation&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1&amp;lt;/span&amp;gt; ] &amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt; we compute the percentages using the regressions presented, then compute the final results following this method:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;S1=e^{(WATSAFE_{ss=unimproved})}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;S2=e^{(WATSAFE_{ss=othimproved})}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WATSAFE_{ss=unimproved}=\frac{S1}{S1+S2+1}*100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WATSAFE_{ss=otherimproved}=\frac{S2}{S1+S2+1}*100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WATSAFE_{ss=piped}=\frac{1}{S1+S2+1}*100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Where UnimpSWat% is the percentage of people with access to unimproved safe water, OthImpSWat% is the percentage of people with other improved access to safe water and PipedSWat% is the percentage of people with access to piped safe water. The same method is applied for estimating access to improved sanitation.&lt;br /&gt;
&lt;br /&gt;
In order to compute the appropriate PAFs, IFs calculates the proportion of the population that falls into each of the following five categories:&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;Category II&#039;&#039;: minimum of (share of population with piped connection for water supply, share of population with improved connection for sanitation)&lt;br /&gt;
*&#039;&#039;Category IV&#039;&#039;: minimum of (share with other improved or piped water supply not in category Vb or II, share with basic or improved sanitation not in category Va or II)&lt;br /&gt;
*&#039;&#039;Category Va&#039;&#039;: minimum of (share with basic or improved sanitation, remainder of those without other improved or piped connection for water supply that are not already in category VI)&lt;br /&gt;
*&#039;&#039;Category Vb&#039;&#039;: minimum of (share with other improved or piped water supply, remainder of those without shared or improved access for sanitation that are not already in category VI)&lt;br /&gt;
*&#039;&#039;Category VI&#039;&#039;: minimum of (share without other improved or piped connection for water supply, share without shared or improved connection for sanitation)&lt;br /&gt;
&lt;br /&gt;
Each category has a different Relative Risk associated with it:&lt;br /&gt;
&lt;br /&gt;
*Category II: 2.5&lt;br /&gt;
*Category IV: 6.9&lt;br /&gt;
*Category Va: 6.9&lt;br /&gt;
*Category Vb: 8.7&lt;br /&gt;
*Category VI: 11&lt;br /&gt;
&lt;br /&gt;
The theoretical minimum or international reference is assumed to be 1, and thus the PAF equation gets simplified to:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;PAF=1-\frac{1}{\sum{RR(x)*P(x)}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; data-mce-mark=&amp;quot;1&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; &amp;lt;/span&amp;gt; For more detail on this formulation please refer to Rothman, Dale. 2009 (Feb). “Formulae for Predicting Shares 23 Feb 2009.doc”, unpublished internal Pardee Center working note.&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:large;&amp;quot;&amp;gt;Indoor Air Pollution&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Indoor air pollution affects forecasts of under-5 mortality related to respiratory infection and adult (30+) mortality related to respiratory disease.&amp;amp;nbsp; IFs uses the percentage of people using solid fuels as their primary source of energy (ENSOLFUEL) as a proxy for indoor air pollution.&lt;br /&gt;
&lt;br /&gt;
The full model calculation is:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENSOLFUEL_{r,t}=\frac{100}{1+e^{-(2.823+0.166*GDPPCP_{r,t}+0.032*INFRAELECACC(national)_{r,t})}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
ENSOLFUEL = ratio of electricity use to total primary energy demand, in percentage&lt;br /&gt;
&lt;br /&gt;
GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars&lt;br /&gt;
&lt;br /&gt;
INFRAELECACC(national, that is, not urban or rural but total) = percent of total population with access to electricity in percentage&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;multiplicative shift factor:&amp;amp;nbsp;ENSOLFUELShift; never converges&#039;&#039;&lt;br /&gt;
*&#039;&#039;multiplier:&amp;amp;nbsp;&#039;&#039;&#039;ensolfuelm&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;targeting&amp;amp;nbsp;parameters:&amp;amp;nbsp;&#039;&#039;&#039;ensolfuelsetar, ensolfueltrgtyr, ensolfuelsetar, ensolfuelseyrtar&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*&#039;&#039;hold switch:&amp;amp;nbsp;&#039;&#039;&#039;ensolflhldsw&#039;&#039;&#039;, fixes value of ENSOLFUEL at initial year value&#039;&#039;&lt;br /&gt;
*&#039;&#039;cross-sectional&amp;amp;nbsp;data, GLM regression, R-squared = 0.81&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
The distal driver formulation for ENSOLFUEL uses the following formula, which relies also on EDYRSAG25 and average years of formal education for adults over 25.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;z=2.9538-1.0694*ln(GDPPCP_r)+1.0668*ln(EDYRSAG25_r)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENSOLFUEL_r=100*\frac{e^z}{1+e^z}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
We use a multiplicative shift factor to match initialization data in the first year, and keep it constant in our forecast. Following work done through the WHO Desai and others (2004), IFs adjusts in the full formulation (not the distal one) the percentage of population exposed to indoor smoke from solid fuels by a ventilation coefficient (&#039;&#039;&#039;&#039;&#039;ensfvent&#039;&#039; &#039;&#039;&#039;) that ranges from 0 to 1. A coefficient of 0 indicates no exposure to pollutants from solid fuel use, whereas a coefficient of 1 indicates full exposure:&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%; border: 1px solid #cccccc&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 10px; padding-right: 10px&amp;quot; colspan=&amp;quot;2&amp;quot; | &#039;&#039;&#039;Recommended Ventilation Coefficients to use in Conjunction with Percentage of Population Exposed to Indoor Smoke from Solid Fuels&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 10px; padding-right: 10px&amp;quot; | Country&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 10px; padding-right: 10px&amp;quot; | Ventilation Coefficient&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px; padding-right: 10px&amp;quot; | Albania, Belarus, Bosnia &amp;amp; Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Macedonia, Moldova, Poland, Romania, Russia, Slovakia, Slovenia, Ukraine, Yugoslavia (Serbia and Montenegro)&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px; padding-right: 10px&amp;quot; | 0.20&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px; padding-right: 10px&amp;quot; | China&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px; padding-right: 10px&amp;quot; | 0.25 for children; 0.50 for adults&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px; padding-right: 10px&amp;quot; | All Others&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px; padding-right: 10px&amp;quot; | 1.0&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px; padding-right: 10px&amp;quot; colspan=&amp;quot;2&amp;quot; | From Desai and others (2004)&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
Since in the case of indoor air pollution there are only 2 categories–exposed or not exposed, in which case RR = 1 - the mortality effect can then be simplified to:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ME=\frac{(RR-1)P_{full}+1}{(RR-1)P_{distal}+1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Where P is the percentage of population exposed to indoor smoke from solid fuel, adjusted for ventilation; and RR is the relative risk for the exposed population&amp;lt;sup&amp;gt;[1] &amp;lt;/sup&amp;gt;.&amp;amp;nbsp; The table below lists RRs used in IFs. &amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%; border: 1px solid #cccccc&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; colspan=&amp;quot;3&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Relative risk estimates for Mortality from Indoor Smoke from Solid Fuels&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; align=&amp;quot;center&amp;quot; | Health Outcome&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; align=&amp;quot;center&amp;quot; | Groups Impacted&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; align=&amp;quot;center&amp;quot; | Relative Risk&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; align=&amp;quot;center&amp;quot; | Respiratory Infections&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; align=&amp;quot;center&amp;quot; | Children under 5&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; align=&amp;quot;center&amp;quot; | 2.30 (1.90, 2.70)&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; rowspan=&amp;quot;2&amp;quot; align=&amp;quot;center&amp;quot; | Respiratory Diseases&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; align=&amp;quot;center&amp;quot; | Females over 30&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; align=&amp;quot;center&amp;quot; | 3.20 (2.30, 4.80)&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; align=&amp;quot;center&amp;quot; | Males over 30&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; align=&amp;quot;center&amp;quot; | 1.80 (1.00, 3.20)&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5x; padding-right: 10px&amp;quot; colspan=&amp;quot;3&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;• From Desai and others (2004)&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;• 95% confidence intervals in parentheses&amp;lt;br/&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
|}&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;div&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; More information is available on Dale’s documents: “Incorporating Indoor Air Pollution 9 October 2009.docx”, unpublished internal Pardee Center working note.&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Outdoor Air Pollution&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
IFs uses PM 2.5 concentration in urban areas (ENVPM2PT5) as a proxy for outdoor air pollution. Outdoor air pollution impacts mortality related to respiratory infections, respiratory disease, and cardiovascular disease for adults 30 or older.&lt;br /&gt;
&lt;br /&gt;
The distal driver formulation for ENVPM2PT5 uses the following formula:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENVPM10EXP_r=4.8929+0.2145*ln(GDPPCP_r)-0.0995*(ln(GDPPCP_r))^2-0.451*ln(EDYRSAG25_r-0.0181*(Year-1989)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
In the above equation GDPPCP is GDP per capita at purchasing power parity and EDYRSAG25 is average years of formal education for adults over 25.&lt;br /&gt;
&lt;br /&gt;
The full driver formulation for ENVPM2PT5 uses the following formula:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENVPM10EXP_r=5.1238+0.2533*ln(GDPPCP_r)-0.056957*(ln(GDPPCP_r))^2-0.4758*ln(EDYRSAG25_r)-0.0137*(Year-1989)-0.14*\frac{GDS_{r,g=health}}{GDP_r}*100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENVPM10_r=e^{ENVPM10EXP_r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENVPM2PT5_r=ENVPM10_r*ENVPM2PT5ConvFct_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
T is time expressed as the current year&lt;br /&gt;
&lt;br /&gt;
GDSHealth%GDP is the government expenditures in health as a percentage of GDP&lt;br /&gt;
&lt;br /&gt;
The first formula returns PM10 concentration levels which then are converted to PM2.5 using a conversion factor. The WHO (Ostro 2004 and EBD spreadsheet) recommends the following conversion factors:&lt;br /&gt;
&lt;br /&gt;
*0.5 - developing countries outside of Europe&lt;br /&gt;
*0.65 - developed countries outside of Europe&lt;br /&gt;
*0.73&amp;amp;nbsp; - European countries&lt;br /&gt;
&lt;br /&gt;
In the case of outdoor air pollution we can assume that all persons in urban areas are exposed to the same level of air pollution and therefore the same relative risk. Therefore we can simplify the mortality effect as follows:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ME=(\frac{PM2.5_{Full}+1}{PM2.5_{Distal}+1})^{\beta}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;where the recommended value for β is 0.1161.&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;div&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; &amp;lt;/span&amp;gt; More Information on: Rothman,&amp;amp;nbsp; Dale. 2009 (Feb). “Incorporating Outdoor air pollution 5 October 2009.docx”&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Smoking Rate and Smoking Impact&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
==== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ====&lt;br /&gt;
&lt;br /&gt;
The ultimate purpose of forecasting smoking (HLSMOKING) by country/region r and sex p is to forecast smoking impact (HLSMOKINGIMP) by country/region, age category a, and sex p.&amp;amp;nbsp; We provide some background on the general approach surrounding smoking impact and the some specific elements of its implementation in IFs (some of the background comes directly from Hughes et al. 2011: 41-42).&lt;br /&gt;
&lt;br /&gt;
In 1992 Peto et al. proposed a method for calculating the proportion of deaths caused by smoking that was not dependent on statistics on prevalence of tobacco consumption.&amp;amp;nbsp; This method involved developing an indicator for accumulated smoking risk termed the smoking impact ratio (SIR). Ezzatti and Lopez (2004: 888) defined the SIR as “population lung cancer mortality in excess of never-smokers, relative to excess lung cancer mortality for a known reference group of smokers.” In other words, the ratio is derived by comparing actual population lung cancer mortality with the expected lung cancer mortality in a reference population of nonsmokers. Because the SIR is derived from age-sex lung cancer mortality it can also provide an indication of the “maturity” of the smoking epidemic (the extent to which the population had been exposed to tobacco in the past (Ezzati and Lopez 2004: 888). Once the SIR has been determined, one can then use it to estimate the proportions of deaths from other diseases attributable to smoking (Peto et al. 1992).&lt;br /&gt;
&lt;br /&gt;
For the GBD project, Mathers and Loncar developed country-level smoking impact (SI) projections to 2030 (Mathers and Loncar 2006; and Mathers and Loncar, Protocol S1 Technical Appendix, n.d.) and used them as part of their distal-driver formulation.&amp;amp;nbsp; The SI projections rely upon expert judgment, and it was not possible for the IFs project to improve on them; thus we used those projections without change.&lt;br /&gt;
&lt;br /&gt;
Forecasting beyond 2030 required, however, that the IFs project extend those series, taking into account a long lag between smoking rates and smoking impact. We therefore wanted smoking rates themselves to drive our approach. The development of a structural forecast system for those rates involved several main steps. First, we created a historical series of estimated smoking rates. Second, we constructed cross-sectional relationships that suggest expected rates of smoking based on GDP per capita at PPP for males and females separately. Third, we initialized a moving average rate of change in smoking rate with the compound rate of change between 1995 and 2005 and used that as the basis for forecasting longer-term. Finally, for forecasting smoking impact longer term we used the same process in reverse that we had earlier used to estimate the historical smoking series, that is, we calculated smoking impact from smoking rate using a 25-year lag.&lt;br /&gt;
&lt;br /&gt;
In more recent work (beyond that supporting the Hughes et al. 2011 volume, we have introduced an alternative approach to forecasting change in smoking rate over time, one that uses a structural (and heavily algorithmic) smoking stages model.&amp;amp;nbsp; In the sections below we discuss the four steps of the original model and then the revised approach to forecasting smoking rates (work in progress).&lt;br /&gt;
&lt;br /&gt;
==== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Historical Smoking Rates&amp;lt;/span&amp;gt; ====&lt;br /&gt;
&lt;br /&gt;
We found it necessary to compute historical smoking rates because we found historical smoking rate data (taken from WHO) to be exceptionally sparse, and we needed to understand the patterns and trajectory of smoking behavior over time as a subsequent basis for forecasting. We may revisit this in the future because we now have data from the WDI for 1977 and more recent years.&lt;br /&gt;
&lt;br /&gt;
We built the historical imputed smoking series on the most recent smoking rate data point of each country and the smoking impact forecasts of the Global Burden of Disease (GBD). Those GBD forecasts of smoking impact cover the period from 2005 through 2030, provide considerable country coverage, and represent age in four quite large categories: 30-44, 45-59, 60-69, and 70 and older. These can be found in our tables SeriesHealthSmokingImpactMales30to44, SeriesHealthSmokingImpactMales45to59, SeriesHealthSmokingImpactMales60to69, SeriesHealthSmokingImpactMales70to100 in IFsHistSeries.mdb (and the same four tables for females).&lt;br /&gt;
&lt;br /&gt;
Assuming a direct 25-year lag between smoking rate and smoking impact, we used year-to-year percentage changes in the smoking impact series to change smoking rates before and after our smoking data point. In spite of the simplicity of this approach, and the fact that smoking impact reflects more than smoking rates, we found that the constructed series tended to match relatively well when more than one historical point for smoking rate existed.&lt;br /&gt;
&lt;br /&gt;
This is done in a procedure invoked under the IFs menu option Extended Features called Generate Historical Smoking Rate Estimates. The procedure starts by estimating historic smoking rates by age category (4 categories corresponding to the 4 smoking impact age categories of the GBD forecasts) and sex assuming a lag of 25 years (that is, filling in the historical smoking series from BaseYear – 25 to Base Year using GBD smoking impact data from Base Year to Base Year + 25).&amp;amp;nbsp; Then an all-age estimate for smoking rate is found with a simple average across the 4 smoking impact age categories. Next we compute an additive shift factor for each country to match the most recent WHO smoking rate data (from SeriesHealthSmokingPrevalenceWHOFemales% and the same table for males), and then we apply the same shift factor to smoking rate data for previous years. In cases where there are no smoking rate data we compute aggregated shifts using WHO Regions and apply the regional shift to the member country(ies) with no data.&amp;amp;nbsp; The final result of this process are 25 year-long series on smoking rates in the tables SeriesHealthSmokingMales%SI and SeriesHealthSmokingFemales%SI in IFsHistSeries.mdb&lt;br /&gt;
&lt;br /&gt;
==== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Smoking as a Function of GDP per Capita&amp;lt;/span&amp;gt; ====&lt;br /&gt;
&lt;br /&gt;
Once we have an historical rate of smoking, the next step is forecasting it so as to drive forecasts of smoking impact beyond the 2030 of the GBD estimates.&amp;amp;nbsp; Forecasting smoking in IFs is actually a two-step process beginning with the construction of&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
cross-sectional relationships for expected rates of smoking for males and females separately, based on GDP per capita at PPP.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Female smoking rate cross-sectional relationship:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;SmokRate_{r,p,t}=5.6634+0.6893*GDPPCP_r-0.00573*GDPPCP^2_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Male smoking rate cross-sectional relationship:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;SmokRate_{r,p,t}=38.3996+0.3386*GDPPCP_r-0.00224*GDPPCP^2_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Where Smok_Rate by country/region r, sex p, and year t is an initial estimate of smoking rate (percent), and GDPPCP is GDP per capita at PPP ($1,000).&amp;amp;nbsp; Regression results are kept constant after they reach a GDPPCP of $30,000 (females) or $50,0000 (males).&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
The cross-sectional calculation is initial in part because it will not produce results consistent with the data for countries, even in the first or base year of a model run.&amp;amp;nbsp; In the first year an additive shift is computed for both male and female smoking rates to reconcile 2010 values with regression results.&amp;amp;nbsp; In future model years, the additive shift is evaluated based on regression results: if it is positive (thus producing the forecasted value to be above the expected value given by the regression) or for all non-high-income countries (initial GDPPCP &amp;lt;= 25k) the shift is converged to 0 over 100 years. If it is negative and the country is high income, the shift is kept constant for the entire run horizon. The resulting adjusted shift is added to the smoking rate produced by the regression.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
==== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Forecasting Changes in Smoking using Past Behavior and GDP per Capita Expectations&amp;lt;/span&amp;gt; ====&lt;br /&gt;
&lt;br /&gt;
It is important to recognize not just the initial empirical (or estimated) value of smoking rate in our base year for each country, but the trajectory of country-specific change in smoking rates. Our approach to capturing the trajectory is a variation on a moving-average approach.&amp;amp;nbsp; Every year our first step is to compute a compound growth rate of the smoking rate over the last 10 years.&amp;amp;nbsp; In the second step we also compute the rate of change that one would expect based solely on applying the cross-sectional formulation in two consecutive years.&amp;amp;nbsp; The third step is to compute a slowly-changing moving average by combining those two growth rate computations, weighting the compound historical growth rate 90 percent and the expectation of growth from the cross-sectional formulation 10 percent.&lt;br /&gt;
&lt;br /&gt;
In the first step described above we obtain every year the compounded growth rate (Comp_Gr_Rt) over the previous 10 years. That growth rate is based on historical (or constructed) smoking rate data (Smok_Rate).&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1&amp;lt;/span&amp;gt; ]&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CompGrRt_{r,p,t=1}=(\frac{SmokRate_{r,p,t=1}}{SmokRate_{r,p,t=1-10}})^{1/10}-1&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
In the second step described above we compute the annual expectation of growth in the smoking rate (Exp_Gr_Rt) based on the cross-sectional formulation expectations (Smok_Rate_Exp) for the current and previous year.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ExpGrRt_{r,p,t}=\frac{SmokRateExp_{r,p,t}-SmokRateExp_{r,p,t-1}}{SmokRateExp_{r,p,t-1}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
In the third step, every year we compute what is effectively a moving average of change in smoking rates (Mov_Gr_Rt) by combining the compound growth rate for the last 10 years with the expected value from the preceding to the current year (with 90 percent and 10 percent weighting, respectively).&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;MovGrRt_{r,p,t}=0.9*CompGrRt_{r,p,t}+0.1*ExpGrRt_{r,p,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
In the above computation we introduced a number of other algorithmic rules to produce what appeared to be reasonable forecasts of smoking rates given the general notion of a bell-shaped curve (or rise and then fall) of smoking with income and time. These included bounding the cross-sectional expected value formulations at $30,000 for females and $50,000 for males so as to avoid complete collapse of smoking rates at high income levels.&lt;br /&gt;
&lt;br /&gt;
It is then possible to apply the moving average to obtain what is actually a preliminary forecast of smoking rate (although not used in the model, we can call it HLSMOKINGP) based on that in the previous year.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;HLSMOKING_{r,p,t}=HLSMOKING_{r,p,t-1}*(1+MovGrRt_{r,p,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
This preliminary smoking rate is then converged to match the result of the regression equation over a period of 100 years and yield a near final smoking rate (HLSMOKING)&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;HLSMOKING_{r,p,t}=ConvergeOverTime(HLSMOKINGP_{r,p,t},SmokRateExp_{r,p,t},(BaseYear+100)-t)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
High income countries (with initial GDP per capita at PPP or GDPPCPI &amp;gt; $25,000) then are checked to avoid smoking rate growth after they have started to drop, i.e:&lt;br /&gt;
&lt;br /&gt;
:&#039;&#039;If&#039;&#039; GDPPCPI &amp;gt; $25,000&lt;br /&gt;
&lt;br /&gt;
:and &amp;lt;math&amp;gt;HLSMOKING_{r,p,t}&amp;gt;HLSMOKING_{r,p,t-1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:and &amp;lt;math&amp;gt;HLSMOKING_{r,p,t-1}&amp;lt;=HLSMOKING_{r,p,t-2}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:then&amp;lt;math&amp;gt;HLSMOKING_{r,p,t-1}=HLSMOKING_{r,p,t-1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Finally, a multiplier (&#039;&#039;&#039;&#039;&#039;hlsmokingm&#039;&#039; &#039;&#039;&#039;) by country and sex is available and applied to the smoking rate to compute the final value for the country-sex-year; the default value of the multiplier is 1 and alternative values introduce scenarios. Note that having a multiplier for a specific sex (e.g. 0.9 for males) and another for “total” or both sexes (e.g. 0.8) will produce a multiplicative effect on the forecast of 0.72.&amp;amp;nbsp; This ability to stack multipliers for individual sexes with total or both is not the standard practice in IFs; this may be the only example of it.&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;div&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; The constructed historical smoking data contributes to this computation of the compound growth rate for the first 10 years of its computation; thereafter, the computation will rely only on values generated by our forecasting.&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
==== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Computing Future Smoking Impact from Future Smoking Rates&amp;lt;/span&amp;gt; ====&lt;br /&gt;
&lt;br /&gt;
With the year-to-year percentage change in smoking rate forecasts from 2010 forward, we change the year-to-year values of the smoking impact series 25 years later.&amp;amp;nbsp; Thus, we use the reverse process used earlier–rather than using changes in smoking impact to compute smoking rates, we use changes in smoking rates to compute smoking impact.&lt;br /&gt;
&lt;br /&gt;
One complication is that we need smoking impact by age-category. We first compute smoking impact in the 4 big age categories of the GBD data. For purposes of affecting mortality we then apply the large age category values to each of the underlying 5-year categories; for example we compute smoking impact for 30-44 year olds, and then we apply the value for 30-34, 35-39, 40-44 year olds.&lt;br /&gt;
&lt;br /&gt;
Even in the big categories, however, as well as in the smaller 5-year categories, a consequence of our approach is that the changes in smoking impact year-to-year are identical in each age category.&amp;amp;nbsp; This is an inevitable consequence of our not having smoking rates by age–we have no basis for positing different patterns of smoking over time by age and therefore for changing the smoking impact by age.&lt;br /&gt;
&lt;br /&gt;
An unfortunate corollary consequence of this procedure is that, in the years between our initial forecast year and 2030, a period for which we have the smoking impact forecasts of the GBD but are also computing our own smoking impact forecasts, our forecasts will differ somewhat from those of the GBD.&amp;amp;nbsp;&amp;amp;nbsp; Remember, however, that we used GBD forecasts to create historical smoking rate series, and in those forecast years through 2030 are computing smoking impact from the historical smoking rates; thus our smoking impact forecasts through 2030 will not differ greatly from those of the GBD. &amp;amp;nbsp;It would be possible for us to actually use the GBD smoking impact forecasts for the 4 age categories through 2030 and then initiate our own changes to all 4 categories; we judged that the computational intensity of using theirs through 2030 offset the advantages of doing so.&lt;br /&gt;
&lt;br /&gt;
Note that it is possible to stop changes in the Smoking Impact forecast in year 2030 and beyond by turning off the smoking impact switch, &#039;&#039;&#039;&#039;&#039;hlsmimpsw&#039;&#039; &#039;&#039;&#039; = 0.&lt;br /&gt;
&lt;br /&gt;
==== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Revised Approach: Forecasting Smoking Rates using a Stages Model&amp;lt;/span&amp;gt; ====&lt;br /&gt;
&lt;br /&gt;
Smoking rate itself is computed in two different ways.&amp;amp;nbsp; The basic formulation uses only the initial condition, historical rates of growth in smoking, and a cross-sectionally estimated function linked to the simple and squared values of GDP per capita at PPP.&amp;amp;nbsp; The more extended formulation (still in development and testing and therefore not turned on as the default approach) is an algorithmic one based on the same general concept of a pattern that initially rises with GDP per capita, peaks, and then falls, but with a series of parameters that allow much more control over the stages.&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1&amp;lt;/span&amp;gt; ] &amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt;&amp;amp;nbsp; This staged algorithmic approach (see Lopez et al. 1994; Shibuya et al. 2005; Ploeg et al. 2009) is turned on with a switch (&#039;&#039;&#039;&#039;&#039;hlsmokingstsw&#039;&#039; &#039;&#039;&#039;=1; the default =0).&amp;amp;nbsp; Based on country- and sex-specific trends, plus initial prevalence, in the first year each country is placed into one of four sequential stages: rising, peak, falling, late.&amp;amp;nbsp; During the forecast horizon they can move through other stages.&amp;amp;nbsp; The approach is heavily algorithmic and the logic is explained below.&lt;br /&gt;
&lt;br /&gt;
===== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Early to Rising (Stage 1)&amp;lt;/span&amp;gt; =====&lt;br /&gt;
&lt;br /&gt;
Conditions for being in group are: Male prevalence rises during historic estimate and current prevalence is &amp;lt;=15%. Female prevalence rises at least until the last 5 years of history and current prevalence is &amp;lt;=1%.&lt;br /&gt;
&lt;br /&gt;
The forecasting strategy is: Ongoing increase. Use compound growth rate (10-year moving average from current year) when it is &amp;amp;lt;5% per year, increase growth rate to 5%/year over the next ten years. Continue 5% growth rate until country moves into next group (i.e. male smoking prevalence &amp;amp;gt; 15%, so country moves to stage 2). In the case of females, the previous year growth rate is kept constant until transition to next stage (female smoking prevalence &amp;gt; 1%).&lt;br /&gt;
&lt;br /&gt;
===== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Rising to Peak (Stage 2)&amp;lt;/span&amp;gt; =====&lt;br /&gt;
&lt;br /&gt;
Conditions for being in this stage are: Male prevalence rises during historic estimate and current prevalence is &amp;gt;15%. Female prevalence has been rising at least until last 5 years of history and current prevalence is &amp;gt; 1% and &amp;lt; 45%.&lt;br /&gt;
&lt;br /&gt;
The forecasting strategy is: Exponential increase. If exponential rate of increase &amp;lt; e&amp;lt;sup&amp;gt;.01x&amp;lt;/sup&amp;gt;, where x=year of forecast (1,2,3,… end of forecast horizon), then increase = e&amp;lt;sup&amp;gt;.01x&amp;lt;/sup&amp;gt;, otherwise use constant growth. For females we use an exponential regression (y=ab^x), limit annual growth to a maximum of 0.5%. If country moves from stage 1 to stage 2 during forecast horizon, exponential rate of increase = e&amp;lt;sup&amp;gt;.01x&amp;lt;/sup&amp;gt;, where x=year of forecast (1,2,3,… end of forecast horizon - x begins again at 1 when country moves to stage 2 both for males and females). In base case forecast the ceiling is 65% male prevalence for countries with increasing smoking trends.&amp;amp;nbsp; If male prevalence rises to 65% during forecast, country moves to next stage (3). The limit for females is 45% prevalence.&lt;br /&gt;
&lt;br /&gt;
===== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Peak to Falling (Stage 3)&amp;lt;/span&amp;gt; =====&lt;br /&gt;
&lt;br /&gt;
Conditions for being in stage are: Male prevalence peaks during historic estimate (20 years before the base year or later). Female prevalence peaks during the last 5 years of history and current prevalence is &amp;gt;= 45%.&lt;br /&gt;
&lt;br /&gt;
The forecasting strategy is: If smoking rate peaks during historic estimate then do 10 years of exponential decrease from last year of peak (y = ab^x). In other words, regression based on historic estimate starting with last year of peak (not all years of historic estimate).&lt;br /&gt;
&lt;br /&gt;
After 10 years, faster exponential decrease:&lt;br /&gt;
&lt;br /&gt;
:Prev&amp;lt;sub&amp;gt;x+1&amp;lt;/sub&amp;gt;=Prev&amp;lt;sub&amp;gt;x&amp;lt;/sub&amp;gt;*e&amp;lt;sup&amp;gt;-.01x&amp;lt;/sup&amp;gt;, where x=year (1,2,3,… end of forecast horizon - x begins again at 1 when country begins faster decrease). &amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
If male prevalence falls to &amp;lt;=20%, then flat rate to end of forecast horizon.&lt;br /&gt;
&lt;br /&gt;
If smoking rate &amp;amp;nbsp;peaks during forecast slow exponential decrease for first 10 years:&lt;br /&gt;
&lt;br /&gt;
:Prev&amp;lt;sub&amp;gt;x+1&amp;lt;/sub&amp;gt;=Prev&amp;lt;sub&amp;gt;x&amp;lt;/sub&amp;gt;*e&amp;lt;sup&amp;gt;-.001x &amp;lt;/sup&amp;gt;, where x=year (1 to 10)&lt;br /&gt;
&lt;br /&gt;
Faster exponential decrease for rest of the forecast:&lt;br /&gt;
&lt;br /&gt;
:Prev&amp;lt;sub&amp;gt;x+1&amp;lt;/sub&amp;gt;=Prev&amp;lt;sub&amp;gt;x&amp;lt;/sub&amp;gt; *e&amp;lt;sup&amp;gt;-.01x&amp;lt;/sup&amp;gt;, where x=year (1,2,3,… end of forecast horizon - x begins again at 1 when country begins faster decrease).&lt;br /&gt;
&lt;br /&gt;
If male prevalence falls to &amp;lt;=20%, then flat rate to end of forecast horizon.&lt;br /&gt;
&lt;br /&gt;
For Females simply do a slow logarithmic decline:&lt;br /&gt;
&lt;br /&gt;
S:mRt(t) = SmRt(t-1) – 0.05 LN(BaseYear + Yr – 1 – PeakYear)&lt;br /&gt;
&lt;br /&gt;
If female prevalence falls to &amp;lt;= 15%, then flat rate to end of forecast horizon.&lt;br /&gt;
&lt;br /&gt;
===== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Late (Stage 4)&amp;lt;/span&amp;gt; =====&lt;br /&gt;
&lt;br /&gt;
Conditions for being in the stage are: Male prevalence declines throughout at least the last twenty years of historic estimation (ie peak year&amp;lt; BaseYear - 20). Female prevalence declines at least the last 5 years of historic estimation.&lt;br /&gt;
&lt;br /&gt;
The forecasting strategy is: If current male smoking rate (prevalence) &amp;gt;=30% exponential&amp;amp;nbsp; decrease (y=ab^x) beginning with last year of peak. In other words, regression based on historical data starting with last year of peak (not necessarily all years of historical data). Trend continues through forecast horizon or until male prevalence &amp;lt;=20%. If male prevalence &amp;lt;=20% then flat rate to end of forecast horizon&lt;br /&gt;
&lt;br /&gt;
IF current male prevalence &amp;lt;30% logarithmic decline (y=a+b*ln(x)) beginning with last year of peak. In other words, regression based on historical data starting with last year of peak (not necessarily all years of historical data). Trend continues through forecast horizon or until male prevalence &amp;lt;=20%. If male prevalence &amp;lt;=20% then flat rate to end of forecast horizon.&lt;br /&gt;
&lt;br /&gt;
Female forecast simply uses a logarithmic decline (y=a+b*ln(x)). Trend continues through forecast horizon or until female prevalence &amp;lt;=15%. If female prevalence &amp;lt;=15% then flat rate to end of forecast horizon.&lt;br /&gt;
&lt;br /&gt;
There are a number of parameters for the stages approach to forecasting smoking rates. Because control of tobacco is a major policy objective in many countries, a number of these relate to the representation of a tobacco control score on a 100-point scale (&#039;&#039;&#039;&#039;&#039;hlsmokingtcs&#039;&#039; &#039;&#039;&#039;) with an&amp;amp;nbsp; associated parameter to control the elasticity of smoking with that score (&#039;&#039;&#039;&#039;&#039;hlsmokingtcsel&#039;&#039; &#039;&#039;&#039;), as well as a multiplier on the score (&#039;&#039;&#039;&#039;&#039;hlsmokingtcsm&#039;&#039; &#039;&#039;&#039;).&amp;amp;nbsp; The parameters related to tobacco control are:&lt;br /&gt;
&lt;br /&gt;
*Tobacco Control Score:&#039;&#039;&#039;&#039;&#039;hlsmokingtcs&#039;&#039; &#039;&#039;&#039;, higher scores reduce growing trend or accelerate reduction.&lt;br /&gt;
*Elasticity of relationship between tobacco control score and smoking rate (&#039;&#039;&#039;&#039;&#039;hlsmokingtcsel).&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
*Multiplier on tobacco control score (&#039;&#039;&#039;&#039;&#039;hlsmokingtcsm)&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
Other parameters related to the stage approach are:&lt;br /&gt;
&lt;br /&gt;
*Smoking Multiplier for Increasing Trend: &#039;&#039;&#039;&#039;&#039;hlsmokingincm&#039;&#039; &#039;&#039;&#039;, only affects countries in stage 2.&lt;br /&gt;
*Smoking Multiplier for Decreasing Trend: &#039;&#039;&#039;&#039;&#039;hlsmolingdecm&#039;&#039; &#039;&#039;&#039;, only affects countries in stage 3.&lt;br /&gt;
*Smoking Ceiling and Floor can be controlled using: &#039;&#039;&#039;&#039;&#039;hlsmokingceiling&#039;&#039; &#039;&#039;&#039; and &#039;&#039;&#039;&#039;&#039;hlsmokingfloor&#039;&#039; &#039;&#039;&#039;.&lt;br /&gt;
*Smoking Peak Year can be controlled using:&#039;&#039;&#039;&#039;&#039;hlsmokingpeakyr&#039;&#039; &#039;&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
When the stages approach is turned on the computation of smoking impact from the approach is changed from that described for the basic smoking model based on the quadratic equation with GDP per capita.&amp;amp;nbsp; To compute smoking impact when using smoking in stages we use linear regressions between Smoking Prevalence and estimated Smoking Impact (smoking impact lagged 25 years) to estimate Smoking Impact by large GBD age category. &amp;amp;nbsp;For instance, one of those functions is “Male Smoking Prevalence (2005) Versus Male 30 to 44 Smoking Impact (2030) Linear”. The functions were computed using smoking rates in 2005 and GBD smoking impact forecasts for 2030. In forecasting we apply those functions to smoking rates 25 and 26 years earlier, and obtain two values for smoking impact.&amp;amp;nbsp; We compute the growth rate between those two values of smoking impact and apply it to the previous year’s value of smoking impact for each of the 4 large GBD age categories. We then reproduce the smoking impact value for each 5-year age category in the larger GBD categories.&lt;br /&gt;
&lt;br /&gt;
For small values of SI (lower than 1) we restrict the annual change rate to be between -50% and 100%. If SI is 0 in the base year then it stays at 0. If the estimated value of SI reaches 0, and the next year is positive, then a 100% growth rate is used, but if the next year is negative then a -50% growth rate is used.&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; Cecilia Peterson developed this approach for IFs.&amp;lt;/div&amp;gt;&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Outcomes&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
There are two main mortality-related outcomes from the Health Module: deaths by cause-category (DEATHCAT) and life expectancy (LIFEXPHLM).&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;sup&amp;gt;&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1&amp;lt;/span&amp;gt; ] &amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt; &amp;lt;/span&amp;gt;&amp;amp;nbsp; From these, IFs calculates other relevant outcome variables including: years of life lost (YLL; HLYLL in IFs); years lived with disability (YLD; HLYLD); disability adjusted life years (DALY; HLDALY); YLLs and YLDs among the working-age population (HLYLLWORK and HLYLDWORK,&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;sup&amp;gt;&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[2&amp;lt;/span&amp;gt; ] &amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt; &amp;lt;/span&amp;gt; respectively, the sum of which would be DALYs for the working population); morbidity and the probability of mortality during a user-defined age-range.&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;sup&amp;gt;&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[3&amp;lt;/span&amp;gt; ]&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The computation of life expectancy in the health module is a replica of the one in the population module, with the only difference being the mortality distribution.&amp;amp;nbsp; In the initial year, these match because of the normalization process but they grow apart as the model advances.&amp;amp;nbsp; DEATHCAT is computed by multiplying the mortality distribution from the Health Module by population age categories.&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;sup&amp;gt;&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[4&amp;lt;/span&amp;gt; ]&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Years of Life Lost (YLL)&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Years of Life Lost (YLLs) are computed using the number of deaths in each age category multiplied by the number of years they died prematurely (the potential life expectancy at the age category as determined by a stored vector of standard life expenctancy):&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1&amp;lt;/span&amp;gt; ]&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&#039;&#039;&#039;Years of Life Lost = Deaths in age category * StdLifeExp for age&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
There are two extra elements that are considered in the computation of YLL – discounting and age weighting.&amp;amp;nbsp; Discounting represents the preference of individuals for current, rather than future, benefits.&amp;amp;nbsp; Age weighting is an attempt to capture age-specific social roles, with working ages more heavily weighted than children or older adults. While controversial, both elements are used in GBD studies and are therefore useful for standard comparisons.&lt;br /&gt;
&lt;br /&gt;
If we initially considered that the number of years lost per each death is the expected life expectancy minus the average age in a given category, then we now need to use the following formula to apply both discounting and age weighting:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;years=\frac{-AWC*e^{(-\beta*Avg(j))}}{(\beta+r)^2}*(e^{(-(\beta+r)*(StdLE(j)))*(1+(\beta+r)*StdLE)-(1+(\beta+r)*Avg(j))})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Where AWC is the age weighting correction factor (0.1658), this constant compensates so that the global estimated burden of disease remains the same as if not using age weighting. β is the parameter from the age weighting function (0.04). These two constants are related and if you want to change the form of the weighting function by changing β, then you would have to change AWC too. R is the Discount Rate, which we have set to 3 percent to match the GBD study.&lt;br /&gt;
&lt;br /&gt;
YLLWORK uses the same formulations, but only includes the population of 20 to 64 year-olds.&amp;amp;nbsp;&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; Randall Kuhn provide the hard-coded vector for standard life expectancy.&amp;lt;/div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Years of Life Lost to Disability (YLD)&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
External to the model, the IFs project calculated an initial ratio of YLD to YLL based on data from the World Health Organization. &amp;amp;nbsp;Those ratios are regional but specific to cause of death category. In the base year of the model, IFs initializes the value for YLDs by applying this ratio to YLL.&amp;amp;nbsp; In subsequent years IFs calculates the growth rate for mortality&amp;amp;nbsp; from the previous year and uses that to forecast the change in morbidity.&amp;amp;nbsp; The percentage decline in disability relative to decline in mortality can be controlled by the parameter (&#039;&#039;&#039;&#039;&#039;hlmorbtomortgthport&#039;&#039; &#039;&#039;&#039;).&amp;amp;nbsp; IFs estimates YLD from the ratio of regional morbidity to mortality (YLD/YLL). This ratio is used for initialization and then the growth rate for mortality is computed and applied to forecast morbidity. Morbidity results can then be adjusted with a multiplier (&#039;&#039;&#039;&#039;&#039;hlmorbtomortgthport&#039;&#039; &#039;&#039;&#039;) that can be handled by the user across time. This multiplier is specific for each subtype, with default values detailed in the table below. &amp;amp;nbsp;As an example, the value of 50 percent for cardiovascular disease &amp;amp;nbsp;(&#039;&#039;&#039;&#039;&#039;hlmorbtomortgthport&#039;&#039; &#039;&#039;&#039;=0.5) suggests that morbidity decreases at half the rate of mortality.&amp;amp;nbsp; Because malaria is set at 100 percent (&#039;&#039;&#039;&#039;&#039;hlmorbtomortgthport&#039;&#039; &#039;&#039;&#039;=1.0), morbidity will change at exactly the same rate as mortality; a higher value for the parameter could be used to represent a situation in which morbidity declined faster than mortality.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&amp;lt;span style=&amp;quot;text-decoration: underline&amp;quot;&amp;gt;Percentage decline in disability relative to decline in mortality&amp;lt;/span&amp;gt;&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot; border=&amp;quot;0&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width: 50%&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Group I&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Percentage Charge in Disability&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;Diarrhea&amp;lt;br/&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;padding-left: 10px; text-align: left&amp;quot; align=&amp;quot;center&amp;quot; | 75 (&#039;&#039;&#039;&#039;&#039;hlmorbtomortgthport&#039;&#039; &#039;&#039;&#039;=.75)&amp;amp;nbsp;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;Malaria&amp;lt;br/&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;100&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;Respiratory&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;100&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;Other communicable&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;75&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;HIV/AIDS&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Modeled separately&amp;lt;/div&amp;gt;&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot; border=&amp;quot;0&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width: 50%&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Group II&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Percentage Charge in Disability&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;Cardiovascular&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;padding-left: 10px; text-align: left&amp;quot; align=&amp;quot;center&amp;quot; | 50&amp;amp;nbsp;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;Disgestive disorders&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;100&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;Malignant neoplasms&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;100&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;Diabetes&amp;lt;br/&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;100&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;Mental health&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Not applicable; given value of -1&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;Chronic respiratory&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;100&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;Other NCDs&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;50&amp;lt;/div&amp;gt;&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot; border=&amp;quot;0&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width: 50%&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Group III&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Percentage Charge in Disability&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;International injuries&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;75&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;Traffic accidents&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;75&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;Other non-intentional injuries&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;75&amp;lt;/div&amp;gt;&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
Once morbidity is computed, the calculations to find YLDs (HLYLD) are identical to those for YLLs (HLYLL)–using morbidity instead of mortality to compute the number of people affected by a given disease.&amp;amp;nbsp; HLYLDWORK again focuses only on the population aged between 20 and 64.&lt;br /&gt;
&lt;br /&gt;
Morbidity related to mental health is the one exception to the above methodology.&amp;amp;nbsp; IFs computes an initial ratio of YLD/POP based on WHO data, keeping the ratio constant over time.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Disability adjusted life years (DALY)&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
DALYs are computed by summing YLLs and YLDs.&amp;amp;nbsp; YLLWORK and YLDWORK (the sum of which would be DALYs for the working population) include only the 20 to 64 year-old population.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Morbidity&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
We have added 3 parameters to control Morbidity.&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%; border: 1px solid #cccccc&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Name&#039;&#039;&#039;&amp;lt;br/&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Description&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Dimensions&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Default&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center&amp;quot; valign=&amp;quot;middle&amp;quot; align=&amp;quot;center&amp;quot; | &#039;&#039;&#039;Horizon&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;hlmorbm&#039;&#039;&#039;&#039;&#039;&amp;lt;br/&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Target Morbidity Multiplier&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | None&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | -1&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | All Years&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;hlmorbconv&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Convergence to Target Morbidity&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | None&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 20&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | All Years&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | &#039;&#039;&#039;&#039;&#039;hlmorbtomortgthport&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Morbidity to Mortality growth portion&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | Disease sub-types (15)&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | 1&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; valign=&amp;quot;middle&amp;quot; | All Years&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
The first one works as a both a switch and a multiplier; in terms of its functioning as a switch, a value of -1 will turn it off.&amp;amp;nbsp; When it is given a value above 0 it functions as a multiplier. The multiplier works as a portion of initial morbidity to compute Target morbidity, so that if you set it to 1, it will keep morbidity constant, and if you set it to 0 it will &amp;amp;nbsp;eliminate morbidity. The second parameter is used to indicate how many years to converge from the regular Health Module morbidity to the Target morbidity. &amp;amp;nbsp;Note that if you want to keep morbidity constant you need to change the convergence parameter to 0 to avoid using Health Module morbidity at all.&lt;br /&gt;
&lt;br /&gt;
The last parameter controls the change rate of morbidity as described [[Health#Years_of_Life_Lost_to_Disability_(YLD)|in the topic on this issue]].&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Mortality probabilities&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
IFs allows the user to compute the probability that a person of a given age (e.g. 15) will live to reach another age (e.g. 60)–to compute it look for under the Display Type Options&amp;amp;nbsp; on the specialized display for a J-curve (there are probabilities and rates for children and adults).&amp;amp;nbsp; In this example, the probability of a 15-year old dying before she reaches 60 equals 1 minus the cumulative probability of surviving (lx) to 59 given that she has survived to 15:&lt;br /&gt;
&lt;br /&gt;
:P(15-59) = 1 – lx(59) / lx(15)&lt;br /&gt;
&lt;br /&gt;
In order to compute lx at age j we need to consider the cumulative effect of the previous age category and the probability of death in the current age category (nqx). Lx(0) is assumed to be 1, given that we are only considering deaths for people that are born alive:&lt;br /&gt;
&lt;br /&gt;
:lx(j) = lx(j-1) * (1 - nqx)&lt;br /&gt;
&lt;br /&gt;
The probability of death at the current category is computed based on the mortality of the age category nMx, the number of years in the given category N (5 for most of the IFs age categories), and the average years lived within the same category nax, which in most cases is 2.5, but can be lower for shorter age categories:&lt;br /&gt;
&lt;br /&gt;
:nqx = (N * nMx) / (1 + ((N - nax) * nMx))&lt;br /&gt;
&lt;br /&gt;
This adjustment is necessary because nMx is the mortality rate of the 5 year period (which is not the same for each of the 1 year periods within it). The mortality rate nMx already considers that some people die in the middle of the period, which we don’t need for the probability nqx, which is why in general probabilities are lower than mortality rates and more pertinent for older ages where people tend to die earlier in the age category.&lt;br /&gt;
&lt;br /&gt;
Although the infant age category covers a shorter age range, the rate correction is in some ways a bigger issue because infants tend to die within the first days of life. The basic framework for understanding nax in this category is thus that the higher the mortality, the higher the average years lived nax.&amp;amp;nbsp; In a highly developed country such as Sweden, nearly all infant mortality takes place in the neonatal period (so nax is almost = 0).&amp;amp;nbsp; Alternately, in a country such as the Congo, infant mortality takes place throughout the year (though it is still concentrated in the neonatal period) and nax rises fairly consistently with nMx. &amp;amp;nbsp;This has been implemented in the following way:&lt;br /&gt;
&lt;br /&gt;
Average years lived by those who die (per Keyfitz) for Infants&lt;br /&gt;
&lt;br /&gt;
:If nMx &amp;gt;= 0.107 Then&lt;br /&gt;
&lt;br /&gt;
:nax = 0.34&lt;br /&gt;
&lt;br /&gt;
:Else&lt;br /&gt;
&lt;br /&gt;
:nax = 0.049 + 2.742 * nMx&lt;br /&gt;
&lt;br /&gt;
When estimating nax for children aged between 1 and 4, the logic becomes that child deaths between age 1 and 5 are a prolonged extension of infant mortality. Estimations have thus shown that nax is more directly tied to infant mortality than it is to nMx for its own age category. In other words, countries with very high infant mortality also experience elevated child mortality, mostly concentrated in the 1 - 2 age range.&amp;amp;nbsp; Thus as infant mortality rises so does child mortality, pulling nax away from 2:&lt;br /&gt;
&lt;br /&gt;
Average years lived by those who die (per Keyfitz) for children 1 to 4&lt;br /&gt;
&lt;br /&gt;
:If infMort &amp;gt;= 0.107 Then&lt;br /&gt;
&lt;br /&gt;
:nax = 1.356&lt;br /&gt;
&lt;br /&gt;
:Else&lt;br /&gt;
&lt;br /&gt;
:nax = 1.587 - 2.167 * infMort&amp;lt;br/&amp;gt;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Forward Linkages&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Chapter 7 of Hughes, Kuhn, Peterson, Rothman, and Solorzano (2011) elaborated the forward linkages of the health model to other parts of the IFs system at the time of that volume&#039;s completion.&amp;amp;nbsp; It began by discussing a controversy in the literature about whether the effects on economic well-being (as indicated by GDP per capita) of improvements in life expectancy are positive or negative.&amp;amp;nbsp; It went on to devote much attention to three major and general pathways of impact between health and GDP, each of which corresponds to a major element in standard production functions and that in IFs:&amp;amp;nbsp; labor, capital, and multifactor productivity.&amp;amp;nbsp; The equation documentation uses the same division and then provides some attention to potential forward linkages that do not involve those same three paths (notably the impact of health on public spending on health on the years of education attained by members of society and the quality of that education.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Population and Labor Supply&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The IFs demographic model captures the mechanical or accounting effects of mortality on population.&amp;amp;nbsp; A key pathway passes from mortality through adult age population to labor supply (including aging-related lags).&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; &amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt;&amp;amp;nbsp; Similarly, IFs captures the mechanical effect of mortality on fertility through the death of women of childbearing age.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
The most important non-mechanical linkage is almost certainly the relationship between child mortality and fertility. IFs forecasts fertility as a relationship with infant mortality, the log of educational level of those aged 15 and older (neither the education of women alone nor the education of those 15-24 work as well), and the percentage use of modern contraception.&amp;amp;nbsp; Adding the rate of infant mortality boosted the overall adjusted R-squared to 0.84.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;TFR_{\gamma,t}=3.8812+0.21*INFMORT_{\gamma,t}-0.8327*LN(EDYRS15_{\gamma,t})-0.009*CONTRUSE_{\gamma,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
R-squared = 0.8345; other terms and algorithmic specifications modify the relationship, most importantly a term that slowly shifts TFR over time and the specification of a minimum level toward which the function slowly converges if it overshoots on the downward side.&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;div&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; &amp;lt;/span&amp;gt; IFs also includes income-based formulations for changing the female participation rate.&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Capital Stocks&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Most capital stock consists of buildings and machinery for producing goods and services; some representations may include land also, but most treat land separately and largely as a constant (although land developed for crop production or grazing can, in fact, be highly variable).&amp;amp;nbsp; Most immediately, investment increases capital stock and depreciation reduces it.&amp;amp;nbsp; Although there is certainly some impact of morbidity and mortality on the rate of depreciation of both built physical and natural capital, the relationship may not be substantial and we do not understand it well enough to model it.&amp;amp;nbsp; Turning our gaze to the paths that affect investment, the three major ones run though health spending, which can crowd out savings and investment, through the age-structure of societies, which affects the savings rate, and through investment from abroad, which can augment that generated domestically.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
The IFs modeling system treats capital stock dynamically over time, investing in it and allowing it to depreciate.&amp;amp;nbsp; Investment is responsive to both domestic savings and foreign flows.&amp;amp;nbsp; Thus, the necessary elements for considering the impact of morbidity and mortality, via paths such as those across health spending and age structure, are part of the economic model’s core structure.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
With respect to health spending, to which we return later, the IFs model uses a social accounting matrix (SAM) structure.&amp;amp;nbsp; Thus the flow of funds into health spending automatically competes with other consumption uses and with savings and investment.&amp;amp;nbsp; In addition, [[Health#Additional_Potential_Forward_Linkages|health spending does affect mortality]]. We focus here on the paths versus the age-structure and foreign direct investment.&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Age Structure and Domestic Savings&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
The paths in IFs that link age structure most directly to domestic savings have two important elements.&amp;amp;nbsp; The most fundamental one represents the understanding of life-cycle dynamics in income, consumption and savings.&amp;amp;nbsp; The cycle for income is fairly clear-cut with a peak in the middle to latter periods of the working years.&amp;amp;nbsp; Workers set aside some portion of income as savings and that portion, too, tends to peak in the middle and late period of working years.&amp;amp;nbsp; Society-wide savings themselves become negative after retirement age (65 in the Base Case scenario) even though some portion of the population will continue to work. The second fundamental element is that both the horizon of life expectancy and the average income level of a society can have an impact on the portion set aside for savings and the degree to which it rises and then falls.&amp;amp;nbsp; Thus, for example, the life-cycle “bulge” of savings may be earlier and flatter in developing countries.&lt;br /&gt;
&lt;br /&gt;
We implemented the representation of savings and investment in accord with that understanding.&amp;amp;nbsp; Relying upon analyses of selected countries that Fernández-Villaverde and Kruegger (2004 and 2005) and Deaton&amp;amp;nbsp; and Paxson (2000) undertook, we extracted general stylized patterns of the savings life cycle to represent more and less developed (and lower life expectancy) countries. In forecasting we use the pattern for less developed countries when life expectancy falls below 40 years, use that for more developed countries when life expectancy exceeds 80 years, and interpolate in between for all other countries.&amp;amp;nbsp; The result of this largely algorithmic approach&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1&amp;lt;/span&amp;gt; ] &amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt; is an adjustment factor (SavingsAgeAdj) that augments or reduces investment.&lt;br /&gt;
&lt;br /&gt;
In addition, investment is somewhat augmented or reduced as a direct result of changing life expectancy.&amp;amp;nbsp; Life expectancy is compared over time with an expected value (tied to cross-sectional estimation with income).&amp;amp;nbsp; That difference is compared to the difference in the initial year and, if it rises, augments investment.&lt;br /&gt;
&lt;br /&gt;
Although conceptually tied to savings rates, neither the life-cycle analysis nor the life-expectancy term directly affect savings in IFs.&amp;amp;nbsp; Instead, they affect investment directly and savings indirectly via the dynamics in IFs that balance savings and investment over time.&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Foreign Direct Investment&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
The path linking health to foreign direct investment is potentially quite important.&amp;amp;nbsp; Alsan, Bloom and Canning (2006: 613) report that one additional year of life expectancy boosts FDI inflows by 9 percent, controlling for other variables.&amp;amp;nbsp; We have implemented that relationship in IFs.&amp;amp;nbsp; The representation of FDI in IFs captures the accumulation over time of FDI inflows in stocks of FDI, as well as the accumulation of FDI outflows in stocks.&amp;amp;nbsp; In addition, the stocks set up their own dynamics, including the tendency for stocks to reinforce flows.&amp;amp;nbsp; For that reason, we have set the base case parameter for the impact of each year of life expectancy on FDI flows to 0.05 (5 percent), lower than the estimate of Alsan, Bloom and Canning (2006).&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; &amp;lt;/span&amp;gt; See the subroutine SavingsDemogAdj in routine Populat.bas, which draws upon table IncConSav in IFs.mdb&amp;amp;nbsp; with different patterns of income, consumption, and savings for more developed countries (MDCs) and&amp;amp;nbsp; less developed countries (LDCs) across age categories; in general, peaks of income, consumption, savings occur the in late 40s and savings turn negative at 65.&amp;lt;/div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Productivity&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Health outcomes impact productivity through a variety of pathways (see Figure 8.3).&amp;amp;nbsp; Overall the function for multifactor productivity from human capital (MFPHC) is a sum of a term linked to educational expenditures (EDEXPCONTRIB) and three terms of interest to us here because mortality and morbidity affect them.&amp;amp;nbsp; Those three of interest are adult stunting (STUNTCONTRIB), disability (DISABCONTRIB) and years of education (EDYRSCONTRIB).&amp;amp;nbsp;&amp;amp;nbsp; We detail these three contributions in turn.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;MFPHC^t_{\gamma}=EDEXPCONTRIB^t_{\gamma}+STUNTCONTRIB^t_{\gamma}+DISABCONTRIB^t_{\gamma}+EDYRSCONTRIB^t_{\gamma}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Stunting Contribution&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
In the IFs health module, the prevalence of adult stunting (HLSTUNT) relates negatively to overall productivity (an elasticity of -0.025, in mfpstunt).&amp;amp;nbsp; In extreme cases, stunting could cost as much as 1 percent of economic growth.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;STUNTCONTRIB^t_{\gamma}=HLSTUNT^{t-1}_{\gamma}-STUNTINGCOMP^t_{\gamma}*mfpstunt&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;where&amp;lt;/span&amp;gt;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;STUNTINGCOMP^t_{\gamma}=F(GDPPCP^t_{\gamma})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
We compute HLSTUNT in the health model itself.&amp;amp;nbsp; We initialize adult stunting in a long-term lagged relationship (using a moving average of 25 years)&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1&amp;lt;/span&amp;gt; ] &amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt; with child malnutrition and forecast it as a function of both malnutrition and child mortality as a proxy for morbidity.&amp;amp;nbsp; Initial values in 2005 range up to about 55 percent for India and Bangladesh and even over 80 percent for Somalia; in the base case these generally but not universally decrease.&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[2&amp;lt;/span&amp;gt; ] &amp;lt;/span&amp;gt; &amp;lt;/sup&amp;gt;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
To initialize HLSTUNT in the preprocessor, IFs must first estimate historic levels of childhood undernutrition (MALNCHP):&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[3&amp;lt;/span&amp;gt; ]&amp;lt;/span&amp;gt;&amp;lt;/sup&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;MN=23.853*GDPPCP^{-0.6721}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
First we find the result of this function with GDPPCP numbers from 2005, then we compute an additive shift factor to match initialization data for MALNCHP(2005).&amp;amp;nbsp; Second we compute the result of the function with GDPPCP from 1980 and apply the shift factor to estimate HLSTUNT in 2005.&amp;amp;nbsp; We use a limit of 80% for the maximum possible stunting value.&lt;br /&gt;
&lt;br /&gt;
IFs forecasts HLSTUNT using an extremely slowly moving average:&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;HLSTUNT(t)=(HLSTUNT(t-1)*24+MALCHP(t))/25&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Childhood malnutrition and morbidity do not give rise to all disability in working years; much also comes from disabilities arising during the working years. IFs therefore also calculates millions of years of living with disability related to mortality rates specific to the working aged-population.&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[4]&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Disability Contribution&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Turning to the forecasting relationship between disability and productivity, the IFs approach drives changes in the growth of productivity from the changing difference between computed and expected values of disability.&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[5]&amp;lt;/span&amp;gt;&amp;amp;nbsp;Because we have replicated the practice of the GBD project and kept mental health disability rates constant over time, and because mental health generally dominates disability, forecasts of this disability term are relatively stable over time. Thus analysis with respect to this variable will depend on scenarios that increase or decrease those disability rates.&amp;amp;nbsp; In IFs, changes in disability levels result in a -0.5 change in productivity (mfphlyld = 0.5 in the Base Case).&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;where&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;DISABILITY^t_{\gamma}=\frac{SUMHLYLDWORK^t_{\gamma}}{POP15TO65^t_{\gamma}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WORLDDISAVG^t=\frac{WORLDDISABILITY^t}{WORLDWORKPOP^t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;&amp;lt;span lang=&amp;quot;EN-GB&amp;quot;&amp;gt;where&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;SUMHLYLDWORK^t_{\gamma}=\sum^MHLYLDWORK^t_{\gamma,m}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WORLDDISABILITY^t=\sum^RSUMHLYLDWORK^t_{\gamma}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WORLDWORKPOP^t=\sum^RPOP15TO65^t_{\gamma}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Education Years Contribution&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Soares (2006: 72) found in cross-sectional analysis that 10 years of additional life expectancy add 0.7 years to the average years of education attained.&amp;amp;nbsp; Ashraf, Lester, and Weil (2008: 10) built on other work on seven sub-Saharan African countries to conclude that 20 years of additional life expectancy add 0.386 average years of education.&amp;amp;nbsp; We used this analysis to support our relationship between life expectancy (as a proxy for morbidity including that of children) to productivity via increased education.&amp;amp;nbsp; IFs associates each year of incremental life expectancy with a value of 0.035 years of education for those 15 years of age and older (thus, in essence using education years as a proxy for quality as well as quantity of educational attainment).&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDYRSCONTRIB^t_{\gamma}=EDYRSAG15^{t-1}_{\gamma}+(LIFEXPEDYRSBOOST^t_{\gamma}-YRSEDCOMP^t_{\gamma})*mfpedyrs&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;LIFEXPEDYRSBOOST^t_{\gamma}=(LIFEXP^{t-1}_{\gamma}-LIFEXPCOMP^t_{\gamma})*0.035&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;LIFEXPCOMP^t_{\gamma}=F(GDPPCP^t_{\gamma})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;YRSEDCOMP^t_{\gamma}=F(GDPPCP^t_{\gamma})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt; The lag is the difference from the midpoint of childhood (7.5) to the midpoint of adulthood (32.5).&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[2]&amp;lt;/span&amp;gt; Global data on stunting among adults appear nearly nonexistent. UNICEF (2009:5) suggests that under-5 stunting exceeds that of malnutrition (200 versus 130 million) and that stunting is nearly irreversible with aging; these facts suggest very high percentages of stunting among global adults, concentrated in Africa and Asia.&amp;amp;nbsp;&lt;br /&gt;
&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[3]&amp;lt;/span&amp;gt; While IFs includes historic data series from WDI and WHO for child undernutrition, many countries do not have data for 1980 (25 years prior to 2005, our initial year).&amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[4]&amp;lt;/span&amp;gt;&amp;amp;nbsp;As a quick reality check on those numbers, dividing disability years for the working population by population aged 15-65 generates numbers in 2005 that range from around 0.20-0.27 at the top end of the range (Timor-Leste, Afghanistan, Montenegro, Puerto Rico, Cambodia, and mostly other African countries) to 0.05-0.06 at the bottom end (Kuwait, UAE, Cape Verde, Algeria, Japan and mostly other rich countries). &amp;amp;nbsp;&amp;amp;nbsp; &amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[5]&amp;lt;/span&amp;gt;&amp;amp;nbsp;Because mental health rates do not change in base forecasts, we used the world average as an “expected” value—in 2005 that value, with mental health included, is .097 (mental health alone accounts for .025).&amp;amp;nbsp; That is, we calculate about 0.1 year of disability per worker across a working life. &amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span style=&amp;quot;color: #990000&amp;quot; lang=&amp;quot;EN-GB&amp;quot;&amp;gt;[5]&amp;lt;/span&amp;gt;&amp;amp;nbsp;Because mental health rates do not change in base forecasts, we used the world average as an “expected” value—in 2005 that value, with mental health included, is .097 (mental health alone accounts for .025).&amp;amp;nbsp; That is, we calculate about 0.1 year of disability per worker across a working life.&amp;amp;nbsp;&lt;br /&gt;
&amp;lt;/div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Additional Potential Forward Linkages&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
There are at least three additional forward linkages that could be usefully added to the model.&amp;amp;nbsp;&amp;amp;nbsp; First, we know that morbidity affects health spending, but the model does not have that relationship (change in health spending is linked instead to change in GDP per capita).&amp;amp;nbsp; Because health spending competes with other government spending (IFs does not represent private health spending separately from public), adding such a linkage would affect other spending and/or the total expenditure and revenue balance and therefore the level of taxation.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Second, we know that health affects the ability of populations to obtain education and the quality of that received.&amp;amp;nbsp; IFs includes enrolment terms to which health could be added. Third, health will affect distribution of income and economic well-being, although that relationship might be particularly difficult to represent.&lt;br /&gt;
Certainly there are other forward linkages of mortality and morbidity that the model might include.&amp;lt;/div&amp;gt;&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Health Bibliography&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
Adams 1987.&amp;amp;nbsp;[http://www.geog.ucl.ac.uk/~jadams/PDFs/smeed&#039;s%20law.pdf &amp;quot;Smeed&#039;s Law: some further thoughts.&amp;quot;]&amp;amp;nbsp;&#039;&#039;Traffic Engineering and Control&#039;&#039;&amp;amp;nbsp;(Feb) 70-73.&lt;br /&gt;
&lt;br /&gt;
Alsan, Marcella, David E. Bloom, and David Canning. 2006. “The Effects of Population Health on Foreign Direct Investment Inflows to Low- and Middle-Income Countries,”&amp;amp;nbsp;&#039;&#039;World Development&#039;&#039;&amp;amp;nbsp;34(4): 613-630.&lt;br /&gt;
&lt;br /&gt;
Anand, Sudhir and Martin Ravallion. 1993. “Human development in poor countries: on the role of private incomes and public services,”&amp;amp;nbsp;&#039;&#039;Journal of Economic Perspectives&#039;&#039;&amp;amp;nbsp;7(1): 133–150.&lt;br /&gt;
&lt;br /&gt;
Ashraf, Quamrul H., Ashley Lester, and David N. Weil. 2008. “When Does Improving Health Raise GDP?”&amp;amp;nbsp; NBER Working Paper No. 14449. National Bureau of Economic Research, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
Bidani, Benu and Martin Ravallion. 1997. “Decomposing social indicators using distributional data.”&amp;amp;nbsp;&#039;&#039;Journal of Econometrics&#039;&#039;&amp;amp;nbsp;77: 125–139.&lt;br /&gt;
&lt;br /&gt;
Bloom, David E., and David Canning. 2004. “Global Demographic Change: Dimensions and Economic Significance.” NBER Working Paper No. 10817.&amp;amp;nbsp; National Bureau of Economic Research, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
Blössner, Monika, and Mercedes de Onis. 2005.&amp;amp;nbsp;&#039;&#039;Malnutrition: quantifying the health impact at national and local levels.&#039;&#039;&amp;amp;nbsp;Geneva, World Health Organization. (WHO Environmental Burden of Disease Series, No. 12).&lt;br /&gt;
&lt;br /&gt;
Dargay, Gately, and Sommer 2007. “Vehicle Ownership and Income Growth, Worldwide: 1960-2030”. Joyce Dargay, Dermot Gately and Martin Sommer, January 2007.&lt;br /&gt;
&lt;br /&gt;
Deaton, Angus, and Christina Paxson. 2000 (May). “Growth and Savings Among Individuals and Households.”&amp;amp;nbsp;&#039;&#039;The Review of Economics and Statistics&#039;&#039;&amp;amp;nbsp;82(2): 212-225.&lt;br /&gt;
&lt;br /&gt;
Desai, Manish A., Sumi Mehta, and Kirk R. Smith. 2004. “Indoor smoke from solid fuels: Assessing the environmental burden of disease.”WHOEnvironmental Burden of Disease Series No. 4&#039;&#039;.&amp;amp;nbsp;&#039;&#039;Annette Pruss-Üstun, Diamid Campbell-Lendrum, Carlos Corvalán, and Alistair Woodward, series eds. World Health Organization, Geneva.&lt;br /&gt;
&lt;br /&gt;
Ezzati, Majid and Alan D. Lopez. 2004. “Smoking and oral tobacco use.” In Majid Ezzati, Alan D. Lopez, Anthony Rodgers, and Cristopher J.L. Murray, eds.,&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors&#039;&#039;. Geneva: World Health Organization, 883-957.&amp;amp;nbsp; Retrieved 4 Feb 2009, from&amp;amp;nbsp;[http://www.who.int/publications/cra/chapters/volume1/part4/en/index.html http://www.who.int/publications/cra/chapters/volume1/part4/en/index.html].&lt;br /&gt;
&lt;br /&gt;
Ezzati, Majid, Alan D. Lopez, Anthony Rodgers, Christopher J.L. Murray, eds. 2004.&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors&#039;&#039;. Geneva: World Health Organization.&lt;br /&gt;
&lt;br /&gt;
Fernández-Villaverde, Jesús, and Dirk Kruegger. 2004 (September 14). “Consumption over the Life Cycle: Facts from Consumer Expenditure Survey Data,” unpublished manuscript, University of Pennsylvania and University of Frankfort.&amp;amp;nbsp;&amp;amp;nbsp;[http://www.dklevine.com/archive/refs4506439000000000304.pdf http://www.dklevine.com/archive/refs4506439000000000304.pdf]&lt;br /&gt;
&lt;br /&gt;
Fernández-Villaverde, Jesús, and Dirk Kruegger. 2005 (December 19). “Consumption over the Life Cycle: How Important are Consumer Durables?,” unpublished manuscript, University of Pennsylvania and Goethe University.&amp;amp;nbsp;&amp;amp;nbsp;[http://journals.cambridge.org/action/displayAbstract?fromPage=online&amp;amp;aid=8466457 http://journals.cambridge.org/action/displayAbstract?fromPage=online&amp;amp;aid=8466457]&lt;br /&gt;
&lt;br /&gt;
Gakidou, Emmanuela, Shefali Oza, Cecilia Vidal Fuertes, Amy Y. Li, Diana K. Lee, Angelica Sousa, Margaret C. Hogan, Stephen Vander Hoorn, and Majid Ezzati. 2007.” Improving Child Survival Through Environmental and Nutritional Interventions: The Importance of Targeting Interventions Toward the Poor.”&amp;amp;nbsp;&#039;&#039;Journal of the American Medical Association&#039;&#039;&amp;amp;nbsp;298(16): 1876-1887.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. and Hillebrand, Evan E. 2006. “Exploring and shaping International Futures”. Boulder, CO: Paradigm Publishers.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., Randall Kuhn, Cecilia Peterson, Dale Rothman, and Jose Solorzano. 2011.&amp;amp;nbsp;&amp;amp;nbsp;&#039;&#039;Improving Global Health: Patterns of Potential Human Progress, Volume 3&#039;&#039;.&amp;amp;nbsp; Paradigm Publishing and Oxford India.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2005.&amp;amp;nbsp; “Productivity in IFs.” Pardee Center for International Futures Working Paper, University of Denver, Denver, CO.&amp;amp;nbsp;&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx http://www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
James, W. Philip T., Rachel Jackson-Leach , Cliona Ni Mhurchu, Eleni Kalamara, Maryam Shayeghi, Neville J. Rigby, Chizuru Nishida, and Anthony Rodgers. 2004.&amp;amp;nbsp; “Overweight and obesity (high body mass index).” In Majid Ezzati, Alan D. Lopez, Anthony Rodgers and Christopher J.L. Murray, eds.,&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors.&#039;&#039;&amp;amp;nbsp;Geneva: World Health Organization, 959-1108.&lt;br /&gt;
&lt;br /&gt;
Jamison, Dean T., Jia Wang, Kenneth Hill, and Juan-Luis Londono. 1996. “Income, Mortality and Fertility in Latin America: Country-Level Performance, 1960 - 90.”&amp;amp;nbsp;&#039;&#039;Analisis Economico&#039;&#039;11(2): 219-261.&lt;br /&gt;
&lt;br /&gt;
Kelly, Christopher, Nora Pashayan, Sreetharan Munisamy, and Joshn W. Powles. 2009.&amp;amp;nbsp; “Mortality attributable to excess adiposity in England and Wales in 2003 and 2015: explorations with a spreadsheet implementation of the Comparative Risk Assessment mentodology.”&amp;amp;nbsp;&#039;&#039;Population Health Metrics&#039;&#039;&amp;amp;nbsp;7(11): 1-7.&lt;br /&gt;
&lt;br /&gt;
Lopez, Alan D., Neil E. Collishaw, and Tapani Piha. 1994. “A descriptive model of the cigarette epidemic in developed countries.”&amp;amp;nbsp;&#039;&#039;Tobacco Control&#039;&#039;&amp;amp;nbsp;3(3): 242-247. &amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Mathers, Colin D., and Dejan Loncar. 2005. &amp;quot;Updated Projections of Global Mortality and Burden of Disease, 2002-2030: Data Sources, Methods and Results.&amp;quot; Evidence and Information for Policy Working Paper. World Health Organization, Geneva.&lt;br /&gt;
&lt;br /&gt;
Mathers, Colin D., and Dejan Loncar. 2006. &amp;quot;Projections of Global Mortality and Burden of Disease from 2002 to 2030.&amp;quot;&amp;amp;nbsp;&#039;&#039;PLoS Medicine&#039;&#039;&amp;amp;nbsp;3(11): e442, 2011-2030.&amp;amp;nbsp; Retrieved 13 March 2009. doi:10.1371/journal.pmed.0030442.&lt;br /&gt;
&lt;br /&gt;
Mathers, Colin D., and Dejan Loncar. 2006b. “New projections of global mortality and burden of disease from 2002 to 2030.” Protocol S1. Technical Appendix to Mathers and Loncar 2006.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Mathers, Colin D., and Dejan Loncar. 2006c. “Results of Regressions of Age–Sex-Specific Mortality for Detailed Causes on the Respective Cause Cluster Based on the Full Country Panel Dataset, 1950–2002.” Technical Appendix to Mathers and Loncar 2006.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Nixon, John, and Philippe Ulmann. 2006. “The Relationship Between Health Care Expenditure and Health Outcomes: Evidence and caveats for a Causal Link.”&amp;amp;nbsp;&#039;&#039;European Journal of Health Economics&#039;&#039;&amp;amp;nbsp;7: 7-18.&lt;br /&gt;
&lt;br /&gt;
Peto, Richard, Jillian Boreham, Alan D. Lopez, Michael Thun, and Clark Heath, Jr. 1992. “Mortality from Tobacco in Developed Countries: Indirect Estimation from National Vital Statistics.”&amp;amp;nbsp;&#039;&#039;Lancet&amp;amp;nbsp;&#039;&#039;339(8804): 1268–1278. doi:10.1016/0140- 6736(92)91600-D.&lt;br /&gt;
&lt;br /&gt;
Ploeg, Martine, Katja K. H. Aben, and Lambertus A. Kiemeney. 2009. “The Present and Future Burden of Urinary Bladder Cancer in the World.”&amp;amp;nbsp;&#039;&#039;World Journal of Urology&#039;&#039;&amp;amp;nbsp;27(3): 289-293. doi:[http://dx.doi.org/10.1007/s00345-009-0383-3 &amp;amp;nbsp;10.1007/s00345-009-0383-3&amp;amp;nbsp;]. &amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Shibuya, Kenji, Mie Inoue, and Alan D. Lopez. 2005. “Statistical Modeling and Projections of Lung Cancer Mortality in 4 Industrialized Countries.”&amp;amp;nbsp;&#039;&#039;International Journal of Cancer&#039;&#039;&amp;amp;nbsp;117(3): 476-485. doi:[http://dx.doi.org/10.1002/ijc.21078 &amp;amp;nbsp;10.1002/ijc.21078&amp;amp;nbsp;]. &amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Smeed, RJ 1949. &amp;quot;Some statistical aspects of road safety research&amp;quot;.&amp;amp;nbsp;[http://en.wikipedia.org/wiki/Royal_Statistical_Society &#039;&#039;Royal Statistical Society&#039;&#039;], Journal (A) CXII (Part I, series 4). 1-24.&lt;br /&gt;
&lt;br /&gt;
Smith, Lisa C. and Lawrence Haddad. 2000. “Explaining Child Malnutrition in Developing Countries: A Cross-Sectional Analysis.” Washington, D.C.: International Food Policy Research Institute.&lt;br /&gt;
&lt;br /&gt;
Soares, Rodrigo R. 2007. “On the Determinants of Mortality Reductions in the Developing World.”&amp;amp;nbsp;&#039;&#039;Population and Development Review&amp;amp;nbsp;&#039;&#039;33(2): 247-287.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2003.&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;World Population Prospects: The 2002 Revision, Highlight.&#039;&#039;&amp;amp;nbsp; New York:&amp;amp;nbsp; United Nations. Department of Economics and Social Affairs.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2009.&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;World Population Prospects: The 2008 Revision, Highlights.&#039;&#039;&amp;amp;nbsp; New York:&amp;amp;nbsp; United Nations. Department of Economics and Social Affairs.&lt;br /&gt;
&lt;br /&gt;
Wagstaff, Adam. 2002. “Inequalities in Health in Developing Countries: Swimming Against the Tide?” Unpublished Manuscript&lt;br /&gt;
[[Category:Pages with broken file links]]&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8317</id>
		<title>Introduction to IFs</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8317"/>
		<updated>2017-09-07T21:45:16Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Purposes&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;International Futures (IFs) is a tool for thinking about long-term global trends and planning more strategically for the&amp;amp;nbsp;future.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
IFs can help you:&lt;br /&gt;
&lt;br /&gt;
*Understand&amp;amp;nbsp;the state of major&amp;amp;nbsp;global systems&lt;br /&gt;
*Explore&amp;amp;nbsp;long-term trends and consider where they might take&amp;amp;nbsp;us&lt;br /&gt;
*Learn about the dynamic&amp;amp;nbsp;interactions between global systems&lt;br /&gt;
*Clarify long-term organizational goals/priorities&lt;br /&gt;
*Develop alternative scenarios (if-then statements) about the future&lt;br /&gt;
*Investigate how different groups (households, firms or governments) can shape the future&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;IFs development and analysis depend&amp;amp;nbsp;on core, underlying assumptions, including the following.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*Global issues are becoming&amp;amp;nbsp;more significant as the scope of human interaction and human impact on the broader environment grow.&lt;br /&gt;
*Goals and priorities for human systems are becoming clearer and are more frequently and consistently communicated.&lt;br /&gt;
*Understanding of the dynamics of human systems is improving&amp;amp;nbsp;rapidly.&lt;br /&gt;
*The domain of human choice and action is broadening.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;What issues can you&amp;amp;nbsp;investigate with IFs?&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*[[Environment]]: Atmospheric carbon dioxide levels, world forest area, fossil fuel usage&lt;br /&gt;
*[[Socio-Political|Socio-Political Change]]: Life expectancy, literacy rate, democracy level, status of women, value change&lt;br /&gt;
*[[Population|Demographics]]: Population levels and growth, fertility, mortality, migration&lt;br /&gt;
*[[Agriculture|Food and Agriculture]]: Land use and production levels, calorie availability, malnutrition rates&lt;br /&gt;
*[[Energy]]: Resource and production levels, demand patterns, renewable energy share&lt;br /&gt;
*[[Economics]]: Sectoral production, consumption, and trade patterns and structural change&lt;br /&gt;
*[[Interstate_Politics_(IP)|Geopolitics]]: Country and regional power levels&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Visual Representation&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
[[File:IFsOverviewChart.jpg|frame|center|Visual representation of IFs structure]]&lt;br /&gt;
&lt;br /&gt;
Among the philosophical premises of the International Futures (IFs) project is that the model cannot be a &amp;quot;black box&amp;quot; to users and be truly useful. Model users must be able to examine the structures of IFs in order (1) to have confidence in them, and (2) learn from them.&lt;br /&gt;
&lt;br /&gt;
There is available (see topics under Understanding the Mode in the contents of this help system):&lt;br /&gt;
&lt;br /&gt;
*[[Understand_IFs#Dominant_Relations|Dominant Relations]] of the model structure&lt;br /&gt;
*[[Understand_IFs#2.2|Structure-Based and Agent-Class Driven Modeling]]&lt;br /&gt;
*[[Understand_IFs#Equation_Notation|Equation Notation]]&lt;br /&gt;
*[[Introduction_to_IFs#IFs_Bibliography|IFs Bibliography]] of data and data sources&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Quick Survey&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;population&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents 22 age-sex cohorts to age 100+&lt;br /&gt;
*calculates change in fertility and mortality rates in response to income, income distribution, and analysis multipliers&lt;br /&gt;
*computes average life expectancy at birth, literacy rate, and overall measures of human development (HDI) and physical quality of life&lt;br /&gt;
*represents migration and HIV/AIDS&lt;br /&gt;
*includes a newly developing submodel of formal education across primary, secondary, and tertiary levels&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;economic&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents the economy in six sectors: agriculture, materials, energy, industry, services, and ICT (other sectors could be configured, using raw data from the GTAP project)&lt;br /&gt;
*computes and uses input-output matrices that change dynamically with development level&lt;br /&gt;
*is a general equilibrium-seeking model that does not assume exact equilibrium will exist in any given year; rather it uses inventories as buffer stocks and to provide price signals so that the model chases equilibrium over time&lt;br /&gt;
*contains an endogenous production function that represents contributions to growth in multifactor productivity from R&amp;amp;D, education, worker health, economic policies (&amp;quot;freedom&amp;quot;), and energy prices (the &amp;quot;quality&amp;quot; of capital)&lt;br /&gt;
*uses a Linear Expenditure System to represent changing consumption patterns&lt;br /&gt;
*utilizes a &amp;quot;pooled&amp;quot; rather than the bilateral trade approach for international trade&lt;br /&gt;
*is being imbedded during 2002 in a social accounting matrix (SAM) envelope that will tie economic production and consumption to intra-actor financial flows&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;agricultural&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents production, consumption and trade of crops and meat; it also carries ocean fish catch and aquaculture in less detail&lt;br /&gt;
*maintains land use in crop, grazing, forest, urban, and &amp;quot;other&amp;quot; categories&lt;br /&gt;
*represents demand for food, for livestock feed, and for industrial use of agricultural products&lt;br /&gt;
*is a partial equilibrium model in which food stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the agricultural sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;energy&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*portrays production of six energy types: oil, gas, coal, nuclear, hydroelectric, and other renewable&lt;br /&gt;
*represents consumption and trade of energy in the aggregate&lt;br /&gt;
*represents known reserves and ultimate resources of the fossil fuels&lt;br /&gt;
*portrays changing capital costs of each energy type with technological change as well as with draw-downs of resources&lt;br /&gt;
*is a partial equilibrium model in which energy stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the energy sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The two &#039;&#039;&#039;socio-political&#039;&#039;&#039; sub-modules:&lt;br /&gt;
&lt;br /&gt;
Within countries or geographic groupings&lt;br /&gt;
&lt;br /&gt;
*represents fiscal policy through taxing and spending decisions&lt;br /&gt;
*shows six categories of government spending: military, health, education, R&amp;amp;D, foreign aid, and a residual category&lt;br /&gt;
*represents changes in social conditions of individuals (like fertility rates or literacy levels), attitudes of individuals (such as the level of materialism/postmaterialism of a society from the World Value Survey), and the social organization of people (such as the status of women)&lt;br /&gt;
*represents the evolution of democracy&lt;br /&gt;
*represents the prospects for state instability or failure&lt;br /&gt;
&lt;br /&gt;
Between countries or groupings of countries&lt;br /&gt;
&lt;br /&gt;
*traces changes in power balances across states and regions&lt;br /&gt;
*allows exploration of changes in the level of interstate threat&lt;br /&gt;
*represents possible action-reaction processes and arms races with associated potential for conflict among countries&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;environmental&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows tracking of remaining resources of fossil fuels, of the area of forested land, of water usage, and of atmospheric carbon dioxide emissions&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;technology&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows changes in assumptions about rates of technological advance in agriculture, energy, and the broader economy&lt;br /&gt;
*explicitly represents the extent of electronic networking of individuals in societies&lt;br /&gt;
*is tied to the governmental spending model with respect to R&amp;amp;D spending&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Background&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
International Futures (IFs) has evolved since 1980 through three &amp;quot;generations,&amp;quot; with a fourth generation now taking form.&lt;br /&gt;
&lt;br /&gt;
The first generation had deep roots in the world models of the 1970s, including those of the Club of Rome. In particular, IFs drew on the Mesarovic-Pestel or World Integrated Model (Mesarovic and Pestel 1974). The author of IFs had contributed to that project, including the construction of the energy submodel. IFs consciously also drew on the Leontief World Model (Leontief et al. 1977), the Bariloche Foundation’s world model (Herrera et al. 1976), and Systems Analysis Research Unit Model (SARU 1977), following comparative analysis of those models by Hughes (1980). That generation was written in FORTRAN and available for use on main-frame computers through CONDUIT, an educational software distribution center at the University of Iowa. Although the primary use of that and subsequent generations was by students, IFs has always had some policy analysis capability that has appealed to specialists. For example, the U.S. Foreign Service Institute used the first generation of IFs in a mid-career training program.&lt;br /&gt;
&lt;br /&gt;
The second generation of International Futures moved to early microcomputers in 1985, using the DOS platform. It was a very simplified version of the original IFs without regional or country differentiation.&lt;br /&gt;
&lt;br /&gt;
The third generation, first available in 1993, became a full-scale microcomputer model. The third generation improved earlier representations of demographic, energy, and food systems, but added new environmental and socio-political content. It built upon the collaboration of the author with the GLOBUS project, and it adopted the economic submodel of GLOBUS (developed by the author). GLOBUS had been created with the inspiration of Karl Deutsch and under the leadership of Stuart Bremer (1987) at the Wissenschaftszentrum in Berlin.&lt;br /&gt;
&lt;br /&gt;
The third generation has produced three editions/major releases of IFs, each accompanied by a book also called International Futures (Hughes 1993, 1996, 1999). The second edition moved to a Visual Basic platform that allowed a much improved menu-driven interface, running under Windows. The third edition incorporated an early global mapping capability and an initial ability to do cross-sectional and longitudinal data analysis.&lt;br /&gt;
&lt;br /&gt;
The fourth generation has been taking shape since early 2000. It has been heavily influenced by the usage of the model in an increasingly policy-analysis mode by several important organizations. First, General Motors commissioned a specialized version of IFs named CoVaTrA (Consumer Values Trends Analysis) with a need for updated and extended demographic modeling and representation of value change. An alliance was established with the World Values Survey, directed by Ronald Inglehart, to create that version. Second, the Strategic Assessments Group of the Central Intelligence Agency commissioned a specialized version named IFs for SAG. The work involved in preparing that greatly extended and enhanced the socio-political representations of the model, both domestic and international. Third, the European Commission sponsored a project named TERRA which has led to a specialized version named IFs for TERRA. IFs for TERRA work led to enhancements across the model, including improved representation of economic sectors, updated IO matrices and a basic Social Accounting Matrix, GINI and Lorenz curves, and preparing for extended environmental impact representation (drawing upon the Advanced Sustainability Analysis framework of the Finland Futures Research Center).&lt;br /&gt;
&lt;br /&gt;
Throughout this emergence of a fourth generation IFs (incorporating all of the above elements for additional users) there has been also a heavy emphasis on enhanced usability. Ideas from Robert Pestel in the TERRA project led to the creation of a new tree-structure for scenario creation and management. Ideas from Ronald Inglehart led to the development of the Guided Use structure and a somewhat more game-like character within that structure. Inglehart also help arrange funding to support the programming of Guided Use through the European Union Center of the University of Michigan.&lt;br /&gt;
&lt;br /&gt;
The fifth version of IFs is currently in use and represents broad strides to improving the model and its usability. It is the first version of this software to be placed online due to the help of the National Intelligence Council ([http://www.ifs.du.edu http://www.ifs.du.edu]). Also, usability has been increased as Packaged Displays and Flex Packaged Displays were introduced that allowed for the creation of very specific lists of countries/regions, groups or Glists. A new education model has also been incorporated into the broader IFs model. New scenarios were created for UNEP (focusing on environmental change) and Pardee (focusing on poverty). Finally, one of the largest changes made was incorporating 182 countries into the Base-Case scenario used by IFs. Previous versions of IFs used broader regions to forecast global trends. This change also did away with the Student and Professional versions.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Geographic Representation of the World&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
186 countries underpin the functioning of IFs and these countries can be displayed separately or as parts of larger groups that users can determine.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;Below is a visual representation of how different entities are organized into Countries/Regions, Groups or Glists:&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
[[File:Geo 186.png|frame|right|Visual representation of IF&#039;s definition of regions/countries/groups/glists]]&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;*Note: In older versions of IFs, Regions were used as intermediaries between Countries and Groups. In the future, they, or some similarly named unit, will be a sub-unit of Countries. Regions, acting as a sub-unit of Countries, are currently not a feature of IFs. See the image located at the bottom of this Help topic.&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
When using IFs, there are many occasions where the user is asked whether or not they would like to display their results as a product of single countries, or larger groups. This is typically a toggle switch that moves between Country/Region and Groups, however, it might be a three-way-toggle that includes Country/Region, Group and Glist.&lt;br /&gt;
&lt;br /&gt;
Countries/Regions are currently the smallest geographical unit that users can represent. The ability to split countries down into smaller regions, or states, is under development. There are 186 different countries/regions that users can display.&lt;br /&gt;
&lt;br /&gt;
Groups are variably organized geographically or by memberships in international institutions/regimes. You can find out who is represented in each group and add or delete members by exploring the [[Extended_Features#Manage_Groups/Regions|Managing Regionalization]] function.&lt;br /&gt;
&lt;br /&gt;
Glists merge both Groups and Countries/Regions. These lists are mostly geographically bound. In the future, the Glist distinction will become more important as some users may want to place, for example, both the Indian state of Kerala in a Glist with Sri Lanka and Nepal.&lt;br /&gt;
&lt;br /&gt;
Users may also want to [[Extended_Features#Change_Grouping/Regionalization|create]] their own groups or [[Extended_Features#Identify_Groups_or_Country/Region_Members|explore]] what countries are members of what groups.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Time Horizon&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Future Forecasts.&#039;&#039;&#039; IFs begins computation with data from 2000 and can dynamically calculate values for all variables annually through 2100.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Historical Analysis and &amp;quot;Forecasts.&amp;quot;&#039;&#039;&#039; IFs also includes an extensive and growing historical data base starting in 1960. The data basis allows analysis of relationships among variables across countries and across time.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Instructional Use&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The standard modes for using IFs in a classroom are:&lt;br /&gt;
&lt;br /&gt;
1. Assigning class members to an issue area or topic. Consider identifying specific questions for them to address.&lt;br /&gt;
&lt;br /&gt;
2. Assigning class members to a country/geographic region. Again, specificity helps.&lt;br /&gt;
&lt;br /&gt;
Most often, students will work independently or in groups on projects and share information after completing them. It is possible, however, to have students work interactively, by assigning them topics or regions, letting them begin work, and then have the interacting groups (or individuals) create a collective model run with the changes that each group proposes by topic or region. That process, although more difficult to organize, allows the class as whole to investigate the interaction of their topics or regions (and to share learning about model use).&lt;br /&gt;
&lt;br /&gt;
There is a&amp;amp;nbsp;[http://portfolio.du.edu/bhughes web site]&amp;amp;nbsp;available in support of the educational use of IFs. You will find syllabi at that site. There are several [[Introduction_to_IFs#Publications_on_IFs|publications]] on IFs, including a book structured specifically for educational use.&lt;br /&gt;
&lt;br /&gt;
Donald Borock has described his classroom use of IFs in print. Borock, Donald. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot; Advances in Social Science and Computers 4, 103-127.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Acknowledgements&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The author gratefully recognizes critical contributions in the forms of:&lt;br /&gt;
&lt;br /&gt;
:1. Testing and suggestions for development of IFs in one or more of multiple generations. By Donald Borock, Richard Chadwick, William Dixon, Dale Rothman, Phil Schrodt, Douglas Stuart, Donald Sylvan, Jonathan Wilkenfeld, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
:2. Computer assistance across many releases. By Michael Niemann, Terrance Peet-Lukes, Douglas McClure, Mohammod Irfan, and Jose Solorzano.&lt;br /&gt;
&lt;br /&gt;
:3. Data gathering and general assistance. By James Chung, Padma Padula, Shannon Brady, David Horan, Michael Ferrier, Kay Drucker, Warren Christopher, and Anwar Hossain.&lt;br /&gt;
&lt;br /&gt;
:4. Long-term encouragement and support. By Harold Guetzkow, Karl Deutsch, Richard Chadwick, Gerald Barney, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
:5. Association in related world modeling projects and projects building upon IFs. By Mihajlo Mesarovic, Aldo Barsotti, Juan Huerta, John Richardson, Thomas Shook, Patricia Strauch, and other members of the World Integrated Model (WIM) team. By Stuart Bremer, Peter Brecke, Thomas Cusack, Wolf Dieter-Eberwein, Brian Pollins, and Dale Smith of the GLOBUS modeling project. By Evan Hillebrand, Paul Herman, and others of the IFs for SAG project. By Rob Lempert and Steve Bankes at RAND, Santa Monica. By Robert Pestel, Jonathan Cave, Ronald Inglehart, Sergei Parinov, Pentti Malaska, and many others in the IFs for TERRA project.&lt;br /&gt;
&lt;br /&gt;
:6. Financial assistance (without responsibility for the form of the evolving product). By the National Science Foundation, the Cleveland Foundation, the Exxon Education Foundation, the Kettering Family Foundation, the Pacific Cultural Foundation, the United States Institute of Peace, General Motors, the Strategic Assessments Group of the Central Intelligence Agency, the European Commission (Information Society Technology) Programme, the European Union Center of the University of Michigan, the National Intelligence Council (for web conversion), and Frederick S. Pardee. &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Feedback&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
Feedback on how to improve IFs is always appreciated, especially if you find something that is not working. Compliments are also accepted. Please contact. To send the IFs team an e-mail, click on&amp;amp;nbsp;[mailto:pardee.center@du.edu Pardee Center]&amp;amp;nbsp;in stand-alone versions or on the web.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Support for IFs Use&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Publications on IFs&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
To obtain additional information about IFs and its use, consult:&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes and Evan E. Hillebrand, &#039;&#039;&#039;Exploring and Shaping International Futures.&#039;&#039;&#039; Boulder, CO: Paradigm Publishers, 2006. Specifically, see chapter 4.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &#039;&#039;&#039;International Futures: Choices in the Face of Uncertainty,&#039;&#039;&#039; 3rd ed. Boulder, CO: Westview Press, 1999. This volume is built around IFs and contains detailed suggestions for its use. Version 3.17 of IFs, which runs under Windows 95, is distributed with the third edition of the book. The second edition contained a version for Windows 3.1, and the first edition ran under DOS. Chapter 4 of the 2nd edition of IFs included Flow Charts of Worldviews , reproduced now in this Help system.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &#039;&#039;&#039;Continuity and Change in World Politics,&#039;&#039;&#039; 4th ed. Englewood Cliffs, N.J.: Prentice Hall, 2000. IFs can also usefully supplement this textbook on global politics.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &amp;quot;The International Futures (IFs) Modeling Project. 1999. &#039;&#039;&#039;Simulation and Gaming&#039;&#039;&#039; 30, No. 3 (September): 304-326.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;IFs Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Alcamo, Joseph, Rik Leemans and Eric Kreileman, eds. 1998.&amp;amp;nbsp;&#039;&#039;Global Change Scenarios of the 21st Century: Results from the IMAGE 2.1 Model&#039;&#039;. The Netherlands: Pergamon.&lt;br /&gt;
&lt;br /&gt;
Alcamo, Joseph. 1994.&amp;amp;nbsp;&#039;&#039;IMAGE 2.0: Integrated Modeling of Global Climate Change&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
&lt;br /&gt;
Alexandratos, Nikos, ed. 1995.&amp;amp;nbsp;&#039;&#039;World Agriculture: Towards 2010&#039;&#039;&amp;amp;nbsp;(An FAO Study). New York: FAO and John Wiley and Sons.&lt;br /&gt;
&lt;br /&gt;
Allen, R. G. D. 1968.&amp;amp;nbsp;&#039;&#039;Macro-Economic Theory: A Mathematical Treatment&#039;&#039;. New York: St. Martin&#039;s Press.&lt;br /&gt;
&lt;br /&gt;
Avery, Dennis. 1995. &amp;quot;Saving the Planet with Pesticides,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 50-82.&lt;br /&gt;
&lt;br /&gt;
Bailey, Ronald, ed. 1995.&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;. New York: The Free Press.&lt;br /&gt;
&lt;br /&gt;
Barbieri, Kathleen. 1996. &amp;quot;Economic Interdependence: A Path to Peace or a Source of Interstate Conflict?&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Peace Research&#039;&#039;&amp;amp;nbsp;33: 29-50.&lt;br /&gt;
&lt;br /&gt;
Barker, T.S. and A.W.A. Peterson, eds. 1987.&amp;amp;nbsp;&#039;&#039;The Cambridge Multisectoral Dynamic Model of the British Economy&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Barney, Gerald O., W. Brian Kreutzer, and Martha J. Garrett, eds. 1991.&amp;amp;nbsp;&#039;&#039;Managing a Nation&#039;&#039;, 2nd ed. Boulder: Westview Press.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. 1997.&amp;amp;nbsp;&#039;&#039;Determinants of Economic Growth: A Cross-Country Empirical Study&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. and Xavier Sala-i-Martin. 1999.&amp;amp;nbsp;&#039;&#039;Economic Growth&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
&lt;br /&gt;
Bennett, D. Scott, and Allan Stam. 2003.&amp;amp;nbsp;&#039;&#039;The Behavioral Origins of War: Cumulation and Limits to Knowledge in Understanding International Conflict&#039;&#039;. Ann Arbor: University of Michigan Press&lt;br /&gt;
&lt;br /&gt;
Birg, Herwig. 1995.&amp;amp;nbsp;&#039;&#039;World Population Projections for the 21st Century&#039;&#039;. Frankfurt: Campus Verlag.&lt;br /&gt;
&lt;br /&gt;
Borock, Donald M. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot;&amp;amp;nbsp;&#039;&#039;Advances in Social Science and Computers&#039;&#039;&amp;amp;nbsp;4, 103-127.&lt;br /&gt;
&lt;br /&gt;
Bos, Eduard, My T. Vu, Ernest Massiah, and Rodolfo A. Bulatao. 1994.&amp;amp;nbsp;&#039;&#039;World Population Projections 1994-95 Edition&#039;&#039;&amp;amp;nbsp;[editions are biannual] Baltimore: Johns Hopkins Press.&lt;br /&gt;
&lt;br /&gt;
Boulding, Elise and Kenneth E. Boulding. 1995.&amp;amp;nbsp;&#039;&#039;The Future: Images and Processes&#039;&#039;. Thousand Oaks, CA: Sage Publications.&lt;br /&gt;
&lt;br /&gt;
Brecke, Peter. 1993. &amp;quot;Integrated Global Models that Run on Personal Computers,&amp;quot;&amp;amp;nbsp;&#039;&#039;Simulation&#039;&#039;60 (2).&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. 1977.&amp;amp;nbsp;&#039;&#039;Simulated Worlds: A Computer Model of National Decision-Making&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A., ed. 1987.&amp;amp;nbsp;&#039;&#039;The GLOBUS Model: Computer Simulation of World-wide Political and Economic Developments&#039;&#039;. Boulder, CO: Westview.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. and Walter Gruhn. 1988.&amp;amp;nbsp;&#039;&#039;Micro GLOBUS: A Computer Model of Long-Term Global Political and Economic Processes&#039;&#039;. Berlin: edition sigma.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. and Barry B. Hughes. 1990.&amp;amp;nbsp;&#039;&#039;Disarmament and Development: A Design for the Future?&#039;&#039;&amp;amp;nbsp;Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Brockmeier, Martina and Channing Arndt (presentor). 2002. Social Accounting Matrices. Powerpoint presentation on GTAP and SAMs (June 21). Found on the web.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1981.&amp;amp;nbsp;&#039;&#039;Building a Sustainable Society&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1988. &amp;quot;Analyzing the Demographic Trap,&amp;quot; in&amp;amp;nbsp;&#039;&#039;State of the World 1987&#039;&#039;, eds. Lester R. Brown and others. New York: W.W. Norton, pp. 20-37.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1995.&amp;amp;nbsp;&#039;&#039;Who Will Feed China?&#039;&#039;&amp;amp;nbsp;New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1996.&amp;amp;nbsp;&#039;&#039;Tough Choices: Facing the Challenge of Food Scarcity&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., et al. 1996&amp;amp;nbsp;&#039;&#039;State of the World 1996&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., Nicholas Lenssen, and Hal Kane. 1995.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1995. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., Christopher Flavin, and Hal Kane. 1996.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1996. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Burkhardt, Helmut. 1995. &amp;quot;Priorities for a Sustainable Civilization,&amp;quot; unpublished conference paper. Department of Physics, Ryerson Polytechnic University, Toronto, Canada.&lt;br /&gt;
&lt;br /&gt;
Bussolo, Maurizio, Mohamed Chemingui and David O’Connor. 2002. A Multi-Region Social Accounting Matrix (1995) and Regional Environmental General Equilibrium Model for India (REGEMI). Paris: OECD Development Centre (February). Available at&amp;amp;nbsp;[http://www.oecd.org/dev/technics www.oecd.org/dev/technics].&lt;br /&gt;
&lt;br /&gt;
British Petroleum Company. 1995.&amp;amp;nbsp;&#039;&#039;BP Statistical Review of World Energy&#039;&#039;. London: British Petroleum Company.&lt;br /&gt;
&lt;br /&gt;
Central Intelligence Agency (CIA). 1991.&amp;amp;nbsp;&#039;&#039;Handbook of Economic Statistics, 1991&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
&lt;br /&gt;
Central Intelligence Agency (CIA). 1994.&#039;&#039;&amp;amp;nbsp;The World Factbook 1994&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
&lt;br /&gt;
Chang, Sheldon S. L. 1961.&amp;amp;nbsp;&#039;&#039;Synthesis of Optimum Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Chenery, Hollis and Moises Syrquin. 1975.&amp;amp;nbsp;&#039;&#039;Patterns of Development 1950-1970&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Cipolla, Carlo M. 1962.&amp;amp;nbsp;&#039;&#039;The Economic History of World Population&#039;&#039;. Baltimore: Penguin.&lt;br /&gt;
&lt;br /&gt;
Cook, Earl. 1976.&amp;amp;nbsp;&#039;&#039;Man, Energy, Society&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
&lt;br /&gt;
Committee on the Strategic Assessment of the U.S. Department of Energy’s Coal Program. 1995.&amp;amp;nbsp;&#039;&#039;Coal: Energy for the Future&#039;&#039;. Washington, D.C.: National Academy Press.&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1981.&amp;amp;nbsp;&#039;&#039;The Global 2000 Report to the President&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1981b.&amp;amp;nbsp;&#039;&#039;Environmental Trends&#039;&#039;. Washington, D.C. (July).&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1991.&amp;amp;nbsp;&#039;&#039;21st Annual Report&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Crescenzi, Mark J.C. and Andrew J. Enterline. 2001. &amp;quot;Time Remembered: A Dynamic Model of Interstate Interaction,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;45, no. 3 (September): 409-431.&lt;br /&gt;
&lt;br /&gt;
Crosson, Pierre, and Jock R. Anderson. 1992.&amp;amp;nbsp;&#039;&#039;Resources and Global Food Prospects&#039;&#039;. Washington, D.C.: The World Bank. World Bank Technical Paper Number 184.&lt;br /&gt;
&lt;br /&gt;
Cusack, Thomas R. and Richard J. Stoll. 1990.&amp;amp;nbsp;&#039;&#039;Exploring Realpolitik: Probing International Relations with Computer Simulatio&#039;&#039;n. Boulder: Lynne Rienner Publishers.&lt;br /&gt;
&lt;br /&gt;
Dargay, Joyce and Dermot Gately. 1999. &amp;quot;Income’s Effect on Car and Vehicle Ownership, Worldwide: 1960-2015,&amp;quot;&amp;amp;nbsp;&#039;&#039;Transportation Research Part A&#039;&#039;&amp;amp;nbsp;33: 101-138.&lt;br /&gt;
&lt;br /&gt;
Dall, P., Kaspar, F. and Alcamo, J. 1998. &amp;quot;Modeling World-wide Water Availability and Water Use Under the Influence of Climate Change,&amp;quot;&amp;amp;nbsp;&#039;&#039;Proceedings of the Second International Conference on Climate and Water&#039;&#039;, July 17-20, Espoo, Finland.&lt;br /&gt;
&lt;br /&gt;
Dimaranan, Betina V. and Robert A. McDougall, eds. 2002.&amp;amp;nbsp;&#039;&#039;Global Trade, Assistance, and Production: The GTAP 5 Data Base&#039;&#039;. Center for Global Trade Analysis, Purdue University. Available at [http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp].&lt;br /&gt;
&lt;br /&gt;
Dowlatabadi, H., and Morgan, M.G. 1993. &amp;quot;A Model Framework for Integrated Studies of the Climate Problem,&amp;quot;&amp;amp;nbsp;&#039;&#039;Energy Policy&#039;&#039;&amp;amp;nbsp;(March): 209-221.&lt;br /&gt;
&lt;br /&gt;
Duchin, Faye. 1998.&amp;amp;nbsp;&#039;&#039;Structural Economics: Measuring Change in Technology, Lifestyles, and the Environment&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
&lt;br /&gt;
Edwards, Stephen R. 1995. &amp;quot;Conserving Biodiversity,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 212-265.&lt;br /&gt;
&lt;br /&gt;
Edmonds, J., and Reilly, J.M. 1985.&amp;amp;nbsp;&#039;&#039;Global Energy: Assessing the Future&#039;&#039;. Oxford, UK: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Edmonds, J., Pitcher, H. Rosenberg, N., and Wigley, T. &amp;quot;Design for the Global Change Assessment Model.&amp;quot;&amp;amp;nbsp;&#039;&#039;Integrative Assessment of Mitigation, Impacts and Adaptation to Climate Change&#039;&#039;. Laxenburg, Austria.&lt;br /&gt;
&lt;br /&gt;
Ehrlich, Paul R. and Anne H. Ehrlich. 1972.&amp;amp;nbsp;&#039;&#039;Population, Resources, Environment&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
&lt;br /&gt;
Eicher, Carl. 1982. &amp;quot;Facing up to Africa&#039;s Food Crisis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Foreign Affairs&#039;&#039;&amp;amp;nbsp;61, no. 1 (Fall): 151-74.&lt;br /&gt;
&lt;br /&gt;
Eberstadt, Nicholas. 1995. &amp;quot;Population, Food, and Income,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 8-47.&lt;br /&gt;
&lt;br /&gt;
Esty, Daniel C., Jack A. Goldstone, Ted Robert Gurr, Barbara Harff, Marc Levy, Geoffrey D. Dabelko, Pamela T. Surko, and Alan N. Unger. 1998. State Failure Task Force Report: Phase II Findings. Volume provided courtesy of Ted Robert Gurr.&lt;br /&gt;
&lt;br /&gt;
Flavin, Christopher. 1996. &amp;quot;Facing Up to the Risks of Climate Change,&amp;quot; in Lester R. Brown and others, eds., State of the World 1996 (New York: W.W. Norton), pp. 21-39.&lt;br /&gt;
&lt;br /&gt;
Forrester, Jay W. 1968.&amp;amp;nbsp;&#039;&#039;Principles of Systems&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Gilpin, Robert. 1981.&amp;amp;nbsp;&#039;&#039;War and Change in World Politics&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Globerman, Steven. 2000 (May). Linkages Between Technological Change and Productivity Growth. Industry Canada Research Publications Program: Occasional Paper 23.&lt;br /&gt;
&lt;br /&gt;
Grant, Lindsey. 1982.&amp;amp;nbsp;&#039;&#039;The Cornucopian Fallacies&#039;&#039;. Washington, D.C.: Environmental Fund.&lt;br /&gt;
&lt;br /&gt;
Griffith, Rachel, Stephen Redding, and John Van Reenen. 2000.&amp;amp;nbsp;&#039;&#039;Mapping the Two Faces of R&amp;amp;D: Productivity Growth in a Panel of OECD Industries&#039;&#039;. Institute for Fiscal Studies (January)&lt;br /&gt;
&lt;br /&gt;
Gwartney, James and Robert Lawson with Dexter Samida. 2000.&amp;amp;nbsp;&#039;&#039;Economic Freedom of the World: 2000 Annual Report&#039;&#039;. Vancouver, B.C.: the Fraser Institute.&lt;br /&gt;
&lt;br /&gt;
Hammond, Allen. 1998.&amp;amp;nbsp;&#039;&#039;Which World? Scenarios for the 21st Century&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
&lt;br /&gt;
Harff, Barbara, with Ted Robert Gurr and Alan Unger. 1999. Preconditions of Genocide and Politicide: 1955-1998. Paper prepared for the State Failure Task Force and provided courtesy of Barbara Harff and Ted Gurr.&lt;br /&gt;
&lt;br /&gt;
Henderson, Hazel. 1996. &amp;quot;Changing Paradigms and Indicators: Implementing Equitable, Sustainable and Participatory Development,&amp;quot; in Jo Marie Griesgraber and Bernhard G. Gunter,&amp;amp;nbsp;&#039;&#039;Development: New Paradigms and Principles for the 21st Century&#039;&#039;. East Haven, CT: Pluto Press, pp. 103-136.&lt;br /&gt;
&lt;br /&gt;
Herrera, Amilcar O., et al. 1976.&#039;&#039;&amp;amp;nbsp;Catastrophe or New Society? A Latin American World Model&#039;&#039;. Ottawa: International Development Research Centre.&lt;br /&gt;
&lt;br /&gt;
Hoekstra, A.Y. 1998.&amp;amp;nbsp;&#039;&#039;Perspectives on Water: An Integrated Model-Based Exploration of the Future&#039;&#039;. Utrecht, the Netherlands: International Books.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1980.&amp;amp;nbsp;&#039;&#039;World Modeling&#039;&#039;. Lexington, Mass: Lexington Books.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1982.&amp;amp;nbsp;&#039;&#039;International Futures Simulation: User&#039;s Manual&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985a.&amp;amp;nbsp;&#039;&#039;International Futures Simulation&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985b. &amp;quot;World Models: The Bases of Difference,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;29, 77-101.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985c.&amp;amp;nbsp;&#039;&#039;World Futures: A Critical Analysis of Alternatives&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1987. &amp;quot;Domestic Economic Processes,&amp;quot; in Stuart A. Bremer, ed.,&amp;amp;nbsp;&#039;&#039;The Globus Model: Computer Simulation of Worldwide Political Economic Development&#039;&#039;&amp;amp;nbsp;(Frankfurt and Boulder: Campus and Westview), pp. 39-158.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1988. &amp;quot;International Futures: History and Status,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Science Microcomputer Review&#039;&#039;&amp;amp;nbsp;6, 43-48.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1999. &amp;quot;The International Futures (IFs) Modeling Project.&#039;&#039;&amp;amp;nbsp;Simulation and Gaming&#039;&#039;&amp;amp;nbsp;Vol 30, No. 3 (September): 304-326.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1999.&amp;amp;nbsp;&#039;&#039;International Futures&#039;&#039;, 3rd edition Boulder: Westview Press, 1999.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2000.&amp;amp;nbsp;&#039;&#039;Continuity and Change in World Politics&#039;&#039;. Englewood Cliffs, N.J.: Prentice-Hall, fourth edition.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2001. &amp;quot;Global Social Transformation: The Sweet Spot, the Steady Slog, and the Systemic Shift,&amp;quot;&amp;amp;nbsp;&#039;&#039;Economic Development and Cultural Change&#039;&#039;&amp;amp;nbsp;49, No. 2 (January): 423-458.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2002.&amp;amp;nbsp;&#039;&#039;Theats and Opportunities Analysis&#039;&#039;. Living document prepared for the Strategic Assessments Group, Office of Transnational Issues, Central Intelligence Agency, August 2002.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. and Anwar Hossain. 2003. Long-Term Socio-Economic Modeling: With Universal, Globally-Integrated Social Accounting Matrices (SAMs) in a General Equilibrium Model Structure. IFs Project Living Document, University of Denver.&lt;br /&gt;
&lt;br /&gt;
Huth, Paul. 1996.&amp;amp;nbsp;&#039;&#039;Standing Your Ground: Territorial Disputes and International Conflict&#039;&#039;. Ann Arbor, MI: University of Michigan Press.&lt;br /&gt;
&lt;br /&gt;
Inglehart, Ronald. 1997.&amp;amp;nbsp;&#039;&#039;Modernization and Postmodernization: Cultural, Economic, and Political Change in 43 Societies&#039;&#039;. Ewing, NJ: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1995.&amp;amp;nbsp;&#039;&#039;Oil, Gas, and Coal Supply Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996.&amp;amp;nbsp;&#039;&#039;World Energy Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996b.&amp;amp;nbsp;&#039;&#039;The Strategic Value of Fossil Fuels: Challenges and Responses&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;International Financial Statistics&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;World Economic Outlook&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
Intergovernmental Panel on Climate Change (IPCC). 1995. Several volumes by various working groups. Published by Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Jansen, Karel and Rob Vos, eds. 1997.&amp;amp;nbsp;&#039;&#039;External Finance and Adjustment: Failure and Success in the Developing World&#039;&#039;. London: Macmillan Press Ltd.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1998.&amp;amp;nbsp;&#039;&#039;Modeling Global Change: The Art of Integrated Assessment Modelling&#039;&#039;. Cheltenham, UK: Edward Elgar.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1996.&amp;amp;nbsp;&#039;&#039;Meeting Targets: Tools to Support Integrated Modelling of Global Change&#039;&#039;. Den Haag: CIP-Gegevens Koninklijke Bibliotheek.&lt;br /&gt;
&lt;br /&gt;
Jansson, Kurt, Michael Harris, Angela Penrose. 1987.&amp;amp;nbsp;&#039;&#039;The Ethiopian Famine&#039;&#039;. London: Zed Books Ltd.&lt;br /&gt;
&lt;br /&gt;
Jeffreys, Kent. 1995. &amp;quot;Rescuing the Oceans,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 296-338.&lt;br /&gt;
&lt;br /&gt;
Jones, Daniel M., Stuart A. Bremer, and J. David Singer. 1996. &amp;quot;Militarized Interstate Disputes, 1816-1992: Rationale, Coding Rules, and Empirical Patterns,&amp;quot;&amp;amp;nbsp;&#039;&#039;Conflict Management and Peace Science&#039;&#039;&amp;amp;nbsp;XV, No. 2: 163-215.&lt;br /&gt;
&lt;br /&gt;
Khan, Haider A. 1998.&amp;amp;nbsp;&#039;&#039;Technology, Development and Democracy&#039;&#039;. Northhampton, Mass: Edward Elgar Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Kahn, Herman, William Brown, and Leon Martel. 1976.&amp;amp;nbsp;&#039;&#039;The Next 200 Years&#039;&#039;. New York: William Morrow.&lt;br /&gt;
&lt;br /&gt;
Kalymon, Basil A. 1975. &amp;quot;Economic Incentives in OPEC Oil Pricing Policy.&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Development Economics&#039;&#039;&amp;amp;nbsp;2: 337-362.&lt;br /&gt;
&lt;br /&gt;
Kaplan, Robert. 1994. &amp;quot;The Coming Anarchy,&amp;quot;&amp;amp;nbsp;&#039;&#039;The Atlantic Monthly&#039;&#039;&amp;amp;nbsp;273 (February): .&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999a. &amp;quot;Aggregating Governance Indicators&amp;quot;. World Bank Policy Research Department Working Paper No. 2195.&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999b. &amp;quot;Governance Matters&amp;quot;. World Bank Policy Research Department Working Paper No. 2196.&lt;br /&gt;
&lt;br /&gt;
Keepin, B. and B. Wynne. 1984. &amp;quot;Technical Analysis of the IIASA Energy Scenarios,&amp;quot;&amp;amp;nbsp;&#039;&#039;Nature&#039;&#039;312: 691-695.&lt;br /&gt;
&lt;br /&gt;
Kehoe, Timothy J. 1996. Social Accounting Matrices and Applied General Equilibrium Models. Federal Reserve Bank of Minneapolis, Working Paper 563.&lt;br /&gt;
&lt;br /&gt;
Kennedy, Paul. 1993.&amp;amp;nbsp;&#039;&#039;Preparing for the Twenty-First Century&#039;&#039;. New York: Random House.&lt;br /&gt;
&lt;br /&gt;
Klein, Lawrence R. and Fu-chen Lo, eds. 1995.&amp;amp;nbsp;&#039;&#039;Modeling Global Change&#039;&#039;. Tokyo: United Nations University Press.&lt;br /&gt;
&lt;br /&gt;
Kornai, J. 1971.&amp;amp;nbsp;&#039;&#039;Anti-Equilibrium&#039;&#039;. Amsterdam: North Holland.&lt;br /&gt;
&lt;br /&gt;
Kwasnicki, Witold and Halina Kwasnicka. 1996. &amp;quot;Long-Term Diffusion Factors of Technological Development: An Evolutionary Model and Case Study,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 31-57.&lt;br /&gt;
&lt;br /&gt;
Leontief, Wassily, Anne Carter and Peter Petri. 1977.&amp;amp;nbsp;&#039;&#039;The Future of the World Economy&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander H., and Elizabeth R. Ducot. 1976. &amp;quot;AGRIMOD: A Simulation Model for the Analysis of U.S. Food Policies.&amp;quot; Paper delivered at Conference on Systems Analysis of Grain Reserves, Joint Annual Meeting of GRSA and TIMS, Philadelphia, Pa., March 31-April 2.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander, H., et al. 1977. Energy in Agriculture: On Modeling Inputs in AGRIMOD. Final Report to U.S. Department of Energy. Palo Alto: Systems Control, Inc., August, available through NTIS.&lt;br /&gt;
&lt;br /&gt;
Lichbach, Mark Irving. 1989. &amp;quot;An Evaluation of ‘Does Economic Inequality Breed Political Conflict?,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;, Vol 41 , No. 4 (July 1989): 431-470.&lt;br /&gt;
&lt;br /&gt;
Liverman, Dianne. 1983.&amp;amp;nbsp;&#039;&#039;The Use of Global Simulation Models in Assessing Climate Impacts on the World Food System&#039;&#039;. Dissertation, University of California, Los Angeles.&lt;br /&gt;
&lt;br /&gt;
Londregan, John B. and Keith T. Poole. 1996. &amp;quot;Does High Income Promote Democrary?&amp;quot;,&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 1 (October): 1-30.&lt;br /&gt;
&lt;br /&gt;
MacKenzie, James J. 1996. &amp;quot;Oil as a Finite Resource: When is Global Production Likely to Peak?&amp;quot; Paper of the World Resources Institute. Washington, D.C.: WRI.&lt;br /&gt;
&lt;br /&gt;
Maddison, Angus. 1995.&amp;amp;nbsp;&#039;&#039;Monitoring the World Economy 1820-1992&#039;&#039;. Paris: OECD.&lt;br /&gt;
&lt;br /&gt;
Malthus, Thomas. 1798.&amp;amp;nbsp;&#039;&#039;An Essay on the Principle of Population as It Affects the Future Improvement of Society&#039;&#039;. London (reprinted many times).&lt;br /&gt;
&lt;br /&gt;
Mansfield, Edward D. 1994.&amp;amp;nbsp;&#039;&#039;Power, Trade, and War&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Marchetti, Cesare, Perrin S. Meyer, and Jesse H. Ausubel. 1996. &amp;quot;Human Population Dynamics Revisited with the Logistic Model: How Much Can be Modeled and Predicted?,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 1-30.&lt;br /&gt;
&lt;br /&gt;
Martens, Pim and Jan Rotmans, eds. 1999.&amp;amp;nbsp;&#039;&#039;Climate Change: An Integrated Perspective&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
&lt;br /&gt;
Martens, W.J.M. 1997. &amp;quot;Health Impacts of Climate Change and Ozone Depletion: An Eco-Epidemiological Approach,&amp;quot; Maastricht, the Netherlands: Maastricht University.&lt;br /&gt;
&lt;br /&gt;
Mason, Andrew. 1997. &amp;quot;The Role of Population Change in the Asian Economic Miracle,&amp;quot; Honolulu, Hawaii: East-West Center, AsiaPacific Issues, No. 33 (October), 8 pages.&lt;br /&gt;
&lt;br /&gt;
McMahon, Walter W. 1997.&amp;amp;nbsp;&#039;&#039;Education and Development: Measuring the Social Benefits&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, Jorgen Randers, and William K. Behrens, III. 1972.&amp;amp;nbsp;&#039;&#039;Limits to Growth&#039;&#039;. New York: Universe Books.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, and Jorgen Randers. 1992.&amp;amp;nbsp;&#039;&#039;Beyond the Limits&#039;&#039;. Post Mills, Vermont: Chelsea Green Publishing Company.&lt;br /&gt;
&lt;br /&gt;
Meadows, Dennis L. et al. 1974.&amp;amp;nbsp;&#039;&#039;Dynamics of Growth in a Finite World&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Mesarovic, Mihajlo D. and Eduard Pestel. 1974.&amp;amp;nbsp;&#039;&#039;Mankind at the Turning Point&#039;&#039;. New York: E.P. Dutton &amp;amp; Co.&lt;br /&gt;
&lt;br /&gt;
Mishkin, Eli. And Ludwig Braun, ed. 1961.&amp;amp;nbsp;&#039;&#039;Adaptive Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Moore, Will H., Ronny Lindstrom, and Valerie O’Regan. 1996. &amp;quot;Land Reform, Political Violence and the Economic Inequality-Political Conflict Nexus: A Longitudinal Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Interactions&#039;&#039;&amp;amp;nbsp;21, No. 4: 335-363.&lt;br /&gt;
&lt;br /&gt;
Mori, Shunsuke and Masato Takahaashi, 1997. An Integrated Assessment Model for the Evaluation of New Energy Technologies and Food Production, accepted by&amp;amp;nbsp;&#039;&#039;International Journal of Global Energy Issues&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
Naill, Roger F. 1977.&amp;amp;nbsp;&#039;&#039;Managing the Energy Transition&#039;&#039;. Vols. 1 and 2. Cambridge, Mass: Ballinger Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1992. &amp;quot;The DICE Model: Background and Structure of a Dynamic Integrated Climate Economy,&amp;quot; New Haven: Yale University.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1979.&amp;amp;nbsp;&#039;&#039;The Efficient Use of Energy Resources&#039;&#039;. New Haven, CT: Yale University Press.&lt;br /&gt;
&lt;br /&gt;
Oneal, John R. and Bruce M. Russett. 1997. The Classical Liberals were Right: Democracy, Interdependence, and Conflict, 1950-1985.&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;41, no. 2 (June): 267-294.&lt;br /&gt;
&lt;br /&gt;
Pan, Xiaoming. 2000 (January). &amp;quot;Social and Ecological Accounting Matrix: an Empirical Study for China,&amp;quot; paper submitted for the Thirteenth International Conference on Input-Output Techniques, Macerata, Italy, August 21-25, 2000.&lt;br /&gt;
&lt;br /&gt;
Pesaran, M. Hashem and G. C. Harcourt. 1999. Life and Work of John Richard Nicholas Stone.&lt;br /&gt;
&lt;br /&gt;
Pirages, Dennis. 1989.&amp;amp;nbsp;&#039;&#039;Global Technopolitics&#039;&#039;. Pacific Grove, Calif: Brooks/Cole Publishing.&lt;br /&gt;
&lt;br /&gt;
Prinn, R. H.J., A. Sokolov, C. Wand, X. Xiao, Z. Yang, R. Eckhaus, P. Stone, D. Ellerman, J Melilo, J. Fitzmaurice, D. Kicklighter, and Y. Liu. 1996. &amp;quot;Integrated Global System Model for Climate Policy Analysis: Model Framework and Sensitivity Analysis.&amp;quot; Cambridge, Mass: Global Change Center, Massachusetts Institute of Technology.&lt;br /&gt;
&lt;br /&gt;
Przeworski, Adam and Fernando Limongi. 1997. &amp;quot;Modernization: Theories and Facts,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 2 (January): 155-183.&lt;br /&gt;
&lt;br /&gt;
Population Reference Bureau. 1996. World Population Data Sheet 1996. Washington, D.C.: Population Reference Bureau.&lt;br /&gt;
&lt;br /&gt;
Postel, Sandra. 1996.&amp;amp;nbsp;&#039;&#039;Dividing the Waters: Food Security, Ecosystem Health, and the New Politics of Scarcity&#039;&#039;. Worldwatch Paper 132. Washington, D.C.: Worldwatch Institute, September.&lt;br /&gt;
&lt;br /&gt;
Pyatt, G. and J.I. Round, eds. 1985.&amp;amp;nbsp;&#039;&#039;Social Accounting Matrices: A Basis for Planning&#039;&#039;. Washington, D.C.: The World Bank.&lt;br /&gt;
&lt;br /&gt;
Raskin, P., T. Banuri, G. Gallopín, P. Gutman, A. Hammond, R. Kates, and R. Swart. 2001. Great Transition:&amp;amp;nbsp;&#039;&#039;The Promise and Lure of the Times Ahead&#039;&#039;. Forthcoming.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1990.&amp;amp;nbsp;&#039;&#039;Global Politics&#039;&#039;, 4th edition. Boston: Houghton Mifflin.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1995.&amp;amp;nbsp;&#039;&#039;Democracy and International Conflict&#039;&#039;. Columbia: University of South Carolina Press.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee and J. David Singer. 1973. &amp;quot; Measuring the Concentration of Power in the International System,&amp;quot;&#039;&#039;&amp;amp;nbsp;Sociological Methods and Research&#039;&#039;&amp;amp;nbsp;1, no. 4: 403-436. Reprinted in&amp;amp;nbsp;&#039;&#039;Measuring the Correlates of War&#039;&#039;, edited by J. David Singer and Paul Diehl. Ann Arbor: University of Michigan Press, 1990.&lt;br /&gt;
&lt;br /&gt;
Rayner. S. 1992. &amp;quot;Cultural Theory and Risk Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Theory of Risk&#039;&#039;, ed. G. D. Preagor. Westport, USA.&lt;br /&gt;
&lt;br /&gt;
Repetto, Robert and Duncan Austin. 1997.&amp;amp;nbsp;&#039;&#039;The Costs of Climate Protection&#039;&#039;. Washington, D.C.: World Resources Institute.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis Fry. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Chicago: Quadrangle Books.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis F. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Pittsburgh: Boxwood Press.&lt;br /&gt;
&lt;br /&gt;
Romer, Paul M. 1994. &amp;quot;The Origins of Endogenous Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Economic Perspectives&#039;&#039;Vol 8, No. 1 (Winter): 3-22.&lt;br /&gt;
&lt;br /&gt;
Root T. and Stephen Schneider. 1995. &amp;quot;Ecology and Climate: Research Strategies and Implications,&amp;quot; Science 269 (52): 334-341.&lt;br /&gt;
&lt;br /&gt;
Rosegrant, Mark W., Mercedita Agcaoili-Sombilla, and Nicostrato D. Perez. 1995. &amp;quot;Global Food Projections to 2020: Implications for Investment.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 5.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1999. Integrated Assessment Models: Uncertainty, Quality and Use. Maastricht, the Netherlands: Maastricht University, International Centre for Integrative Studies (ICIS), Working Paper 199-E005.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and Burt de Vries, eds. 1997.&amp;amp;nbsp;&#039;&#039;Perspectives on Global Change: The Targets Approach&#039;&#039;. Cambridge, UK: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and M.B.A. van Asselt. 1996. &amp;quot;Integrated Assessment: A Growing Child on its Way to Maturity,&amp;quot;&amp;amp;nbsp;&#039;&#039;Climatic Change&#039;&#039;&amp;amp;nbsp;34 (3-4): 327-336.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1990.&amp;amp;nbsp;&#039;&#039;IMAGE: An Integrated Model to Assess the Greenhouse Effect&#039;&#039;. Dordrecht, the Netherlands: Kluwer Academics.&lt;br /&gt;
&lt;br /&gt;
Saaty, Thomas L. 1996. The Analytic Network Process: Decision Making with Dependence and Feedback. Pittsburgh: RWS Publications.&lt;br /&gt;
&lt;br /&gt;
Schafer, Andreas and David G. Victor. 1997. The Future Mobility of the World Population. Massachusetts Institute of Technology and International Institute for Applied Systems Analysis, Discussion Paper 97-6-4 (revision 2, September).&lt;br /&gt;
&lt;br /&gt;
Scheer, Sara J. and Satya Yadav. 1996. &amp;quot;Land Degradation in the Developing World: Implications for Food, Agriculture, and the Environment to 2020.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 14.&lt;br /&gt;
&lt;br /&gt;
Schneider, Stephen. 1997. &amp;quot;Integrated Assessment Modeling of Climate Change: Transparent Rational Tool for Policy Making or Opaque Screen Hiding Value-Laden Assumptions?&amp;quot;&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(4): 229-250.&lt;br /&gt;
&lt;br /&gt;
Schwartz, Peter. 1996.&#039;&#039;&amp;amp;nbsp;The Art of the Long View.&#039;&#039;&amp;amp;nbsp;New York: Doubleday.&lt;br /&gt;
&lt;br /&gt;
Sedjo, Roger A. 1995. &amp;quot;Forests: Conflicting Signals,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 178-209.&lt;br /&gt;
&lt;br /&gt;
Shane, Harold G. and Gary A. Sojka. 1990. &amp;quot;John Elfreth Watkins, Jr.: Forgotten Genius of Forecasting,&amp;quot; in Edward Cornish, ed.,&#039;&#039;&amp;amp;nbsp;The 1990s and Beyond&#039;&#039;. Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Shaw, Timothy W. and Clement E. Adibe. 1995-96. &amp;quot;Africa and Global Developments in the Twenty-First Century,&amp;quot; International Journal 51 (Winter): 1-26.&lt;br /&gt;
&lt;br /&gt;
Siegmann, Heinrich. 1985.&amp;amp;nbsp;&#039;&#039;Recent Developments in World Modeling&#039;&#039;. Berlin: Science Center.&lt;br /&gt;
&lt;br /&gt;
Simon, Julian. 1981.&amp;amp;nbsp;&#039;&#039;The Ultimate Resource&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Singer, J. David, Stuart Bremer, and John Stuckey. 1972. &amp;quot;Capability Distribution, Uncertainty, and Major Power Wars, 1820-1965.&amp;quot; In Bruce Russett, ed.,&amp;amp;nbsp;&#039;&#039;Peace, War, and Numbers.&#039;&#039;&amp;amp;nbsp;Beverly Hills: Sage.&lt;br /&gt;
&lt;br /&gt;
Sivard, Ruth Leger. 1993.&amp;amp;nbsp;&#039;&#039;World Military and Social Expenditures 1993.&#039;&#039;&amp;amp;nbsp;Washington, D.C. 20007: World Priorities, Box 25140.&lt;br /&gt;
&lt;br /&gt;
Solow, Robert M. 1956. &amp;quot;A Contribution to the Theory of Economic Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Quarterly Journal of Economics&#039;&#039;&amp;amp;nbsp;70, 1 (February): 65-94.&lt;br /&gt;
&lt;br /&gt;
Stanford University. 1978.&amp;amp;nbsp;&#039;&#039;Stanford Pilot Energy/Economic Model&#039;&#039;. Stanford: Department of Research, Interim Report, Vol. 1.&lt;br /&gt;
&lt;br /&gt;
Stockholm International Peace Research Institute (SIPRI). 1994.&amp;amp;nbsp;&#039;&#039;SIPRI Yearbook&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Stone, Richard. 1986. &amp;quot;The Accounts of Society,&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Applied Econometrics&#039;&#039;&amp;amp;nbsp;1, no. 1 (January): 5-28.&lt;br /&gt;
&lt;br /&gt;
Strategic Assessments Group (SAG), Office of Transnational Issues, Directorate of Intelligence. 2001 (February). The Global Economy in the Long Term. OTI IR 2001-013.&lt;br /&gt;
&lt;br /&gt;
Systems Analysis Research Unit (SARU). 1977.&amp;amp;nbsp;&#039;&#039;SARUM 76 Global Modeling Project&#039;&#039;. Departments of the Environment and Transport, 2 Marsham Street, London, 3WIP 3EB.&lt;br /&gt;
&lt;br /&gt;
Tammen, Ronald L, Jacek Kugler, Douglas Lemke, Allan C. Stam III, Carole Alsharabati, Mark Andrew Abdollahian, Brian Efird, and A.F.K. Organski. 2000. Power Transitions: Strategies for the 21st Century. New York: Chatham House Publishers.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1975. &amp;quot;Theoretical Foundations and Technical Implications.&amp;quot; in Charles Blitzer, Peter Clark and Lance Taylor, eds.,&amp;amp;nbsp;&#039;&#039;Economy-Wide Models and Development Planning.&#039;&#039;&amp;amp;nbsp;Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1979.&amp;amp;nbsp;&#039;&#039;Macro Models for Developing Countries&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Thirlwall, A. P. 1977.&amp;amp;nbsp;&#039;&#039;Growth and Development&#039;&#039;. New York: John Wiley and Sons.&lt;br /&gt;
&lt;br /&gt;
Thompson, M. 1997. Cultural Theory and Integrated Assessment.&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(3): 139-150.&lt;br /&gt;
&lt;br /&gt;
Thompson, M., R. Ellis and A. Wildavsky. 1990.&amp;amp;nbsp;&#039;&#039;Cultural Theory&#039;&#039;. Boulder, Co: Westview Press.&lt;br /&gt;
&lt;br /&gt;
Thorbecke, Erik. 2001. &amp;quot;The Social Accounting Matrix: Deterministic or Stochastic Concept?&amp;quot;, paper prepared for a conference in honor of Graham Pyatt&#039;s retirement, at the Institute of Social Studies, The Hague, Netherlands (November 29 and 30). Available at [http://people.cornell.edu/pages/et17/etpapers.html http://people.cornell.edu/pages/et17/etpapers.html].&lt;br /&gt;
&lt;br /&gt;
United Nations, Department of Economic and Social Affairs. 1956.&amp;amp;nbsp;&#039;&#039;Methods of Population Projections by Sex and Age&#039;&#039;. New York: United Nations, ST/SOA Series A.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1992.&amp;amp;nbsp;&#039;&#039;Long-Range World Population Projections. Two Centuries of Population Growth: 1950-2150&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1993.&amp;amp;nbsp;&#039;&#039;World Population Prospects - the 1992 Revision&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations Development Program (UNDP). 1995.&amp;amp;nbsp;&#039;&#039;Human Development Report&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1992.&amp;amp;nbsp;&#039;&#039;Production Yearbook.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1995.&#039;&#039;&amp;amp;nbsp;World Agriculture: Towards 2010.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 1999. The World at Six Billion New York: UN.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2000. Replacement Migration: Is it a Solution to Declining and Ageing Populations? New York: UN.&lt;br /&gt;
&lt;br /&gt;
United States Arms Control and Disarmament Agency (ACDA). 1995.&amp;amp;nbsp;&#039;&#039;World Military Expenditures and Arms Transfers 1995&#039;&#039;. Washington, D.C.: Arms Control and Disarmament Agency.&lt;br /&gt;
&lt;br /&gt;
United States Bureau of the Census. 1991.&amp;amp;nbsp;&#039;&#039;World Population Profile: 1991&#039;&#039;. Report WP/91 Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Walters, Robert S. and David H. Blake. 1992.&amp;amp;nbsp;&#039;&#039;The Politics of Global Economic Relations&#039;&#039;, 4th edition. Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Waltz, Kenneth N. 1959. Man, the State, and War: A Theoretical Analysis. New York: Columbia University Press.&lt;br /&gt;
&lt;br /&gt;
Watkins, John Elfreth, Jr. 1990. &amp;quot;What May Happen in the Next Hundred Years,&amp;quot; in Edward Cornish, ed.,&amp;amp;nbsp;&#039;&#039;The 1990s and Beyond.&#039;&#039;&amp;amp;nbsp;Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Wildavsky, Aaron, and Ellen Tenenbaum. 1981.&amp;amp;nbsp;&#039;&#039;The Politics of Mistrust&#039;&#039;. Beverly Hills: Sage Publications.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1991b.&amp;amp;nbsp;&#039;&#039;World Tables 1991&#039;&#039;. New York: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1995&amp;amp;nbsp;&#039;&#039;World Development Report 1995&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
World Energy Council (WEC) Commission. 1993.&amp;amp;nbsp;&#039;&#039;Energy for Tomorrow’s World.&#039;&#039;&amp;amp;nbsp;New York: St. Martin’s Press.&lt;br /&gt;
&lt;br /&gt;
World Resources Institute (WRI). 1994.&amp;amp;nbsp;&#039;&#039;World Resources 1994-95.&#039;&#039;&amp;amp;nbsp;New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Wortman, Sterling and Ralph W. Cummings, Jr. 1978.&#039;&#039;&amp;amp;nbsp;To Feed This World&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Zinnes, Dina A. and John W. Gillespie, eds. 1976.&amp;amp;nbsp;&#039;&#039;Mathematical Models in International Relations&#039;&#039;&amp;amp;nbsp;(New York: Preaeger).&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Health Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Adams 1987.&amp;amp;nbsp;[http://www.geog.ucl.ac.uk/~jadams/PDFs/smeed&#039;s%20law.pdf &amp;quot;Smeed&#039;s Law: some further thoughts.&amp;quot;]&amp;amp;nbsp;&#039;&#039;Traffic Engineering and Control&#039;&#039;&amp;amp;nbsp;(Feb) 70-73.&lt;br /&gt;
&lt;br /&gt;
Alsan, Marcella, David E. Bloom, and David Canning. 2006. “The Effects of Population Health on Foreign Direct Investment Inflows to Low- and Middle-Income Countries,”&amp;amp;nbsp;&#039;&#039;World Development&#039;&#039;&amp;amp;nbsp;34(4): 613-630.&lt;br /&gt;
&lt;br /&gt;
Anand, Sudhir and Martin Ravallion. 1993. “Human development in poor countries: on the role of private incomes and public services,”&amp;amp;nbsp;&#039;&#039;Journal of Economic Perspectives&#039;&#039;&amp;amp;nbsp;7(1): 133–150.&lt;br /&gt;
&lt;br /&gt;
Ashraf, Quamrul H., Ashley Lester, and David N. Weil. 2008. “When Does Improving Health Raise GDP?”&amp;amp;nbsp; NBER Working Paper No. 14449. National Bureau of Economic Research, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
Bidani, Benu and Martin Ravallion. 1997. “Decomposing social indicators using distributional data.”&amp;amp;nbsp;&#039;&#039;Journal of Econometrics&#039;&#039;&amp;amp;nbsp;77: 125–139.&lt;br /&gt;
&lt;br /&gt;
Bloom, David E., and David Canning. 2004. “Global Demographic Change: Dimensions and Economic Significance.” NBER Working Paper No. 10817.&amp;amp;nbsp; National Bureau of Economic Research, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
Blössner, Monika, and Mercedes de Onis. 2005.&amp;amp;nbsp;&#039;&#039;Malnutrition: quantifying the health impact at national and local levels.&#039;&#039;&amp;amp;nbsp;Geneva, World Health Organization. (WHO Environmental Burden of Disease Series, No. 12).&lt;br /&gt;
&lt;br /&gt;
Dargay, Gately, and Sommer 2007. “Vehicle Ownership and Income Growth, Worldwide: 1960-2030”. Joyce Dargay, Dermot Gately and Martin Sommer, January 2007.&lt;br /&gt;
&lt;br /&gt;
Deaton, Angus, and Christina Paxson. 2000 (May). “Growth and Savings Among Individuals and Households.”&amp;amp;nbsp;&#039;&#039;The Review of Economics and Statistics&#039;&#039;&amp;amp;nbsp;82(2): 212-225.&lt;br /&gt;
&lt;br /&gt;
Desai, Manish A., Sumi Mehta, and Kirk R. Smith. 2004. “Indoor smoke from solid fuels: Assessing the environmental burden of disease.”WHOEnvironmental Burden of Disease Series No. 4&#039;&#039;.&amp;amp;nbsp;&#039;&#039;Annette Pruss-Üstun, Diamid Campbell-Lendrum, Carlos Corvalán, and Alistair Woodward, series eds. World Health Organization, Geneva.&lt;br /&gt;
&lt;br /&gt;
Ezzati, Majid and Alan D. Lopez. 2004. “Smoking and oral tobacco use.” In Majid Ezzati, Alan D. Lopez, Anthony Rodgers, and Cristopher J.L. Murray, eds.,&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors&#039;&#039;. Geneva: World Health Organization, 883-957.&amp;amp;nbsp; Retrieved 4 Feb 2009, from&amp;amp;nbsp;[http://www.who.int/publications/cra/chapters/volume1/part4/en/index.html http://www.who.int/publications/cra/chapters/volume1/part4/en/index.html].&lt;br /&gt;
&lt;br /&gt;
Ezzati, Majid, Alan D. Lopez, Anthony Rodgers, Christopher J.L. Murray, eds. 2004.&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors&#039;&#039;. Geneva: World Health Organization.&lt;br /&gt;
&lt;br /&gt;
Fernández-Villaverde, Jesús, and Dirk Kruegger. 2004 (September 14). “Consumption over the Life Cycle: Facts from Consumer Expenditure Survey Data,” unpublished manuscript, University of Pennsylvania and University of Frankfort.&amp;amp;nbsp;&amp;amp;nbsp;[http://www.dklevine.com/archive/refs4506439000000000304.pdf http://www.dklevine.com/archive/refs4506439000000000304.pdf]&lt;br /&gt;
&lt;br /&gt;
Fernández-Villaverde, Jesús, and Dirk Kruegger. 2005 (December 19). “Consumption over the Life Cycle: How Important are Consumer Durables?,” unpublished manuscript, University of Pennsylvania and Goethe University.&amp;amp;nbsp;&amp;amp;nbsp;[http://journals.cambridge.org/action/displayAbstract?fromPage=online&amp;amp;aid=8466457 http://journals.cambridge.org/action/displayAbstract?fromPage=online&amp;amp;aid=8466457]&lt;br /&gt;
&lt;br /&gt;
Gakidou, Emmanuela, Shefali Oza, Cecilia Vidal Fuertes, Amy Y. Li, Diana K. Lee, Angelica Sousa, Margaret C. Hogan, Stephen Vander Hoorn, and Majid Ezzati. 2007.” Improving Child Survival Through Environmental and Nutritional Interventions: The Importance of Targeting Interventions Toward the Poor.”&amp;amp;nbsp;&#039;&#039;Journal of the American Medical Association&#039;&#039;&amp;amp;nbsp;298(16): 1876-1887.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. and Hillebrand, Evan E. 2006. “Exploring and shaping International Futures”. Boulder, CO: Paradigm Publishers.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., Randall Kuhn, Cecilia Peterson, Dale Rothman, and Jose Solorzano. 2011.&amp;amp;nbsp;&amp;amp;nbsp;&#039;&#039;Improving Global Health: Patterns of Potential Human Progress, Volume 3&#039;&#039;.&amp;amp;nbsp; Paradigm Publishing and Oxford India.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2005.&amp;amp;nbsp; “Productivity in IFs.” Pardee Center for International Futures Working Paper, University of Denver, Denver, CO.&amp;amp;nbsp;&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx http://www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
James, W. Philip T., Rachel Jackson-Leach , Cliona Ni Mhurchu, Eleni Kalamara, Maryam Shayeghi, Neville J. Rigby, Chizuru Nishida, and Anthony Rodgers. 2004.&amp;amp;nbsp; “Overweight and obesity (high body mass index).” In Majid Ezzati, Alan D. Lopez, Anthony Rodgers and Christopher J.L. Murray, eds.,&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors.&#039;&#039;&amp;amp;nbsp;Geneva: World Health Organization, 959-1108.&lt;br /&gt;
&lt;br /&gt;
Jamison, Dean T., Jia Wang, Kenneth Hill, and Juan-Luis Londono. 1996. “Income, Mortality and Fertility in Latin America: Country-Level Performance, 1960 - 90.”&amp;amp;nbsp;&#039;&#039;Analisis Economico&#039;&#039;11(2): 219-261.&lt;br /&gt;
&lt;br /&gt;
Kelly, Christopher, Nora Pashayan, Sreetharan Munisamy, and Joshn W. Powles. 2009.&amp;amp;nbsp; “Mortality attributable to excess adiposity in England and Wales in 2003 and 2015: explorations with a spreadsheet implementation of the Comparative Risk Assessment mentodology.”&amp;amp;nbsp;&#039;&#039;Population Health Metrics&#039;&#039;&amp;amp;nbsp;7(11): 1-7.&lt;br /&gt;
&lt;br /&gt;
Lopez, Alan D., Neil E. Collishaw, and Tapani Piha. 1994. “A descriptive model of the cigarette epidemic in developed countries.”&amp;amp;nbsp;&#039;&#039;Tobacco Control&#039;&#039;&amp;amp;nbsp;3(3): 242-247. &amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Mathers, Colin D., and Dejan Loncar. 2005. &amp;quot;Updated Projections of Global Mortality and Burden of Disease, 2002-2030: Data Sources, Methods and Results.&amp;quot; Evidence and Information for Policy Working Paper. World Health Organization, Geneva.&lt;br /&gt;
&lt;br /&gt;
Mathers, Colin D., and Dejan Loncar. 2006. &amp;quot;Projections of Global Mortality and Burden of Disease from 2002 to 2030.&amp;quot;&amp;amp;nbsp;&#039;&#039;PLoS Medicine&#039;&#039;&amp;amp;nbsp;3(11): e442, 2011-2030.&amp;amp;nbsp; Retrieved 13 March 2009. doi:10.1371/journal.pmed.0030442.&lt;br /&gt;
&lt;br /&gt;
Mathers, Colin D., and Dejan Loncar. 2006b. “New projections of global mortality and burden of disease from 2002 to 2030.” Protocol S1. Technical Appendix to Mathers and Loncar 2006.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Mathers, Colin D., and Dejan Loncar. 2006c. “Results of Regressions of Age–Sex-Specific Mortality for Detailed Causes on the Respective Cause Cluster Based on the Full Country Panel Dataset, 1950–2002.” Technical Appendix to Mathers and Loncar 2006.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Nixon, John, and Philippe Ulmann. 2006. “The Relationship Between Health Care Expenditure and Health Outcomes: Evidence and caveats for a Causal Link.”&amp;amp;nbsp;&#039;&#039;European Journal of Health Economics&#039;&#039;&amp;amp;nbsp;7: 7-18.&lt;br /&gt;
&lt;br /&gt;
Peto, Richard, Jillian Boreham, Alan D. Lopez, Michael Thun, and Clark Heath, Jr. 1992. “Mortality from Tobacco in Developed Countries: Indirect Estimation from National Vital Statistics.”&amp;amp;nbsp;&#039;&#039;Lancet&amp;amp;nbsp;&#039;&#039;339(8804): 1268–1278. doi:10.1016/0140- 6736(92)91600-D.&lt;br /&gt;
&lt;br /&gt;
Ploeg, Martine, Katja K. H. Aben, and Lambertus A. Kiemeney. 2009. “The Present and Future Burden of Urinary Bladder Cancer in the World.”&amp;amp;nbsp;&#039;&#039;World Journal of Urology&#039;&#039;&amp;amp;nbsp;27(3): 289-293. doi:[http://dx.doi.org/10.1007/s00345-009-0383-3 &amp;amp;nbsp;10.1007/s00345-009-0383-3&amp;amp;nbsp;]. &amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Shibuya, Kenji, Mie Inoue, and Alan D. Lopez. 2005. “Statistical Modeling and Projections of Lung Cancer Mortality in 4 Industrialized Countries.”&amp;amp;nbsp;&#039;&#039;International Journal of Cancer&#039;&#039;&amp;amp;nbsp;117(3): 476-485. doi:[http://dx.doi.org/10.1002/ijc.21078 &amp;amp;nbsp;10.1002/ijc.21078&amp;amp;nbsp;]. &amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Smeed, RJ 1949. &amp;quot;Some statistical aspects of road safety research&amp;quot;.&amp;amp;nbsp;[http://en.wikipedia.org/wiki/Royal_Statistical_Society &#039;&#039;Royal Statistical Society&#039;&#039;], Journal (A) CXII (Part I, series 4). 1-24.&lt;br /&gt;
&lt;br /&gt;
Smith, Lisa C. and Lawrence Haddad. 2000. “Explaining Child Malnutrition in Developing Countries: A Cross-Sectional Analysis.” Washington, D.C.: International Food Policy Research Institute.&lt;br /&gt;
&lt;br /&gt;
Soares, Rodrigo R. 2007. “On the Determinants of Mortality Reductions in the Developing World.”&amp;amp;nbsp;&#039;&#039;Population and Development Review&amp;amp;nbsp;&#039;&#039;33(2): 247-287.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2003.&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;World Population Prospects: The 2002 Revision, Highlight.&#039;&#039;&amp;amp;nbsp; New York:&amp;amp;nbsp; United Nations. Department of Economics and Social Affairs.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2009.&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;World Population Prospects: The 2008 Revision, Highlights.&#039;&#039;&amp;amp;nbsp; New York:&amp;amp;nbsp; United Nations. Department of Economics and Social Affairs.&lt;br /&gt;
&lt;br /&gt;
Wagstaff, Adam. 2002. “Inequalities in Health in Developing Countries: Swimming Against the Tide?” Unpublished Manuscript&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Infrastructure Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Agénor, Pierre-Richard, Mustapha Kamel Nabli, and Tarik M. Yousef. 2007. “Public Infrastructure and Private Investment in the Middle East and North Africa.” In Mustapha Kamel Nabli, ed.,. Breaking the Barriers to Higher Economic Growth: Better Governance and Deeper Reforms in the Middle East and North Africa. Washington, DC: World Bank Publications, 399–422.&lt;br /&gt;
&lt;br /&gt;
Asian Development Bank, Japan Bank for International Cooperation, and World Bank. 2005.&amp;amp;nbsp;&#039;&#039;Connecting East Asia: A New Framework for Infrastructure&#039;&#039;. Tokyo: Asian Development Bank, Japan Bank for International Cooperation, and World Bank.&amp;amp;nbsp;[http://siteresources.worldbank.org/INTEASTASIAPACIFIC/Resources/Connecting-East-Asia.pdf http://siteresources.worldbank.org/INTEASTASIAPACIFIC/Resources/Connecting-East-Asia.pdf].&lt;br /&gt;
&lt;br /&gt;
Bhattacharyay, Biswa Nath. 2010. “Estimating Demand for Infrastructure in Energy, Transport, Telecommunications, Water and Sanitation in Asia and the Pacific: 2010-2020”. Working Paper no. 248. Asian Development Bank Institute, Tokyo.&amp;amp;nbsp;[http://www.adbi.org/working-paper/2010/09/09/4062.infrastructure.demand.asia.pacific/ http://www.adbi.org/working-paper/2010/09/09/4062.infrastructure.demand.asia.pacific/].&lt;br /&gt;
&lt;br /&gt;
Bruinsma, Jelle. 2011. “The Resources Outlook: By How Much Do Land, Water and Crop Yields Need to Increase by 2050?” In Piero Conforti, ed.,.&amp;amp;nbsp;&#039;&#039;Looking Ahead in World Food and Agriculture: Perspectives to 2050&#039;&#039;. Rome: Food and Agriculture Organization of the United Nations (FAO), 233–275.&amp;amp;nbsp;[http://www.fao.org/docrep/014/i2280e/i2280e.pdf http://www.fao.org/docrep/014/i2280e/i2280e.pdf].&lt;br /&gt;
&lt;br /&gt;
Calderón, César, and Luis Servén. 2010a. “Infrastructure and Economic Development in Sub-Saharan Africa.”&amp;amp;nbsp;&#039;&#039;Journal of African Economies&#039;&#039;&amp;amp;nbsp;19(Supplement 1): i13–i87. doi:10.1093/jae/ejp022.&amp;amp;nbsp;[http://jae.oxfordjournals.org/cgi/content/abstract/19/suppl_1/i13 http://jae.oxfordjournals.org/cgi/content/abstract/19/suppl_1/i13].&lt;br /&gt;
&lt;br /&gt;
Calderón, César, and Luis Servén. 2010b. “Infrastructure in Latin America”. World Bank Policy Research Working Paper. Report Number 5317. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Canning, David. 1998. “A Database of World Stocks of Infrastructure, 1950-1995.”&amp;amp;nbsp;&#039;&#039;The World Bank Economic Review&#039;&#039;&amp;amp;nbsp;12(3): 529–548.&lt;br /&gt;
&lt;br /&gt;
Canning, David, and Mansour Farahani. 2007. “A Database of World Stocks of Infrastructure: Update 1950-2005”. Harvard School of Public Health, Boston, MA.&amp;amp;nbsp;[http://www.hsph.harvard.edu/faculty/david-canning/data-sets/ http://www.hsph.harvard.edu/faculty/david-canning/data-sets/].&lt;br /&gt;
&lt;br /&gt;
Cavallo, Eduardo Alfredo, and Christian Daude. 2008. “Public Investment in Developing Countries: A Blessing or a Curse?” RES Working Paper #4597. Inter-American Development Bank (IADB) - Research Department, OECD, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Chatterton, Isabe, and Olga S. Puerto. 2006.&amp;amp;nbsp;&#039;&#039;Estimation of Infrastructure Investment Needs in the South Asia Region: Executive Summary&#039;&#039;. Washington, DC: World Bank.&amp;amp;nbsp;[http://siteresources.worldbank.org/INTSARREGTOPTRANSPORT/Resources/Inf_Investment_Needs_IC_version4.pdf http://siteresources.worldbank.org/INTSARREGTOPTRANSPORT/Resources/Inf_Investment_Needs_IC_version4.pdf].&lt;br /&gt;
&lt;br /&gt;
Congressional Budget Office. 2010.&amp;amp;nbsp;&#039;&#039;Public Spending on Transportation and Water Infrastructure&#039;&#039;. Washington, DC: Congressional Budget Office.&amp;amp;nbsp;[http://www.cbo.gov/publication/21902 http://www.cbo.gov/publication/21902].&lt;br /&gt;
&lt;br /&gt;
Estache, Antonio, and Ana Goicoechea. 2005. “A Research Database on Infrastructure Economic Performance”. Policy Research Working Paper no. 3643. World Bank, Washington, DC.&amp;amp;nbsp;[http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2005/06/16/000016406_20050616100502/Rendered/PDF/wps3643.pdf http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2005/06/16/000016406_20050616100502/Rendered/PDF/wps3643.pdf].&lt;br /&gt;
&lt;br /&gt;
Ezzati, Majid, Alan D. Lopez, Anthony Rodgers, and Christopher J. L. Murray, eds. 2004.&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors&#039;&#039;. Geneva, Switzerland: World Health Organization (WHO).&lt;br /&gt;
&lt;br /&gt;
Fay, Marianne. 2001. “Financing the Future: Infrastructure Needs in Latin America, 2000-05”. Policy Research Working Paper no. 2545. World Bank, Washington, DC.&amp;amp;nbsp;[http://elibrary.worldbank.org/docserver/download/2545.pdf?expires=1375200693&amp;amp;id=id&amp;amp;accname=guest&amp;amp;checksum=DB7E5FB146EE28C93511B5ADAB2FD3CB http://elibrary.worldbank.org/docserver/download/2545.pdf?expires=1375200693&amp;amp;id=id&amp;amp;accname=guest&amp;amp;checksum=DB7E5FB146EE28C93511B5ADAB2FD3CB].&lt;br /&gt;
&lt;br /&gt;
Fay, Marianne, and Tito Yepes. 2003. “Investing in Infrastructure: What Is Needed from 2000 to 2010?” Policy Research Working Paper no. 3102. World Bank, Washington, DC. RePEc.&amp;amp;nbsp;[http://ideas.repec.org/p/wbk/wbrwps/3102.html http://ideas.repec.org/p/wbk/wbrwps/3102.html].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2007. “Forecasting Global Economic Growth with Endogenous Multifactor Productivity: The International Futures (IFs) Approach”. Pardee Center for International Futures Working Paper, University of Denver. Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., Devin Joshi, Jonathan Moyer, Timothy Sisk and José Roberto Solórzano. 2014. Strengthening Governance Globally. vol. 5, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Hughes, Gordon, Paul Chinowsky, and Ken Strzepek. 2009. “The Costs of Adapting to Climate Change for Infrastructure”. Economics of Adaptation to Climate Change Discussion Paper no. 2. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
International Transport Forum, and Organisation for Economic Cooperation and Development (OECD). 2011. “Trends in Transport Infrastructure Investment 1995-2009”. Paris.&lt;br /&gt;
&lt;br /&gt;
Kohli, Harpaul Alberto, and Phillip Basil. 2011. “Requirements for Infrastructure Investment in Latin America Under Alternate Growth Scenarios.”&amp;amp;nbsp;&#039;&#039;Global Journal of Emerging Market Economies&#039;&#039;&amp;amp;nbsp;3(1): 59 –110. doi:10.1177/097491011000300103.&amp;amp;nbsp;[http://eme.sagepub.com/content/3/1/59.abstract http://eme.sagepub.com/content/3/1/59.abstract].&lt;br /&gt;
&lt;br /&gt;
Kim, M. Julie, and Rita Nangia. 2010. “Infrastructure Development in India and China—A Comparative Analysis.” In William Ascher and Corinne Krupp, eds.,.&amp;amp;nbsp;&#039;&#039;Physical Infrastructure Development: Balancing The Growth, Equity, and Environmental Imperatives&#039;&#039;. New York, NY: Palgrave Macmillan, 97–140.&lt;br /&gt;
&lt;br /&gt;
Lora, Eduardo A. 2007.&amp;amp;nbsp;&#039;&#039;Public Investment in Infrastructure in Latin America: Is Debt the Culprit?&#039;&#039;&amp;amp;nbsp;Inter-American Development Bank Working Paper. Washington, DC: Inter-American Development Bank (IADB) - Research Department.&lt;br /&gt;
&lt;br /&gt;
Nelson, Gerald C., Mark W. Rosegrant, Amanda Palazzo, Ian Gray, Christina Ingersoll, Richard Robertson, Simla Tokgoz, Tingju Zhu, Timothy B. Sulser, Claudia Ringler, Siwa Msangi, and Liangzhi You. 2010.&amp;amp;nbsp;&#039;&#039;Food Security, Farming, and Climate Change to 2050: Scenarios, Results, Policy Options&#039;&#039;. Washington, DC: International Food Policy Research Institute.&amp;amp;nbsp;[http://www.ifpri.org/publication/food-security-farming-and-climate-change-2050 http://www.ifpri.org/publication/food-security-farming-and-climate-change-2050].&lt;br /&gt;
&lt;br /&gt;
Organisation for Economic Co-operation and Development. 2006.&amp;amp;nbsp;&#039;&#039;Infrastructure to 2030 Volume 1: Telecom, Land Transport, Water and Electricity&#039;&#039;. Infrastructure to 2030. Paris: Organisation for Economic Cooperation and Development.&lt;br /&gt;
&lt;br /&gt;
Organisation for Economic Co-operation and Development. 2009.&amp;amp;nbsp;&#039;&#039;Going for Growth: Economic Policy Reforms&#039;&#039;. Paris: Organisation for Economic Cooperation and Development (OECD).&lt;br /&gt;
&lt;br /&gt;
Qiang, Christine Zhen-Wei, Carlo M. Rossotto, and Kaoru Kimura. 2009. “Economic Impacts of Broadband.” In World Bank, ed.,.&amp;amp;nbsp;&#039;&#039;2009 Information and Communications for Development: Extending Reach and Increasing Impact&#039;&#039;. Washington, DC: World Bank, 35–50.&lt;br /&gt;
&lt;br /&gt;
Rothman, Dale S. Mohammod T. Irfan, Eli Margolese-Malin, Barry B. Hughes, Jonathan Moyer, and Janet Dickson. 2013.&amp;amp;nbsp;&#039;&#039;Building Global Infrastructure.&amp;amp;nbsp;&#039;&#039;vol. 4, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press. Stambrook, David. 2006. “Key Factors Driving the Future Demand for Surface Transport Infrastructure and Services.” In Organisation for Economic Cooperation and Development (OECD), ed.,.&amp;amp;nbsp;&#039;&#039;Infrastructure to 2030 Volume 1: Telecom, Land Transport, Water and Electricity&#039;&#039;. Infrastructure to 2030. Paris: Organisation for Economic Cooperation and Development (OECD), 185–239.&lt;br /&gt;
&lt;br /&gt;
World Health Organization, and UNICEF. 2013.&amp;amp;nbsp;&#039;&#039;Progress on Sanitation and Drinking-Water - 2013 Update&#039;&#039;. Geneva: World Health Organization.&lt;br /&gt;
&lt;br /&gt;
Yepes, Tito. 2008. “Investment Needs for Infrastructure in Developing Countries 2008-15”. Draft. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Yepes, Tito. 2005.&amp;amp;nbsp;&#039;&#039;Expenditure on Infrastructure in East Asia Region, 2006–2010&#039;&#039;. East Asia Pacific Infrastructure Flagship Study. Manila: Asian Development Bank (ADB), Japan Bank for International Cooperation (JBIC), World Bank.&lt;br /&gt;
&lt;br /&gt;
You, Liangzhi, Claudia Ringler, Ulrike Wood-Sichra, Richard Robertson, Stanley Wood, Tingju Zhu, Gerald Nelson, Zhe Guo, and Yan Sun. 2011. “What Is the Irrigation Potential for Africa? A Combined Biophysical and Socioeconomic Approach.”&amp;amp;nbsp;&#039;&#039;Food Policy&#039;&#039;&amp;amp;nbsp;36(6): 770–782. doi:10.1016/j.foodpol.2011.09.001.&amp;amp;nbsp;[http://www.sciencedirect.com/science/article/pii/S030691921100114X http://www.sciencedirect.com/science/article/pii/S030691921100114X].&lt;br /&gt;
&lt;br /&gt;
== [[Development_Mode_Features|Development Mode Features]] ==&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Governance&amp;diff=8316</id>
		<title>Governance</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Governance&amp;diff=8316"/>
		<updated>2017-09-07T21:44:54Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The most recent and complete governance model documentation is available on Pardee&#039;s [http://pardee.du.edu/ifs-governance-and-socio-cultural-model-documentation website]. Although the text in this interactive system is, for some IFs models, often significantly out of date, you may still find the basic description useful to you.&lt;br /&gt;
&lt;br /&gt;
Governance is the two-way interaction between government and the broader socio-political or, even more broadly, socio-cultural system. Although our documentation and the IFs model itself focuses primarily on three dimensions of that governance interaction, we will need also to direct some attention specifically to that broader socio-cultural system and how it might change over time.&lt;br /&gt;
&lt;br /&gt;
The conceptual foundation for the representation of governance in IFs owes much to an analysis of the evolution of governance in countries around the world over several centuries. That analysis (see Chapter 1 of the Strengthening Governance Globally volume by Hughes et al. 2014) identified three dimensions of governance: security, capacity, and inclusion. It traced them over time and noted their largely sequential unfolding for currently developed countries and their currently simultaneous progression in many lower-income countries.&lt;br /&gt;
&lt;br /&gt;
The three dimensions interact closely and bi-directionally with each other. They also interact bi-directionally with broader human development systems. The level of well-being, often captured quantitatively by GDP per capita or the more inclusive human development index, may be especially important, but is hardly alone in helping drive forward advance in governance; for instance, the age structures of populations and economic structures also interact with governance patterns both indirectly through well-being and directly.[[File:Gov1.jpg|frame|right|Visual representation of governance]]&lt;br /&gt;
&lt;br /&gt;
The conceptualization of governance further divides each of the three primary dimensions into two sub-dimensions partly based on the desire to quantify them historically and to facilitate forecasting. For security those are the probability of intrastate conflict and the general level of country performance and risk. The two sub-dimensions of capacity are the ability to raise revenue and the effective use of it and the other tools of government—that is, the competence or quality of governance. We use corruption (that is, control of it) as a proxy for such competence. The first sub-dimension of inclusion is the level of formal democratization, typically assessed in terms of competitive elections. More broadly democratization involves inclusion of population groupings across lines such as ethnicity, religion, sex, and age; we use gender equity as a proxy for the second dimension.&lt;br /&gt;
&lt;br /&gt;
See Hughes et al. (2014), especially Chapter 4, for more background on the development of the governance representations of IFs than this documentation provides. See also Hughes (2002) for earlier and/or complementary work in IFs on socio-political representations (domestic and international); for example, here we do not discuss the formulations for power, interstate threat, and conflict, but that is available in documentation on the International Political model of the IFs system. Finally, we do not provide here the important information about the forward linkages of governance to other elements of IFs, including to the production function of the economic model and to the broader financial flows of the social accounting matrix representation. See documentation on the economic model for that information.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Structure and Agent System: Governance&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot; border=&amp;quot;0&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width: 30%&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;System/Subsystem&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Governance&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Organizing Structure&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Three dimensions with two sub-dimensions each; highly interactive, bi-directional relationships among dimensions and with socio-economic development, demographics, and economics&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Stocks&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Socio-economic development levels (e.g. level of education, gender relationships, size of the economy); past patterns of governance; also cultural patterns are a stock&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; valign=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Flows&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Government spending on human capital, infrastructure, development generally; accretion of changes in governance over time&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Key Aggregate&amp;amp;nbsp;&#039;&#039;&#039; &#039;&#039;&#039;Relationships&amp;amp;nbsp;&#039;&#039;&#039;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;(illustrative, not comprehensive)&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Probability of intrastate conflict is a function of past conflict, neighborhood effects, economic growth rate (inverse), trade openness (inverse), youth bulge, infant mortality, democracy (inverted-U), state repression (inverse), and external intervention.&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Vulnerability to intrastate conflict is a function of past intrastate conflict, energy trade dependence (as a proxy for broader natural resource dependence), economic growth rate (inverse), youth bulge, urbanization rate, poverty level, infant mortality, life expectancy (inverse) undernutrition, HIV prevalence, primary net enrollment (inverse), adult education levels (inverse), corruption, democracy (inverse), gender empowerment (inverse), governance effectiveness (inverse), freedom (inverse), inequality, and water stress&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Government revenues are a function of past revenue as percentage of GDP, GDP per capita, and social expenditures (that is, inversely to fiscal balance).&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Corruption is a function of past corruption level, GDP per capita (inverse), energy trade dependence, democracy (inverse), gender empowerment (inverse), and probability of intrastate conflict.&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Democracy is a function of past democracy level, youth bulge (inverse), and gender empowerment.&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Gender empowerment is a function of past gender empowerment level, GDP per capita, youth bulge (inverse), and primary net enrollment.&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; valign=&amp;quot;center&amp;quot; | &amp;lt;div style=&amp;quot;text-align: left&amp;quot;&amp;gt;&#039;&#039;&#039;Key Agent-Class Behavior&amp;amp;nbsp;&#039;&#039;&#039; &#039;&#039;&#039;Relationships&#039;&#039;&#039;&amp;lt;/div&amp;gt;&amp;lt;div style=&amp;quot;text-align: left&amp;quot;&amp;gt;(illustrative, not comprehensive)&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Social sub-group relationships, especially historical conflict patterns and gender relationships; government revenue and expenditure&amp;lt;br/&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Dominant Relations: Governance&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The drivers of change on each dimension and sub-dimension of governance range widely.&amp;amp;nbsp; A quick summary (see also the table below) is that:[[File:Gov2.png|frame|right|Drivers of change on each dimension and sub-dimension of governance]]&lt;br /&gt;
&lt;br /&gt;
*Probability of intrastate conflict is a function of past conflict, neighborhood effects, economic growth rate (inverse), trade openness (inverse), youth bulge, infant mortality, democracy (inverted-U), state repression (inverse), and external intervention (inverse).&lt;br /&gt;
*Vulnerability to intrastate conflict is a function of energy trade dependence, economic growth rate (inverse), urbanization rate, poverty level, infant mortality, undernutrition, HIV prevalence, primary net enrollment (inverse), intrastate conflict probability, corruption, democracy (inverse), governance effectiveness (inverse), freedom (inverse), and water stress.&lt;br /&gt;
*Government revenues are a function of past revenue as percentage of GDP, GDP per capita, and fiscal balance (inverse).&lt;br /&gt;
*Corruption is a function of past corruption level, GDP per capita (inverse), energy trade dependence, democracy (inverse), gender empowerment (inverse), and probability of intrastate conflict.&lt;br /&gt;
*Democracy is a function of past democracy level, economic growth rate (inverse), youth bulge (inverse), and gender empowerment.&lt;br /&gt;
*Gender empowerment is a function of past gender empowerment level, GDP per capita, youth bulge (inverse), and primary net enrollment.&lt;br /&gt;
&lt;br /&gt;
There are some general insights with respect to elaboration of the formulations (equations and algorithms) that drive change on each dimension and sub-dimension of governance:&lt;br /&gt;
&lt;br /&gt;
*In almost each case there are path dependencies that supplement the basic relationships—social change has considerable inertia.&lt;br /&gt;
*The driving and driven variables clearly constitute a complex syndrome of mutually interdependent developmental interactions, not a simple causal sequence.&lt;br /&gt;
*There is a tendency for the dimensions of governance traditionally developing later to feed back to earlier ones, notably for inclusion to affect capacity via reduced corruption and also for inclusion and capacity to reduce the probability of internal conflict.&lt;br /&gt;
*Behaviorally, the bi-directional structures suggest the possibility that reinforcing processes may accelerate as governance strengthens, setting up a kind of tipping from one equilibrium to another; vicious cycles of deterioration would also be possible.&lt;br /&gt;
&lt;br /&gt;
For detailed discussion of the model&#039;s causal dynamics, see the discussions of flow charts (block diagrams) and equations.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Governance Flow Charts&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
We can show and briefly describe a block diagram for each of the three dimensions of governance and the two sub-dimensions of those: security (probability of intrastate or internal war and risk of conflict); capacity (ability to mobilize revenues and the effectiveness of their use); inclusiveness (formal democracy and broader inclusiveness, using gender empowerment as a proxy).&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Security&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Internal War&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Internal or intrastate war (SFINTLWAR) is heavily determined by a moving average of a society&#039;s past experience with such conflict (SFINTLWARMA) in what is a positive feedback system. The probability of such conflict will, however, typically converge to that determined by more basic underlying drivers, and the user can control the speed of such convergence by specifying the years to convergence (&#039;&#039;&#039;&#039;&#039;sfconv&#039;&#039;&#039; &#039;&#039;).[[File:Gov3.jpg|frame|right|Visual representation of internal war]]&lt;br /&gt;
&lt;br /&gt;
The major driving variables in a statistical estimation are the level of infant mortality (INFMORT) as a proxy for quality of government performance and trade openness or exports (X) plus imports (M) as a share of GDP. In addition democracy level (DEMOCPOLITY) enters in a non-linear and algorithmic fashion, as do youth bulge (YTHBULGE) and a moving average of economic growth rate (GDPRMA).&lt;br /&gt;
&lt;br /&gt;
Although less often used and turned off in the Base Case scenario, external interventions (&#039;&#039;&#039;&#039;&#039;wpextinterv&#039;&#039;&#039; &#039;&#039;) and mass repression (&#039;&#039;&#039;&#039;&#039;sfmassrep&#039;&#039;&#039; &#039;&#039;) can cause or at least temporarily dampen internal war, respectively.&lt;br /&gt;
&lt;br /&gt;
Finally, the user can multiply resultant endogenous values of internal war (&#039;&#039;&#039;&#039;&#039;sfintlwarm&#039;&#039;&#039; &#039;&#039;) in order to generate user-controlled scenarios.&lt;br /&gt;
&lt;br /&gt;
The IFs system also includes a representation of instability short of internal war (&#039;&#039;&#039;SFINSTABALL&#039;&#039;&#039; and &#039;&#039;&#039;SFINSTABMAG&#039;&#039;&#039;), linking them to the category of abrupt regime change in the classification developed by Ted Robert Gurr and used by the Political Instability Task Force. The forecasting representation was developed before the revision and update of that for internal war, however, and we recommend less attention to it until its own revision is done.&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Vulnerability and Risk of Conflict&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
The IFs treatment of societal/governance performance risk and related vulnerability to conflict does not involve an estimated formulation. Instead, like other such efforts, it involves the creation of an index. The figure below, a screen capture of the form (reached via Specialized Displays) uses variables related both directly to governance and to performance. A [[Governance#Performance_Risk_Analysis_Form|specialized Help topic]] on this form is available.&lt;br /&gt;
&lt;br /&gt;
Although many users will be interested in the rankings of countries (see the Global Rank column for ranks on individual variables and the summary measure for overall, variable-weighted rank), others will be interested in the summary value across all variables, shown at the bottom of the first column. Those values are also available in the model as the variable named government risk (GOVRISK).&lt;br /&gt;
&lt;br /&gt;
[[File:Govchart04.png|frame|center|1035x690px|Variables related both directly to governance and to performance]]&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Capacity&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Government Revenues&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
The ability to raise government revenues (GOVREV as a share of GDP) is one of the dimensions of capacity in governance. Its basic calculation is a very simple ratio. The key drivers of GOVREV, however, documented [[Governance#Equations:_Broader_Regime_Capacity|elsewhere]], are very complex. For instance, GOVREV is responsive in an equilibration process to government expenditures, both transfer payments and direct government expenditures in categories such as military, health, education, and infrastructure, as well as to external revenues, notably foreign aid receipts.[[File:Gov42.jpg|frame|center|Visual representation of government revenues]]&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Effectiveness of Government&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
The central measure of governance effectiveness in Hughes et al. (2014) was defined to be corruption or GOVCORRUPT (actually the absence thereof, or level of transparency). The model computes several additional measures of effectiveness or capacity, however, including regulatory quality (REGQUALITY) and effectiveness (GOVEFFECT), both related to the World Bank&#039;s World Governance Indicator project (Kaufmann, Kraay, and Mastruzzi 2010). In addition, many analysts point to the level of economic freedom (ECONFREE) or liberalization as a measure of effectiveness, in spite of considerable debate around their doing so.&lt;br /&gt;
&lt;br /&gt;
Among the drivers of governance corruption is resource dependence, for which we use as a proxy the value of energy exports (ENX) at energy prices (ENPRI) as a share of GDP. Energy exports tend to me the largest such category globally. Further drivers are the extent of gender empowerment (GEM) and the level of democracy (DEMOCPOLITY), both of which indicate the extent of inclusiveness but which make independent statistical contributions to corruption level.[[File:Gov5.jpg|frame|right|Visual representation of government effectiveness]]&lt;br /&gt;
&lt;br /&gt;
The drivers do not, of course, fully determine the level of corruption and there is much historical path dependence in societies related to other variables. The user can control the speed of elimination of such dependence and therefore of convergence to the basic formulation with a conversion years parameter (&#039;&#039;&#039;&#039;&#039;goveffconv&#039;&#039;&#039; &#039;&#039;).&lt;br /&gt;
&lt;br /&gt;
There are times when the user will wish to introduce normatively controlled target values for corruption. One approach is use of the &amp;quot;brute force&amp;quot; multiplier on corruption (&#039;&#039;&#039;&#039;&#039;govcorruptm&#039;&#039;&#039; &#039;&#039;). A second approach involves the specification of target values relative to a function of the key drivers estimated cross-sectionally across countries. This second approach allows, for instance, the [[Understand_IFs#Standard_Error_Targeting|specification of a target level]] 1 or 2 standard errors (SE) above the level expected of a country given those drivers. The SE target parameter is &#039;&#039;&#039;&#039;&#039;govcorruptsetar&#039;&#039;&#039; &#039;&#039;and the &#039;&#039;&#039;&#039;&#039;govcorruptseyrtar&#039;&#039;&#039; &#039;&#039;carries the years to achieve the target. There are similar control parameters (not shown the diagram) for regulatory quality (&#039;&#039;&#039;&#039;&#039;govregqualsetar&#039;&#039;&#039; &#039;&#039;and &#039;&#039;&#039;&#039;&#039;govreqqualseyrtar&#039;&#039;&#039; &#039;&#039;) and for effectiveness (&#039;&#039;&#039;&#039;&#039;goveffectsetar&#039;&#039;&#039; &#039;&#039;and &#039;&#039;&#039;&#039;&#039;goveffectseyrtar&#039;&#039;&#039; &#039;&#039;), but not for economic freedom.&lt;br /&gt;
&lt;br /&gt;
Theoretically, internal war (SFINTLWAR) could affect all of the capacity variables, but the only linkage identified in IFs is that to economic freedom. Setting the control switch (&#039;&#039;&#039;&#039;&#039;confforsw&#039;&#039;&#039; &#039;&#039;) to 1 turns on that impact.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Inclusiveness&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Democracy&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Three variables dominate the forecasting [[Governance#Equations:_Gender_Empowerment|formulation for democracy]] (DEMOCPOLITY): the gender empowerment measure (GEM) as a measure of broad social inclusion (positive linkage), the youth bulge (YTHBULGE) as an indicator of the age structure of society (negative linkage), and the dependence of the country on raw materials exports, a negative linkage using energy export share (ENX) times energy prices (ENPRI) as a share of the GDP as a proxy. An exogenous multiplier (&#039;&#039;&#039;&#039;&#039;democm&#039;&#039;&#039; &#039;&#039;) allows the user to directly manipulate the democracy level.[[File:Gov6.jpg|frame|right|Visual representation of democracy]]&lt;br /&gt;
&lt;br /&gt;
Two other variables can affect the democracy level but are turned off in the Base Case and will seldom be used. The first is the neighborhood effects of swing states in a regional neighborhood (e.g. Russia among former states of the Soviet Union). The swing states effect switch (&#039;&#039;&#039;&#039;&#039;sweffects&#039;&#039;&#039; &#039;&#039;) turns it on when set to 1.&lt;br /&gt;
&lt;br /&gt;
The more complicated additional factor is that of democracy waves (DEMOCWAVE). Relative to the initial condition a democracy wave can add or subtract democracy to the basic formulation&#039;s calculation of it (an algorithm based on historical experience allows upward swings to be larger than downward ones depending on EffectMul). The basic magnitude of increments depends of an exogenous specification of the impetus provided to democracy by the leading power (&#039;&#039;&#039;&#039;&#039;democwvus&#039;&#039;&#039; &#039;&#039;) and by other powers (&#039;&#039;&#039;&#039;&#039;democimpoth&#039;&#039;&#039; &#039;&#039;), the former&#039;s impact controlled by an elasticity (&#039;&#039;&#039;&#039;&#039;eldemocimp&#039;&#039;&#039; &#039;&#039;). Because waves rise and ebb, another parameter controls the length (&#039;&#039;&#039;&#039;&#039;democlen&#039;&#039;&#039; &#039;&#039;) and still another sets the maximum rise (&#039;&#039;&#039;&#039;&#039;democwvmax&#039;&#039;&#039; &#039;&#039;). A counter keeps track of the running and receding of a wave (DEMOCWVCOUNT) and a pointer keeps track of the direction its operation (DEMOCWVDIR); these two parameters are linked with the magnitude of the wave in a positive loop.&lt;br /&gt;
&lt;br /&gt;
The calculation from the basic formulation, before the addition of wave and swing state or neighborhood effects, can also be overridden by the use of [[Understand_IFs#Standard_Error_Targeting|external targeting]] directed by specifications of standard error targets relative to the formulation (&#039;&#039;&#039;&#039;&#039;democpolitysetar&#039;&#039;&#039; &#039;&#039;) to be achieved by a target year (&#039;&#039;&#039;&#039;&#039;democpolityseyrtar&#039;&#039;&#039; &#039;&#039;).&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Gender Empowerment and Freedom&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
[[Governance#Equations:_Gender_Empowerment|Gender empowerment (GEM)]], a broader measure of inclusion, joins democracy as the second key measure of governance inclusiveness. Its three basic drivers are youth bulge size (YTHBULGE), GDP per capita as purchasing power parity (GDPPCP), and the years of formal education obtained by female adults (EDYRSAG15).&lt;br /&gt;
&lt;br /&gt;
A user can control the progression of gender empowerment with a simple multiplier (&#039;&#039;&#039;&#039;&#039;gemm&#039;&#039;&#039; &#039;&#039;) or via setting a target value for it movement to some number of standard errors above or below a cross-sectionally estimated function (&#039;&#039;&#039;&#039;&#039;gemsetar&#039;&#039;&#039; &#039;&#039;) across a set number of years (&#039;&#039;&#039;&#039;&#039;gemseyrtar&#039;&#039;&#039; &#039;&#039;).&lt;br /&gt;
&lt;br /&gt;
Although IFs uses the Polity measure of democracy (DEMOCPOLITY) as its main measure of more formal, electoral inclusion, Freedom House&#039;s freedom measure (FREEDOM) is a logical alternative and the second of that measure&#039;s sub-dimensions, civil liberties, is a more inclusive measure. We therefore compute it also, using again GDP per capita and educational years (of all adults, not just females) as drivers. And there is a brute force multiplier for it also (&#039;&#039;&#039;&#039;&#039;freedomm&#039;&#039;&#039; &#039;&#039;). There is no SE targeting mechanism in place for the freedom variable.[[File:Gov7.jpg|frame|center|Visual representation of gender empowerment and freedom]]&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Aggregate Governance Indicators&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The major way of exploring the possible future of the three dimensions of governance is separately to use the two variables that represent each. But it is also useful to have more aggregate indices, first for each dimension and also across the three.&lt;br /&gt;
&lt;br /&gt;
The governance security index (GOVINDSECUR) is computed as an unweighted average of internal war probability (SFINTLWAR) and governance/society performance risk (GOVRISK). Similarly, the governance capacity index (GOINDCAP) is an unweighted average of government revenue (GOVREV) as a portion of GDP and government corruption, while the governance inclusion index (GOVINCLIND) averages democracy (DEMOCPOLITY) and gender empowerment (GEM). The overall governance index (GOVINDTOTAL) is a simple average of those across dimensions.&lt;br /&gt;
&lt;br /&gt;
[[File:Gov8.jpg|frame|center|Visual representation of governance index]] In reality, creating the indices for each dimension requires some attention to scaling issues and valence. See the description of the equations for details.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Life Conditions and the Human Development Index&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The condition of individuals and society are both the ultimate focus of governance and the font of it. The IFs system computes many of the relevant variables across its various models. It also aggregates a number of those into the widely used Human Development Index (HDI), based on heath (life expectancy), education or knowledge (both expectations for youth and attainment for adults), and GDP per capita.&lt;br /&gt;
&lt;br /&gt;
[[File:Gov9.png|frame|center|Visual representation of life conditions and HDI]]&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Social Values and Cultural Evolution&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Understanding societies fully requires going even more deeply than their governance and social conditions in order to look at the values and cultural foundations. IFs computes change in three cultural dimensions identified by the World Values Survey (Inglehart 1997). Those are dimensions of materialism/post-materialism, survival/self-expression, and traditional/secular-rational values.&lt;br /&gt;
&lt;br /&gt;
Inglehart has identified large cultural regions that have substantially different patterns on these value dimensions and IFs represents those regions, using them to compute shifts in value patterns specific to them.&lt;br /&gt;
&lt;br /&gt;
Levels on the three cultural dimensions are predicted not only for the country/regional populations as a whole, but in each of 6 age cohorts. Not shown in the flow chart is the option, controlled by the parameter &amp;quot;&#039;&#039;&#039;&#039;&#039;wvsagesw&#039;&#039;&#039; &#039;&#039;,&amp;quot; of computing country/region change over time in the three dimensions by functions for each cohort (value of &#039;&#039;&#039;&#039;&#039;wvsagesw&#039;&#039;&#039; &#039;&#039;= 1) or by computing change only in the first cohort and then advancing that through time (value of &#039;&#039;&#039;&#039;&#039;wvsagesw&#039;&#039;&#039; &#039;&#039;= 2).&lt;br /&gt;
&lt;br /&gt;
The model uses country-specific data from the World Values Survey project to compute a variety of parameters in the first year by cultural region (English-speaking, Orthodox, Islamic, etc.). The key parameters for the model user are the three country/region-specific additive factors on each value/cultural dimension (&#039;&#039;&#039;&#039;&#039;matpostradd&#039;&#039;&#039; &#039;&#039;, etc.).&lt;br /&gt;
&lt;br /&gt;
Finally, the model contains data on the size (percentage of population) of the two largest ethnic/cultural groupings. At this point these parameters have no forward linkages to other variables in the model.&amp;amp;nbsp;[[File:Gov10.png|frame|center|Visual representation of social values and cultural evolution]]&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Governance Equations&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Like the block diagrams for governance in IFs, the equations fall into the categories of the three dimensions (security, capacity, and inclusion), with detail for each of two sub-dimensions on each.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Governance Security Dimension&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
IFs represents two different types of measures related to domestic conflict and security. The first has roots in the work of the Political Instability Task Force (PITF); see Esty et al. (1998) and Goldstone et al. (2010). The PITF database allows us to see the actual pattern of conflict in countries over time and to use that historical conflict pattern to compute an initial probability of conflict. The second type of measure includes indices of vulnerability to conflict, generally presented in terms of rankings of countries with respect to their vulnerability (see Chapter 2 of Hughes et al. 2014, especially Box 2.3). Because these indices are not rooted as solidly in past conflict patterns, we cannot interpret their values or the rankings based on them as probabilities of conflict, but rather as propensities for conflict (and as indicators more generally of country performance and risk).&lt;br /&gt;
&lt;br /&gt;
In order to establish forecasting approaches for both types of measures within IFs, we looked to earlier work (see Chapter 3 of Chapter 2 of Hughes et al. 2014), did our own statistical analysis to create an underlying base formulation for overt conflict probability, and augmented the basic approach via more algorithmic elements—algorithms or logical procedures, like recipes, help guide forecasting through steps that analytical functions cannot easily represent. The algorithmic elements are tied in part to our efforts to fit the IFs forecasting approach at least relatively well to historical data from 1960 through 2010. Chapter 4 of Hughes et al. 2014 elaborates more fully the development process for the representation of security provided in this Help system.&lt;br /&gt;
&lt;br /&gt;
=== Equations: Internal Conflict or War Probability ===&lt;br /&gt;
&lt;br /&gt;
The PITF defined state failure in terms of four different types of events (with specific magnitude thresholds)—namely, adverse regime change (such as coups), revolutionary wars, ethnic wars, and genocides or politicides (Esty et al. 1998). On the recommendation of Ted Robert Gurr, one of the founding fathers of the PITF data project and approach, IFs builds two categories of insecurity from those four types: instability (adverse regime change); and internal war (combining revolutionary war, ethnic war, and genocide or politicide).&lt;br /&gt;
&lt;br /&gt;
Presence of any one of the three types of war, either as an initiation or continuation, leads us to code a country as 1; otherwise we code the country as 0. This distinction between instability and internal war helps differentiate among what Easton (1965) identified as regime, state, and polity levels within the sociopolitical system, by at least differentiating the regime level (where adverse regime changes occur) from the more fundamental state and polity levels. The forces of change and generally the extent of violence around change differ significantly at these different levels.&lt;br /&gt;
&lt;br /&gt;
Looking at the historical patterns of conflict in global regions across time (see Chapter 4 of Hughes et al. 2014) and doing our own statistical analysis it is clear that the &amp;quot;usual suspect&amp;quot; variables will not explain those patterns, and that in many cases they cannot therefore be very effective in forecasting. We found:&lt;br /&gt;
&lt;br /&gt;
*Normed infant mortality proves statistically interesting, being associated with (explaining or being explained by, using a second-order polynomial form) about 12 percent of cross-country variation in intrastate conflict in the most recent data-year (8.9 percent in panel analysis across the 1960–2000 period). Thus in forecasting it may help us understand general propensity for conflict, but its slow variation over time means it cannot possibly explain the big historical surges of warfare within regions and their country members.&lt;br /&gt;
&lt;br /&gt;
*Trade openness (which we define as the sum of exports and imports as a percentage of GDP) can be helpful in understanding variations in conflict and does vary within countries more rapidly than infant mortality. In cross-sectional analysis with most recent data, infant mortality and trade openness (inverse relationship) together account for 15 percent of the variation in intrastate conflict (trade openness itself is associated with 11 percent of the variance within intrastate conflict in a logarithmic formulation). Moreover, its increase coincides with the reduction of conflict historically within the countries of East Asia. But openness perversely increased over time in South Asia as intrastate conflict also rose. And its statistical power is good but not great. Again, causality could run in either direction or be a spurious result of a third variable; for instance, the end of Indochina wars and a change in economic policy in socialist countries could have led to greater trade there.&lt;br /&gt;
&lt;br /&gt;
*Factionalism, which can have many bases, including ethnicity or the intensity of feelings around ethnicity, is of surprisingly little use in forecasting. Most underlying social divisions change very slowly over time. Although intensity of factionalism around those divisions may change much more rapidly (for instance, as &amp;quot;conflict entrepreneurs&amp;quot; inflame passions), we arguably cannot anticipate when that might happen. Nor do we believe we can we anticipate changes in other potential ideational drivers, such as ideologies. Further, historical measurement of change in factionalism risks using conflict as a proxy, thereby creating the danger that correlations between it and conflict are simply a tautological artifact of that measurement. Finally, our own analysis of various measures of ethnic and/or religious factionalism and intrastate conflict suggests lower relationship than we expected.&lt;br /&gt;
&lt;br /&gt;
*Youth bulges are a potentially more useful driver in forecasting because our demographic forecasts are stronger than those of variables like factionalism or even trade openness, and because demographic structures exhibit clear and non-monotonic variation over time. There were many bulges in East Asia during the 1970s, as there have been many recently in South Asia and as there are today in the Middle East and North Africa. In cross-sectional analysis of recent data, a linear relationship with youth bulge size accounts for 7 percent of the variation in conflict (in panel analysis since 1960, however, only 3.5 percent).&lt;br /&gt;
&lt;br /&gt;
*Consistent with studies that have found anocracy rather than autocracy primarily related to conflict, the relationship of measures of regime type with conflict has an inverted U-shaped character. Using a third-order polynomial, we found that the Polity measure of regime type explains 4 percent of variation in recent intrastate war. The Freedom House measure&amp;amp;nbsp;(see [http://www.freedomhouse.org/ http://www.freedomhouse.org/]) actually explains 10 percent, but we used the Polity Project measure (see [http://www.systemicpeace.org/polity/polity4.htm http://www.systemicpeace.org/polity/polity4.htm])&amp;amp;nbsp;because it is a purer measure of political democracy (rather than civil liberties as well) and because it is our primary measure of regime in forecasting.&lt;br /&gt;
&lt;br /&gt;
*Downturns in economic growth rates preceded the collapse of communism in Europe and Central Asia, the rise of internal conflict in both Latin America and the Middle East in the 1980s, and more recently the events of the Arab Spring. Analysis of the magnitude of downturn required to generate conflict and the lag between downturn and conflict is complex. We found, through experimentation directed at fitting historical conflict patterns (running IFs against historical patterns since 1960), that a 1.0 percent drop in a moving average of economic growth (carrying 60 percent of the moving average forward) is associated with a 0.04 point increase on a 0-1 scale for the rate of internal war.&lt;br /&gt;
&lt;br /&gt;
*Conflict begets conflict. We found, again through historical analysis, a 60 percent carryover of past conflict levels to current ones.&lt;br /&gt;
&lt;br /&gt;
For IFs forecasting, we conceptualize and operationalize intrastate war not as a 0 or 1 outcome as in the data (no war or war), but as a probability of conflict in any country-year. We initialize country probabilities at the beginning of a forecast horizon with average conflict rates across the preceding 20 years. The development of our own basic forecasting formulation for these probabilities involved not just literature and statistical analysis, but testing of the formulation in runs of the model from 1960 through 2010 and comparisons of our historical forecasts with the data on intrastate war. We let the historical forecasts run without the frequently used annual adjustment/correction by the historical conflict data for the full 50 years. We experimented with a number of algorithmic elements in order to improve the historical fit. This analysis yielded the following basic formulation:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;SFINTLWAR_{r,t}=((0.1420+0.0012*INFMOR_{r,t}-0.0006*TRADEOPEN_{r,t})+F(POLITYDEMOC_{r,t},YTHBULGE_{r,t},GDPMA_{r,t},SFINTLWARMA_{r,t}))*\mathbf{sfintlwarm}_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;TRADEOPEN_{r,t}=(X_{r,t}+M_{r,t})/GDP_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:SFINTLWAR=probability of internal war or state failure&lt;br /&gt;
&lt;br /&gt;
:INFMOR=infant mortality, normed globally&lt;br /&gt;
&lt;br /&gt;
:TRADEOPEN=trade openness ratio&lt;br /&gt;
&lt;br /&gt;
:X=exports in billion dollars&lt;br /&gt;
&lt;br /&gt;
:M=imports in billion dollars&lt;br /&gt;
&lt;br /&gt;
:GDP=gross domestic product in billion dollars&lt;br /&gt;
&lt;br /&gt;
:POLITYDEMOC=Polity’s 21-point scale of democracy; asymmetrical curvilinear relationship with a peak at 9 and a sharper fall than rise&lt;br /&gt;
&lt;br /&gt;
:YTHBULGE=population age 15–29 as a portion of all adults; algorithmic adjustment with GDP/capita explained in text&lt;br /&gt;
&lt;br /&gt;
:GDPRMA=gross domestic product growth rate, algorithmic moving average carrying forward 60 percent past year’s value; algorithmic adjustment with GDP/capita explained in text; inverse relationship&lt;br /&gt;
&lt;br /&gt;
:SFINTLWARMA=moving average of past internal war probability&amp;amp;nbsp; (i.e., carrying forward past forecast values, not past data values)&lt;br /&gt;
&lt;br /&gt;
:&#039;&#039;&#039;sfintlwarm&#039;&#039;&#039;=an exogenous multiplier for scenario analysis&lt;br /&gt;
&lt;br /&gt;
:Algorithm on regional contagion explained in text&lt;br /&gt;
&lt;br /&gt;
:R-squared = 0.22 in 50-year historical simulation without annual correction (see text for elaboration)&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Our historical and extended analytical explorations of the core statistical formulation with infant mortality and trade openness led us to make a number of algorithmic changes to it in creating our basic formulation. We found that $18,000 per capita (in 2005 dollars at PPP) is a point above which economic downturns and youth bulges tend not to increase the probability of internal war, so we greatly dampened the affects of both of those variables above that level. We also found it important to add a regional contagion effect; courtesy of data provided by Paul Diehl we combined three of the Correlates of War Project distance categories (contiguous, less than 12 miles separation, and less than 24 miles separation) and added 0.1 to conflict probability for a country for each neighbor with computed conflict probability of its own above 0.2— because of conflict carryover across time, this algorithm can also lead to a positive feedback loop of neighborhood contagion.&lt;br /&gt;
&lt;br /&gt;
We further found that the intrastate war formulation is sensitive to actual GDP levels, not just because of the growth rate term, but because within the broader IFs system GDP per capita also affects the endogenously calculated youth bulge and democracy variables (we will return to discussion of the latter). To deal with this sensitivity, we forced the IFs historical base to be historically accurate with respect to GDP growth—otherwise the entire historical forecast of IFs after 1960 was endogenously determined in recursive annual calculation only by initial conditions and formulations rather than with annual corrective terms often used in historical validation exercises.&lt;br /&gt;
&lt;br /&gt;
This basic initial formulation generated a pattern of historical forecasts (which can be generated using the file HistoricalNoMassRepOrExtInterv.sce) of intrastate warfare probabilities that showed some of the characteristics of the historical data, including a peak for the Middle East and North Africa in the 1980s and one for developing Europe and Central Asia in the early 1990s (both related to growth downturns). Visual comparison quickly suggested, however, that the overall pattern was not a good historical fit. In particular, the bulges of conflict in East Asia in the early years and of South Asia more recently were missing; in addition, because of the infant mortality and economic growth terms, the model generated a bulge of conflict within Africa in the early 1980s (when growth and social advance was very weak) that did not appear in the data. Moreover, statistically, the forecasts correlated at the region level with data across the 1960-2010 time period with only a 0.19 R-squared level.&lt;br /&gt;
&lt;br /&gt;
We therefore explored the bases of the historical patterns further, and concluded that additional factors were missing. One is the extreme or totalitarian repression that lowered conflict in developing Europe and Central Asia until about the time of General Secretary Mikhail Gorbachev; we added a repression parameter (wpextinterv) for exogenous manipulation. More controversially perhaps, we also found it necessary to extend the suppression of conflict to sub-Saharan Africa in the middle period of the historical run; the underlying assumption is that the domestic prestige and power of liberation movement leaders, backed by their domestic and superpower supporters, helped dampen conflict significantly in the face of poor, and even deteriorating, domestic economic and social conditions.&lt;br /&gt;
&lt;br /&gt;
A second type of factor missing in our basic statistical analysis is external interventions, such as those of the U.S. in Southeast Asia in the 1960s and those of the former USSR and then the U.S. in South Asia after 1980; we added another exogenous parameter (sfmassrep) to represent such interventions.&lt;br /&gt;
&lt;br /&gt;
Although still not a terribly strong match to actual history, this revised historical forecast some remarkable similarities, including the initially high level of conflict in East Asia and the Pacific and a relatively high rate for South Asia in recent decades. The adjusted R-squared rises to 0.61 from 0.19 (before the addition of the repression and intervention variables). The major problems that remained in our historical forecast include the generation by the model of too much conflict for Latin America and the Caribbean in the 1980s, when economic and social conditions in that region deteriorated significantly; and the relatively high levels of conflict in sub-Saharan Africa beyond the end of the Cold War, again associated in our forecast with a combination of absolute and relative deterioration in socioeconomic conditions of many countries. Thus the additional parameters may be useful in scenario analysis.&lt;br /&gt;
&lt;br /&gt;
It is possible that our relatively high historical forecasts for conflict in post-Cold War sub-Saharan Africa, even after formulation enhancements, may reflect the remaining omission of yet another systemic variable, namely regional and global efforts to dampen conflict there. There is no parameter to represent that variable, but the user can use the overall multiplier (&#039;&#039;&#039;&#039;&#039;sfintlwarm&#039;&#039;&#039; &#039;&#039;) in scenario analysis.&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Equations: Political Stability/Instability&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
The State Failure project has analyzed the propensity for different types of state failures within countries, including those associated with revolution, ethnic conflict, genocide-politicide, and abrupt regime change (using categories and data pioneered by Ted Robert Gurr. Upon the advice of Gurr, IFs groups the first three as internal war and the last as political instability. The model formulations for political instability are older and less well developed than those for internal war; we therefore recommend focus on internal war. Nonetheless, we document the approach to instability here.&lt;br /&gt;
&lt;br /&gt;
The extensive database of the project includes many measures of failure. IFs has variables representing the probability of the first year or a continuing year of instability (SFINSTABALL) and the magnitude of a first year or continuing event (SFINSTABMAG).&lt;br /&gt;
&lt;br /&gt;
Using data from the State Failure project, formulations were estimated for each variable using up to five independent variables that exist in the IFs model: democracy as measured on the Polity scale (DEMOCPOLITY), infant mortality (INFMOR) relative to the global average (WINFMOR), trade openness as indicated by exports (X) plus imports (M) as a percentage of GDP, GDP per capita at purchasing power parity (GDPPCP), and the average number of years of education of the population at least 25 years old (EDYRSAG25). The first three of these terms were used because of the state failure project findings of their importance and the last two were introduced because they were found to have very considerable predictive power with historic data.&lt;br /&gt;
&lt;br /&gt;
The IFs project developed an analytic function capability for functions with multiple independent variables that allows the user to change the parameters of the function freely within the modeling system. The default values seldom draw upon more than 2-3 of the independent variables, because of the high correlation among many of them. Those interested in the empirical analysis should look to a project document (Hughes 2002) prepared for the CIA&#039;s Strategic Assessment Group (SAG), or to the model for the default values.&lt;br /&gt;
&lt;br /&gt;
One additional formulation issue grows out of the fact that the initial values predicted for countries or regions by the six estimated equations are almost invariably somewhat different, and sometimes quite different than the empirical rate of failure. There may well be additional variables, some perhaps country-specific, that determine the empirical experience, and it is somewhat unfortunate to lose that information. Therefore the model computes three different forecasts of the six variables, depending on the user&#039;s specification of a state failure history use parameter (sfusehist). If the value is 0, forecasts are based on predictive equations only. The equation below illustrates the formulation. The analytic function obviously handles various formulations including linear and logarithmic.&lt;br /&gt;
&lt;br /&gt;
:if &amp;lt;math&amp;gt;\mathbf{sfusehist}=0 &amp;lt;/math&amp;gt; then (no history)&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;SFINSTABALL_{r,t}=PredictedTerm_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;PredictedTerm_{r,t}=ANALFUNC(GDPPCP_{r,t},DemocTerm_t,InfMorTerm_t,TradeTerm_t, Educ25Term_t)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;DemocTerm=DemoPolity_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;InfMorTerm=\frac{INFMOR_r}{WINFMOR}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;TradeTerm=\frac{X_r+M_r}{GDP}*100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;Educ25Term=EDYRSAG25_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
If the value of the sfusehist parameter is 1, the historical values determine the initial level for forecasting, and the predictive functions are used to change that level over time. Again the equation is illustrative.&lt;br /&gt;
&lt;br /&gt;
:if &amp;lt;math&amp;gt;\mathbf{sfusehist}=1&amp;lt;/math&amp;gt; then (use history)&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;SFINSTABALL_{r,t}=\frac{PredictedTerm_{f,t}}{PredictedTerm_{f,t=1}}*\mathbf{SFINSTABALL}_{r,t=1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;PredictedTerm_{r,t}=ANALFUNC(GDPPCP_{r,t},DemocTerm_t,InfMorTerm_t,TradeTerm_t,Educ25Term_t)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;DemocTerm=DemoPolity_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;InfMorTerm=\frac{INFMOR_r}{WINFMOR}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;TradeTerm=\frac{X_r+M_r}{GDP}*100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;Educ25Term=EDYRSAG25_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
If the value of the sfusehist parameter is 2, the historical values determine the initial level for forecasting, the predictive functions are used to change the level over time, and the forecast values converge over time to the predictive ones, gradually eliminating the influence of the country-specific empirical base. That is, the second formulation above converges linearly towards the first over years specified by a parameter (polconv), using the CONVERGE function of IFs.&lt;br /&gt;
&lt;br /&gt;
:if &amp;lt;math&amp;gt;\mathbf{sfusehist}=2&amp;lt;/math&amp;gt; then (converge)&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;SFINSTABALLBase_{r,t}=\frac{PredictedTerm_{f,t}}{PredictedTerm_{f,t=1}}*\mathbf{SFINSTABALL}_{r,t=1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;SFINSTABALL_{r,t}=ConvergeOverTime(SFINSTABALLBase_{r,t},PredictedTerm_{f,t},\mathbf{polconv})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;PredictedTerm=ANALFUNC(GDPPCP_{r,t},DemocTerm_t,InfMorTerm_t,TradeTerm_t,Educ25Term_t)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;DemocTerm=DemoPolity_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;InfMorTerm=\frac{INFMOR_r}{WINFMOR}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;TradeTerm=\frac{X_r+M_r}{GDP}*100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;Educ25Term=EDYRSAG25_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Equations: Vulnerability to Conflict (and Performance Risk Analysis)&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
The second approach to analyzing risk of violent internal conflict (and broader country risks) involves the creation of indices that tend to rank states according to generalized performance. The projects creating such indices—variously referred to as measures of state fragility, state weakness, political instability, or failed states—most often do not intend to convey a probability of violent internal conflict. Rather they try to suggest greater or lower propensities for conflict as well as broader country risk, for instance that which foreign investors might face with respect to socioeconomic conditions. .&lt;br /&gt;
&lt;br /&gt;
Generally, these indices combine variables in four categories: social, political, economic, and security. Developers may supplement variables that mostly focus on the average values for countries with select variables focusing on distribution (such as the Gini index). They commonly weight variables within categories equally and/or weight the categories equally when aggregating them to final index values. While individual variables have theoretical and empirical links to conflict or lack of security, such simple combination of large numbers of highly intercorrelated variables into a formulation of conflict vulnerability is very difficult to interpret. Moreover, because reports generally present an index with no simple interpretation of scale, analysts focus heavily on rankings of countries.&lt;br /&gt;
&lt;br /&gt;
The IFs project has created its own Performance Risk Index (see variable GOVRISK) along the lines of these approaches, and for the purposes of forecasting has uniquely made it responsive to endogenous long-term change in the underlying variables. Like those of other projects, the IFs measure draws upon social, political, economic, and security variables, but we impose a different conceptual or analytical structure on them (see the example risk analysis form provided here). We divide the variables of the index into three general categories: governance, (deep) risk drivers, and performance. We further divide the governance variables into our three dimensions of security, capacity and inclusion, the deep risk factors into demographic, environmental, and international categories, and the performance factors into economic, health, and education categories.&lt;br /&gt;
&lt;br /&gt;
[[File:Govchart11.png|frame|center|1080x728px|Performance Risk Index]]&lt;br /&gt;
&lt;br /&gt;
The Performance Risk Index (GOVRISK) and the probability of intrastate conflict (SFINTLWAR) provide quite different images of security in states, in part because the probability of intrastate war has a power-law distribution across countries and risk indices have a more nearly linear distribution (see Chapter 2 of Hughes et al 2014). In 2010 the correlation between the two measures in IFs has an adjusted R-squared of only 0.25. Presumably the probability of conflict measure should be the better indicator of its likelihood. In fact, beyond their drawing our attention to the highest ranked and therefore most fragile countries, risk indices seldom are used to identify conflict likelihood and more often suggest a wider variety of risks, including overall poor state performance, only some of which may be so severe as to lead to conflict.&lt;br /&gt;
&lt;br /&gt;
Because vulnerability or risk indices often include GDP per capita or other highly correlated indicators, they generally assign greater risk to poorer countries. Another way of using such risk information it to compare performance of countries to expectations that control for their level of GDP per capita (with a cross-sectional analysis). The column in the Performance Risk Analysis form showing standard errors helps us do that. In 2010 Angola&#039;s performance on infant mortality was 2.4 standard errors worse than the expected value. Thus its performance on that variable was not only very poor relative to other countries around the world, but also relative to countries at its own income level.&lt;br /&gt;
&lt;br /&gt;
Unlike our analysis with the probability of conflict, it is not possible to compare the IFs Governance Risk Index with other measures across the full 1960–2010 historical time period, because those other measures tend to be quite recent and to cover only a small number of years. For instance, the Brookings Institution&#039;s Index of State Weakness for the Developing World (Rice and Patrick 2008) was produced only for a single year (2008). The measures with the greatest time series are the Fund for Peace&#039;s Index of State Failure (2005–2012) and the Center for Systemic Peace&#039;s (CSP&#039;s) State Fragility Index (1995-2011); see Marshall and Cole 2008; 2009; 2011). In order to assess the risk index of IFs, we again did a historical run of the model, without any extraordinary interventions, from 1960 through 2010—the run computes the IFs Country Performance Risk Index for all years. The R-squared of 0.71 indicates the remarkably close correlation, even after 50 years of forecasting with the full integrated IFs model. In fact, the R-squared is 0.70 across all years for which the SFI is available.&lt;br /&gt;
&lt;br /&gt;
For much more detail on the structure and computations of the Performance Risk Analysis form, see the separate discussion of it (see [[Governance#Performance_Risk_Analysis_Form|Performance Risk Analysis Form]]).&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Governance Capacity Dimension&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
The capacity dimension has two primary elements. The first is the ability to raise revenue. The second is the effective use of it and the other tools of government—that is, the competence or quality of governance.&lt;br /&gt;
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=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Equations: Government Finance&amp;lt;/span&amp;gt; ===&lt;br /&gt;
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Government finance in IFs sits within a broader [[Economics#Social_Accounting_Matrix_Approach_in_IFs|social accounting matrix (SAM) structure]] that accounts for, and in the process balances, all domestic and international financial exchanges among firms, households, and governments. The IFs system is unique, not only in the representation of flows within and across so many countries of the world, but also in maintaining, insofar as the sparse data allow, stocks (accumulations of net flows, such as government debt and assets of firms) that provide signals for equilibration processes that require changes in flows (like [[Economics#Government_Revenue|revenues]]&amp;amp;nbsp;and [[Economics#Government_Expenditure|expenditures]]) over time. Like the goods and services markets of the economic model, the government finance representation in IFs (its representation of revenues and expenditures) does not seek an exact equilibrium in every time point, but rather [[Economics#Government_Balances_and_Dynamics|chases equilibrium over time]]. The variables computed (see the links) are GOVREV, GOVEXP (with direct government consumption or GOVCON as a subset), and GOVBAL. This approach is both more realistic and more computationally efficient.&lt;br /&gt;
&lt;br /&gt;
The desired IFs treatment of government is of consolidated or general government. Beyond our use of the OECD&#039;s general government expenditure data for its members, however, our main data source for finance is the World Bank&#039;s World Development Indicators (Kaufmann, Kraay, and Mastruzzi 2010), which appear to provide mostly data for central government. In fact, for most countries there are quite incomplete and inconsistent systems of national accounts on which to build social accounting matrices generally, or a full mapping of government finance more specifically. Thus the &amp;quot;preprocessor&amp;quot; in IFs plays a big role in creating a consistent and complete initial image of government finance.&lt;br /&gt;
&lt;br /&gt;
With respect to government finance and the SAM more generally, the preprocessor both fills holes for missing data series of many countries, using cross-sectionally estimated functions or algorithms, and otherwise cleans and balances the SAM data. The preprocessor first builds on data to estimate total governmental revenues and expenditures for the model&#039;s base year and then uses available data on the breakdown of revenues and expenditures to calculate initial values of those streams consistent with the totals. Those who wish to understand the entire social accounting system, both initialization and forecast, should look to Hughes and Hossain (2003). More generally, the IFs [http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf preprocessor&#039;s computational rules] assist in the initialization of all models within the IFs system and the connections among them, including reconciliation of physical systems such as energy and agriculture with financial ones.&lt;br /&gt;
&lt;br /&gt;
We make simplifying assumptions to move from limited data to initial values for total general government expenditures and revenues of all countries as a percentage of GDP. For OECD countries we have general government expenditure data (from the OECD), and we assume that the general government revenue share of GDP differs from the expenditures share by the same percentage as central government expenditure and revenue shares differ in WDI data; the implicit assumption is that local government expenditures and revenues are in balance For non-OECD countries we have only central government expenditures and revenues, and we estimate a size for local government revenues and expenditures that rises progressively from 2 percent for the lowest income countries to 14 percent for high-income countries—the latter being the contemporary average of OECD countries, and both the former and the rise being apparent in the data and discussion of North, Wallis, and Weingast (2009: 10).&lt;br /&gt;
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In the forecasting itself, there is similar attention to revenues and expenditures, but also attention to the cumulative imbalance between them and how that imbalance affects their dynamics over time. The model represents five revenue streams from taxes on household and firm income: household income taxes, household social security/welfare taxes, firm income taxes, firm social security/welfare taxes, and indirect taxes. In the absence of cross-country data on other revenue streams such as property taxes, the preprocessor allocates them in the base year to household taxes, a category for which data are especially weak. Total domestic government revenue is computed from the five streams. Foreign assistance augments domestic revenue in computing the fiscal balance with expenditures.&lt;br /&gt;
&lt;br /&gt;
[[Economics#Government_Expenditure|Government expenditures]] (GOVEXP) combine direct consumption expenditures (GOVCON) and transfer payments, especially to households (GOVHHTRN). Direct government consumption as a portion of GDP is computed from functions linking GDP per capita (PPP) to key elements of spending such as military, health, and education; total government consumption generally rises with GDP per capita. An additional optional term in the equation is a Wagner term (set to zero in the Base Case), after the discoverer of the long-term behavioral tendency for government consumption to rise as a share of GDP. The final division of government consumption into target destination categories, namely military, education, health, research and development, infrastructure (two subcategories) and an &amp;quot;other&amp;quot; or residual category, depends on a combination of functions and broader algorithmic and modeling elements specific to each spending category (including, for instance, demand for expenditures from the education and infrastructure models). The model normalizes across spending categories to assure that they equal total government consumption. As a general rule, transfer payments grow with GDP per capita more rapidly than does direct government consumption. And within the category of transfer payments, pension payments grow especially rapidly in many countries, particularly in more economically developed ones. Computation of government transfers involves integrating two different behavioral logics, a top-down one depending on general relationships to income and a bottom-up one. The bottom-up logic is especially important in the analysis of pensions, because it is responsive to the changing size of the elderly population.&lt;br /&gt;
&lt;br /&gt;
With completed computations of revenues and expenditures, it is possible to compute the [[Economics#Government_Balances_and_Dynamics|government fiscal balance]], an annual flow variable. That allows the update of cumulative government financial assets or debt and a calculation of their magnitude relative to GDP. IFs uses this cumulative total as a percentage of GDP in its equilibrating dynamics for annual government revenues and expenditures.&lt;br /&gt;
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=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Equations: Broader Regime Capacity&amp;lt;/span&amp;gt; ===&lt;br /&gt;
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Forecasting of variables that relate to broader regime capacity in IFs has three elements: (1) a basic statistical formulation; (2) a recognition of country-specific differences (tied in part to path dependencies); (3) an algorithmic linkage to internal conflict. A fourth potential element could be factors external to the country including global waves and neighborhood effects, but we introduce those only through scenario analysis.&lt;br /&gt;
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Corruption is one of the most powerful indicators of capacity (or more accurately, lack of capacity) as well as accountability. We rely in our analysis on the Transparency International index of corruption perceptions (CPI), which is actually a measure of transparency (higher values are more transparent or less corrupt). The basic formulation in IFs for corruption/transparency (below) contains four statistically significant drivers, which collectively account for nearly 80 percent of the cross-country variation in corruption in the most recent year of data. The first term, and the one identified with the most variation, involves a variable representing long-term development, namely GDP per capita (years of education plays that same role in forecasting formulations for some other governance variables, such as democracy).&lt;br /&gt;
&lt;br /&gt;
Interestingly, a second very powerful driving variable is the Gender Empowerment Measure (GEM), which, in spite of its high correlation with GDP per capita, makes its own contribution and suggests the power of inclusion in affecting capacity. In fact, still another driving variable is the extent of democracy, further suggesting the power that inclusion may have to increase accountability and transparency, reducing corruption. A less-powerful but still-significant variable is the dependence of the country on exports of energy—in a few years, and in the aftermath of the Arab Spring beginning in 2011, this term may drop out of cross-sectional analyses of change in governance capacity but will still probably remain very important for those countries with low levels of development and inclusion. (We find that the same drivers work well (an R-squared of 0.62) for the IFs economic freedom variable, based on the Fraser Institute/Economic Freedom Network measure.) A multiplier for scenario analysis is the only exogenous element added to the basic formulation.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GOVCORRUPT_{r,t}=(1.576+0.1133*GDPPCP_{r,t}+2.270*GEM_{t,r}+0.02779*DEMOCPOLITY_{r,t}-0.04566*(ENX_{r,t}*(\frac{ENPRI_{r,t}}{GDP_{r,t}}))*\mathbf{govcorruptm}_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:GOVCORRUPT= the Transparency International corruption perception index (for which higher values are more transparent or less corrupt)&lt;br /&gt;
&lt;br /&gt;
:GDPPCP=GDP per capita at purchasing power parity in thousand dollars&lt;br /&gt;
&lt;br /&gt;
:GEM=Gender Empowerment Measure (values below 1 indicate female disadvantage)&lt;br /&gt;
&lt;br /&gt;
:DEMOCPOLITY=Polity’s 20-point scale of democracy; inverse relationship&lt;br /&gt;
&lt;br /&gt;
:ENX=energy exports in physical terms (billion barrels of oil equivalent)&lt;br /&gt;
&lt;br /&gt;
:ENPRI=energy price per barrel&lt;br /&gt;
&lt;br /&gt;
:GDP=gross domestic product in billion constant 2000 dollars (market prices)&lt;br /&gt;
&lt;br /&gt;
:&#039;&#039;&#039;govcorruptm&#039;&#039;&#039;=an exogenous multiplier for scenario analysis&lt;br /&gt;
&lt;br /&gt;
:R-squared in 2010 = 0.75&lt;br /&gt;
&lt;br /&gt;
We compute an additive adjustment term (not shown in the equation) on top of the basic formulation in the base year to capture any difference between the value anticipated in the formulation and the value from data. In most of our formulations we use additive or multiplicative terms in this manner, and the adjustment term introduces the impact of other variables not in the statistically estimated equation (such as historical path dependencies and cultural differences). The additive adjustment term gradually converges to zero over time in our forecasts. The logic behind such convergence is twofold: first, many differences from initial anticipated values are the result of transient factors and even data errors; second, ongoing global processes tend to lead to a convergence of patterns across countries.&lt;br /&gt;
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There is every reason to believe that the presence of domestic conflict will reduce governmental capacity, including leading to lower levels of transparency (higher corruption). In fact, the inverse relationship between the IFs internal war variable (SFINTLWARALL) and transparency is strong. Even when added to the full equation above it remains quite strong (a T-score of -1.97). Because conflict tends to be quite variable over time, however, we undertook more analysis rather than simply adding conflict to the equation for corruption. Specifically, we experimented with different coefficients in analysis across the historical period (1960-2010). In doing so, we reinforced the result of the pure statistical analysis that a movement from 0 (no conflict) to 1 (conflict) appears to increase corruption (to lower the TI measure) by 0.6 points. We algorithmically overlaid this relationship on the basic equation above.&lt;br /&gt;
&lt;br /&gt;
There are times when the user will wish to introduce normatively controlled target values for corruption. One approach is use of the &amp;quot;brute force&amp;quot; multiplier on corruption (&#039;&#039;&#039;&#039;&#039;govcorruptm&#039;&#039;&#039; &#039;&#039;). A second approach involves the specification of target values relative to a function of the key drivers estimated cross-sectionally across countries. This second approach allows, for instance, the specification of a target level 1 or 2 standard errors (SE) above the level expected of a country given those drivers. The SE target parameter is &#039;&#039;&#039;&#039;&#039;govcorruptsetar&#039;&#039;&#039; &#039;&#039;and the &#039;&#039;&#039;&#039;&#039;govcorruptseyrtar&#039;&#039;&#039; &#039;&#039;carries the years to achieve the target. Relevant to the discussion below, there are similar control parameters for regulatory quality (&#039;&#039;&#039;&#039;&#039;govregqualsetar&#039;&#039;&#039; &#039;&#039;and &#039;&#039;&#039;&#039;&#039;govreqqualseyrtar&#039;&#039;&#039; &#039;&#039;) and for effectiveness (&#039;&#039;&#039;&#039;&#039;goveffectsetar&#039;&#039;&#039; &#039;&#039;and &#039;&#039;&#039;&#039;&#039;goveffectseyrtar&#039;&#039;&#039; &#039;&#039;), but not for economic freedom.&lt;br /&gt;
&lt;br /&gt;
Looking beyond the corruption/transparency measure of Transparency International, IFs also forecasts a number of capacity-related variables from the World Bank&#039;s World Governance Indicators project (Kaufmann, Kraay, and Mastruzzi 2010) that we did not use to define the capacity dimension, but that are still of significant interest (used, for instance, in forward linkages to the building of infrastructure). These include the quality of government regulation and government effectiveness. The approaches are identical to those used for corruption and involve the same drivers. The R-squared values are again high (0.74 and 0.72, respectively).&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GOVREGQUAL_{r,t}=(-1.018+0.726*ln(GDPPCP_{r,t})+0.2085*EDYRSAG15_{r,t}+2.5*\mathbf{govregqualm}_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:GOVREGQUAL=government regulatory quality using the World Bank WGI scale, shifting it 2.5 points so that it runs from 0-5 instead of from -2.5 to 2.5&lt;br /&gt;
&lt;br /&gt;
:GDPPCP=GDP per capita at purchasing power parity&lt;br /&gt;
&lt;br /&gt;
:EDYRSAG15=average years of education for adults aged 15 or older&lt;br /&gt;
&lt;br /&gt;
:&#039;&#039;&#039;govregqualm&#039;&#039;&#039;=an exogenous multiplier for the model user&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GOVEFFECT_{r,t}=(-1.1029+0.08*ln(GDPPCP_{r,t})+0.21205*EDYRSAG15_{r,t}+2.5*\mathbf{goveffectm}_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:GOVEFFECT=government effectiveness using the World Bank WGI scale, shifting it 2.5 points so that it runs from 0-5 instead of from -2.5 to 2.5&lt;br /&gt;
&lt;br /&gt;
:GDPPCP=GDP per capita at purchasing power parity&lt;br /&gt;
&lt;br /&gt;
:EDYRSAG15=average years of education for adults aged 15 or older&lt;br /&gt;
&lt;br /&gt;
:&#039;&#039;&#039;goveffectm&#039;&#039;&#039;=an exogenous multiplier for the model user&lt;br /&gt;
&lt;br /&gt;
We have also computed multivariate functions (using GDP per capita and education as drivers) for the other four WGI measures, voice and accountability, political stability, corruption, and rule of law. But we have not yet added them to IFs.&lt;br /&gt;
&lt;br /&gt;
Turning to policy orientations, we compute an economic freedom variable based on the measures of the Economic Freedom Institute (with leadership from the Fraser Institute; see Gwartney and Lawson with Samida, 2000):&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ECONFREE_{r,t}=(5.4097+0.5971ln(GDPPCP_{r,t}))*\mathbf{econfreem}_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:ECONFREE= economic freedom using the Fraser Institute/Economic Freedom Network freedom indicator (higher values are freer)&lt;br /&gt;
&lt;br /&gt;
:GDPPCP=GDP per capita at purchasing power parity&lt;br /&gt;
&lt;br /&gt;
:&#039;&#039;&#039;econfreem&#039;&#039;&#039;=an exogenous multiplier for the model user&lt;br /&gt;
&lt;br /&gt;
:R-squared = .5038&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;The Inclusion Dimension&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Inclusion has many elements that reach beyond democratization or regime type and gender empowerment. For reasons including conceptual clarity, data availability and parsimony, we limit our forecasting to those two elements.&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Equations: Regime Type&amp;lt;/span&amp;gt; ===&lt;br /&gt;
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As with capacity, the forecasting of regime type in IFs has multiple elements: (1) a basic statistical formulation; (2) a recognition of country-specific differences (tied in part to path dependencies); and (3) algorithmic specification of a number of additional factors, including global waves and neighborhood effects.&lt;br /&gt;
&lt;br /&gt;
A look at the historical patterns since 1960 of democratization across global regions shows a substantial almost global increase in democracy levels in the late 1970s and 1980s. That suggests reasons that a multi-element and potentially algorithmic forecasting formulation can be useful. Most analyses of democratization place much emphasis on a developmental variable such as GDP per capita. Note, for instance, that the general upward movement of democracy across most developing regions could be forecast with a basic formulation tied to the traditionally-identified development drivers of democracy, including income and education increase. Again, however, this historical pattern, with a clear dip in the early years of the post-1960 period and an accelerated advance in the later decades is consistent with a global wave that a formulation tied only to quite steadily growing long-term developmental variables could not generate. Further, a formulation tied only to such drivers would be unlikely to generate initial conditions for 1960 or 2010 consistent with the actual history, because country and regional values in those years also reflect historical path dependencies.&lt;br /&gt;
&lt;br /&gt;
In building an initial, statistically-based formulation, we looked, as usual, at the power of two highly-correlated long-term development variables (notably GDP per capita and average education years attained by adults). The better broad developmental driving variable proved to be years of adults&#039; education. With additional exploration, however, we found a slight further advantage for the Gender Empowerment Measure, and so replaced the education variable with the GEM (which is, itself, strongly influenced by adults&#039; education). On top of that we found the size of the youth bulge (YTHBULGE) and extent of dependence on energy exports (ENX times the price ENPRI) as a share of GDP to be quite useful (see the discussions in these variables in Chapter 3 of Hughes et al. 2014).&lt;br /&gt;
&lt;br /&gt;
In the equation below, the basic IFs formulation, all terms are significant with T-scores above 2.0 in absolute terms. In earlier work we also explored a linkage to the survival/self-expression dimension of the World Value Survey, but have found that other development variables statistically force it out of the relationship.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;DEMOCPOLITYBase_{r,t}=(13.4+11.4*GEM_{r,t}-9.73*YTHBULGE_{r,t}-0.232*(ENX_{r,t}*\frac{ENPRI_{r,t}}{GDP_{r,t}}))*\mathbf{democm}_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:DEMOCPOLITYBase=basic or initial democracy using the Polity scale (in our case a combined 20-point scale built from historical democracy and autocracy series)&lt;br /&gt;
&lt;br /&gt;
:GEM=Gender Empowerment Measure (values below 1 indicate female disadvantage)&lt;br /&gt;
&lt;br /&gt;
:YTHBULGE=the youth bulge, the population aged 15–29 as a portion of the entire adult population&lt;br /&gt;
&lt;br /&gt;
:ENX=energy exports in physical terms (billion barrels of oil equivalent)&lt;br /&gt;
&lt;br /&gt;
:ENPRI=energy price per barrel&lt;br /&gt;
&lt;br /&gt;
:GDP=gross domestic product in billion constant 2000 dollars, market prices&lt;br /&gt;
&lt;br /&gt;
:&#039;&#039;&#039;democm=&#039;&#039;&#039;an exogenous multiplier for scenario analysis&lt;br /&gt;
&lt;br /&gt;
:r=country (geographic region in IFs terminology)&lt;br /&gt;
&lt;br /&gt;
:R-squared in 2010 = 0.41&lt;br /&gt;
&lt;br /&gt;
The initial conditions of democracy in countries carry a considerable amount of idiosyncratic, country-specific influence, much of which can be expected to erode over time. Therefore a revised base level is computed that converges over time from the base component with the empirical initial condition built in to the value expected purely on the base of the analytic formulation. The user can control the rate of convergence with a parameter that specifies the years over which convergence occurs (&#039;&#039;&#039;&#039;&#039;polconv&#039;&#039;&#039; &#039;&#039;) and, in fact, basically shut off convergence by sitting the years very high.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;DEMOCPOLITYBaseRev_{r,t}=ConvergeOverTime(DEMOCPOLITYBase_{r,t},DEMOCEXP_{r,t},\mathbf{polconv})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The endogenous movement of this basic calculation can also be overridden by the users via the specification of a target value for democracy some number of standard errors (&#039;&#039;&#039;&#039;&#039;democpolitysetar&#039;&#039;&#039; &#039;&#039;) above or below the cross-sectional estimation of the formulation and the movement of the basic value to that target over a specified number of years (&#039;&#039;&#039;&#039;&#039;democpolityseyrtar&#039;&#039;&#039; &#039;&#039;). Such targeting of important variables is done in an [http://www.du.edu/ifs/help/understand/equations/specialized/setargeting.html algorithm described elsewhere].&lt;br /&gt;
&lt;br /&gt;
Additionally we built structures, largely algorithmic, that allow forecasting with waves of democratization influenced by the impetus provided by systemic leadership, computing the magnitude of the global wave effect for all countries (DemGlobalEffects). Those depend on the amplitude of waves (DEMOCWAVE) relative to their initial condition and on a multiplier (EffectMul) that translates the amplitude into effects on states in the system. Because democracy and democratic wave literature often suggests that the countries in the middle of the democracy range are most susceptible to movements in the level of democracy, the analytic function enhances the affect in the middle range and dampens it at the high and low ends.&lt;br /&gt;
&lt;br /&gt;
The democratic wave amplitude is a level that shifts over time (DemocWaveShift) with a normal maximum amplitude (&#039;&#039;&#039;&#039;&#039;democwvmax&#039;&#039;&#039; &#039;&#039;) and wave length (&#039;&#039;&#039;&#039;&#039;democwvlen&#039;&#039;&#039; &#039;&#039;), both specified exogenously, with the wave shift controlled by a endogenous parameter of wave direction that shifts with the wave length (DEMOCWVDIR). The normal wave amplitude can be affected also by impetus towards or away from democracy by a systemic leader (DemocImpLead), assumed to be the exogenously specified impetus from the United States (&#039;&#039;&#039;&#039;&#039;democimpus&#039;&#039;&#039; &#039;&#039;) compared to the normal impetus level from the U.S. (&#039;&#039;&#039;&#039;&#039;democimpusn&#039;&#039;&#039; &#039;&#039;) and the net impetus from other countries/forces (&#039;&#039;&#039;&#039;&#039;democimpoth&#039;&#039;&#039; &#039;&#039;).&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;DEMOCWAVE_t=DEMOCWAVE_{t-1}+DemocimpLead+\mathbf{democimpoth}+DemocWaveShift&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;DemocimpLead=\frac{(\mathbf{democimpus}-\mathbf{democimpusn})*\mathbf{eldemocimp}}{\mathbf{democwvlen}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;DemocWaveShift=\frac{\mathbf{democwvmax}}{\mathbf{democwvlen}}*DEMOCWVDIR&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Our historical analysis suggests the waves could have magnitudes (trough to peak) of as much as 6 points on the 20-point Polity scale of combined democracy and autocracy, although we found in historical analysis that downward shifts tend to be only one-third as great as upward movements. We found that the swings appear greatest in the anocracies, and that countries with higher incomes appear unaffected by them. We have structured and then &amp;quot;tuned&amp;quot; the general IFs representation of such effects so that the representation appears generally consistent with behavior over our 1960–2010 period of historical analysis. Nonetheless, we have no basis for forecasting the impetus that the U.S. or other systemic leadership might provide in the future, and we therefore set parameters for forecasting so that the effect is neutralized unless model users decide to introduce such an impetus on a scenario basis. The parameter for the U.S. impetus (&#039;&#039;&#039;&#039;&#039;democimpus&#039;&#039;&#039; &#039;&#039;) is set equal to the parameter for &amp;quot;normal&amp;quot; impetus (&#039;&#039;&#039;&#039;&#039;democimpusn&#039;&#039;&#039; &#039;&#039;), and that for other sources of impetus (&#039;&#039;&#039;&#039;&#039;democimpoth&#039;&#039;&#039; &#039;&#039;) is set to 0.&lt;br /&gt;
&lt;br /&gt;
On top of the country-specific calculation and the global wave effect sits an (optional) regional or swing state effect calculation (SwingEffects), turned on by setting the swing states parameter (&#039;&#039;&#039;&#039;&#039;swseffects&#039;&#039;&#039; &#039;&#039;) to 1. The countries set as default neighborhood leaders are Brazil, Indonesia, Mexico, Nigeria, Pakistan, Russian Federation, South Africa, Turkey, and the Ukraine.&lt;br /&gt;
&lt;br /&gt;
The swing effects term has three components. The first is a world effect, whereby the democracy level in any given state (the &amp;quot;swingee&amp;quot;) is affected by the world average level, with a parameter of impact (&#039;&#039;&#039;&#039;&#039;swingstdem&#039;&#039;&#039; &#039;&#039;) and a time adjustment (&#039;&#039;&#039;&#039;&#039;timeadj&#039;&#039;&#039; &#039;&#039;). The second is a regionally powerful state factor, the regional &amp;quot;swinger&amp;quot; effect, with similar parameters. The third is a swing effect based on the average level of democracy in the region (RgDemoc). The size of the swing effects is further constrained algorithmically by an external parameter (&#039;&#039;&#039;&#039;&#039;swseffmax&#039;&#039;&#039; &#039;&#039;), not shown in the equation below.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;SwingEffects_{r,t}=timeadj*\mathbf{swingstsdem}_{r=Swinger,p=1}*(WDemoc_{t-1}-DEMOCPOLITY_{r=Swingee,t-1}+timadj*\mathbf{swingstdem_{r=Swinger,p=2}}*(DEMOCPOLITY_{r=Swinger,t-1}-DEMOCPOLITY_{r=Swingee,t-1})+timadj*\mathbf{swingstdem_{r=Swinger,p=3}}*(RgDemoc-DEMOCPOLITY_{r=Swingee,t-1})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where timeadj=.2&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WDemoc_{t-1}=\frac{\sum^RDEMOCPOLITY_{r,t-1}}{R}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
else&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;SwingEffects_{r,t}=0&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
David Epstein of Columbia University did extensive estimation of the parameters (the adjustment parameter on each term is 0.2). Unfortunately, the levels of significance were inconsistent across swing states and regions. Moreover, the term with the largest impact is the global term, already represented somewhat redundantly in the democracy wave effects. Hence, these swing effects are normally turned off (the sweffects parameter is 0 in the Base Case scenario) and are available for optional use.&lt;br /&gt;
&lt;br /&gt;
Further, we anticipated and explored for an impact of internal war on democratization, as discussed in some of the literature. Although there is a cross-sectional relationship, it is weak. Further, when the variable is added to a formulation with a long-term driver such as GEM, it actually reverses sign (more war is associated with greater democracy) and the significance drops further. One of the analytical difficulties is that a number of countries, like India and Israel, are both democratic and prone to internal conflict. Internal conflict conceptualization and measurement probably need refinement to take into consideration the actual threat level that internal war poses to regimes. We have explored the relationship using the PITF data on conflict magnitude rather than simply event occurrence and have found similar difficulties. Given our analysis, we have not built a relationship from intrastate conflict into our forecasting of democracy.&lt;br /&gt;
&lt;br /&gt;
Thus the final equation for democracy adds the global wave effects and the swing effects (both turned off in the base case) to the revised basic calculation of it.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;DEMOCPOLITY_{r,t}=DEMOCPOLITYBaseRev_{r,t}+SwingEffects_{r,t}+DemGlobalEffects_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
IFs has the capability of doing an historical simulation between 1960 and 2010 so that we can compare with data. We undertook such an analysis using the basic democratization formulation and wave-based modifications to it described above. Although we introduced an historical wave exogenously, no other interventions were made to affect the course of the forecasts for level of democracy. The R-squared in a cross-sectional analysis comparing the IFs regional forecast for 2010 against Polity data was 0.69 and the value across the entire time period was 0.78. That provides a false sense of the accuracy of our historical forecasts, however. At the country level the R-squared in 2010 was only 0.09 and the value over the entire 50-year period was 0.37. IFs expected higher values than proved to be the case for countries including Qatar, Singapore, Cuba, Kuwait, and Belarus. IFs expected lower values than Polity data show for countries including Nigeria, Ethiopia, Bangladesh and Moldova.&lt;br /&gt;
&lt;br /&gt;
Most significantly, IFs failed to anticipate the large rise in democracy in Africa in the 1990s. More generally, however strong our basic formulations for forecasting democracy may become, they are unlikely to foresee the timing of transitions toward or away from democracy. One approach to helping with that is to try to assess the pressures or unmet demand for democracy. As a small step in that direction, and using the concept of democratic deficit that Chapter 2 introduced, the model also computes an expected democracy variable (DEMOCEXP) directly from the equation above without exogenous multiplier or convergence to the function. This is useful for those who wish to see the magnitude of a country&#039;s democratic deficit or surplus by comparing DEMOC with DEMOCEXP. In fact, in advance of the Arab spring of 2011, IFs analysis (Cilliers, Hughes, and Moyer 2011) had identified the Middle East and North Africa as having exceptionally large democratic deficits.&lt;br /&gt;
&lt;br /&gt;
Although we use the Polity democracy measure as our central indicator of regime type (including its use in the more general measure of governance inclusiveness) IFs also calculates in a simpler fashion a FREEDOM measure (combining the Freedom House political rights and civil liberties scales into one scale running from least to most free). Specifically, the drivers are GDP per capita and adult educational attainment, our two standard long-term development drivers. Interestingly, the R-squared between the democracy and freedom measures in 2010 (using data from both projects) is 0.686 and that in 2060 (using forecasts of IFs for both measures) is a nearly identical 0.689. This suggests that the long-term driver variables in our formulations are doing a quite good job of representing the similarities and differences in the two measures.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;FREEDOM_{r,t}=(6.3718+1.6659*ln(GDPPCP_{r,t})+0.1293*EDYRSAG15_{r,t})*\mathbf{freedomm}_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:FREEDOM=freedom using 14-point Freedom House scale (PL and CL summed), inverted so that higher is more free&lt;br /&gt;
&lt;br /&gt;
:GDPPCP=GDP per capita at purchasing power parity in thousand dollars&lt;br /&gt;
&lt;br /&gt;
:EDYRSAG15=average years of education for adults aged 15 or older&lt;br /&gt;
&lt;br /&gt;
:&#039;&#039;&#039;freedomm&#039;&#039;&#039;=an exogenous multiplier for the model user&lt;br /&gt;
&lt;br /&gt;
:R-squared=0.402&lt;br /&gt;
&lt;br /&gt;
Although IFs uses the Polity measure of democracy (DEMOCPOLITY) as its main measure of more formal, electoral inclusion, Freedom House&#039;s freedom measure (FREEDOM) is a logical alternative and the second of that measure&#039;s sub-dimensions, civil liberties, is a more inclusive measure. We therefore compute it also, using again GDP per capita and educational years (of all adults, not just females) as drivers. And there is a brute force multiplier for it also (&#039;&#039;&#039;&#039;&#039;freedomm&#039;&#039;&#039; &#039;&#039;). There is no SE targeting mechanism in place for the freedom variable.&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Equations: Gender Empowerment&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
It is not surprising that a measure of women&#039;s inclusion, such as the Gender Empowerment Measure (GEM) of the UNDP, should correlate highly with GDP per capita or years of formal education of adult women. As we have seen, income and education are closely correlated and one or the other is almost invariably a key driver in our forecasts of change in governance. It is perhaps more surprising, in the formulation below, that together they both make statistically significant contributions to GEM. The relationship between GDP per capita and the GEM has shifted over time—the advance of global education, even in countries with low levels of income, helps explain that shift and almost certainly helps account for the independent contribution of education to higher levels of female empowerment. Interestingly, women&#039;s education does not differ in its statistical contribution from that of men; we nonetheless use that of women in our formulation.&lt;br /&gt;
&lt;br /&gt;
One might expect a strong relationship between total fertility rate and GEM as women who bear fewer children rise in other ways in society. There is, in fact, a strong correlation. Interestingly, however, a stronger one inversely relates the size of the youth bulge to the GEM. The IFs formulation is:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GEM_{r,t}=(0.4429+0.003401*GDPPCP_{r,t}+0.0271*EDYRSAG15_{r,g=f,t}-0.506*YTHBULGE_{r,t})*\mathbf{gemm}_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:GEM=UNDP Gender Empowerment Measure&lt;br /&gt;
&lt;br /&gt;
:GDPPCP=GDP per capita at purchasing power parity in thousand dollars&lt;br /&gt;
&lt;br /&gt;
:EDYRSAG15=average years of education for females age 15 or older&lt;br /&gt;
&lt;br /&gt;
:YTHBULGE=youth bulge, the population aged 15–29 as a portion of the entire adult population&lt;br /&gt;
&lt;br /&gt;
:&#039;&#039;&#039;gemm&#039;&#039;&#039;=an exogenous multiplier for scenario analysis&lt;br /&gt;
&lt;br /&gt;
:R-squared in 2010=0.66&lt;br /&gt;
&lt;br /&gt;
We experimented with a variation on the above formulation in which GDP per capita enters in a logged term, and found nearly as high an R-squared (0.64). However, a problem in longer-term forecasting with such a variation is that the saturation of the log of GDP per capita nearly stops growth in GEM for more developed countries, often well below parity for women.&lt;br /&gt;
&lt;br /&gt;
A user can control the progression of gender empowerment with a simple multiplier (&#039;&#039;&#039;&#039;&#039;gemm&#039;&#039;&#039; &#039;&#039;) or via setting a target value for it movement to some number of standard errors above or below a cross-sectionally estimated function (&#039;&#039;&#039;&#039;&#039;gemsetar&#039;&#039;&#039; &#039;&#039;) across a set number of years (&#039;&#039;&#039;&#039;&#039;gemseyrtar&#039;&#039;&#039; &#039;&#039;).&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Governance Indices&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
[[Governance#Governance|IFs represents three dimensions of governance (security, capacity, and inclusion) and uses two sub-dimensions for each]]. Just as the dimensions themselves show considerable conceptual independence, the sub-dimensions tend not to be highly correlated.&lt;br /&gt;
&lt;br /&gt;
Thus there is value in creating an index for each of the three governance dimensions that integrates the two variables representing them as well as an overall index. We have taken the typical basic approach to index construction when there is no clear external referent against which to judge the validity of the resultant index; that is, we have scaled each variable from 0 to 1 and averaged the two variables that make up each dimension. The resultant indices, GOVINDSECUR, GOVINDCAPAC, and GOVINDINCLUS, each have a global average value near 0.5, but the distribution of countries across the component measures varies; for instance, because the intrastate conflict variable of the security index exhibits a power-law distribution, the global average of the security measure is slightly higher than that of the other two indices. The security index uses 1.0 minus the average of the probability of intrastate war and the IFs performance risk index—the relative infrequency of intrastate war causes many states to cluster near 1.0 in the former formulation.&lt;br /&gt;
&lt;br /&gt;
In computing the index for governance capacity, we do not attribute increased capacity to countries when the revenue to GDP ratio rises above 0.45. Migdal (1988: 281) and Joshi (2011) suggest that the appropriate upper limit is 0.30, but their focus is on central government; our own analysis suggests that local government can on average for high-income countries add another 0.15 (15 percent of GDP) to that ratio.&lt;br /&gt;
&lt;br /&gt;
Finally, we compute an overall governance index (GOVINDTOTAL) as the simple average across the three dimensions. Just as the rankings of countries on the three dimensional indices provide some face or subjective validity to the indices, the rankings on the combined index likely correspond to the general perceptions that most analysts have.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Performance Risk Analysis Form&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
IFs includes a Performance Risk Index (GOVRISK) and an associated display to facilitate Performance and Risk Analysis, for instance by changing the weight of variables in the index. The design is intended primarily for analysis of single countries, but the form allows also consideration of country groups. It also facilitates comparison of alternative scenarios, mainly to display single country characteristics, but with the ability to switch to groups, compare different scenarios, different countries or groups.&lt;br /&gt;
&lt;br /&gt;
The overall risk form and index build on nine categories of variables:&lt;br /&gt;
&lt;br /&gt;
:The first three categories correspond to the three dimensions of governance in IFs but do not use precisely the same sub-dimensional variables (in part because the performance risk index is itself a sub-dimension of security and that would create a circularity, but partly also because the risk index is meant to be a dynamic assessment vehicle that allows users to tailor the analysis to their own understanding of what constitutes risk. The three governance dimensions and variables used in the index are: security (instability and internal war); capacity (corruption and effectiveness); and inclusion (democracy, freedom, and the gender empowerment measure).&lt;br /&gt;
&lt;br /&gt;
:The next three categories in the index are associated with drivers that many analysts have associated with country risk. The categories and associated variables are: population (youth bulge, elderly bulge [with a 0-weighting for the developing country oriented analysis of interest to most form users], and urbanization rate); environment (water use as a portion of renewable supplies and climate change); international (power transition).&lt;br /&gt;
&lt;br /&gt;
:The final three categories in the index represent specific arenas of government and societal performance. Again with associated variables they are: the economy (poverty, inequality, resource export dependence, and per capita GDP growth rate); health (infant mortality, life expectancy, malnutrition and HIV prevalence); and education (primary net enrollment and years of formal education of adults).&lt;br /&gt;
&lt;br /&gt;
Information about each country across variables is organized into two clusters of columns. The first cluster provides information about values and ranks:&lt;br /&gt;
&lt;br /&gt;
:The Value column is the actual IFs forecast for each specific variable (for instance, the life expectancy for Angola in 2010 reflects data and is near 50.&lt;br /&gt;
&lt;br /&gt;
:The Min Level and Max Level columns indicate the overall range over which each variable varies across counties and time. These levels are constant across years and countries. They are used in computing the Scaled Levels.&lt;br /&gt;
&lt;br /&gt;
:The Scaled Level column uses the minimum and maximum levels to scale values for each country from 0 to 1. The scaling takes into account the valence of each variable (that is, infant mortality is bad and life expectancy is good). The Summary Measure in the last row of this column is a weighted average of the scaled levels on each variable; this computation is saved as the GOVRISK variable in our forecast files for each country and each year&lt;br /&gt;
&lt;br /&gt;
:The Global Rank column indicates how each country ranks among all countries on each variable. The Summary Measure in the last row at the bottom of the column uses a weighted average of the ranks for each variable to compute the ordinal position of the country when sorting across all countries. Lower Ranks indicate higher risk levels (or worst performance). Clicking on any cell in this column provides a pop-up option for showing the rank of all countries on specific variables or the Summary Measure.&lt;br /&gt;
&lt;br /&gt;
:The Weighting column determines how the variables are combined in computing the summary Scaled Levels and Global Ranks of a country. Clicking on any cell in that column allows the user to change the weight for the associated variable.&lt;br /&gt;
&lt;br /&gt;
[[File:Govchart04.png|frame|center|Performance Risk Index]]&lt;br /&gt;
&lt;br /&gt;
:The color for each variable in the Value column indicates the position of the value relative to the alert and goal levels. Values between the alert and goal levels are yellow, values on undesirable side of the alert level (depending on the valence of the variable) are red, and values on the desirable side of the goal level are green. For the Summary Measure the color coding is a bit different: .red indicates the 40 countries performing least well in the aggregate (numbers 1 through 40 in the Global Rank column), green shows the 40 countries doing best; yellow indicates all other countries.&lt;br /&gt;
&lt;br /&gt;
The second cluster of columns provides evaluation information. Evaluation can be either absolute or relative to income (actually GDP per capita), as determined by the menu option that toggles between those two forms (the column cluster heading changes also with the toggle value). The default approach is absolute evaluation, setting up comparison of countries and evaluation of their performance independently of their development level.&lt;br /&gt;
&lt;br /&gt;
The relative or income-adjusted evaluation approach takes into account the GDP per capita of the country and has a &amp;quot;benchmarking&amp;quot; character. That is, evaluation of countries takes into account the GDP per capita at PPP of countries, expecting different performance at difference levels. The expectations upon which relative evaluation occurs are related to cross-sectionally estimated relationships of the Values for each variable across all countries. For instance, the cross-sectional relationship for Inequality using the Gini index (on the Y-axis) as a function of GDP per capita at PPP (on the X-axis) is the following:[[File:Govchart10.gif|frame|right|Inequality using the Gini index as a function of GDP per capita at PPP]]&lt;br /&gt;
&lt;br /&gt;
Higher values indicate poorer performance or more risk and Colombia is shown on this figure as having a considerably higher than expected level of inequality. We would expect Colombia to be evaluated poorly on this variable both in absolute terms and relative to its income level.&lt;br /&gt;
&lt;br /&gt;
The columns in the Evaluation cluster are:&lt;br /&gt;
&lt;br /&gt;
:Goal and Alert Levels will change depending on the evaluation method. When using absolute evaluation, the level values will not vary across countries (we have set absolute Goal and Alert Levels exogenously based on our own analysis across countries). When using income-adjusted or relative evaluation, the values will be recomputed based on the GDP per capita level of a specific country in a given year. Specifically, in income-adjusted evaluation the Goal Levels are generally set at the value of the function for the GDP per capita of the country in the year being analyzed. The Alert Levels are generally 1 or 2 standard errors below or above the value of the function;&amp;lt;sup&amp;gt;[[http://www.du.edu/ifs/help/understand/governance/performance.html#footnote 1]]&amp;lt;/sup&amp;gt; below or above depends on whether higher or lower values indicate better performance.&lt;br /&gt;
&lt;br /&gt;
:The third evaluation column will show the Standard Deviation of Values for all countries around the global mean in the case of Absolute Evaluation and will show the Standard Error of all countries around the function in the case of income-adjusted evaluation.&lt;br /&gt;
&lt;br /&gt;
Useful information can be obtained beyond that apparent in the table by clicking on particular cells:&lt;br /&gt;
&lt;br /&gt;
:Cells within the Value, Scaled Level, and Standard Deviation/Standard Error columns can be displayed across time by clicking on them and selecting the pop-up menu option.&lt;br /&gt;
&lt;br /&gt;
:You can generate a rank-ordered list of countries based on a given variable by clicking on a cell in the Global Rank column and selecting the pop-up menu option.&lt;br /&gt;
&lt;br /&gt;
:Clicking on a cell in the Value column and selecting the option &amp;quot;Display All Years and All Countries Ranked&amp;quot; produces a table of all values for all countries across time with countries ranked left-to-right from riskier to less risky values in the selected year.&lt;br /&gt;
&lt;br /&gt;
:Clicking on any variable name provides a pop-up menu with useful information related to evaluation. The Cross-Sectional Relationship option on that pop-up shows the function for the variable and selected country&#039;s position relative to the function. The Provide Information option provides information on the Goal and Alert Levels for any specific variable; it also gives a set of information explaining the variable and bibliographic references when available. The Show Count option will display the number of countries in alert level, moderate risk or not at risk using absolute evaluation only.&lt;br /&gt;
&lt;br /&gt;
Additional menu options exist on the form:&amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:On the form called up by Select Multiple Scenarios holding down the Ctrl key allows selecting multiple scenarios. Once selected they can be displayed simultaneously, for instance by clicking on a cell in the Value column and selecting the pop-up option to Show Over Time.&lt;br /&gt;
&lt;br /&gt;
:On the form called up by Select Multiple Country/Regions or Groups holding down the Ctrl key allows selecting multiple countries or groups; again these can be displayed, for instance, by clicking on a cell in the Value column and requesting Show Over Time.&lt;br /&gt;
&lt;br /&gt;
:Using Countries/Regions is the default menu option geographically, but it toggles with click to Using Groups. Groups are displayed with ranks that weight country members by population (the group aggregations of Values use varying weighting variables; for instance, the climate change variable uses GDP).&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
[1] There is subjectivity in this. We mostly use 2 standard errors (11 times); next we use 1 SE (9 times: Elderly Bulge, Poverty Level, Inequality, Rate of per capita Growth, Infant Mortality, Life Expectancy, Malnutrition, Adult Education Years and Urbanization Rate); then use 0.5 twice: Democracy and Freedom,&#039; and finally we use 0.2 for GEM.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;The Broader Socio-Cultural Context&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Governance is rooted in a much broader socio-cultural context including the condition of individuals within society and the values and beliefs they hold. Much of that context is spread across the various modules of IFs. For instance, literacy and educational attainment are determined in the education model. Income levels and income distribution are in the economic model. Here we focus primarily on the aggregation of those into the summary HDI indicator and the expression of them in selected indicators of values and cultural orientations.&lt;br /&gt;
&lt;br /&gt;
To read more, please click on the links below.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Human Development&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Human development measures invariable look to such variables as life expectancy, literacy or other indication of educational attainment, income, etc. These variables are computed in other IFs models, but provide a basis for socio-political analysis.&lt;br /&gt;
&lt;br /&gt;
Literacy is a variable fundamentally tied to educational attainment. In IFs it changes from the initial level for a country because of a multiplier (LITM).&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;LIT_r=\mathbf{LIT}_{r,t=1}*LITM_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The function upon which the literacy multiplier is based represents the cross-sectional relationship globally between the percentage of adults who have completed a primary education (EDPRIPER from the education model) and literacy rate (LIT). Rather than imposing the typical literacy rate from this function (and thereby being inconsistent with initial empirical values), the literacy multiplier is the ratio of typical literacy given future adult primary completion percentage to the normal literacy level at initial primary completion percentage.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;LITM=\frac{AnalFunc(EDPRIPER)}{AnalFunc(\mathbf{EDPRIPER}_{t=1})}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
At one time the IFs system represented an aggregate view of life conditions within a society by using the Physical Quality of Life Index (PQLI) of the Overseas Development Council (ODC, 1977: 147#154). This measure averaged literacy, life expectancy, and infant mortality, first normalizing each indicator so that it ranges from zero to 100.&lt;br /&gt;
&lt;br /&gt;
The United Nations Development Program&#039;s human development index (HDI) has fully supplanted that early measure in the development literature. The HDI began as is a simple average of three sub-indices for life expectancy, education, and GDP per capita (using purchasing power parity).. The GDP per capita index is a logged form that runs from a minimum of 100 to a maximum of $40,000 per capita. The original measure in IFs differs slightly from the original HDI version, because it does not put educational enrollment rates into a broader educational index with literacy.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;HDI_r=\frac{LifeExpInd_r+LitInd+GDPInd}{3}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;LifeExpInd=\frac{LIFEEXP_r-LIFEXPMIN}{LIFEXPMAX-LIFEXPMIN}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;LitInd=LIT_r/100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GDPInd=\frac{Log(GDPPCP_r*1000)-Log(100)}{Log(40000)-Log(100)}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Although the HDI is a wonderful measure for looking at past and current life conditions, it has some limitations when looking at the longer-term future. Specifically, the fixed upper limits for life expectancy and GDP per capita are likely to be exceeded by many countries before the end of the 21st century. IFs therefore introduced a floating version of the HDI, in which the maximums for those two index components are calculated from the maximum performance of any state in the system in each forecast year.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;HDIFLOAT_r=\frac{LifeExpInd_r+LitInd+GDPInd}{3}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;LifeExpInd=\frac{LIFEEXP_r-LIFEXPMIN}{HDILIFEMAXFLOAT-LIFEXPMIN}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;LitInd=LIT_r/100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GDPInd=\frac{Log(GDPPCP_r*1000)-Log(100)}{Log(GDPPCMAX)-Log(100)}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The floating measure, in turn, has some limitations because it introduces relative attainment into the equation rather than absolute attainment. IFs therefore developed still a third version of the original HDI, one that allows the users to specify probable upper limits for life expectancy and GDPPC in the twenty-first century. Those enter into a fixed calculation of which the normal HDI could be considered a special case.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;HDI21stFIX_r=\frac{LifeExpInd_r+LitInd+GDPInd}{3}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;HDILIFEMAX21=\mathbf{hdilifemaxf}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;LifeExpInd=\frac{LIFEEXP_r-LIFEXPMIN}{HDILIFEMAX21-LIFEXPMIN}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;LitInd=LIT_r/100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;LogGDPPCP21=Log(\mathbf{hdigdppcmax}*1000)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GDPInd=\frac{Log(GDPPCP_r*1000)-Log(100)}{Log(GDPPCP21)-Log(100)}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
In 2010 the Human Development Report Office of the UNDP changed its computation of HDI and the IFs model followed suit with a new version named HDINEW. That measure moved to a different aggregation of the components, one that uses a geometric mean of the component elements. It further changed the computation by creating a revised education index that is a geometric mean of two subcomponents, mean years of schooling of adults (EDYRSAG25) and expected years of schooling of school entrants (EDYRSSLE). It continues to use life expectancy (LIFEXP) and gross national income per capita at PPP, for which IFs substitutes GDP per capita at PPP (GDPPCP).&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;HDI_r=(LifeExpInd)^{1/3}*(EdInd)^{1/3}*(GDPInd)^{1/3}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;LifeExpInd=\frac{LIFEEXP_r-LIFEXPMIN}{LIFEXPMAX-LIFEXPMIN}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EdInd=(EDYRSSLEIND)^{1/2}*(EDYRSAG25IND)^{1/2}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GDPInd=\frac{Log(GDPPCP_r*1000)-Log(100)}{Log(40000)-Log(100)}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDYRSSLEIND=EDYRSSLE/EDYRSSLEMAX&amp;lt;/math&amp;gt;&lt;br /&gt;
:&amp;lt;math&amp;gt;EDYRSAG25IND=EDYRSAG25/EDYRSAG25MAX&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
We further compute several global indicators including a world life expectancy (WLIFE) and a world literacy rate (WLIT).&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WLIFE=\frac{\sum^RLIFEXP_r*POP_r}{WPOP}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WLIT=\frac{\sum^RLIT_r*POP_r}{WPOP}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Roots of Culture: Beliefs and Values&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
IFs computes change in three cultural dimensions identified by the World Values Survey (Inglehart 1997). Those are dimensions of materialism/post-materialism (MATPOSTR), survival/self-expression (SURVSE), and traditional/secular-rational values (TRADSRAT). On each dimension the process for calculation is somewhat more complicated than for freedom or gender empowerment, however, because the dynamics for change in the cultural dimensions involves the aging of population cohorts. IFs uses the six population cohorts of the World Values Survey (1= 18-24; 2=25-34; 3=35-44; 4=45-54; 5=55-64; 6=65+). It calculates change in the value orientation of the youngest cohort (c=1) from change in GDP per capita at PPP (GDPPCP), but then maintains that value orientation for the cohort and all others as they age. Analysis of different functional forms led to use of an exponential form with GDP per capita for materialism/postmaterialism and to use of logarithmic forms for the two other cultural dimensions (both of which can take on negative values).&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;MATPOSTR_{r,c=1}=\mathbf{MATPOSTR}_{r,c=1,t=1}*\frac{AnalFunc(GDPPCP_r)}{AnalFunc(GDPPCP_{r,t=1})}+\mathbf{CultShMP}_{r=cultural}+\mathbf{matpostradd}_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;\mathbf{CultShMP_{r=cultural,t}}=F(\mathbf{MATPOSTR}_{r,c=1,t=1},AnalFunc(GDPPCP_{r,t=1})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;SURVSE_{r,c=1}=\mathbf{SURVSE}_{r,c=1,t=1}*\frac{AnalFunc(GDPPCP_r)}{AnalFunc(GDPPCP_{r,t=1})}+\mathbf{CultShSE}_{r=cultural,t}+\mathbf{survseadd}_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;\mathbf{CultShSE}_{r=culutral,t}=F(\mathbf{SURVSE_{r,c=1,t=1}},AnalFunc(GDPPCP_{r,t=1})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;TRADSRAT_{r,c=1}=\mathbf{TRADSRAT}_{r,c=1,t=1}*\frac{AnalFunc(GDPPP_r)}{AnalFunc(GDPPCP_{r,t=1})}+\mathbf{CultShTS_{r=cultural,t}}+\mathbf{tradsratadd}_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;\mathbf{CultShTS}_{r=cultural,t}=F(\mathbf{TRADSRAT_{r,c=1,t=1}},AnalFunc(GDPPCP_{r,t=1})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The user can influence values on each of the cultural dimensions via two parameters. The first is a cultural shift factor (e.g. CultSHMP) that affects all of the IFs countries/regions in a given cultural region as defined by the World Value Survey. Those factors have initial values assigned to them from empirical analysis of how the regions differ on the cultural dimensions (determined by the pre-processor of raw country data in IFs), but the user can change those further, as desired. The second parameter is an additive factor specific to individual IFs countries/regions (e.g. matpostradd). The default values for the additive factors are zero.&lt;br /&gt;
&lt;br /&gt;
Some users of IFs may not wish to assume that aging cohorts carry their value orientations forward in time, but rather want to compute the cultural orientation of cohorts directly from cross-sectional relationships. Those relationships have been calculated for each cohort to make such an approach possible. The parameter (wvsagesw) controls the dynamics associated with the value orientation of cohorts in the model. The standard value for it is 2, which results in the &amp;quot;aging&amp;quot; of value orientations. Any other value for wvsagesw (the WVS aging switch) will result in use of the cohort-specific functions with GDP per capita.&lt;br /&gt;
&lt;br /&gt;
Regardless of which approach to value-change dynamics is used, IFs calculates the value orientation for a total region/country as a population cohort-weighted average.&lt;br /&gt;
&lt;br /&gt;
Although we have explored the forward linkages of value change to other variables, including democracy, the IFs project has not given either the forecasting of value/culture change nor the impacts of it the attention they deserve. This is a great opportunity for creative thinking and modeling in the future.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Governance Bibliography&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. and Jong-Wha Lee. 2001. &amp;quot;International Data on Educational Attainment: Updates and Implications,&amp;quot;&amp;amp;nbsp;&#039;&#039;Oxford Economic Papers&#039;&#039;&amp;amp;nbsp;53(3): 541-563.&lt;br /&gt;
&lt;br /&gt;
Cilliers, Jakkie, Barry Hughes, and Jonathan Moyer. 2011.&amp;amp;nbsp;&#039;&#039;African Futures 2050: The Next 40 Years&#039;&#039;. Pretoria, South Africa and Denver, Colorado: Institute for Security Studies and Frederick S. Pardee Center for International Futures.&lt;br /&gt;
&lt;br /&gt;
Correlates of War Project. 2011. “State System Membership List, v2011.” Online,&amp;amp;nbsp;[http://correlatesofwar.org/ http://correlatesofwar.org&amp;amp;nbsp;].&lt;br /&gt;
&lt;br /&gt;
Diamond, Larry. 1992. “Economic Development and Democracy Reconsidered.”&amp;amp;nbsp;&#039;&#039;American Behavioral Scientist&#039;&#039;&amp;amp;nbsp;35(4/5): 450-499.&lt;br /&gt;
&lt;br /&gt;
Diehl, Paul F., ed. 1999.&amp;amp;nbsp;&#039;&#039;A Roadmap to War: Territorial Dimensions of International Conflict&#039;&#039;, 1&amp;lt;sup&amp;gt;st&amp;lt;/sup&amp;gt;&amp;amp;nbsp;ed. Nashville: Vanderbilt University Press.&lt;br /&gt;
&lt;br /&gt;
Easton, David. 1965.&amp;amp;nbsp;&#039;&#039;A Framework for Political Analysis&#039;&#039;. Englewood Cliffs, New Jersey: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Esty, Daniel C., Jack A. Goldstone, Ted Robert Gurr, Barbara Harff, Marc Levy, Geoffrey D. Dabelko, Pamela Surko, and Alan N. Unger. 1998. “State Failure Task Force Report: Phase II Findings.” Study Commissioned by the Central Intelligence Agency and George Mason University School of Public Policy. Political Instability Task Force, Arlington VA.&lt;br /&gt;
&lt;br /&gt;
Freedom House, Inc. 2009.&amp;amp;nbsp;&#039;&#039;Freedom in the World 2009: The Annual Survey of Political Rights and Civil Liberties&#039;&#039;. Washington, DC: Freedom House, Inc.\&lt;br /&gt;
&lt;br /&gt;
Goldstone, Jack A. 2010. “The New Population Bomb”&amp;amp;nbsp;&#039;&#039;Foreign Affairs&#039;&#039;&amp;amp;nbsp;(January/February): 31-43.&lt;br /&gt;
&lt;br /&gt;
Goldstone, Jack A., Robert H. Bates, David L. Epstein, Ted Robert Gurr, Michael B. Lustik, Monty G. Marshall, Jay Ulfelder, and Mark Woodward. 2010. “A Global Model for Forecasting Political Instability.”&amp;amp;nbsp;&#039;&#039;American Journal of Political Science&#039;&#039;&amp;amp;nbsp;54(1): 190-208. doi: 10.1111/j.1540-5907.2009.00426.x.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2001. “Global Social Transformation: The Sweet Spot, the Steady Slog, and the Systemic Shift.”&amp;amp;nbsp;&#039;&#039;Economic Development and Cultural Change&#039;&#039;&amp;amp;nbsp;49(2): 423-458. doi: 10.1086/452510.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2002. &amp;quot;Threats and Opportunities Analysis,&amp;quot; working document prepared for the Strategic Assessments Group, Office of Transnational Issues, Central Intelligence Agency.&amp;amp;nbsp; Available on the IFs project web site at&amp;amp;nbsp;[http://www.ifs.du.edu/ www.ifs.du.edu].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., and Anwar Hossain. 2003. “Long-Term Socio-Economic Modeling: With Universal, Globally-Integrated Social Accounting Matrices (SAMs) in a General Equilibrium Model Structure.” Working Paper, University of Denver, Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/assets/documents/economyandsamdocument46.pdf http://www.ifs.du.edu/assets/documents/economyandsamdocument46.pdf]&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., Devin Joshi, Jonathan Moyer, Timothy Sisk and José Roberto Solórzano. 2014.&amp;amp;nbsp;&#039;&#039;Strengthening Governance Globally.&amp;amp;nbsp;&#039;&#039;vol. 5, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Huntington, Samuel P. 1991.&amp;amp;nbsp;&#039;&#039;The Third Wave: Democratization in the Late Twentieth Century&#039;&#039;. Norman, OK: University of Oklahoma.&lt;br /&gt;
&lt;br /&gt;
Inglehart, Ronald. 1997.&amp;amp;nbsp;&amp;amp;nbsp;&#039;&#039;Modernization and Postmodernization&#039;&#039;.&amp;amp;nbsp; Princeton: PrincetonUniversity Press.&lt;br /&gt;
&lt;br /&gt;
Joshi, Devin. 2011a. “Good Governance, State Capacity, and the Millennium Development Goals.”&amp;amp;nbsp;&#039;&#039;Perspectives on Global Development and Technology&amp;amp;nbsp;&#039;&#039;10(2): 339-360. doi: 10.1163/156914911X5824.68.&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi. 2010. “The Worldwide Governance Indicators: Methodology and Analytical Issues.” World Bank Policy Research Working Paper no. 5430. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Marshall, Monty G. and Benjamin R. Cole. 2008. “Global Report on Conflict, Governance and State Fragility 2008.”&amp;amp;nbsp;&#039;&#039;Foreign Policy Bulletin&#039;&#039;&amp;amp;nbsp;18: 3-21. doi: 10.1017/S1052703608000014.&lt;br /&gt;
&lt;br /&gt;
Marshall, Monty G., and Benjamin R. Cole. 2009. “Global Report 2009: Conflict, Governance, and State Fragility.” Vienna, VA.: Center for Systemic Peace and Center for Global Policy.&lt;br /&gt;
&lt;br /&gt;
Marshall, Monty G., and Benjamin R. Cole. 2011. &amp;quot;Global Report 2011: Conflict, Governance, and State Fragility.&amp;quot; Vienna, VA. Center for Systemic Peace.&lt;br /&gt;
&lt;br /&gt;
Marshall, Monty G., and Keith Jaggers. 2011. “Polity IV Project: Political Regime Characteristics and Transitions 1800-2010.”&amp;amp;nbsp;[http://www.systemicpeace.org/polity/polity4.htm http://www.systemicpeace.org/polity/polity4.htm]&amp;amp;nbsp;[accessed December 22 2012]&lt;br /&gt;
&lt;br /&gt;
Mauro, Paolo. 1995. “Corruption and Growth.”&amp;amp;nbsp;&#039;&#039;The Quarterly Journal of Economics&#039;&#039;&amp;amp;nbsp;110(3) (August): 681-712.&lt;br /&gt;
&lt;br /&gt;
Migdal, Joel. 1988.&amp;amp;nbsp;&#039;&#039;Strong Societies and Weak Sates: State-Society Relations and State Capabilities in the&amp;amp;nbsp;Third World&#039;&#039;. Princeton: Princeton University Press&lt;br /&gt;
&lt;br /&gt;
Mo, Pak Hung. 2001. “Corruption and Economic Growth.”&amp;amp;nbsp;&#039;&#039;Journal of Comparative Economics&amp;amp;nbsp;&#039;&#039;29(1) (March): 66-79. doi:10.1006/jcec.2000.1703.&lt;br /&gt;
&lt;br /&gt;
North, Douglass C., John Joseph Wallis, and Barry R. Weingast. 2009.&amp;amp;nbsp;&#039;&#039;Violence and Social Orders: A Conceptual Framework for Interpreting Recorded Human History&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Pierson, Paul. 2004.&amp;amp;nbsp;&#039;&#039;Politics in Time: History, Institutions, and Social Analysis&#039;&#039;. Princeton, NJ: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Rice, Susan E., and Stewart Patrick. 2008.&amp;amp;nbsp;&#039;&#039;Index of State Weakness in the Developing World.&#039;&#039;&amp;amp;nbsp;Washington, DC: The Brookings Institution.&lt;br /&gt;
&lt;br /&gt;
Shihata, Ibrahim F. I. 1996. “Corruption - A General Review with an Emphasis on the Role of the World Bank.”&amp;amp;nbsp;&#039;&#039;Dickinson Journal of International Law&#039;&#039;&amp;amp;nbsp;15: 451.&lt;br /&gt;
&lt;br /&gt;
Tanzi, Vito. 1998. “Corruption Around the World: Causes, Consequences, Scope, and Cures.” Staff Papers - International Monetary Fund 45(4) (December): 559-594.&lt;br /&gt;
&lt;br /&gt;
Urdal, H. 2004. “The devil in the demographics: the effect of youth bulges on domestic armed conflict, 1950-2000.” Social Development Papers: Conflict and Reconstruction Paper 14.&lt;br /&gt;
&lt;br /&gt;
Ware, H. 2004. “Pacific instability and youth bulges: the devil in the demography and the economy.” Paper delivered at the 12th Biennial Conference of the Australian Population Association, 15-17.&lt;br /&gt;
&lt;br /&gt;
Wagner, Adolph. 1892.&amp;amp;nbsp;&#039;&#039;Grundlegung der Politischen Ökonomie&#039;&#039;. Leipzig: C.F. Winter Publishing Firm.&lt;br /&gt;
&lt;br /&gt;
World Bank. 2011.&amp;amp;nbsp;&#039;&#039;World Development Indicators 2011.&#039;&#039;&amp;amp;nbsp;Washington, DC: World Bank. Available at&amp;amp;nbsp;[http://data.worldbank.org/data-catalog/world-development-indicators http://data.worldbank.org/data-catalog/world-development-indicators].&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8315</id>
		<title>Introduction to IFs</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8315"/>
		<updated>2017-09-07T21:43:43Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Purposes&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;International Futures (IFs) is a tool for thinking about long-term global trends and planning more strategically for the&amp;amp;nbsp;future.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
IFs can help you:&lt;br /&gt;
&lt;br /&gt;
*Understand&amp;amp;nbsp;the state of major&amp;amp;nbsp;global systems&lt;br /&gt;
*Explore&amp;amp;nbsp;long-term trends and consider where they might take&amp;amp;nbsp;us&lt;br /&gt;
*Learn about the dynamic&amp;amp;nbsp;interactions between global systems&lt;br /&gt;
*Clarify long-term organizational goals/priorities&lt;br /&gt;
*Develop alternative scenarios (if-then statements) about the future&lt;br /&gt;
*Investigate how different groups (households, firms or governments) can shape the future&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;IFs development and analysis depend&amp;amp;nbsp;on core, underlying assumptions, including the following.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*Global issues are becoming&amp;amp;nbsp;more significant as the scope of human interaction and human impact on the broader environment grow.&lt;br /&gt;
*Goals and priorities for human systems are becoming clearer and are more frequently and consistently communicated.&lt;br /&gt;
*Understanding of the dynamics of human systems is improving&amp;amp;nbsp;rapidly.&lt;br /&gt;
*The domain of human choice and action is broadening.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;What issues can you&amp;amp;nbsp;investigate with IFs?&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*[[Environment]]: Atmospheric carbon dioxide levels, world forest area, fossil fuel usage&lt;br /&gt;
*[[Socio-Political|Socio-Political Change]]: Life expectancy, literacy rate, democracy level, status of women, value change&lt;br /&gt;
*[[Population|Demographics]]: Population levels and growth, fertility, mortality, migration&lt;br /&gt;
*[[Agriculture|Food and Agriculture]]: Land use and production levels, calorie availability, malnutrition rates&lt;br /&gt;
*[[Energy]]: Resource and production levels, demand patterns, renewable energy share&lt;br /&gt;
*[[Economics]]: Sectoral production, consumption, and trade patterns and structural change&lt;br /&gt;
*[[Interstate_Politics_(IP)|Geopolitics]]: Country and regional power levels&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Visual Representation&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
[[File:IFsOverviewChart.jpg|frame|center|Visual representation of IFs structure]]&lt;br /&gt;
&lt;br /&gt;
Among the philosophical premises of the International Futures (IFs) project is that the model cannot be a &amp;quot;black box&amp;quot; to users and be truly useful. Model users must be able to examine the structures of IFs in order (1) to have confidence in them, and (2) learn from them.&lt;br /&gt;
&lt;br /&gt;
There is available (see topics under Understanding the Mode in the contents of this help system):&lt;br /&gt;
&lt;br /&gt;
*[[Understand_IFs#Dominant_Relations|Dominant Relations]] of the model structure&lt;br /&gt;
*[[Understand_IFs#2.2|Structure-Based and Agent-Class Driven Modeling]]&lt;br /&gt;
*[[Understand_IFs#Equation_Notation|Equation Notation]]&lt;br /&gt;
*[[Introduction_to_IFs#IFs_Bibliography|IFs Bibliography]] of data and data sources&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Quick Survey&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;population&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents 22 age-sex cohorts to age 100+&lt;br /&gt;
*calculates change in fertility and mortality rates in response to income, income distribution, and analysis multipliers&lt;br /&gt;
*computes average life expectancy at birth, literacy rate, and overall measures of human development (HDI) and physical quality of life&lt;br /&gt;
*represents migration and HIV/AIDS&lt;br /&gt;
*includes a newly developing submodel of formal education across primary, secondary, and tertiary levels&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;economic&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents the economy in six sectors: agriculture, materials, energy, industry, services, and ICT (other sectors could be configured, using raw data from the GTAP project)&lt;br /&gt;
*computes and uses input-output matrices that change dynamically with development level&lt;br /&gt;
*is a general equilibrium-seeking model that does not assume exact equilibrium will exist in any given year; rather it uses inventories as buffer stocks and to provide price signals so that the model chases equilibrium over time&lt;br /&gt;
*contains an endogenous production function that represents contributions to growth in multifactor productivity from R&amp;amp;D, education, worker health, economic policies (&amp;quot;freedom&amp;quot;), and energy prices (the &amp;quot;quality&amp;quot; of capital)&lt;br /&gt;
*uses a Linear Expenditure System to represent changing consumption patterns&lt;br /&gt;
*utilizes a &amp;quot;pooled&amp;quot; rather than the bilateral trade approach for international trade&lt;br /&gt;
*is being imbedded during 2002 in a social accounting matrix (SAM) envelope that will tie economic production and consumption to intra-actor financial flows&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;agricultural&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents production, consumption and trade of crops and meat; it also carries ocean fish catch and aquaculture in less detail&lt;br /&gt;
*maintains land use in crop, grazing, forest, urban, and &amp;quot;other&amp;quot; categories&lt;br /&gt;
*represents demand for food, for livestock feed, and for industrial use of agricultural products&lt;br /&gt;
*is a partial equilibrium model in which food stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the agricultural sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;energy&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*portrays production of six energy types: oil, gas, coal, nuclear, hydroelectric, and other renewable&lt;br /&gt;
*represents consumption and trade of energy in the aggregate&lt;br /&gt;
*represents known reserves and ultimate resources of the fossil fuels&lt;br /&gt;
*portrays changing capital costs of each energy type with technological change as well as with draw-downs of resources&lt;br /&gt;
*is a partial equilibrium model in which energy stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the energy sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The two &#039;&#039;&#039;socio-political&#039;&#039;&#039; sub-modules:&lt;br /&gt;
&lt;br /&gt;
Within countries or geographic groupings&lt;br /&gt;
&lt;br /&gt;
*represents fiscal policy through taxing and spending decisions&lt;br /&gt;
*shows six categories of government spending: military, health, education, R&amp;amp;D, foreign aid, and a residual category&lt;br /&gt;
*represents changes in social conditions of individuals (like fertility rates or literacy levels), attitudes of individuals (such as the level of materialism/postmaterialism of a society from the World Value Survey), and the social organization of people (such as the status of women)&lt;br /&gt;
*represents the evolution of democracy&lt;br /&gt;
*represents the prospects for state instability or failure&lt;br /&gt;
&lt;br /&gt;
Between countries or groupings of countries&lt;br /&gt;
&lt;br /&gt;
*traces changes in power balances across states and regions&lt;br /&gt;
*allows exploration of changes in the level of interstate threat&lt;br /&gt;
*represents possible action-reaction processes and arms races with associated potential for conflict among countries&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;environmental&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows tracking of remaining resources of fossil fuels, of the area of forested land, of water usage, and of atmospheric carbon dioxide emissions&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;technology&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows changes in assumptions about rates of technological advance in agriculture, energy, and the broader economy&lt;br /&gt;
*explicitly represents the extent of electronic networking of individuals in societies&lt;br /&gt;
*is tied to the governmental spending model with respect to R&amp;amp;D spending&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Background&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
International Futures (IFs) has evolved since 1980 through three &amp;quot;generations,&amp;quot; with a fourth generation now taking form.&lt;br /&gt;
&lt;br /&gt;
The first generation had deep roots in the world models of the 1970s, including those of the Club of Rome. In particular, IFs drew on the Mesarovic-Pestel or World Integrated Model (Mesarovic and Pestel 1974). The author of IFs had contributed to that project, including the construction of the energy submodel. IFs consciously also drew on the Leontief World Model (Leontief et al. 1977), the Bariloche Foundation’s world model (Herrera et al. 1976), and Systems Analysis Research Unit Model (SARU 1977), following comparative analysis of those models by Hughes (1980). That generation was written in FORTRAN and available for use on main-frame computers through CONDUIT, an educational software distribution center at the University of Iowa. Although the primary use of that and subsequent generations was by students, IFs has always had some policy analysis capability that has appealed to specialists. For example, the U.S. Foreign Service Institute used the first generation of IFs in a mid-career training program.&lt;br /&gt;
&lt;br /&gt;
The second generation of International Futures moved to early microcomputers in 1985, using the DOS platform. It was a very simplified version of the original IFs without regional or country differentiation.&lt;br /&gt;
&lt;br /&gt;
The third generation, first available in 1993, became a full-scale microcomputer model. The third generation improved earlier representations of demographic, energy, and food systems, but added new environmental and socio-political content. It built upon the collaboration of the author with the GLOBUS project, and it adopted the economic submodel of GLOBUS (developed by the author). GLOBUS had been created with the inspiration of Karl Deutsch and under the leadership of Stuart Bremer (1987) at the Wissenschaftszentrum in Berlin.&lt;br /&gt;
&lt;br /&gt;
The third generation has produced three editions/major releases of IFs, each accompanied by a book also called International Futures (Hughes 1993, 1996, 1999). The second edition moved to a Visual Basic platform that allowed a much improved menu-driven interface, running under Windows. The third edition incorporated an early global mapping capability and an initial ability to do cross-sectional and longitudinal data analysis.&lt;br /&gt;
&lt;br /&gt;
The fourth generation has been taking shape since early 2000. It has been heavily influenced by the usage of the model in an increasingly policy-analysis mode by several important organizations. First, General Motors commissioned a specialized version of IFs named CoVaTrA (Consumer Values Trends Analysis) with a need for updated and extended demographic modeling and representation of value change. An alliance was established with the World Values Survey, directed by Ronald Inglehart, to create that version. Second, the Strategic Assessments Group of the Central Intelligence Agency commissioned a specialized version named IFs for SAG. The work involved in preparing that greatly extended and enhanced the socio-political representations of the model, both domestic and international. Third, the European Commission sponsored a project named TERRA which has led to a specialized version named IFs for TERRA. IFs for TERRA work led to enhancements across the model, including improved representation of economic sectors, updated IO matrices and a basic Social Accounting Matrix, GINI and Lorenz curves, and preparing for extended environmental impact representation (drawing upon the Advanced Sustainability Analysis framework of the Finland Futures Research Center).&lt;br /&gt;
&lt;br /&gt;
Throughout this emergence of a fourth generation IFs (incorporating all of the above elements for additional users) there has been also a heavy emphasis on enhanced usability. Ideas from Robert Pestel in the TERRA project led to the creation of a new tree-structure for scenario creation and management. Ideas from Ronald Inglehart led to the development of the Guided Use structure and a somewhat more game-like character within that structure. Inglehart also help arrange funding to support the programming of Guided Use through the European Union Center of the University of Michigan.&lt;br /&gt;
&lt;br /&gt;
The fifth version of IFs is currently in use and represents broad strides to improving the model and its usability. It is the first version of this software to be placed online due to the help of the National Intelligence Council ([http://www.ifs.du.edu http://www.ifs.du.edu]). Also, usability has been increased as Packaged Displays and Flex Packaged Displays were introduced that allowed for the creation of very specific lists of countries/regions, groups or Glists. A new education model has also been incorporated into the broader IFs model. New scenarios were created for UNEP (focusing on environmental change) and Pardee (focusing on poverty). Finally, one of the largest changes made was incorporating 182 countries into the Base-Case scenario used by IFs. Previous versions of IFs used broader regions to forecast global trends. This change also did away with the Student and Professional versions.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Geographic Representation of the World&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
186 countries underpin the functioning of IFs and these countries can be displayed separately or as parts of larger groups that users can determine.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;Below is a visual representation of how different entities are organized into Countries/Regions, Groups or Glists:&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
[[File:Geo 186.png|frame|right|Visual representation of IF&#039;s definition of regions/countries/groups/glists]]&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;*Note: In older versions of IFs, Regions were used as intermediaries between Countries and Groups. In the future, they, or some similarly named unit, will be a sub-unit of Countries. Regions, acting as a sub-unit of Countries, are currently not a feature of IFs. See the image located at the bottom of this Help topic.&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
When using IFs, there are many occasions where the user is asked whether or not they would like to display their results as a product of single countries, or larger groups. This is typically a toggle switch that moves between Country/Region and Groups, however, it might be a three-way-toggle that includes Country/Region, Group and Glist.&lt;br /&gt;
&lt;br /&gt;
Countries/Regions are currently the smallest geographical unit that users can represent. The ability to split countries down into smaller regions, or states, is under development. There are 186 different countries/regions that users can display.&lt;br /&gt;
&lt;br /&gt;
Groups are variably organized geographically or by memberships in international institutions/regimes. You can find out who is represented in each group and add or delete members by exploring the [[Extended_Features#Manage_Groups/Regions|Managing Regionalization]] function.&lt;br /&gt;
&lt;br /&gt;
Glists merge both Groups and Countries/Regions. These lists are mostly geographically bound. In the future, the Glist distinction will become more important as some users may want to place, for example, both the Indian state of Kerala in a Glist with Sri Lanka and Nepal.&lt;br /&gt;
&lt;br /&gt;
Users may also want to [[Extended_Features#Change_Grouping/Regionalization|create]] their own groups or [[Extended_Features#Identify_Groups_or_Country/Region_Members|explore]] what countries are members of what groups.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Time Horizon&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Future Forecasts.&#039;&#039;&#039; IFs begins computation with data from 2000 and can dynamically calculate values for all variables annually through 2100.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Historical Analysis and &amp;quot;Forecasts.&amp;quot;&#039;&#039;&#039; IFs also includes an extensive and growing historical data base starting in 1960. The data basis allows analysis of relationships among variables across countries and across time.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Instructional Use&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The standard modes for using IFs in a classroom are:&lt;br /&gt;
&lt;br /&gt;
1. Assigning class members to an issue area or topic. Consider identifying specific questions for them to address.&lt;br /&gt;
&lt;br /&gt;
2. Assigning class members to a country/geographic region. Again, specificity helps.&lt;br /&gt;
&lt;br /&gt;
Most often, students will work independently or in groups on projects and share information after completing them. It is possible, however, to have students work interactively, by assigning them topics or regions, letting them begin work, and then have the interacting groups (or individuals) create a collective model run with the changes that each group proposes by topic or region. That process, although more difficult to organize, allows the class as whole to investigate the interaction of their topics or regions (and to share learning about model use).&lt;br /&gt;
&lt;br /&gt;
There is a&amp;amp;nbsp;[http://portfolio.du.edu/bhughes web site]&amp;amp;nbsp;available in support of the educational use of IFs. You will find syllabi at that site. There are several [[Introduction_to_IFs#Publications_on_IFs|publications]] on IFs, including a book structured specifically for educational use.&lt;br /&gt;
&lt;br /&gt;
Donald Borock has described his classroom use of IFs in print. Borock, Donald. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot; Advances in Social Science and Computers 4, 103-127.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Acknowledgements&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The author gratefully recognizes critical contributions in the forms of:&lt;br /&gt;
&lt;br /&gt;
:1. Testing and suggestions for development of IFs in one or more of multiple generations. By Donald Borock, Richard Chadwick, William Dixon, Dale Rothman, Phil Schrodt, Douglas Stuart, Donald Sylvan, Jonathan Wilkenfeld, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
:2. Computer assistance across many releases. By Michael Niemann, Terrance Peet-Lukes, Douglas McClure, Mohammod Irfan, and Jose Solorzano.&lt;br /&gt;
&lt;br /&gt;
:3. Data gathering and general assistance. By James Chung, Padma Padula, Shannon Brady, David Horan, Michael Ferrier, Kay Drucker, Warren Christopher, and Anwar Hossain.&lt;br /&gt;
&lt;br /&gt;
:4. Long-term encouragement and support. By Harold Guetzkow, Karl Deutsch, Richard Chadwick, Gerald Barney, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
:5. Association in related world modeling projects and projects building upon IFs. By Mihajlo Mesarovic, Aldo Barsotti, Juan Huerta, John Richardson, Thomas Shook, Patricia Strauch, and other members of the World Integrated Model (WIM) team. By Stuart Bremer, Peter Brecke, Thomas Cusack, Wolf Dieter-Eberwein, Brian Pollins, and Dale Smith of the GLOBUS modeling project. By Evan Hillebrand, Paul Herman, and others of the IFs for SAG project. By Rob Lempert and Steve Bankes at RAND, Santa Monica. By Robert Pestel, Jonathan Cave, Ronald Inglehart, Sergei Parinov, Pentti Malaska, and many others in the IFs for TERRA project.&lt;br /&gt;
&lt;br /&gt;
:6. Financial assistance (without responsibility for the form of the evolving product). By the National Science Foundation, the Cleveland Foundation, the Exxon Education Foundation, the Kettering Family Foundation, the Pacific Cultural Foundation, the United States Institute of Peace, General Motors, the Strategic Assessments Group of the Central Intelligence Agency, the European Commission (Information Society Technology) Programme, the European Union Center of the University of Michigan, the National Intelligence Council (for web conversion), and Frederick S. Pardee. &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Feedback&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
Feedback on how to improve IFs is always appreciated, especially if you find something that is not working. Compliments are also accepted. Please contact. To send the IFs team an e-mail, click on&amp;amp;nbsp;[mailto:pardee.center@du.edu Pardee Center]&amp;amp;nbsp;in stand-alone versions or on the web.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Support for IFs Use&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Publications on IFs&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
To obtain additional information about IFs and its use, consult:&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes and Evan E. Hillebrand, &#039;&#039;&#039;Exploring and Shaping International Futures.&#039;&#039;&#039; Boulder, CO: Paradigm Publishers, 2006. Specifically, see chapter 4.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &#039;&#039;&#039;International Futures: Choices in the Face of Uncertainty,&#039;&#039;&#039; 3rd ed. Boulder, CO: Westview Press, 1999. This volume is built around IFs and contains detailed suggestions for its use. Version 3.17 of IFs, which runs under Windows 95, is distributed with the third edition of the book. The second edition contained a version for Windows 3.1, and the first edition ran under DOS. Chapter 4 of the 2nd edition of IFs included Flow Charts of Worldviews , reproduced now in this Help system.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &#039;&#039;&#039;Continuity and Change in World Politics,&#039;&#039;&#039; 4th ed. Englewood Cliffs, N.J.: Prentice Hall, 2000. IFs can also usefully supplement this textbook on global politics.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &amp;quot;The International Futures (IFs) Modeling Project. 1999. &#039;&#039;&#039;Simulation and Gaming&#039;&#039;&#039; 30, No. 3 (September): 304-326.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;IFs Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Alcamo, Joseph, Rik Leemans and Eric Kreileman, eds. 1998.&amp;amp;nbsp;&#039;&#039;Global Change Scenarios of the 21st Century: Results from the IMAGE 2.1 Model&#039;&#039;. The Netherlands: Pergamon.&lt;br /&gt;
&lt;br /&gt;
Alcamo, Joseph. 1994.&amp;amp;nbsp;&#039;&#039;IMAGE 2.0: Integrated Modeling of Global Climate Change&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
&lt;br /&gt;
Alexandratos, Nikos, ed. 1995.&amp;amp;nbsp;&#039;&#039;World Agriculture: Towards 2010&#039;&#039;&amp;amp;nbsp;(An FAO Study). New York: FAO and John Wiley and Sons.&lt;br /&gt;
&lt;br /&gt;
Allen, R. G. D. 1968.&amp;amp;nbsp;&#039;&#039;Macro-Economic Theory: A Mathematical Treatment&#039;&#039;. New York: St. Martin&#039;s Press.&lt;br /&gt;
&lt;br /&gt;
Avery, Dennis. 1995. &amp;quot;Saving the Planet with Pesticides,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 50-82.&lt;br /&gt;
&lt;br /&gt;
Bailey, Ronald, ed. 1995.&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;. New York: The Free Press.&lt;br /&gt;
&lt;br /&gt;
Barbieri, Kathleen. 1996. &amp;quot;Economic Interdependence: A Path to Peace or a Source of Interstate Conflict?&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Peace Research&#039;&#039;&amp;amp;nbsp;33: 29-50.&lt;br /&gt;
&lt;br /&gt;
Barker, T.S. and A.W.A. Peterson, eds. 1987.&amp;amp;nbsp;&#039;&#039;The Cambridge Multisectoral Dynamic Model of the British Economy&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Barney, Gerald O., W. Brian Kreutzer, and Martha J. Garrett, eds. 1991.&amp;amp;nbsp;&#039;&#039;Managing a Nation&#039;&#039;, 2nd ed. Boulder: Westview Press.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. 1997.&amp;amp;nbsp;&#039;&#039;Determinants of Economic Growth: A Cross-Country Empirical Study&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. and Xavier Sala-i-Martin. 1999.&amp;amp;nbsp;&#039;&#039;Economic Growth&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
&lt;br /&gt;
Bennett, D. Scott, and Allan Stam. 2003.&amp;amp;nbsp;&#039;&#039;The Behavioral Origins of War: Cumulation and Limits to Knowledge in Understanding International Conflict&#039;&#039;. Ann Arbor: University of Michigan Press&lt;br /&gt;
&lt;br /&gt;
Birg, Herwig. 1995.&amp;amp;nbsp;&#039;&#039;World Population Projections for the 21st Century&#039;&#039;. Frankfurt: Campus Verlag.&lt;br /&gt;
&lt;br /&gt;
Borock, Donald M. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot;&amp;amp;nbsp;&#039;&#039;Advances in Social Science and Computers&#039;&#039;&amp;amp;nbsp;4, 103-127.&lt;br /&gt;
&lt;br /&gt;
Bos, Eduard, My T. Vu, Ernest Massiah, and Rodolfo A. Bulatao. 1994.&amp;amp;nbsp;&#039;&#039;World Population Projections 1994-95 Edition&#039;&#039;&amp;amp;nbsp;[editions are biannual] Baltimore: Johns Hopkins Press.&lt;br /&gt;
&lt;br /&gt;
Boulding, Elise and Kenneth E. Boulding. 1995.&amp;amp;nbsp;&#039;&#039;The Future: Images and Processes&#039;&#039;. Thousand Oaks, CA: Sage Publications.&lt;br /&gt;
&lt;br /&gt;
Brecke, Peter. 1993. &amp;quot;Integrated Global Models that Run on Personal Computers,&amp;quot;&amp;amp;nbsp;&#039;&#039;Simulation&#039;&#039;60 (2).&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. 1977.&amp;amp;nbsp;&#039;&#039;Simulated Worlds: A Computer Model of National Decision-Making&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A., ed. 1987.&amp;amp;nbsp;&#039;&#039;The GLOBUS Model: Computer Simulation of World-wide Political and Economic Developments&#039;&#039;. Boulder, CO: Westview.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. and Walter Gruhn. 1988.&amp;amp;nbsp;&#039;&#039;Micro GLOBUS: A Computer Model of Long-Term Global Political and Economic Processes&#039;&#039;. Berlin: edition sigma.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. and Barry B. Hughes. 1990.&amp;amp;nbsp;&#039;&#039;Disarmament and Development: A Design for the Future?&#039;&#039;&amp;amp;nbsp;Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Brockmeier, Martina and Channing Arndt (presentor). 2002. Social Accounting Matrices. Powerpoint presentation on GTAP and SAMs (June 21). Found on the web.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1981.&amp;amp;nbsp;&#039;&#039;Building a Sustainable Society&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1988. &amp;quot;Analyzing the Demographic Trap,&amp;quot; in&amp;amp;nbsp;&#039;&#039;State of the World 1987&#039;&#039;, eds. Lester R. Brown and others. New York: W.W. Norton, pp. 20-37.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1995.&amp;amp;nbsp;&#039;&#039;Who Will Feed China?&#039;&#039;&amp;amp;nbsp;New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1996.&amp;amp;nbsp;&#039;&#039;Tough Choices: Facing the Challenge of Food Scarcity&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., et al. 1996&amp;amp;nbsp;&#039;&#039;State of the World 1996&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., Nicholas Lenssen, and Hal Kane. 1995.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1995. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., Christopher Flavin, and Hal Kane. 1996.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1996. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Burkhardt, Helmut. 1995. &amp;quot;Priorities for a Sustainable Civilization,&amp;quot; unpublished conference paper. Department of Physics, Ryerson Polytechnic University, Toronto, Canada.&lt;br /&gt;
&lt;br /&gt;
Bussolo, Maurizio, Mohamed Chemingui and David O’Connor. 2002. A Multi-Region Social Accounting Matrix (1995) and Regional Environmental General Equilibrium Model for India (REGEMI). Paris: OECD Development Centre (February). Available at&amp;amp;nbsp;[http://www.oecd.org/dev/technics www.oecd.org/dev/technics].&lt;br /&gt;
&lt;br /&gt;
British Petroleum Company. 1995.&amp;amp;nbsp;&#039;&#039;BP Statistical Review of World Energy&#039;&#039;. London: British Petroleum Company.&lt;br /&gt;
&lt;br /&gt;
Central Intelligence Agency (CIA). 1991.&amp;amp;nbsp;&#039;&#039;Handbook of Economic Statistics, 1991&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
&lt;br /&gt;
Central Intelligence Agency (CIA). 1994.&#039;&#039;&amp;amp;nbsp;The World Factbook 1994&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
&lt;br /&gt;
Chang, Sheldon S. L. 1961.&amp;amp;nbsp;&#039;&#039;Synthesis of Optimum Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Chenery, Hollis and Moises Syrquin. 1975.&amp;amp;nbsp;&#039;&#039;Patterns of Development 1950-1970&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Cipolla, Carlo M. 1962.&amp;amp;nbsp;&#039;&#039;The Economic History of World Population&#039;&#039;. Baltimore: Penguin.&lt;br /&gt;
&lt;br /&gt;
Cook, Earl. 1976.&amp;amp;nbsp;&#039;&#039;Man, Energy, Society&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
&lt;br /&gt;
Committee on the Strategic Assessment of the U.S. Department of Energy’s Coal Program. 1995.&amp;amp;nbsp;&#039;&#039;Coal: Energy for the Future&#039;&#039;. Washington, D.C.: National Academy Press.&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1981.&amp;amp;nbsp;&#039;&#039;The Global 2000 Report to the President&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1981b.&amp;amp;nbsp;&#039;&#039;Environmental Trends&#039;&#039;. Washington, D.C. (July).&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1991.&amp;amp;nbsp;&#039;&#039;21st Annual Report&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Crescenzi, Mark J.C. and Andrew J. Enterline. 2001. &amp;quot;Time Remembered: A Dynamic Model of Interstate Interaction,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;45, no. 3 (September): 409-431.&lt;br /&gt;
&lt;br /&gt;
Crosson, Pierre, and Jock R. Anderson. 1992.&amp;amp;nbsp;&#039;&#039;Resources and Global Food Prospects&#039;&#039;. Washington, D.C.: The World Bank. World Bank Technical Paper Number 184.&lt;br /&gt;
&lt;br /&gt;
Cusack, Thomas R. and Richard J. Stoll. 1990.&amp;amp;nbsp;&#039;&#039;Exploring Realpolitik: Probing International Relations with Computer Simulatio&#039;&#039;n. Boulder: Lynne Rienner Publishers.&lt;br /&gt;
&lt;br /&gt;
Dargay, Joyce and Dermot Gately. 1999. &amp;quot;Income’s Effect on Car and Vehicle Ownership, Worldwide: 1960-2015,&amp;quot;&amp;amp;nbsp;&#039;&#039;Transportation Research Part A&#039;&#039;&amp;amp;nbsp;33: 101-138.&lt;br /&gt;
&lt;br /&gt;
Dall, P., Kaspar, F. and Alcamo, J. 1998. &amp;quot;Modeling World-wide Water Availability and Water Use Under the Influence of Climate Change,&amp;quot;&amp;amp;nbsp;&#039;&#039;Proceedings of the Second International Conference on Climate and Water&#039;&#039;, July 17-20, Espoo, Finland.&lt;br /&gt;
&lt;br /&gt;
Dimaranan, Betina V. and Robert A. McDougall, eds. 2002.&amp;amp;nbsp;&#039;&#039;Global Trade, Assistance, and Production: The GTAP 5 Data Base&#039;&#039;. Center for Global Trade Analysis, Purdue University. Available at [http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp].&lt;br /&gt;
&lt;br /&gt;
Dowlatabadi, H., and Morgan, M.G. 1993. &amp;quot;A Model Framework for Integrated Studies of the Climate Problem,&amp;quot;&amp;amp;nbsp;&#039;&#039;Energy Policy&#039;&#039;&amp;amp;nbsp;(March): 209-221.&lt;br /&gt;
&lt;br /&gt;
Duchin, Faye. 1998.&amp;amp;nbsp;&#039;&#039;Structural Economics: Measuring Change in Technology, Lifestyles, and the Environment&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
&lt;br /&gt;
Edwards, Stephen R. 1995. &amp;quot;Conserving Biodiversity,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 212-265.&lt;br /&gt;
&lt;br /&gt;
Edmonds, J., and Reilly, J.M. 1985.&amp;amp;nbsp;&#039;&#039;Global Energy: Assessing the Future&#039;&#039;. Oxford, UK: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Edmonds, J., Pitcher, H. Rosenberg, N., and Wigley, T. &amp;quot;Design for the Global Change Assessment Model.&amp;quot;&amp;amp;nbsp;&#039;&#039;Integrative Assessment of Mitigation, Impacts and Adaptation to Climate Change&#039;&#039;. Laxenburg, Austria.&lt;br /&gt;
&lt;br /&gt;
Ehrlich, Paul R. and Anne H. Ehrlich. 1972.&amp;amp;nbsp;&#039;&#039;Population, Resources, Environment&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
&lt;br /&gt;
Eicher, Carl. 1982. &amp;quot;Facing up to Africa&#039;s Food Crisis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Foreign Affairs&#039;&#039;&amp;amp;nbsp;61, no. 1 (Fall): 151-74.&lt;br /&gt;
&lt;br /&gt;
Eberstadt, Nicholas. 1995. &amp;quot;Population, Food, and Income,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 8-47.&lt;br /&gt;
&lt;br /&gt;
Esty, Daniel C., Jack A. Goldstone, Ted Robert Gurr, Barbara Harff, Marc Levy, Geoffrey D. Dabelko, Pamela T. Surko, and Alan N. Unger. 1998. State Failure Task Force Report: Phase II Findings. Volume provided courtesy of Ted Robert Gurr.&lt;br /&gt;
&lt;br /&gt;
Flavin, Christopher. 1996. &amp;quot;Facing Up to the Risks of Climate Change,&amp;quot; in Lester R. Brown and others, eds., State of the World 1996 (New York: W.W. Norton), pp. 21-39.&lt;br /&gt;
&lt;br /&gt;
Forrester, Jay W. 1968.&amp;amp;nbsp;&#039;&#039;Principles of Systems&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Gilpin, Robert. 1981.&amp;amp;nbsp;&#039;&#039;War and Change in World Politics&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Globerman, Steven. 2000 (May). Linkages Between Technological Change and Productivity Growth. Industry Canada Research Publications Program: Occasional Paper 23.&lt;br /&gt;
&lt;br /&gt;
Grant, Lindsey. 1982.&amp;amp;nbsp;&#039;&#039;The Cornucopian Fallacies&#039;&#039;. Washington, D.C.: Environmental Fund.&lt;br /&gt;
&lt;br /&gt;
Griffith, Rachel, Stephen Redding, and John Van Reenen. 2000.&amp;amp;nbsp;&#039;&#039;Mapping the Two Faces of R&amp;amp;D: Productivity Growth in a Panel of OECD Industries&#039;&#039;. Institute for Fiscal Studies (January)&lt;br /&gt;
&lt;br /&gt;
Gwartney, James and Robert Lawson with Dexter Samida. 2000.&amp;amp;nbsp;&#039;&#039;Economic Freedom of the World: 2000 Annual Report&#039;&#039;. Vancouver, B.C.: the Fraser Institute.&lt;br /&gt;
&lt;br /&gt;
Hammond, Allen. 1998.&amp;amp;nbsp;&#039;&#039;Which World? Scenarios for the 21st Century&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
&lt;br /&gt;
Harff, Barbara, with Ted Robert Gurr and Alan Unger. 1999. Preconditions of Genocide and Politicide: 1955-1998. Paper prepared for the State Failure Task Force and provided courtesy of Barbara Harff and Ted Gurr.&lt;br /&gt;
&lt;br /&gt;
Henderson, Hazel. 1996. &amp;quot;Changing Paradigms and Indicators: Implementing Equitable, Sustainable and Participatory Development,&amp;quot; in Jo Marie Griesgraber and Bernhard G. Gunter,&amp;amp;nbsp;&#039;&#039;Development: New Paradigms and Principles for the 21st Century&#039;&#039;. East Haven, CT: Pluto Press, pp. 103-136.&lt;br /&gt;
&lt;br /&gt;
Herrera, Amilcar O., et al. 1976.&#039;&#039;&amp;amp;nbsp;Catastrophe or New Society? A Latin American World Model&#039;&#039;. Ottawa: International Development Research Centre.&lt;br /&gt;
&lt;br /&gt;
Hoekstra, A.Y. 1998.&amp;amp;nbsp;&#039;&#039;Perspectives on Water: An Integrated Model-Based Exploration of the Future&#039;&#039;. Utrecht, the Netherlands: International Books.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1980.&amp;amp;nbsp;&#039;&#039;World Modeling&#039;&#039;. Lexington, Mass: Lexington Books.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1982.&amp;amp;nbsp;&#039;&#039;International Futures Simulation: User&#039;s Manual&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985a.&amp;amp;nbsp;&#039;&#039;International Futures Simulation&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985b. &amp;quot;World Models: The Bases of Difference,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;29, 77-101.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985c.&amp;amp;nbsp;&#039;&#039;World Futures: A Critical Analysis of Alternatives&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1987. &amp;quot;Domestic Economic Processes,&amp;quot; in Stuart A. Bremer, ed.,&amp;amp;nbsp;&#039;&#039;The Globus Model: Computer Simulation of Worldwide Political Economic Development&#039;&#039;&amp;amp;nbsp;(Frankfurt and Boulder: Campus and Westview), pp. 39-158.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1988. &amp;quot;International Futures: History and Status,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Science Microcomputer Review&#039;&#039;&amp;amp;nbsp;6, 43-48.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1999. &amp;quot;The International Futures (IFs) Modeling Project.&#039;&#039;&amp;amp;nbsp;Simulation and Gaming&#039;&#039;&amp;amp;nbsp;Vol 30, No. 3 (September): 304-326.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1999.&amp;amp;nbsp;&#039;&#039;International Futures&#039;&#039;, 3rd edition Boulder: Westview Press, 1999.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2000.&amp;amp;nbsp;&#039;&#039;Continuity and Change in World Politics&#039;&#039;. Englewood Cliffs, N.J.: Prentice-Hall, fourth edition.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2001. &amp;quot;Global Social Transformation: The Sweet Spot, the Steady Slog, and the Systemic Shift,&amp;quot;&amp;amp;nbsp;&#039;&#039;Economic Development and Cultural Change&#039;&#039;&amp;amp;nbsp;49, No. 2 (January): 423-458.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2002.&amp;amp;nbsp;&#039;&#039;Theats and Opportunities Analysis&#039;&#039;. Living document prepared for the Strategic Assessments Group, Office of Transnational Issues, Central Intelligence Agency, August 2002.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. and Anwar Hossain. 2003. Long-Term Socio-Economic Modeling: With Universal, Globally-Integrated Social Accounting Matrices (SAMs) in a General Equilibrium Model Structure. IFs Project Living Document, University of Denver.&lt;br /&gt;
&lt;br /&gt;
Huth, Paul. 1996.&amp;amp;nbsp;&#039;&#039;Standing Your Ground: Territorial Disputes and International Conflict&#039;&#039;. Ann Arbor, MI: University of Michigan Press.&lt;br /&gt;
&lt;br /&gt;
Inglehart, Ronald. 1997.&amp;amp;nbsp;&#039;&#039;Modernization and Postmodernization: Cultural, Economic, and Political Change in 43 Societies&#039;&#039;. Ewing, NJ: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1995.&amp;amp;nbsp;&#039;&#039;Oil, Gas, and Coal Supply Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996.&amp;amp;nbsp;&#039;&#039;World Energy Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996b.&amp;amp;nbsp;&#039;&#039;The Strategic Value of Fossil Fuels: Challenges and Responses&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;International Financial Statistics&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;World Economic Outlook&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
Intergovernmental Panel on Climate Change (IPCC). 1995. Several volumes by various working groups. Published by Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Jansen, Karel and Rob Vos, eds. 1997.&amp;amp;nbsp;&#039;&#039;External Finance and Adjustment: Failure and Success in the Developing World&#039;&#039;. London: Macmillan Press Ltd.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1998.&amp;amp;nbsp;&#039;&#039;Modeling Global Change: The Art of Integrated Assessment Modelling&#039;&#039;. Cheltenham, UK: Edward Elgar.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1996.&amp;amp;nbsp;&#039;&#039;Meeting Targets: Tools to Support Integrated Modelling of Global Change&#039;&#039;. Den Haag: CIP-Gegevens Koninklijke Bibliotheek.&lt;br /&gt;
&lt;br /&gt;
Jansson, Kurt, Michael Harris, Angela Penrose. 1987.&amp;amp;nbsp;&#039;&#039;The Ethiopian Famine&#039;&#039;. London: Zed Books Ltd.&lt;br /&gt;
&lt;br /&gt;
Jeffreys, Kent. 1995. &amp;quot;Rescuing the Oceans,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 296-338.&lt;br /&gt;
&lt;br /&gt;
Jones, Daniel M., Stuart A. Bremer, and J. David Singer. 1996. &amp;quot;Militarized Interstate Disputes, 1816-1992: Rationale, Coding Rules, and Empirical Patterns,&amp;quot;&amp;amp;nbsp;&#039;&#039;Conflict Management and Peace Science&#039;&#039;&amp;amp;nbsp;XV, No. 2: 163-215.&lt;br /&gt;
&lt;br /&gt;
Khan, Haider A. 1998.&amp;amp;nbsp;&#039;&#039;Technology, Development and Democracy&#039;&#039;. Northhampton, Mass: Edward Elgar Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Kahn, Herman, William Brown, and Leon Martel. 1976.&amp;amp;nbsp;&#039;&#039;The Next 200 Years&#039;&#039;. New York: William Morrow.&lt;br /&gt;
&lt;br /&gt;
Kalymon, Basil A. 1975. &amp;quot;Economic Incentives in OPEC Oil Pricing Policy.&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Development Economics&#039;&#039;&amp;amp;nbsp;2: 337-362.&lt;br /&gt;
&lt;br /&gt;
Kaplan, Robert. 1994. &amp;quot;The Coming Anarchy,&amp;quot;&amp;amp;nbsp;&#039;&#039;The Atlantic Monthly&#039;&#039;&amp;amp;nbsp;273 (February): .&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999a. &amp;quot;Aggregating Governance Indicators&amp;quot;. World Bank Policy Research Department Working Paper No. 2195.&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999b. &amp;quot;Governance Matters&amp;quot;. World Bank Policy Research Department Working Paper No. 2196.&lt;br /&gt;
&lt;br /&gt;
Keepin, B. and B. Wynne. 1984. &amp;quot;Technical Analysis of the IIASA Energy Scenarios,&amp;quot;&amp;amp;nbsp;&#039;&#039;Nature&#039;&#039;312: 691-695.&lt;br /&gt;
&lt;br /&gt;
Kehoe, Timothy J. 1996. Social Accounting Matrices and Applied General Equilibrium Models. Federal Reserve Bank of Minneapolis, Working Paper 563.&lt;br /&gt;
&lt;br /&gt;
Kennedy, Paul. 1993.&amp;amp;nbsp;&#039;&#039;Preparing for the Twenty-First Century&#039;&#039;. New York: Random House.&lt;br /&gt;
&lt;br /&gt;
Klein, Lawrence R. and Fu-chen Lo, eds. 1995.&amp;amp;nbsp;&#039;&#039;Modeling Global Change&#039;&#039;. Tokyo: United Nations University Press.&lt;br /&gt;
&lt;br /&gt;
Kornai, J. 1971.&amp;amp;nbsp;&#039;&#039;Anti-Equilibrium&#039;&#039;. Amsterdam: North Holland.&lt;br /&gt;
&lt;br /&gt;
Kwasnicki, Witold and Halina Kwasnicka. 1996. &amp;quot;Long-Term Diffusion Factors of Technological Development: An Evolutionary Model and Case Study,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 31-57.&lt;br /&gt;
&lt;br /&gt;
Leontief, Wassily, Anne Carter and Peter Petri. 1977.&amp;amp;nbsp;&#039;&#039;The Future of the World Economy&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander H., and Elizabeth R. Ducot. 1976. &amp;quot;AGRIMOD: A Simulation Model for the Analysis of U.S. Food Policies.&amp;quot; Paper delivered at Conference on Systems Analysis of Grain Reserves, Joint Annual Meeting of GRSA and TIMS, Philadelphia, Pa., March 31-April 2.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander, H., et al. 1977. Energy in Agriculture: On Modeling Inputs in AGRIMOD. Final Report to U.S. Department of Energy. Palo Alto: Systems Control, Inc., August, available through NTIS.&lt;br /&gt;
&lt;br /&gt;
Lichbach, Mark Irving. 1989. &amp;quot;An Evaluation of ‘Does Economic Inequality Breed Political Conflict?,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;, Vol 41 , No. 4 (July 1989): 431-470.&lt;br /&gt;
&lt;br /&gt;
Liverman, Dianne. 1983.&amp;amp;nbsp;&#039;&#039;The Use of Global Simulation Models in Assessing Climate Impacts on the World Food System&#039;&#039;. Dissertation, University of California, Los Angeles.&lt;br /&gt;
&lt;br /&gt;
Londregan, John B. and Keith T. Poole. 1996. &amp;quot;Does High Income Promote Democrary?&amp;quot;,&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 1 (October): 1-30.&lt;br /&gt;
&lt;br /&gt;
MacKenzie, James J. 1996. &amp;quot;Oil as a Finite Resource: When is Global Production Likely to Peak?&amp;quot; Paper of the World Resources Institute. Washington, D.C.: WRI.&lt;br /&gt;
&lt;br /&gt;
Maddison, Angus. 1995.&amp;amp;nbsp;&#039;&#039;Monitoring the World Economy 1820-1992&#039;&#039;. Paris: OECD.&lt;br /&gt;
&lt;br /&gt;
Malthus, Thomas. 1798.&amp;amp;nbsp;&#039;&#039;An Essay on the Principle of Population as It Affects the Future Improvement of Society&#039;&#039;. London (reprinted many times).&lt;br /&gt;
&lt;br /&gt;
Mansfield, Edward D. 1994.&amp;amp;nbsp;&#039;&#039;Power, Trade, and War&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Marchetti, Cesare, Perrin S. Meyer, and Jesse H. Ausubel. 1996. &amp;quot;Human Population Dynamics Revisited with the Logistic Model: How Much Can be Modeled and Predicted?,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 1-30.&lt;br /&gt;
&lt;br /&gt;
Martens, Pim and Jan Rotmans, eds. 1999.&amp;amp;nbsp;&#039;&#039;Climate Change: An Integrated Perspective&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
&lt;br /&gt;
Martens, W.J.M. 1997. &amp;quot;Health Impacts of Climate Change and Ozone Depletion: An Eco-Epidemiological Approach,&amp;quot; Maastricht, the Netherlands: Maastricht University.&lt;br /&gt;
&lt;br /&gt;
Mason, Andrew. 1997. &amp;quot;The Role of Population Change in the Asian Economic Miracle,&amp;quot; Honolulu, Hawaii: East-West Center, AsiaPacific Issues, No. 33 (October), 8 pages.&lt;br /&gt;
&lt;br /&gt;
McMahon, Walter W. 1997.&amp;amp;nbsp;&#039;&#039;Education and Development: Measuring the Social Benefits&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, Jorgen Randers, and William K. Behrens, III. 1972.&amp;amp;nbsp;&#039;&#039;Limits to Growth&#039;&#039;. New York: Universe Books.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, and Jorgen Randers. 1992.&amp;amp;nbsp;&#039;&#039;Beyond the Limits&#039;&#039;. Post Mills, Vermont: Chelsea Green Publishing Company.&lt;br /&gt;
&lt;br /&gt;
Meadows, Dennis L. et al. 1974.&amp;amp;nbsp;&#039;&#039;Dynamics of Growth in a Finite World&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Mesarovic, Mihajlo D. and Eduard Pestel. 1974.&amp;amp;nbsp;&#039;&#039;Mankind at the Turning Point&#039;&#039;. New York: E.P. Dutton &amp;amp; Co.&lt;br /&gt;
&lt;br /&gt;
Mishkin, Eli. And Ludwig Braun, ed. 1961.&amp;amp;nbsp;&#039;&#039;Adaptive Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Moore, Will H., Ronny Lindstrom, and Valerie O’Regan. 1996. &amp;quot;Land Reform, Political Violence and the Economic Inequality-Political Conflict Nexus: A Longitudinal Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Interactions&#039;&#039;&amp;amp;nbsp;21, No. 4: 335-363.&lt;br /&gt;
&lt;br /&gt;
Mori, Shunsuke and Masato Takahaashi, 1997. An Integrated Assessment Model for the Evaluation of New Energy Technologies and Food Production, accepted by&amp;amp;nbsp;&#039;&#039;International Journal of Global Energy Issues&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
Naill, Roger F. 1977.&amp;amp;nbsp;&#039;&#039;Managing the Energy Transition&#039;&#039;. Vols. 1 and 2. Cambridge, Mass: Ballinger Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1992. &amp;quot;The DICE Model: Background and Structure of a Dynamic Integrated Climate Economy,&amp;quot; New Haven: Yale University.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1979.&amp;amp;nbsp;&#039;&#039;The Efficient Use of Energy Resources&#039;&#039;. New Haven, CT: Yale University Press.&lt;br /&gt;
&lt;br /&gt;
Oneal, John R. and Bruce M. Russett. 1997. The Classical Liberals were Right: Democracy, Interdependence, and Conflict, 1950-1985.&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;41, no. 2 (June): 267-294.&lt;br /&gt;
&lt;br /&gt;
Pan, Xiaoming. 2000 (January). &amp;quot;Social and Ecological Accounting Matrix: an Empirical Study for China,&amp;quot; paper submitted for the Thirteenth International Conference on Input-Output Techniques, Macerata, Italy, August 21-25, 2000.&lt;br /&gt;
&lt;br /&gt;
Pesaran, M. Hashem and G. C. Harcourt. 1999. Life and Work of John Richard Nicholas Stone.&lt;br /&gt;
&lt;br /&gt;
Pirages, Dennis. 1989.&amp;amp;nbsp;&#039;&#039;Global Technopolitics&#039;&#039;. Pacific Grove, Calif: Brooks/Cole Publishing.&lt;br /&gt;
&lt;br /&gt;
Prinn, R. H.J., A. Sokolov, C. Wand, X. Xiao, Z. Yang, R. Eckhaus, P. Stone, D. Ellerman, J Melilo, J. Fitzmaurice, D. Kicklighter, and Y. Liu. 1996. &amp;quot;Integrated Global System Model for Climate Policy Analysis: Model Framework and Sensitivity Analysis.&amp;quot; Cambridge, Mass: Global Change Center, Massachusetts Institute of Technology.&lt;br /&gt;
&lt;br /&gt;
Przeworski, Adam and Fernando Limongi. 1997. &amp;quot;Modernization: Theories and Facts,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 2 (January): 155-183.&lt;br /&gt;
&lt;br /&gt;
Population Reference Bureau. 1996. World Population Data Sheet 1996. Washington, D.C.: Population Reference Bureau.&lt;br /&gt;
&lt;br /&gt;
Postel, Sandra. 1996.&amp;amp;nbsp;&#039;&#039;Dividing the Waters: Food Security, Ecosystem Health, and the New Politics of Scarcity&#039;&#039;. Worldwatch Paper 132. Washington, D.C.: Worldwatch Institute, September.&lt;br /&gt;
&lt;br /&gt;
Pyatt, G. and J.I. Round, eds. 1985.&amp;amp;nbsp;&#039;&#039;Social Accounting Matrices: A Basis for Planning&#039;&#039;. Washington, D.C.: The World Bank.&lt;br /&gt;
&lt;br /&gt;
Raskin, P., T. Banuri, G. Gallopín, P. Gutman, A. Hammond, R. Kates, and R. Swart. 2001. Great Transition:&amp;amp;nbsp;&#039;&#039;The Promise and Lure of the Times Ahead&#039;&#039;. Forthcoming.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1990.&amp;amp;nbsp;&#039;&#039;Global Politics&#039;&#039;, 4th edition. Boston: Houghton Mifflin.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1995.&amp;amp;nbsp;&#039;&#039;Democracy and International Conflict&#039;&#039;. Columbia: University of South Carolina Press.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee and J. David Singer. 1973. &amp;quot; Measuring the Concentration of Power in the International System,&amp;quot;&#039;&#039;&amp;amp;nbsp;Sociological Methods and Research&#039;&#039;&amp;amp;nbsp;1, no. 4: 403-436. Reprinted in&amp;amp;nbsp;&#039;&#039;Measuring the Correlates of War&#039;&#039;, edited by J. David Singer and Paul Diehl. Ann Arbor: University of Michigan Press, 1990.&lt;br /&gt;
&lt;br /&gt;
Rayner. S. 1992. &amp;quot;Cultural Theory and Risk Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Theory of Risk&#039;&#039;, ed. G. D. Preagor. Westport, USA.&lt;br /&gt;
&lt;br /&gt;
Repetto, Robert and Duncan Austin. 1997.&amp;amp;nbsp;&#039;&#039;The Costs of Climate Protection&#039;&#039;. Washington, D.C.: World Resources Institute.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis Fry. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Chicago: Quadrangle Books.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis F. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Pittsburgh: Boxwood Press.&lt;br /&gt;
&lt;br /&gt;
Romer, Paul M. 1994. &amp;quot;The Origins of Endogenous Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Economic Perspectives&#039;&#039;Vol 8, No. 1 (Winter): 3-22.&lt;br /&gt;
&lt;br /&gt;
Root T. and Stephen Schneider. 1995. &amp;quot;Ecology and Climate: Research Strategies and Implications,&amp;quot; Science 269 (52): 334-341.&lt;br /&gt;
&lt;br /&gt;
Rosegrant, Mark W., Mercedita Agcaoili-Sombilla, and Nicostrato D. Perez. 1995. &amp;quot;Global Food Projections to 2020: Implications for Investment.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 5.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1999. Integrated Assessment Models: Uncertainty, Quality and Use. Maastricht, the Netherlands: Maastricht University, International Centre for Integrative Studies (ICIS), Working Paper 199-E005.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and Burt de Vries, eds. 1997.&amp;amp;nbsp;&#039;&#039;Perspectives on Global Change: The Targets Approach&#039;&#039;. Cambridge, UK: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and M.B.A. van Asselt. 1996. &amp;quot;Integrated Assessment: A Growing Child on its Way to Maturity,&amp;quot;&amp;amp;nbsp;&#039;&#039;Climatic Change&#039;&#039;&amp;amp;nbsp;34 (3-4): 327-336.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1990.&amp;amp;nbsp;&#039;&#039;IMAGE: An Integrated Model to Assess the Greenhouse Effect&#039;&#039;. Dordrecht, the Netherlands: Kluwer Academics.&lt;br /&gt;
&lt;br /&gt;
Saaty, Thomas L. 1996. The Analytic Network Process: Decision Making with Dependence and Feedback. Pittsburgh: RWS Publications.&lt;br /&gt;
&lt;br /&gt;
Schafer, Andreas and David G. Victor. 1997. The Future Mobility of the World Population. Massachusetts Institute of Technology and International Institute for Applied Systems Analysis, Discussion Paper 97-6-4 (revision 2, September).&lt;br /&gt;
&lt;br /&gt;
Scheer, Sara J. and Satya Yadav. 1996. &amp;quot;Land Degradation in the Developing World: Implications for Food, Agriculture, and the Environment to 2020.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 14.&lt;br /&gt;
&lt;br /&gt;
Schneider, Stephen. 1997. &amp;quot;Integrated Assessment Modeling of Climate Change: Transparent Rational Tool for Policy Making or Opaque Screen Hiding Value-Laden Assumptions?&amp;quot;&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(4): 229-250.&lt;br /&gt;
&lt;br /&gt;
Schwartz, Peter. 1996.&#039;&#039;&amp;amp;nbsp;The Art of the Long View.&#039;&#039;&amp;amp;nbsp;New York: Doubleday.&lt;br /&gt;
&lt;br /&gt;
Sedjo, Roger A. 1995. &amp;quot;Forests: Conflicting Signals,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 178-209.&lt;br /&gt;
&lt;br /&gt;
Shane, Harold G. and Gary A. Sojka. 1990. &amp;quot;John Elfreth Watkins, Jr.: Forgotten Genius of Forecasting,&amp;quot; in Edward Cornish, ed.,&#039;&#039;&amp;amp;nbsp;The 1990s and Beyond&#039;&#039;. Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Shaw, Timothy W. and Clement E. Adibe. 1995-96. &amp;quot;Africa and Global Developments in the Twenty-First Century,&amp;quot; International Journal 51 (Winter): 1-26.&lt;br /&gt;
&lt;br /&gt;
Siegmann, Heinrich. 1985.&amp;amp;nbsp;&#039;&#039;Recent Developments in World Modeling&#039;&#039;. Berlin: Science Center.&lt;br /&gt;
&lt;br /&gt;
Simon, Julian. 1981.&amp;amp;nbsp;&#039;&#039;The Ultimate Resource&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Singer, J. David, Stuart Bremer, and John Stuckey. 1972. &amp;quot;Capability Distribution, Uncertainty, and Major Power Wars, 1820-1965.&amp;quot; In Bruce Russett, ed.,&amp;amp;nbsp;&#039;&#039;Peace, War, and Numbers.&#039;&#039;&amp;amp;nbsp;Beverly Hills: Sage.&lt;br /&gt;
&lt;br /&gt;
Sivard, Ruth Leger. 1993.&amp;amp;nbsp;&#039;&#039;World Military and Social Expenditures 1993.&#039;&#039;&amp;amp;nbsp;Washington, D.C. 20007: World Priorities, Box 25140.&lt;br /&gt;
&lt;br /&gt;
Solow, Robert M. 1956. &amp;quot;A Contribution to the Theory of Economic Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Quarterly Journal of Economics&#039;&#039;&amp;amp;nbsp;70, 1 (February): 65-94.&lt;br /&gt;
&lt;br /&gt;
Stanford University. 1978.&amp;amp;nbsp;&#039;&#039;Stanford Pilot Energy/Economic Model&#039;&#039;. Stanford: Department of Research, Interim Report, Vol. 1.&lt;br /&gt;
&lt;br /&gt;
Stockholm International Peace Research Institute (SIPRI). 1994.&amp;amp;nbsp;&#039;&#039;SIPRI Yearbook&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Stone, Richard. 1986. &amp;quot;The Accounts of Society,&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Applied Econometrics&#039;&#039;&amp;amp;nbsp;1, no. 1 (January): 5-28.&lt;br /&gt;
&lt;br /&gt;
Strategic Assessments Group (SAG), Office of Transnational Issues, Directorate of Intelligence. 2001 (February). The Global Economy in the Long Term. OTI IR 2001-013.&lt;br /&gt;
&lt;br /&gt;
Systems Analysis Research Unit (SARU). 1977.&amp;amp;nbsp;&#039;&#039;SARUM 76 Global Modeling Project&#039;&#039;. Departments of the Environment and Transport, 2 Marsham Street, London, 3WIP 3EB.&lt;br /&gt;
&lt;br /&gt;
Tammen, Ronald L, Jacek Kugler, Douglas Lemke, Allan C. Stam III, Carole Alsharabati, Mark Andrew Abdollahian, Brian Efird, and A.F.K. Organski. 2000. Power Transitions: Strategies for the 21st Century. New York: Chatham House Publishers.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1975. &amp;quot;Theoretical Foundations and Technical Implications.&amp;quot; in Charles Blitzer, Peter Clark and Lance Taylor, eds.,&amp;amp;nbsp;&#039;&#039;Economy-Wide Models and Development Planning.&#039;&#039;&amp;amp;nbsp;Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1979.&amp;amp;nbsp;&#039;&#039;Macro Models for Developing Countries&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Thirlwall, A. P. 1977.&amp;amp;nbsp;&#039;&#039;Growth and Development&#039;&#039;. New York: John Wiley and Sons.&lt;br /&gt;
&lt;br /&gt;
Thompson, M. 1997. Cultural Theory and Integrated Assessment.&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(3): 139-150.&lt;br /&gt;
&lt;br /&gt;
Thompson, M., R. Ellis and A. Wildavsky. 1990.&amp;amp;nbsp;&#039;&#039;Cultural Theory&#039;&#039;. Boulder, Co: Westview Press.&lt;br /&gt;
&lt;br /&gt;
Thorbecke, Erik. 2001. &amp;quot;The Social Accounting Matrix: Deterministic or Stochastic Concept?&amp;quot;, paper prepared for a conference in honor of Graham Pyatt&#039;s retirement, at the Institute of Social Studies, The Hague, Netherlands (November 29 and 30). Available at [http://people.cornell.edu/pages/et17/etpapers.html http://people.cornell.edu/pages/et17/etpapers.html].&lt;br /&gt;
&lt;br /&gt;
United Nations, Department of Economic and Social Affairs. 1956.&amp;amp;nbsp;&#039;&#039;Methods of Population Projections by Sex and Age&#039;&#039;. New York: United Nations, ST/SOA Series A.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1992.&amp;amp;nbsp;&#039;&#039;Long-Range World Population Projections. Two Centuries of Population Growth: 1950-2150&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1993.&amp;amp;nbsp;&#039;&#039;World Population Prospects - the 1992 Revision&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations Development Program (UNDP). 1995.&amp;amp;nbsp;&#039;&#039;Human Development Report&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1992.&amp;amp;nbsp;&#039;&#039;Production Yearbook.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1995.&#039;&#039;&amp;amp;nbsp;World Agriculture: Towards 2010.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 1999. The World at Six Billion New York: UN.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2000. Replacement Migration: Is it a Solution to Declining and Ageing Populations? New York: UN.&lt;br /&gt;
&lt;br /&gt;
United States Arms Control and Disarmament Agency (ACDA). 1995.&amp;amp;nbsp;&#039;&#039;World Military Expenditures and Arms Transfers 1995&#039;&#039;. Washington, D.C.: Arms Control and Disarmament Agency.&lt;br /&gt;
&lt;br /&gt;
United States Bureau of the Census. 1991.&amp;amp;nbsp;&#039;&#039;World Population Profile: 1991&#039;&#039;. Report WP/91 Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Walters, Robert S. and David H. Blake. 1992.&amp;amp;nbsp;&#039;&#039;The Politics of Global Economic Relations&#039;&#039;, 4th edition. Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Waltz, Kenneth N. 1959. Man, the State, and War: A Theoretical Analysis. New York: Columbia University Press.&lt;br /&gt;
&lt;br /&gt;
Watkins, John Elfreth, Jr. 1990. &amp;quot;What May Happen in the Next Hundred Years,&amp;quot; in Edward Cornish, ed.,&amp;amp;nbsp;&#039;&#039;The 1990s and Beyond.&#039;&#039;&amp;amp;nbsp;Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Wildavsky, Aaron, and Ellen Tenenbaum. 1981.&amp;amp;nbsp;&#039;&#039;The Politics of Mistrust&#039;&#039;. Beverly Hills: Sage Publications.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1991b.&amp;amp;nbsp;&#039;&#039;World Tables 1991&#039;&#039;. New York: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1995&amp;amp;nbsp;&#039;&#039;World Development Report 1995&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
World Energy Council (WEC) Commission. 1993.&amp;amp;nbsp;&#039;&#039;Energy for Tomorrow’s World.&#039;&#039;&amp;amp;nbsp;New York: St. Martin’s Press.&lt;br /&gt;
&lt;br /&gt;
World Resources Institute (WRI). 1994.&amp;amp;nbsp;&#039;&#039;World Resources 1994-95.&#039;&#039;&amp;amp;nbsp;New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Wortman, Sterling and Ralph W. Cummings, Jr. 1978.&#039;&#039;&amp;amp;nbsp;To Feed This World&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Zinnes, Dina A. and John W. Gillespie, eds. 1976.&amp;amp;nbsp;&#039;&#039;Mathematical Models in International Relations&#039;&#039;&amp;amp;nbsp;(New York: Preaeger).&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Governance Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. and Jong-Wha Lee. 2001. &amp;quot;International Data on Educational Attainment: Updates and Implications,&amp;quot;&amp;amp;nbsp;&#039;&#039;Oxford Economic Papers&#039;&#039;&amp;amp;nbsp;53(3): 541-563.&lt;br /&gt;
&lt;br /&gt;
Cilliers, Jakkie, Barry Hughes, and Jonathan Moyer. 2011.&amp;amp;nbsp;&#039;&#039;African Futures 2050: The Next 40 Years&#039;&#039;. Pretoria, South Africa and Denver, Colorado: Institute for Security Studies and Frederick S. Pardee Center for International Futures.&lt;br /&gt;
&lt;br /&gt;
Correlates of War Project. 2011. “State System Membership List, v2011.” Online,&amp;amp;nbsp;[http://correlatesofwar.org/ http://correlatesofwar.org&amp;amp;nbsp;].&lt;br /&gt;
&lt;br /&gt;
Diamond, Larry. 1992. “Economic Development and Democracy Reconsidered.”&amp;amp;nbsp;&#039;&#039;American Behavioral Scientist&#039;&#039;&amp;amp;nbsp;35(4/5): 450-499.&lt;br /&gt;
&lt;br /&gt;
Diehl, Paul F., ed. 1999.&amp;amp;nbsp;&#039;&#039;A Roadmap to War: Territorial Dimensions of International Conflict&#039;&#039;, 1&amp;lt;sup&amp;gt;st&amp;lt;/sup&amp;gt;&amp;amp;nbsp;ed. Nashville: Vanderbilt University Press.&lt;br /&gt;
&lt;br /&gt;
Easton, David. 1965.&amp;amp;nbsp;&#039;&#039;A Framework for Political Analysis&#039;&#039;. Englewood Cliffs, New Jersey: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Esty, Daniel C., Jack A. Goldstone, Ted Robert Gurr, Barbara Harff, Marc Levy, Geoffrey D. Dabelko, Pamela Surko, and Alan N. Unger. 1998. “State Failure Task Force Report: Phase II Findings.” Study Commissioned by the Central Intelligence Agency and George Mason University School of Public Policy. Political Instability Task Force, Arlington VA.&lt;br /&gt;
&lt;br /&gt;
Freedom House, Inc. 2009.&amp;amp;nbsp;&#039;&#039;Freedom in the World 2009: The Annual Survey of Political Rights and Civil Liberties&#039;&#039;. Washington, DC: Freedom House, Inc.\&lt;br /&gt;
&lt;br /&gt;
Goldstone, Jack A. 2010. “The New Population Bomb”&amp;amp;nbsp;&#039;&#039;Foreign Affairs&#039;&#039;&amp;amp;nbsp;(January/February): 31-43.&lt;br /&gt;
&lt;br /&gt;
Goldstone, Jack A., Robert H. Bates, David L. Epstein, Ted Robert Gurr, Michael B. Lustik, Monty G. Marshall, Jay Ulfelder, and Mark Woodward. 2010. “A Global Model for Forecasting Political Instability.”&amp;amp;nbsp;&#039;&#039;American Journal of Political Science&#039;&#039;&amp;amp;nbsp;54(1): 190-208. doi: 10.1111/j.1540-5907.2009.00426.x.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2001. “Global Social Transformation: The Sweet Spot, the Steady Slog, and the Systemic Shift.”&amp;amp;nbsp;&#039;&#039;Economic Development and Cultural Change&#039;&#039;&amp;amp;nbsp;49(2): 423-458. doi: 10.1086/452510.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2002. &amp;quot;Threats and Opportunities Analysis,&amp;quot; working document prepared for the Strategic Assessments Group, Office of Transnational Issues, Central Intelligence Agency.&amp;amp;nbsp; Available on the IFs project web site at&amp;amp;nbsp;[http://www.ifs.du.edu/ www.ifs.du.edu].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., and Anwar Hossain. 2003. “Long-Term Socio-Economic Modeling: With Universal, Globally-Integrated Social Accounting Matrices (SAMs) in a General Equilibrium Model Structure.” Working Paper, University of Denver, Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/assets/documents/economyandsamdocument46.pdf http://www.ifs.du.edu/assets/documents/economyandsamdocument46.pdf]&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., Devin Joshi, Jonathan Moyer, Timothy Sisk and José Roberto Solórzano. 2014.&amp;amp;nbsp;&#039;&#039;Strengthening Governance Globally.&amp;amp;nbsp;&#039;&#039;vol. 5, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Huntington, Samuel P. 1991.&amp;amp;nbsp;&#039;&#039;The Third Wave: Democratization in the Late Twentieth Century&#039;&#039;. Norman, OK: University of Oklahoma.&lt;br /&gt;
&lt;br /&gt;
Inglehart, Ronald. 1997.&amp;amp;nbsp;&amp;amp;nbsp;&#039;&#039;Modernization and Postmodernization&#039;&#039;.&amp;amp;nbsp; Princeton: PrincetonUniversity Press.&lt;br /&gt;
&lt;br /&gt;
Joshi, Devin. 2011a. “Good Governance, State Capacity, and the Millennium Development Goals.”&amp;amp;nbsp;&#039;&#039;Perspectives on Global Development and Technology&amp;amp;nbsp;&#039;&#039;10(2): 339-360. doi: 10.1163/156914911X5824.68.&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi. 2010. “The Worldwide Governance Indicators: Methodology and Analytical Issues.” World Bank Policy Research Working Paper no. 5430. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Marshall, Monty G. and Benjamin R. Cole. 2008. “Global Report on Conflict, Governance and State Fragility 2008.”&amp;amp;nbsp;&#039;&#039;Foreign Policy Bulletin&#039;&#039;&amp;amp;nbsp;18: 3-21. doi: 10.1017/S1052703608000014.&lt;br /&gt;
&lt;br /&gt;
Marshall, Monty G., and Benjamin R. Cole. 2009. “Global Report 2009: Conflict, Governance, and State Fragility.” Vienna, VA.: Center for Systemic Peace and Center for Global Policy.&lt;br /&gt;
&lt;br /&gt;
Marshall, Monty G., and Benjamin R. Cole. 2011. &amp;quot;Global Report 2011: Conflict, Governance, and State Fragility.&amp;quot; Vienna, VA. Center for Systemic Peace.&lt;br /&gt;
&lt;br /&gt;
Marshall, Monty G., and Keith Jaggers. 2011. “Polity IV Project: Political Regime Characteristics and Transitions 1800-2010.”&amp;amp;nbsp;[http://www.systemicpeace.org/polity/polity4.htm http://www.systemicpeace.org/polity/polity4.htm]&amp;amp;nbsp;[accessed December 22 2012]&lt;br /&gt;
&lt;br /&gt;
Mauro, Paolo. 1995. “Corruption and Growth.”&amp;amp;nbsp;&#039;&#039;The Quarterly Journal of Economics&#039;&#039;&amp;amp;nbsp;110(3) (August): 681-712.&lt;br /&gt;
&lt;br /&gt;
Migdal, Joel. 1988.&amp;amp;nbsp;&#039;&#039;Strong Societies and Weak Sates: State-Society Relations and State Capabilities in the&amp;amp;nbsp;Third World&#039;&#039;. Princeton: Princeton University Press&lt;br /&gt;
&lt;br /&gt;
Mo, Pak Hung. 2001. “Corruption and Economic Growth.”&amp;amp;nbsp;&#039;&#039;Journal of Comparative Economics&amp;amp;nbsp;&#039;&#039;29(1) (March): 66-79. doi:10.1006/jcec.2000.1703.&lt;br /&gt;
&lt;br /&gt;
North, Douglass C., John Joseph Wallis, and Barry R. Weingast. 2009.&amp;amp;nbsp;&#039;&#039;Violence and Social Orders: A Conceptual Framework for Interpreting Recorded Human History&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Pierson, Paul. 2004.&amp;amp;nbsp;&#039;&#039;Politics in Time: History, Institutions, and Social Analysis&#039;&#039;. Princeton, NJ: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Rice, Susan E., and Stewart Patrick. 2008.&amp;amp;nbsp;&#039;&#039;Index of State Weakness in the Developing World.&#039;&#039;&amp;amp;nbsp;Washington, DC: The Brookings Institution.&lt;br /&gt;
&lt;br /&gt;
Shihata, Ibrahim F. I. 1996. “Corruption - A General Review with an Emphasis on the Role of the World Bank.”&amp;amp;nbsp;&#039;&#039;Dickinson Journal of International Law&#039;&#039;&amp;amp;nbsp;15: 451.&lt;br /&gt;
&lt;br /&gt;
Tanzi, Vito. 1998. “Corruption Around the World: Causes, Consequences, Scope, and Cures.” Staff Papers - International Monetary Fund 45(4) (December): 559-594.&lt;br /&gt;
&lt;br /&gt;
Urdal, H. 2004. “The devil in the demographics: the effect of youth bulges on domestic armed conflict, 1950-2000.” Social Development Papers: Conflict and Reconstruction Paper 14.&lt;br /&gt;
&lt;br /&gt;
Ware, H. 2004. “Pacific instability and youth bulges: the devil in the demography and the economy.” Paper delivered at the 12th Biennial Conference of the Australian Population Association, 15-17.&lt;br /&gt;
&lt;br /&gt;
Wagner, Adolph. 1892.&amp;amp;nbsp;&#039;&#039;Grundlegung der Politischen Ökonomie&#039;&#039;. Leipzig: C.F. Winter Publishing Firm.&lt;br /&gt;
&lt;br /&gt;
World Bank. 2011.&amp;amp;nbsp;&#039;&#039;World Development Indicators 2011.&#039;&#039;&amp;amp;nbsp;Washington, DC: World Bank. Available at&amp;amp;nbsp;[http://data.worldbank.org/data-catalog/world-development-indicators http://data.worldbank.org/data-catalog/world-development-indicators].&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Health Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Adams 1987.&amp;amp;nbsp;[http://www.geog.ucl.ac.uk/~jadams/PDFs/smeed&#039;s%20law.pdf &amp;quot;Smeed&#039;s Law: some further thoughts.&amp;quot;]&amp;amp;nbsp;&#039;&#039;Traffic Engineering and Control&#039;&#039;&amp;amp;nbsp;(Feb) 70-73.&lt;br /&gt;
&lt;br /&gt;
Alsan, Marcella, David E. Bloom, and David Canning. 2006. “The Effects of Population Health on Foreign Direct Investment Inflows to Low- and Middle-Income Countries,”&amp;amp;nbsp;&#039;&#039;World Development&#039;&#039;&amp;amp;nbsp;34(4): 613-630.&lt;br /&gt;
&lt;br /&gt;
Anand, Sudhir and Martin Ravallion. 1993. “Human development in poor countries: on the role of private incomes and public services,”&amp;amp;nbsp;&#039;&#039;Journal of Economic Perspectives&#039;&#039;&amp;amp;nbsp;7(1): 133–150.&lt;br /&gt;
&lt;br /&gt;
Ashraf, Quamrul H., Ashley Lester, and David N. Weil. 2008. “When Does Improving Health Raise GDP?”&amp;amp;nbsp; NBER Working Paper No. 14449. National Bureau of Economic Research, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
Bidani, Benu and Martin Ravallion. 1997. “Decomposing social indicators using distributional data.”&amp;amp;nbsp;&#039;&#039;Journal of Econometrics&#039;&#039;&amp;amp;nbsp;77: 125–139.&lt;br /&gt;
&lt;br /&gt;
Bloom, David E., and David Canning. 2004. “Global Demographic Change: Dimensions and Economic Significance.” NBER Working Paper No. 10817.&amp;amp;nbsp; National Bureau of Economic Research, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
Blössner, Monika, and Mercedes de Onis. 2005.&amp;amp;nbsp;&#039;&#039;Malnutrition: quantifying the health impact at national and local levels.&#039;&#039;&amp;amp;nbsp;Geneva, World Health Organization. (WHO Environmental Burden of Disease Series, No. 12).&lt;br /&gt;
&lt;br /&gt;
Dargay, Gately, and Sommer 2007. “Vehicle Ownership and Income Growth, Worldwide: 1960-2030”. Joyce Dargay, Dermot Gately and Martin Sommer, January 2007.&lt;br /&gt;
&lt;br /&gt;
Deaton, Angus, and Christina Paxson. 2000 (May). “Growth and Savings Among Individuals and Households.”&amp;amp;nbsp;&#039;&#039;The Review of Economics and Statistics&#039;&#039;&amp;amp;nbsp;82(2): 212-225.&lt;br /&gt;
&lt;br /&gt;
Desai, Manish A., Sumi Mehta, and Kirk R. Smith. 2004. “Indoor smoke from solid fuels: Assessing the environmental burden of disease.”WHOEnvironmental Burden of Disease Series No. 4&#039;&#039;.&amp;amp;nbsp;&#039;&#039;Annette Pruss-Üstun, Diamid Campbell-Lendrum, Carlos Corvalán, and Alistair Woodward, series eds. World Health Organization, Geneva.&lt;br /&gt;
&lt;br /&gt;
Ezzati, Majid and Alan D. Lopez. 2004. “Smoking and oral tobacco use.” In Majid Ezzati, Alan D. Lopez, Anthony Rodgers, and Cristopher J.L. Murray, eds.,&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors&#039;&#039;. Geneva: World Health Organization, 883-957.&amp;amp;nbsp; Retrieved 4 Feb 2009, from&amp;amp;nbsp;[http://www.who.int/publications/cra/chapters/volume1/part4/en/index.html http://www.who.int/publications/cra/chapters/volume1/part4/en/index.html].&lt;br /&gt;
&lt;br /&gt;
Ezzati, Majid, Alan D. Lopez, Anthony Rodgers, Christopher J.L. Murray, eds. 2004.&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors&#039;&#039;. Geneva: World Health Organization.&lt;br /&gt;
&lt;br /&gt;
Fernández-Villaverde, Jesús, and Dirk Kruegger. 2004 (September 14). “Consumption over the Life Cycle: Facts from Consumer Expenditure Survey Data,” unpublished manuscript, University of Pennsylvania and University of Frankfort.&amp;amp;nbsp;&amp;amp;nbsp;[http://www.dklevine.com/archive/refs4506439000000000304.pdf http://www.dklevine.com/archive/refs4506439000000000304.pdf]&lt;br /&gt;
&lt;br /&gt;
Fernández-Villaverde, Jesús, and Dirk Kruegger. 2005 (December 19). “Consumption over the Life Cycle: How Important are Consumer Durables?,” unpublished manuscript, University of Pennsylvania and Goethe University.&amp;amp;nbsp;&amp;amp;nbsp;[http://journals.cambridge.org/action/displayAbstract?fromPage=online&amp;amp;aid=8466457 http://journals.cambridge.org/action/displayAbstract?fromPage=online&amp;amp;aid=8466457]&lt;br /&gt;
&lt;br /&gt;
Gakidou, Emmanuela, Shefali Oza, Cecilia Vidal Fuertes, Amy Y. Li, Diana K. Lee, Angelica Sousa, Margaret C. Hogan, Stephen Vander Hoorn, and Majid Ezzati. 2007.” Improving Child Survival Through Environmental and Nutritional Interventions: The Importance of Targeting Interventions Toward the Poor.”&amp;amp;nbsp;&#039;&#039;Journal of the American Medical Association&#039;&#039;&amp;amp;nbsp;298(16): 1876-1887.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. and Hillebrand, Evan E. 2006. “Exploring and shaping International Futures”. Boulder, CO: Paradigm Publishers.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., Randall Kuhn, Cecilia Peterson, Dale Rothman, and Jose Solorzano. 2011.&amp;amp;nbsp;&amp;amp;nbsp;&#039;&#039;Improving Global Health: Patterns of Potential Human Progress, Volume 3&#039;&#039;.&amp;amp;nbsp; Paradigm Publishing and Oxford India.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2005.&amp;amp;nbsp; “Productivity in IFs.” Pardee Center for International Futures Working Paper, University of Denver, Denver, CO.&amp;amp;nbsp;&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx http://www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
James, W. Philip T., Rachel Jackson-Leach , Cliona Ni Mhurchu, Eleni Kalamara, Maryam Shayeghi, Neville J. Rigby, Chizuru Nishida, and Anthony Rodgers. 2004.&amp;amp;nbsp; “Overweight and obesity (high body mass index).” In Majid Ezzati, Alan D. Lopez, Anthony Rodgers and Christopher J.L. Murray, eds.,&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors.&#039;&#039;&amp;amp;nbsp;Geneva: World Health Organization, 959-1108.&lt;br /&gt;
&lt;br /&gt;
Jamison, Dean T., Jia Wang, Kenneth Hill, and Juan-Luis Londono. 1996. “Income, Mortality and Fertility in Latin America: Country-Level Performance, 1960 - 90.”&amp;amp;nbsp;&#039;&#039;Analisis Economico&#039;&#039;11(2): 219-261.&lt;br /&gt;
&lt;br /&gt;
Kelly, Christopher, Nora Pashayan, Sreetharan Munisamy, and Joshn W. Powles. 2009.&amp;amp;nbsp; “Mortality attributable to excess adiposity in England and Wales in 2003 and 2015: explorations with a spreadsheet implementation of the Comparative Risk Assessment mentodology.”&amp;amp;nbsp;&#039;&#039;Population Health Metrics&#039;&#039;&amp;amp;nbsp;7(11): 1-7.&lt;br /&gt;
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Lopez, Alan D., Neil E. Collishaw, and Tapani Piha. 1994. “A descriptive model of the cigarette epidemic in developed countries.”&amp;amp;nbsp;&#039;&#039;Tobacco Control&#039;&#039;&amp;amp;nbsp;3(3): 242-247. &amp;amp;nbsp;&lt;br /&gt;
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Mathers, Colin D., and Dejan Loncar. 2005. &amp;quot;Updated Projections of Global Mortality and Burden of Disease, 2002-2030: Data Sources, Methods and Results.&amp;quot; Evidence and Information for Policy Working Paper. World Health Organization, Geneva.&lt;br /&gt;
&lt;br /&gt;
Mathers, Colin D., and Dejan Loncar. 2006. &amp;quot;Projections of Global Mortality and Burden of Disease from 2002 to 2030.&amp;quot;&amp;amp;nbsp;&#039;&#039;PLoS Medicine&#039;&#039;&amp;amp;nbsp;3(11): e442, 2011-2030.&amp;amp;nbsp; Retrieved 13 March 2009. doi:10.1371/journal.pmed.0030442.&lt;br /&gt;
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Mathers, Colin D., and Dejan Loncar. 2006b. “New projections of global mortality and burden of disease from 2002 to 2030.” Protocol S1. Technical Appendix to Mathers and Loncar 2006.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Mathers, Colin D., and Dejan Loncar. 2006c. “Results of Regressions of Age–Sex-Specific Mortality for Detailed Causes on the Respective Cause Cluster Based on the Full Country Panel Dataset, 1950–2002.” Technical Appendix to Mathers and Loncar 2006.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Nixon, John, and Philippe Ulmann. 2006. “The Relationship Between Health Care Expenditure and Health Outcomes: Evidence and caveats for a Causal Link.”&amp;amp;nbsp;&#039;&#039;European Journal of Health Economics&#039;&#039;&amp;amp;nbsp;7: 7-18.&lt;br /&gt;
&lt;br /&gt;
Peto, Richard, Jillian Boreham, Alan D. Lopez, Michael Thun, and Clark Heath, Jr. 1992. “Mortality from Tobacco in Developed Countries: Indirect Estimation from National Vital Statistics.”&amp;amp;nbsp;&#039;&#039;Lancet&amp;amp;nbsp;&#039;&#039;339(8804): 1268–1278. doi:10.1016/0140- 6736(92)91600-D.&lt;br /&gt;
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Ploeg, Martine, Katja K. H. Aben, and Lambertus A. Kiemeney. 2009. “The Present and Future Burden of Urinary Bladder Cancer in the World.”&amp;amp;nbsp;&#039;&#039;World Journal of Urology&#039;&#039;&amp;amp;nbsp;27(3): 289-293. doi:[http://dx.doi.org/10.1007/s00345-009-0383-3 &amp;amp;nbsp;10.1007/s00345-009-0383-3&amp;amp;nbsp;]. &amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Shibuya, Kenji, Mie Inoue, and Alan D. Lopez. 2005. “Statistical Modeling and Projections of Lung Cancer Mortality in 4 Industrialized Countries.”&amp;amp;nbsp;&#039;&#039;International Journal of Cancer&#039;&#039;&amp;amp;nbsp;117(3): 476-485. doi:[http://dx.doi.org/10.1002/ijc.21078 &amp;amp;nbsp;10.1002/ijc.21078&amp;amp;nbsp;]. &amp;amp;nbsp;&lt;br /&gt;
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Smeed, RJ 1949. &amp;quot;Some statistical aspects of road safety research&amp;quot;.&amp;amp;nbsp;[http://en.wikipedia.org/wiki/Royal_Statistical_Society &#039;&#039;Royal Statistical Society&#039;&#039;], Journal (A) CXII (Part I, series 4). 1-24.&lt;br /&gt;
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Smith, Lisa C. and Lawrence Haddad. 2000. “Explaining Child Malnutrition in Developing Countries: A Cross-Sectional Analysis.” Washington, D.C.: International Food Policy Research Institute.&lt;br /&gt;
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Soares, Rodrigo R. 2007. “On the Determinants of Mortality Reductions in the Developing World.”&amp;amp;nbsp;&#039;&#039;Population and Development Review&amp;amp;nbsp;&#039;&#039;33(2): 247-287.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2003.&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;World Population Prospects: The 2002 Revision, Highlight.&#039;&#039;&amp;amp;nbsp; New York:&amp;amp;nbsp; United Nations. Department of Economics and Social Affairs.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2009.&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;World Population Prospects: The 2008 Revision, Highlights.&#039;&#039;&amp;amp;nbsp; New York:&amp;amp;nbsp; United Nations. Department of Economics and Social Affairs.&lt;br /&gt;
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Wagstaff, Adam. 2002. “Inequalities in Health in Developing Countries: Swimming Against the Tide?” Unpublished Manuscript&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Infrastructure Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Agénor, Pierre-Richard, Mustapha Kamel Nabli, and Tarik M. Yousef. 2007. “Public Infrastructure and Private Investment in the Middle East and North Africa.” In Mustapha Kamel Nabli, ed.,. Breaking the Barriers to Higher Economic Growth: Better Governance and Deeper Reforms in the Middle East and North Africa. Washington, DC: World Bank Publications, 399–422.&lt;br /&gt;
&lt;br /&gt;
Asian Development Bank, Japan Bank for International Cooperation, and World Bank. 2005.&amp;amp;nbsp;&#039;&#039;Connecting East Asia: A New Framework for Infrastructure&#039;&#039;. Tokyo: Asian Development Bank, Japan Bank for International Cooperation, and World Bank.&amp;amp;nbsp;[http://siteresources.worldbank.org/INTEASTASIAPACIFIC/Resources/Connecting-East-Asia.pdf http://siteresources.worldbank.org/INTEASTASIAPACIFIC/Resources/Connecting-East-Asia.pdf].&lt;br /&gt;
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Bhattacharyay, Biswa Nath. 2010. “Estimating Demand for Infrastructure in Energy, Transport, Telecommunications, Water and Sanitation in Asia and the Pacific: 2010-2020”. Working Paper no. 248. Asian Development Bank Institute, Tokyo.&amp;amp;nbsp;[http://www.adbi.org/working-paper/2010/09/09/4062.infrastructure.demand.asia.pacific/ http://www.adbi.org/working-paper/2010/09/09/4062.infrastructure.demand.asia.pacific/].&lt;br /&gt;
&lt;br /&gt;
Bruinsma, Jelle. 2011. “The Resources Outlook: By How Much Do Land, Water and Crop Yields Need to Increase by 2050?” In Piero Conforti, ed.,.&amp;amp;nbsp;&#039;&#039;Looking Ahead in World Food and Agriculture: Perspectives to 2050&#039;&#039;. Rome: Food and Agriculture Organization of the United Nations (FAO), 233–275.&amp;amp;nbsp;[http://www.fao.org/docrep/014/i2280e/i2280e.pdf http://www.fao.org/docrep/014/i2280e/i2280e.pdf].&lt;br /&gt;
&lt;br /&gt;
Calderón, César, and Luis Servén. 2010a. “Infrastructure and Economic Development in Sub-Saharan Africa.”&amp;amp;nbsp;&#039;&#039;Journal of African Economies&#039;&#039;&amp;amp;nbsp;19(Supplement 1): i13–i87. doi:10.1093/jae/ejp022.&amp;amp;nbsp;[http://jae.oxfordjournals.org/cgi/content/abstract/19/suppl_1/i13 http://jae.oxfordjournals.org/cgi/content/abstract/19/suppl_1/i13].&lt;br /&gt;
&lt;br /&gt;
Calderón, César, and Luis Servén. 2010b. “Infrastructure in Latin America”. World Bank Policy Research Working Paper. Report Number 5317. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Canning, David. 1998. “A Database of World Stocks of Infrastructure, 1950-1995.”&amp;amp;nbsp;&#039;&#039;The World Bank Economic Review&#039;&#039;&amp;amp;nbsp;12(3): 529–548.&lt;br /&gt;
&lt;br /&gt;
Canning, David, and Mansour Farahani. 2007. “A Database of World Stocks of Infrastructure: Update 1950-2005”. Harvard School of Public Health, Boston, MA.&amp;amp;nbsp;[http://www.hsph.harvard.edu/faculty/david-canning/data-sets/ http://www.hsph.harvard.edu/faculty/david-canning/data-sets/].&lt;br /&gt;
&lt;br /&gt;
Cavallo, Eduardo Alfredo, and Christian Daude. 2008. “Public Investment in Developing Countries: A Blessing or a Curse?” RES Working Paper #4597. Inter-American Development Bank (IADB) - Research Department, OECD, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Chatterton, Isabe, and Olga S. Puerto. 2006.&amp;amp;nbsp;&#039;&#039;Estimation of Infrastructure Investment Needs in the South Asia Region: Executive Summary&#039;&#039;. Washington, DC: World Bank.&amp;amp;nbsp;[http://siteresources.worldbank.org/INTSARREGTOPTRANSPORT/Resources/Inf_Investment_Needs_IC_version4.pdf http://siteresources.worldbank.org/INTSARREGTOPTRANSPORT/Resources/Inf_Investment_Needs_IC_version4.pdf].&lt;br /&gt;
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Congressional Budget Office. 2010.&amp;amp;nbsp;&#039;&#039;Public Spending on Transportation and Water Infrastructure&#039;&#039;. Washington, DC: Congressional Budget Office.&amp;amp;nbsp;[http://www.cbo.gov/publication/21902 http://www.cbo.gov/publication/21902].&lt;br /&gt;
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Estache, Antonio, and Ana Goicoechea. 2005. “A Research Database on Infrastructure Economic Performance”. Policy Research Working Paper no. 3643. World Bank, Washington, DC.&amp;amp;nbsp;[http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2005/06/16/000016406_20050616100502/Rendered/PDF/wps3643.pdf http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2005/06/16/000016406_20050616100502/Rendered/PDF/wps3643.pdf].&lt;br /&gt;
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Ezzati, Majid, Alan D. Lopez, Anthony Rodgers, and Christopher J. L. Murray, eds. 2004.&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors&#039;&#039;. Geneva, Switzerland: World Health Organization (WHO).&lt;br /&gt;
&lt;br /&gt;
Fay, Marianne. 2001. “Financing the Future: Infrastructure Needs in Latin America, 2000-05”. Policy Research Working Paper no. 2545. World Bank, Washington, DC.&amp;amp;nbsp;[http://elibrary.worldbank.org/docserver/download/2545.pdf?expires=1375200693&amp;amp;id=id&amp;amp;accname=guest&amp;amp;checksum=DB7E5FB146EE28C93511B5ADAB2FD3CB http://elibrary.worldbank.org/docserver/download/2545.pdf?expires=1375200693&amp;amp;id=id&amp;amp;accname=guest&amp;amp;checksum=DB7E5FB146EE28C93511B5ADAB2FD3CB].&lt;br /&gt;
&lt;br /&gt;
Fay, Marianne, and Tito Yepes. 2003. “Investing in Infrastructure: What Is Needed from 2000 to 2010?” Policy Research Working Paper no. 3102. World Bank, Washington, DC. RePEc.&amp;amp;nbsp;[http://ideas.repec.org/p/wbk/wbrwps/3102.html http://ideas.repec.org/p/wbk/wbrwps/3102.html].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2007. “Forecasting Global Economic Growth with Endogenous Multifactor Productivity: The International Futures (IFs) Approach”. Pardee Center for International Futures Working Paper, University of Denver. Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., Devin Joshi, Jonathan Moyer, Timothy Sisk and José Roberto Solórzano. 2014. Strengthening Governance Globally. vol. 5, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Hughes, Gordon, Paul Chinowsky, and Ken Strzepek. 2009. “The Costs of Adapting to Climate Change for Infrastructure”. Economics of Adaptation to Climate Change Discussion Paper no. 2. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
International Transport Forum, and Organisation for Economic Cooperation and Development (OECD). 2011. “Trends in Transport Infrastructure Investment 1995-2009”. Paris.&lt;br /&gt;
&lt;br /&gt;
Kohli, Harpaul Alberto, and Phillip Basil. 2011. “Requirements for Infrastructure Investment in Latin America Under Alternate Growth Scenarios.”&amp;amp;nbsp;&#039;&#039;Global Journal of Emerging Market Economies&#039;&#039;&amp;amp;nbsp;3(1): 59 –110. doi:10.1177/097491011000300103.&amp;amp;nbsp;[http://eme.sagepub.com/content/3/1/59.abstract http://eme.sagepub.com/content/3/1/59.abstract].&lt;br /&gt;
&lt;br /&gt;
Kim, M. Julie, and Rita Nangia. 2010. “Infrastructure Development in India and China—A Comparative Analysis.” In William Ascher and Corinne Krupp, eds.,.&amp;amp;nbsp;&#039;&#039;Physical Infrastructure Development: Balancing The Growth, Equity, and Environmental Imperatives&#039;&#039;. New York, NY: Palgrave Macmillan, 97–140.&lt;br /&gt;
&lt;br /&gt;
Lora, Eduardo A. 2007.&amp;amp;nbsp;&#039;&#039;Public Investment in Infrastructure in Latin America: Is Debt the Culprit?&#039;&#039;&amp;amp;nbsp;Inter-American Development Bank Working Paper. Washington, DC: Inter-American Development Bank (IADB) - Research Department.&lt;br /&gt;
&lt;br /&gt;
Nelson, Gerald C., Mark W. Rosegrant, Amanda Palazzo, Ian Gray, Christina Ingersoll, Richard Robertson, Simla Tokgoz, Tingju Zhu, Timothy B. Sulser, Claudia Ringler, Siwa Msangi, and Liangzhi You. 2010.&amp;amp;nbsp;&#039;&#039;Food Security, Farming, and Climate Change to 2050: Scenarios, Results, Policy Options&#039;&#039;. Washington, DC: International Food Policy Research Institute.&amp;amp;nbsp;[http://www.ifpri.org/publication/food-security-farming-and-climate-change-2050 http://www.ifpri.org/publication/food-security-farming-and-climate-change-2050].&lt;br /&gt;
&lt;br /&gt;
Organisation for Economic Co-operation and Development. 2006.&amp;amp;nbsp;&#039;&#039;Infrastructure to 2030 Volume 1: Telecom, Land Transport, Water and Electricity&#039;&#039;. Infrastructure to 2030. Paris: Organisation for Economic Cooperation and Development.&lt;br /&gt;
&lt;br /&gt;
Organisation for Economic Co-operation and Development. 2009.&amp;amp;nbsp;&#039;&#039;Going for Growth: Economic Policy Reforms&#039;&#039;. Paris: Organisation for Economic Cooperation and Development (OECD).&lt;br /&gt;
&lt;br /&gt;
Qiang, Christine Zhen-Wei, Carlo M. Rossotto, and Kaoru Kimura. 2009. “Economic Impacts of Broadband.” In World Bank, ed.,.&amp;amp;nbsp;&#039;&#039;2009 Information and Communications for Development: Extending Reach and Increasing Impact&#039;&#039;. Washington, DC: World Bank, 35–50.&lt;br /&gt;
&lt;br /&gt;
Rothman, Dale S. Mohammod T. Irfan, Eli Margolese-Malin, Barry B. Hughes, Jonathan Moyer, and Janet Dickson. 2013.&amp;amp;nbsp;&#039;&#039;Building Global Infrastructure.&amp;amp;nbsp;&#039;&#039;vol. 4, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press. Stambrook, David. 2006. “Key Factors Driving the Future Demand for Surface Transport Infrastructure and Services.” In Organisation for Economic Cooperation and Development (OECD), ed.,.&amp;amp;nbsp;&#039;&#039;Infrastructure to 2030 Volume 1: Telecom, Land Transport, Water and Electricity&#039;&#039;. Infrastructure to 2030. Paris: Organisation for Economic Cooperation and Development (OECD), 185–239.&lt;br /&gt;
&lt;br /&gt;
World Health Organization, and UNICEF. 2013.&amp;amp;nbsp;&#039;&#039;Progress on Sanitation and Drinking-Water - 2013 Update&#039;&#039;. Geneva: World Health Organization.&lt;br /&gt;
&lt;br /&gt;
Yepes, Tito. 2008. “Investment Needs for Infrastructure in Developing Countries 2008-15”. Draft. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Yepes, Tito. 2005.&amp;amp;nbsp;&#039;&#039;Expenditure on Infrastructure in East Asia Region, 2006–2010&#039;&#039;. East Asia Pacific Infrastructure Flagship Study. Manila: Asian Development Bank (ADB), Japan Bank for International Cooperation (JBIC), World Bank.&lt;br /&gt;
&lt;br /&gt;
You, Liangzhi, Claudia Ringler, Ulrike Wood-Sichra, Richard Robertson, Stanley Wood, Tingju Zhu, Gerald Nelson, Zhe Guo, and Yan Sun. 2011. “What Is the Irrigation Potential for Africa? A Combined Biophysical and Socioeconomic Approach.”&amp;amp;nbsp;&#039;&#039;Food Policy&#039;&#039;&amp;amp;nbsp;36(6): 770–782. doi:10.1016/j.foodpol.2011.09.001.&amp;amp;nbsp;[http://www.sciencedirect.com/science/article/pii/S030691921100114X http://www.sciencedirect.com/science/article/pii/S030691921100114X].&lt;br /&gt;
&lt;br /&gt;
== [[Development_Mode_Features|Development Mode Features]] ==&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Energy&amp;diff=8314</id>
		<title>Energy</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Energy&amp;diff=8314"/>
		<updated>2017-09-07T21:43:27Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;The most recent and complete energy model documentation is available on Pardee&#039;s [http://pardee.du.edu/ifs-energy-model-documentation website]. Although the text in this interactive system is, for some IFs models, often significantly out of date, you may still find the basic description useful to you.&lt;br /&gt;
&lt;br /&gt;
The energy model combines a growth process in production with a partial equilibrium process.&amp;amp;nbsp; The energy model automatically replaces the energy sector in the full economic model unless the user disconnects that linkage.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
For energy, the partial equilibrium structures have distinct demand and supply sides, using price to seek a balance.&amp;amp;nbsp; As in the economic model, however, no effort is made to obtain a precise equilibrium in any time step.&amp;amp;nbsp; Instead stocks serve as a temporary buffer and the model again chases equilibrium over time.&lt;br /&gt;
&lt;br /&gt;
Gross domestic product (GDP) from the economic model provides the basis for energy demand calculations. &amp;amp;nbsp;Energy demand elasticities tend, however, to be quite high.&amp;amp;nbsp; Thus the physical constraints on the supply side are terribly important in determining the dynamics of the energy model.&lt;br /&gt;
&lt;br /&gt;
IFs distinguishes six energy production categories: oil, natural gas, coal, hydroelectric, nuclear, and other renewables. &amp;amp;nbsp;For each category both conventional and unconventional sources are considered, but these have only been fully implemented for oil.&amp;amp;nbsp; IFs computes only aggregated regional or national energy demands and prices, however, on the assumption of high levels of long-term substitutability across energy types and a highly integrated market.&amp;amp;nbsp; The model also conducts energy trade only in a single, combined energy category. &amp;amp;nbsp;Finally, at the moment, there is not a full connection between the energy model and access to electricity and electricity production (see the IFs Infrastructure Model Documentation for a description of the electricity aspects of IFs).&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Structure and Agent System: Energy&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot; border=&amp;quot;0&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width: 50%&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;System/Subsystem&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Energy&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Organizing Structure&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Partial market&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Stocks&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Capital, resources, reserves&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; valign=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Flows&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Production, consumption, trade, discoveries, investment&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Key Aggregate&amp;amp;nbsp;&#039;&#039;&#039; &#039;&#039;&#039;Relationships&amp;amp;nbsp;&#039;&#039;&#039;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;(illustrative, not comprehensive)&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Production function with exogenous technology change;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Energy demand relative to GDP;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Price determination&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; valign=&amp;quot;center&amp;quot; | &amp;lt;div style=&amp;quot;text-align: left&amp;quot;&amp;gt;&#039;&#039;&#039;Key Agent-Class Behavior&amp;amp;nbsp;&#039;&#039;&#039; &#039;&#039;&#039;Relationships&#039;&#039;&#039;&amp;lt;/div&amp;gt;&amp;lt;div style=&amp;quot;text-align: left&amp;quot;&amp;gt;(illustrative, not comprehensive)&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Government taxes, subsidies&amp;lt;br/&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Dominant Relations: Energy&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
Energy demand (ENDEM) is a function of GDP and the energy demand per unit of GDP (ENRGDP). &amp;amp;nbsp;Energy production (ENP) is a function of capital stock in each energy type, the capital/output ratio (QE) for that energy type, and a capacity utilization factor (CPUTF).&lt;br /&gt;
&lt;br /&gt;
The following key dynamics are directly linked to the dominant relations:&lt;br /&gt;
&lt;br /&gt;
DEMAND Energy demand per unit of GDP depends on GDP per capita, energy prices, and an autonomous trend in energy efficiency. &amp;amp;nbsp;The first two of these are computed endogenously, the latter exogenously. &amp;amp;nbsp;The user can control the price elasticity of energy demand (&#039;&#039;&#039;&#039;&#039;elasde&#039;&#039; &#039;&#039;&#039;) and the autonomous trend in efficiency of energy use (&#039;&#039;&#039;&#039;&#039;enrgdpgr&#039;&#039; &#039;&#039;&#039;). &amp;amp;nbsp;The user can also use an energy demand multiplier (&#039;&#039;&#039;&#039;&#039;endemm&#039;&#039; &#039;&#039;&#039;) to directly modify energy demand.&lt;br /&gt;
&lt;br /&gt;
PRODUCTION For fossils fuels and hydro, there are upper bounds on production.&amp;amp;nbsp; For fossil fuels, these are based on reserve production ratios, as well as user-specified upper bounds (&#039;&#039;&#039;&#039;&#039;enpoilmax&#039;&#039; &#039;&#039;&#039;, &#039;&#039;&#039;&#039;&#039;enpgasmax&#039;&#039; &#039;&#039;&#039;, and &#039;&#039;&#039;&#039;&#039;enpcoalmax&#039;&#039; &#039;&#039;&#039;).&amp;amp;nbsp; For hydro, the upper bound relates to hydropower potential. &amp;amp;nbsp;The model user can also control production using an energy demand multiplier (&#039;&#039;&#039;&#039;&#039;enpm&#039;&#039; &#039;&#039;&#039;) to directly modify energy production by energy type.&lt;br /&gt;
&lt;br /&gt;
CAPITAL/OUTPUT RATIO The capital/output (capital/production) ratios for all fuel types decline over time due to technological improvements at rates determined by two user controllable parameters (&#039;&#039;&#039;&#039;&#039;etechadv&#039;&#039; &#039;&#039;&#039; and &#039;&#039;&#039;&#039;&#039;etechadvuncon&#039;&#039; &#039;&#039;&#039;). &amp;amp;nbsp;For fossil fuels, this is counteracted by a factor that increases the capital/output ratio as the amount of remaining resources decreases. &amp;amp;nbsp;Something similar happens for hydro and other renewables, but here the capital/output ratios increase as production approaches a maximum possible level. &amp;amp;nbsp;The user can further modify the capital/output ratios with the multipliers (&#039;&#039;&#039;&#039;&#039;qem&#039;&#039; &#039;&#039;&#039; and &#039;&#039;&#039;&#039;&#039;qeunconm&#039;&#039; &#039;&#039;&#039;).&lt;br /&gt;
&lt;br /&gt;
CAPITAL Energy capital, by fuel type, is initialized based on the initial levels of production and capital/output ratios.&amp;amp;nbsp; Energy capital depreciates at a rate determined by the lifetime of energy capital (&#039;&#039;&#039;&#039;&#039;lke&#039;&#039; &#039;&#039;&#039;) and it grows with investment.&amp;amp;nbsp; Total desired investment in energy capital is influenced by many factors, including existing capital, domestic and global energy demand, the production of other renewables, changes in the global capital/output ratio, world and domestic energy stocks, expected overall profits in the energy sector, and imports.&amp;amp;nbsp; Users can influence this in the aggregate (via &#039;&#039;&#039;&#039;&#039;eninvm&#039;&#039; &#039;&#039;&#039;) and can also control the effect of expected profits (&#039;&#039;&#039;&#039;&#039;eleniprof&#039;&#039; &#039;&#039;&#039; and &#039;&#039;&#039;&#039;&#039;eleniprof2&#039;&#039; &#039;&#039;&#039;) and world energy stocks (&#039;&#039;&#039;&#039;&#039;elenpr&#039;&#039; &#039;&#039;&#039; and &#039;&#039;&#039;&#039;&#039;elenpr2&#039;&#039; &#039;&#039;&#039;). &amp;amp;nbsp;Desired investment by energy type increases with individual profit expectations, but also by limits related to reserve production factors (for fossil fuels and hydro), any exogenous restrictions on maximum production (for fossil fuels), ultimate potential (for hydro), and other, unspecified factors (nuclear).&amp;amp;nbsp; Users can influence the effect of profit expectations by fuel type (via &#039;&#039;&#039;&#039;&#039;elass&#039;&#039; &#039;&#039;&#039;) as well as influence the desired investment by energy type in the aggregate (via &#039;&#039;&#039;&#039;&#039;eninvtm&#039;&#039; &#039;&#039;&#039;).&amp;amp;nbsp; The user can also specify an exogenous growth rate for energy investment by fuel type (&#039;&#039;&#039;&#039;&#039;eprodr&#039;&#039; &#039;&#039;&#039;). &amp;amp;nbsp;The economic model ultimately determines whether all of the investment needs can be met; in case of shortfalls, the investment in each type of energy is reduced proportionately.&lt;br /&gt;
&lt;br /&gt;
RESOURCES/RESERVES/STOCKS IFs separately represents ultimate resources and reserves, where the latter are the amount of energy resources available to be produced. &amp;amp;nbsp;Resources and reserves, both conventional and unconventional, are set in the pre-processor.&amp;amp;nbsp; The user can modify the default assumptions on ultimate resources, either directly (&#039;&#039;&#039;&#039;&#039;resor&#039;&#039; &#039;&#039;&#039;, &#039;&#039;&#039;&#039;&#039;resoruncon&#039;&#039; &#039;&#039;&#039;) or via the use of multipliers (&#039;&#039;&#039;&#039;&#039;resorm&#039;&#039; &#039;&#039;&#039;, &#039;&#039;&#039;&#039;&#039;resorunconm&#039;&#039; &#039;&#039;&#039;).&amp;amp;nbsp; Reserves decline with production and increase with discoveries. &amp;amp;nbsp;The rate of discovery depends on the ultimate resources remaining, the intensity of current production, world energy prices, and a base rate of discovery (&#039;&#039;&#039;&#039;&#039;rdi&#039;&#039; &#039;&#039;&#039;). &amp;amp;nbsp;The user can control the effect of world prices on discovery (&#039;&#039;&#039;&#039;&#039;elasdi&#039;&#039; &#039;&#039;&#039;), augment the base rate of discovery (&#039;&#039;&#039;&#039;&#039;rdinr&#039;&#039; &#039;&#039;&#039;), and use a multiplier to affect the rates of discovery (&#039;&#039;&#039;&#039;&#039;rdm&#039;&#039; &#039;&#039;&#039;).&amp;amp;nbsp; Finally, IFs keeps track of any production not used in the current year, i.e., stocks, and shortages.&lt;br /&gt;
&lt;br /&gt;
ENERGY PRICES Domestic energy prices are influenced by world stocks, domestic stocks, and the ratio of capital to production at the global level. &amp;amp;nbsp;The user can control the effect of domestic stocks on prices (&#039;&#039;&#039;&#039;&#039;epra&#039;&#039; &#039;&#039;&#039; and &#039;&#039;&#039;&#039;&#039;eprafs&#039;&#039; &#039;&#039;&#039;).&amp;amp;nbsp; Users can also include a “cartel premium” (&#039;&#039;&#039;&#039;&#039;encartpp&#039;&#039; &#039;&#039;&#039;) and a carbon tax (&#039;&#039;&#039;&#039;&#039;carbtax&#039;&#039; &#039;&#039;&#039;). &amp;amp;nbsp;More directly users can set domestic energy prices exogenously for just the first year (&#039;&#039;&#039;&#039;&#039;enprixi&#039;&#039; &#039;&#039;&#039;) or for multiple future years (&#039;&#039;&#039;&#039;&#039;enprix&#039;&#039; &#039;&#039;&#039;).&amp;amp;nbsp; The world energy price is calculated as a weighted sum of the domestic prices.&lt;br /&gt;
&lt;br /&gt;
TRADE The energy model also provides representation and control over energy trade.&amp;amp;nbsp; The levels of imports and exports depend upon levels of production and demand, as well as past propensities to import and export energy.&amp;amp;nbsp; The user can set maximum limits on of energy imports (&#039;&#039;&#039;&#039;&#039;enml&#039;&#039; &#039;&#039;&#039;) and energy exports (&#039;&#039;&#039;&#039;&#039;enxl&#039;&#039; &#039;&#039;&#039;), as well as general limits on trade (&#039;&#039;&#039;&#039;&#039;trademax&#039;&#039; &#039;&#039;&#039;).&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Energy Flow Charts&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== Overview ===&lt;br /&gt;
&lt;br /&gt;
The production growth process in energy is simpler than that in agriculture or the full economic model.&amp;amp;nbsp; Because energy is a very capital-intensive sector, production depends only on capital stocks and changes in the capital-output ratio, which represents technological sophistication and other factors (such as decreasing resource bases) that affect production costs.&lt;br /&gt;
&lt;br /&gt;
The key equilibrating variable is again inventories.&amp;amp;nbsp; It works via investment to control capital stock and therefore production, and via prices to control domestic consumption.&amp;amp;nbsp; Production and consumption, in turn, control trade.&lt;br /&gt;
&lt;br /&gt;
Specifically, as inventories rise, investment falls, restraining capital stock and energy production, and thus holding down inventory growth.&amp;amp;nbsp; As inventories rise, prices fall, thereby increasing domestic consumption, which also holds down inventory growth.&lt;br /&gt;
&lt;br /&gt;
[[File:Eng1.png|frame|center|Visual representation of the energy production growth process]]&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Energy Production Detail&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Energy production is a function of the capital stock in energy and the capital-output ratios, modified by a capacity utilization factor and exogenous multipliers and production limits.&amp;amp;nbsp; The capital-output ratios are affected by the amount of remaining resources as a share of the initial levels, technological progress, and user-controlled multipliers.&amp;amp;nbsp; The capacity utilization factor is influenced by domestic stocks and shortages.&lt;br /&gt;
&lt;br /&gt;
[[File:Eng2.png|frame|center|Visual representation of energy production]]&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Energy Capital and Investment Detail&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The capital stock by energy type decreases with depreciation and grows with investment.&amp;amp;nbsp; Investment or growth in the capital stock, while affected by numerous factors, is driven heavily by energy profits and stocks (unless the user intervenes with a scenario multiplier), and constrained by the reserves available of each specific energy type and production constraints. &amp;amp;nbsp;The user can use a direct multiplier on total energy investment, multipliers on energy investment by energy type to influence investment, or specify a desired rate of growth in investment by energy type.&lt;br /&gt;
&lt;br /&gt;
[[File:Eng3.png|frame|center|Visual representation of energy capital and investment]]&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Energy Demand Detail&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Energy demand is estimated as a function of the energy demand per unit GDP (in PPP terms) and total GDP (in PPP terms), with adjustments related to energy prices and improvements in energy use efficiency.&amp;amp;nbsp; The energy demand per unit GDP depends on GDP per capita (in PPP Terms).&amp;amp;nbsp; The improvement in energy use efficiency is a combination of autonomous trend in efficiency of energy use (&#039;&#039;&#039;&#039;&#039;enrgdpgr&#039;&#039; &#039;&#039;&#039;) and an additional amount that accelerates the improvements for (non-exporting) countries that have efficiencies below the global average. &amp;amp;nbsp;The price effect takes into account both the domestic and global prices of energy, as well as any carbon tax (&#039;&#039;&#039;&#039;&#039;carbtax&#039;&#039; &#039;&#039;&#039;).&amp;amp;nbsp; The user can control the price elasticity of energy demand (&#039;&#039;&#039;&#039;&#039;elasde&#039;&#039; &#039;&#039;&#039;) and the historical weight used to smooth energy prices (&#039;&#039;&#039;&#039;&#039;ehw&#039;&#039; &#039;&#039;&#039;).&amp;amp;nbsp; Finally, the user can also use an energy demand multiplier (&#039;&#039;&#039;&#039;&#039;endemm&#039;&#039; &#039;&#039;&#039;) to directly modify energy demand.&lt;br /&gt;
&lt;br /&gt;
[[File:Eng4.png|frame|center|Visual representation of energy demand]]&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Energy Resources and Reserves Detail&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
IFs distinguishes between ultimate resources and reserves, where the latter represent the amount of energy actually discovered and available for production.&amp;amp;nbsp; Ultimate resources are initially determined in the pre-processor, but the user can override these estimates using either absolute values (&#039;&#039;&#039;&#039;&#039;resor&#039;&#039; &#039;&#039;&#039;, &#039;&#039;&#039;&#039;&#039;resoruncon&#039;&#039; &#039;&#039;&#039;) or multipliers (&#039;&#039;&#039;&#039;&#039;resorm&#039;&#039; &#039;&#039;&#039;, &#039;&#039;&#039;&#039;&#039;resorunconm&#039;&#039; &#039;&#039;&#039;).&amp;amp;nbsp; There is also a parameter controlling the portion of unconventional oil that is economic to produce (&#039;&#039;&#039;&#039;&#039;enresorunce&#039;&#039; &#039;&#039;&#039;).&amp;amp;nbsp; For non-renewable energy types, i.e., fossil fuels, reserves increase with discoveries and decrease with production.&amp;amp;nbsp; The rate of discovery includes a base rate (&#039;&#039;&#039;&#039;&#039;rdi&#039;&#039; &#039;&#039;&#039;) and an annual increment (&#039;&#039;&#039;&#039;&#039;rdinr&#039;&#039; &#039;&#039;&#039;).&amp;amp;nbsp; There are further adjustments related to the world energy price, the remaining resources, and the current rate of production.&amp;amp;nbsp; The user can control the effect of world prices on discovery (&#039;&#039;&#039;&#039;&#039;elasdi&#039;&#039; &#039;&#039;&#039;) and can also intervene with a discovery multiplier (&#039;&#039;&#039;&#039;&#039;rdm&#039;&#039; &#039;&#039;&#039;).[[File:Eng5.png|frame|center|551x255px|Visual representation of energy resources and reserves]]&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Energy Equations&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== Overview ===&lt;br /&gt;
&lt;br /&gt;
This section of the Help system will present and discuss the equations that are central to the functioning of the energy model: supply, demand, trade, stocks, price, investment, economic linkages, capital, natural resources and energy indicators.&amp;amp;nbsp; Here we follow the order of calculations in all years but the first, noting specific calculations that are made in the first year or pre-processor as necessary.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Energy Demand&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;The key energy demand variable in IFs, ENDEM, tracks total primary energy demand.&amp;amp;nbsp; For the most part, IFs does not represent the transformation of this primary energy into final energy forms, or end-user energy demand.&amp;amp;nbsp; The one exception relates to electricity use, which is described in the documentation of the Infrastructure model.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;In the first year, total primary energy demand is calculated as an apparent demand, with attention paid to stocks and expected growth in production.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENDEM_{r,t=1}=\sum_eENP_{r,e,t=1}+ENM_{r,t=1}-ENX_{r,t=1}-ENST_{r,t=1}*AVEPR_{r,t=1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*ENP, ENM, ENX, ENST, and AVEPR are energy production, energy imports, energy exports, energy stocks, and an average of the expected growth in production across all energy types.&amp;amp;nbsp; The calculations of the initial values of these variables are described later in the Equations section under the appropriate headings.&lt;br /&gt;
&lt;br /&gt;
Note that this calculation does not directly use the historical data on total primary energy demand and there can be a significant difference between the initialized value of ENDEM and the actual historical data for the base year.&amp;amp;nbsp; This information is used by the variable ENDEMSH, which is described in the Infrastructure documentation.&lt;br /&gt;
&lt;br /&gt;
In future years, the calculation of total primary energy demand begins with an estimate of the predicted amount of energy demand per unit of GDP (in PPP terms), compendemperunit, as a function of GDP per capita (in PPP terms).[1] This function is show in the figure below[2]:[[File:Eng6.png|frame|right|Total primary energy demand]]&lt;br /&gt;
&lt;br /&gt;
A small amount, 0.0005, is added to this computed value to account for the fact that the demand data used to estimate the function above is less than apparent demand globally.&lt;br /&gt;
&lt;br /&gt;
The initial data for countries is unlikely to fall exactly on this function.&amp;amp;nbsp; To reconcile this fact, IFs calculates values for both predicted energy demand per unit GDP in the first year, compendemperuniti, and empirical demand per unit GDP (in PPP terms) in the first year, actendemperuniti. &amp;amp;nbsp;Over a time period controlled by the parameter &#039;&#039;&#039;&#039;&#039;enconv&#039;&#039; &#039;&#039;&#039;, IFs gradually adjusts the difference between these two values so that the estimate of energy demand per unit GDP (in PPP terms) eventually does fall on the function.&lt;br /&gt;
&lt;br /&gt;
IFs then calculates an initial estimate of total energy demand, endemba, by multiplying this adjusted value of energy demand per unit GDP (in PPP terms), endemperunit, by GDP (in PPP terms).[3]&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;endemba_r=GDPP_r*endemperunit_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
IFs then considers the effect of price on total primary energy demand.&amp;amp;nbsp; IFs keeps track of the global energy price as both an index (WEP, base year = 100) and as an actual dollar value (WEPBYEAR, $ per BBOE). It also tracks a country level energy price index (ENPRI, base year =100).[4]&amp;amp;nbsp; Finally, it can also consider a tax on carbon, expressed by the variable CarTaxEnPriAdd, which has the units $ per BBOE.&lt;br /&gt;
&lt;br /&gt;
The calculation of the effect of prices on total energy begins with the calculation of a variable called renpri. &amp;amp;nbsp;renpri is a moving average country-level price index that starts at the level of the country level price index in the base year, ENPRII, and then tracks changes in world energy prices and country-level carbon taxes.[5]&amp;amp;nbsp; The historical weight is controlled by the parameter &#039;&#039;&#039;&#039;&#039;ehw&#039;&#039; &#039;&#039;&#039;, so that:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;renpri_{r,t}=\mathbf{ehw}*renpri_{r,t-1}+(1-\mathbf{ehw})*(WEP_{t-1}+CarTaxEnPriAdd_{r,t-1}*\frac{WEP_{t=1}}{WEPBYEAR_{t=1}})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*renpri is the moving average country level price index&lt;br /&gt;
*&#039;&#039;&#039;&#039;&#039;ehw&#039;&#039; &#039;&#039;&#039; is the weight given to the historical value of renpri&lt;br /&gt;
*&#039;&#039;WEP&#039;&#039; is the global energy price index&lt;br /&gt;
*&#039;&#039;WEPBYEAR&#039;&#039; is the global energy price in $ per BBOE&lt;br /&gt;
*CarTaxEnPriAdd is the country level carbon tax in $ per BBOE of total energy and is calculated as the exogenous value of the carbon tax in $ per ton of carbon, &#039;&#039;&#039;&#039;&#039;carbtax&#039;&#039; &#039;&#039;&#039;, times a production weighted average of the carbon contents of oil, gas, and coal, &#039;&#039;&#039;&#039;&#039;carfuel1-3&#039;&#039; &#039;&#039;&#039;:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CarTaxEnPri_r=\frac{\sum_e(ENP_{r,e}*\mathbf{carfuel_e})}{\sum_eENP_{r,e}}*\mathbf{carbtax_r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The parameter specifying the price elasticity of energy demand, &#039;&#039;&#039;&#039;&#039;elasde&#039;&#039; &#039;&#039;&#039;, is adjusted based on the relationship between renpri and and ENPRII to yield a new parameter, elasadjusted.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;elasadjusted_r=\mathbf{elasde_r}*\frac{ENPRII_r}{renpri_r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
This, in effect, decreases the price elasticity of energy demand as prices increase.&lt;br /&gt;
&lt;br /&gt;
This adjusted elasticity is then used to calculate the impact on energy demand, elasterm, as&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;elasterm_r=1+\frac{renpri_r+ENPRII_r}{ENPRII_r}*elasadjusted_{r^6}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The user can also introduce a further adjustment to total primary energy demand with a multiplier, &#039;&#039;&#039;&#039;&#039;endemm&#039;&#039; &#039;&#039;&#039;, yielding:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENDEM_r=endemba_r*elasterm_r*\mathbf{endemm_r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
IFs makes a final adjustment to total primary energy demand related to changes in energy efficiency of the economy unrelated to prices.[6]&amp;amp;nbsp;All countries receive an annual boost in energy efficiency related to technology given by the parameter &#039;&#039;&#039;&#039;&#039;enrgdpr&#039;&#039; &#039;&#039;&#039;.&amp;amp;nbsp; In addition, if a country is not a major energy exporter and its economy is less energy efficient than the global average, measured as ENDEM divided by GDP (in PPP terms)[7], it gets an additional boost to its energy efficiency.&amp;amp;nbsp; This effect is cumulative, so ENDEM is adjusted as follows:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENDEM_r=ENDEM_r*(1+\frac{EnRGDPGRCalc_r}{100})^{iy}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*EnRGDPGRCalc is the annual average boost in energy efficiency&lt;br /&gt;
*iy is the number of years since the base year plus 1&lt;br /&gt;
&lt;br /&gt;
Finally, IFs makes an initial estimate of energy use per unit GDP in MER terms, ENRGDP.&amp;amp;nbsp; An estimate of GDP based on the previous year’s GDP in MER terms and a growth rate is used due to the order of calculations, but this is corrected later in the model sequence.&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;div id=&amp;quot;ftn1&amp;quot;&amp;gt;&lt;br /&gt;
[1] Here, IFs uses GDP from the previous time cycle, with an estimate of growth, to calculate GDPPCP, because the recursive structure of IFs computes current GDP later.&amp;amp;nbsp; The current value of population, POP, has already been computed at this stage.&lt;br /&gt;
&amp;lt;/div&amp;gt;&amp;lt;div id=&amp;quot;ftn2&amp;quot;&amp;gt;&lt;br /&gt;
[2] The exact equation is compendemperunit = 0.0023428 -0.0003878*ln(GDPPCP).&lt;br /&gt;
&lt;br /&gt;
[3]&amp;amp;nbsp;Again, IFs uses GDP from the previous time cycle here, because the recursive structure of IFs computes current GDP later.&lt;br /&gt;
&amp;lt;div id=&amp;quot;ftn1&amp;quot;&amp;gt;&lt;br /&gt;
[4]&amp;amp;nbsp;The model also has a variable representing the price index in each economic sector, one of which is energy. This value is stored in the variable PRI, which uses an index value of 1 in the base year.&amp;amp;nbsp; ENPRI and PRI (energy) track each other, with former having a value 100 times that of the latter due to the different initial index values.&lt;br /&gt;
&amp;lt;/div&amp;gt;&amp;lt;div id=&amp;quot;ftn2&amp;quot;&amp;gt;&lt;br /&gt;
[5]&amp;amp;nbsp;Because energy prices and carbon taxes are computed later in the model sequence, the previous year’s values are used here.&lt;br /&gt;
&amp;lt;div id=&amp;quot;ftn1&amp;quot;&amp;gt;&lt;br /&gt;
[6]&amp;amp;nbsp;This is generally referred to as autonomous energy efficiency improvement, or aeei.&lt;br /&gt;
&amp;lt;/div&amp;gt;&amp;lt;div id=&amp;quot;ftn2&amp;quot;&amp;gt;&lt;br /&gt;
[7]&amp;amp;nbsp;An estimate of this year’s GDPP based on the previous year’s GDPP and a growth rate is used here due to the order of calculations.&lt;br /&gt;
&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Energy Supply&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;The computation of energy production (ENP) is considerably easier than that of gross sectoral production in the economic model or of agricultural production in the agricultural model.&amp;amp;nbsp; Only capital is considered important as a factor of production (not labor, land, or even weather).&amp;amp;nbsp; Energy production is initially estimated by dividing the quotient of capital in each energy category (ken) and the appropriate capital-to-output ratio (QE).&amp;amp;nbsp; A multiplier, &#039;&#039;&#039;&#039;&#039;enpm&#039;&#039; &#039;&#039;&#039;, can be used to increase or decrease production.&amp;amp;nbsp; This yields:&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENP1_{r,e}=\frac{ken_{r,e}}{QE_{r,e}}*\mathbf{enpm_{r,e}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The dynamics of the capital-to-output ratios, QE, are discussed in [[Energy#Resources_and_Reserves:_Capital-to-Output_Ratios_and_Discoveries|this section]].&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;Known reserves (RESER) and exogenously specified maximums pose constraints on production of certain energy types.&amp;amp;nbsp; The affected energy types are oil, gas, coal, and hydro.&amp;amp;nbsp; The impact of reserves is felt via a limit on the fraction of reserves that can be produce in any year. Specifically, the reserve-to-production ratio may not fall below the value of &#039;&#039;&#039;&#039;&#039;prodtf&#039;&#039; &#039;&#039;&#039;, which is initially set in the pre-processor, but can be overridden by the user. &amp;amp;nbsp;In addition, as the actual reserve-to-production ratio approaches this limit, its rate of decrease is limited.&amp;amp;nbsp; The exogenously specified maximums apply only to oil, gas, and coal, and are given by the parameters &#039;&#039;&#039;&#039;&#039;enpoilmax&#039;&#039; &#039;&#039;&#039;, &#039;&#039;&#039;&#039;&#039;enpgasmax&#039;&#039; &#039;&#039;&#039;, and &#039;&#039;&#039;&#039;&#039;enpcoalmax&#039;&#039; &#039;&#039;&#039;.&amp;amp;nbsp; This yields a second estimate for energy production, given as:&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENP2_{r,e}=MIN(\frac{RESER_{r,e}}{MAX(\mathbf{prodtf}_{r,e},sResProdR_{r,e}-1)},enpmax_{r,e})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*e only applies to oil, gas, coal, and hydro&lt;br /&gt;
*&#039;&#039;enpmax&#039;&#039; takes on the value &#039;&#039;&#039;&#039;&#039;enpoilmax&#039;&#039; &#039;&#039;&#039;, &#039;&#039;&#039;&#039;&#039;enpgasmax&#039;&#039; &#039;&#039;&#039;, and &#039;&#039;&#039;&#039;&#039;enpcoalmax&#039;&#039; &#039;&#039;&#039;,depending upon the fuel.&lt;br /&gt;
*sResProdR is the reserve-to-production ratio from the previous year; this limit only takes effect when sResProdR falls below 30 and remains above &#039;&#039;&#039;&#039;&#039;prodtf&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
IFs then selects the minimum of ENP1 and ENP2 as the estimate of energy production ENP.&amp;amp;nbsp; The dynamics of energy reserves are discussed in [[Energy#Resources_and_Reserves:_Capital-to-Output_Ratios_and_Discoveries|this section]].&lt;br /&gt;
&lt;br /&gt;
Two final adjustments are made to energy production.&amp;amp;nbsp; The first accounts for capacity utilization, &#039;&#039;CPUTF&#039;&#039;, and the second only comes into play when a restriction is placed on energy exports.&amp;amp;nbsp; Since these are not calculated until the calculation of energy stocks and shortages, they are described in the appropriate places in the [[Energy#Domestic_Energy_Stocks|Domestic Energy Stocks]] section and the [[Energy#Energy_Prices_and_Final_Adjustments_to_Domestic_Energy_Stocks_and_Capacity_Utilization|Energy Prices and Final Adjustments]] section.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Energy Trade&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The energy model in IFs keeps track of trade in energy in physical quantities; the trade in energy in monetary terms is handled in the economic model. &amp;amp;nbsp;As opposed to the agricultural model, where trade in crops, meat, and fish are treated separately, the energy model considers trade in energy in the aggregate.&amp;amp;nbsp; Furthermore, it only considers production from oil, gas, coal, and hydro as being available for export.&amp;amp;nbsp; Finally, as with other aspects of trade, IFs uses a pooled trade model rather than representing bilateral trade.&lt;br /&gt;
&lt;br /&gt;
The first estimate of energy imports and exports by country are determined based upon a country’s propensity to export, propensity to import, and moving averages of its energy production and demand.&lt;br /&gt;
&lt;br /&gt;
The moving average of energy production, identified as smoothentot, is calculated simply as a moving average of production of energy from oil, gas, coal, and hydro. In the first year of the model:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;smoothentot_{r,t=1}=EnTot_{r,t=1}=\sum_eENP_{r,e,t=1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*e is oil, gas, coal, and hydro&lt;br /&gt;
&lt;br /&gt;
In future years,&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;smoothentot_{r,t}=0.9*smoothentot_{r,t-1}+0.1*\sum_eENP_{r,e,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*e is oil, gas, coal, and hydro&lt;br /&gt;
&lt;br /&gt;
The moving average of energy demand, identified as smoothpendem has a few more nuances, particularly after the first year.&amp;amp;nbsp; In the first year, IFs calculates:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;smoothpendem_{r,t=1}=ENDEM_{r,t=1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
In future years, rather than using the value of ENDEM calculated earlier, the model uses a slightly different measure of energy demand, referred to as pendem.&amp;amp;nbsp; pendem differs from ENDEM in two main ways:&lt;br /&gt;
&lt;br /&gt;
1. rather than using the moving average country-level price index, renpri, to calculate the effect of prices on energy demand, it uses only current values:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;PEnPri_{r,t}=WEP_{t-1}+CarTaxEnPriAdd_{r,t-1}*\frac{WEP_{t=1}}{WEPBYEAR_{t=1}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
2. it does not include the additional boost in energy efficiency beyond &#039;&#039;&#039;&#039;&#039;enrgdpr&#039;&#039; &#039;&#039;&#039; in calculating the autonomous changes in energy efficiency&lt;br /&gt;
&lt;br /&gt;
Thus, in future years, we have&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;smoothpendem_{r,t}=0.8*smoothpendem_{r,t-1}+0.2*pendem_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
A country’s propensities to import and export energy are given by the variables MKAVE and XKAVE.&amp;amp;nbsp; These are moving averages of the ratios of imports to an import base related to energy demand and exports to an export base related to energy production and demand, respectively.&amp;amp;nbsp; MKAVE is initialized to the ratio of energy imports to energy demand in the first year.&amp;amp;nbsp; A maximum value, MKAVMax is also set at this time to the maximum of 1.5 times this initial value or the value of the parameter &#039;&#039;&#039;&#039;&#039;trademax&#039;&#039; &#039;&#039;&#039;.&amp;amp;nbsp; XKAVE is initialized to the ratio of energy exports to the sum of energy production from oil, gas, coal and hydro and energy demand from all energy types in the first year.&amp;amp;nbsp; Its maximum value, XKAVMAX is set to the maximum of this initial value and the parameter &#039;&#039;&#039;&#039;&#039;trademax&#039;&#039; &#039;&#039;&#039;.&amp;amp;nbsp; The updating of MKAVE and XKAVE occur after the calculation of imports and exports, so we will return to that at the end of this section.&lt;br /&gt;
&lt;br /&gt;
The initial estimates of energy exports, ENX, and energy imports, ENM, are calculated as:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENX_r=MIN(XKAVE_r,XKAVMAX_r)*exportbase_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENM_r=MIN(MKAVE_r*pendem_r,MKAVMAX_r*smoothpendem_r)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;exportbase_r=smoothentot_r+smoothpendem_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
At this point, IFs makes some adjustments to energy imports and exports depending upon whether a country is considered in energy surplus or deficit.&amp;amp;nbsp; Where a country sits in this regard involves considering domestic and global stocks in addition to current production and demand.&lt;br /&gt;
&lt;br /&gt;
Domestic energy stocks are computed as the sum of stocks carried over from the previous year, while also considering any shortages&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;stocks_{r,t}=ENST_{r,t-1}-ENSHO_{r,t-1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
A stock base is also calculated as&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;StBase_r=smoothpendem_r+smoothpendemr&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The ratio of stocks to StBase can be defined as domesticstockratio. A moving average of a trade base, smoothtradebase, is also calculated for each country:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;smoothtradebase_{r,t}=MAX(ENDEM_r,0.9*smoothtradebase_{r,t-1}+0.1*2*(ENX_r+ENM_r))&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;smoothtradbase_{r,t+1}=MAX(ENDEM_{r,t=1},2*(ENX_{r,t=1}+ENM_{r,t=1}))&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Global energy stocks, GlobalStocks, and the global stock base, GlobalStBase, are the sum of the domestic stocks and stock bases across countries, and the value of the globalstockratio is defined as GlobalStocks divided by GlobalStBase.&lt;br /&gt;
&lt;br /&gt;
For each country, the level of deficit or surplus, endefsurp, is calculated as:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;endefsurp_r=(globalstockratio-domesticstockratio_r)*StBase_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
This implies that if a countries stock ratio is less (greater) than the global average, it is considered in deficit (surplus).&lt;br /&gt;
&lt;br /&gt;
If a country is in deficit, i.e., endefsurp &amp;gt; 0, IFs will act to reduce its exports and increase its exports.&amp;amp;nbsp; The recomputed value of exports is:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENX_r=MAX(0.5*ENX_r,ENX_r*(1-\frac{endefsurp_r}{smoothtradebase_r}))&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
In words, the decrease in energy exports is determined by the ratio of the level of deficit to the smoothed trade base, but can be no greater than 50 percent.&lt;br /&gt;
&lt;br /&gt;
The recomputed value of imports is:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENM_r=ENM_r*(1+\frac{endefsurp_r}{smoothtradebase_r})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
with a maximum level given as:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENMMax_r=ENM_r+(\frac{pendem_r*MKAVMAX_r-ENM_r}{5})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Similarly, if a country is in surplus, i.e., endefsurp &amp;lt; 0, IFs will act to increase exports and reduce imports.&amp;amp;nbsp; The amount of increase in exports is controlled, in part, by the exchange rate for the country, EXRATE, specifically its difference from a target level of 1 and its change from the previous year.&amp;amp;nbsp; As with other adjustment factors of this type, the ADJSTR function is used, yielding a factor named mul.&amp;amp;nbsp; After first multiplying ENX by a value that is bound from above by 1.05 and from below by the maximum of 0.95 and mul, the recomputed value of ENX is:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENX_r=ENX_r*(1-\frac{endefsurp_r}{smoothtradebase_r})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Here, a maximum level is given as:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENXMax_r=ENX_r+(\frac{exportbase_r*XKAVMAX_r-ENX_r}{5})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039; this maximum value is computed prior to the adjustments to ENX noted above.&lt;br /&gt;
&lt;br /&gt;
The recomputed value of imports is:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENM_r=MAX(0.5*ENM_r,ENM_r*(1+\frac{endefsurp_r}{smoothtradebase_r}))&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
In words, the decrease in energy imports is determined by the ratio of the level of surplus to the smoothed trade base, but can be no greater than 50 percent.&lt;br /&gt;
&lt;br /&gt;
Because of the frequent use and importance of government trade restrictions in energy trade, model users may want to establish absolute export (&#039;&#039;&#039;&#039;&#039;enxl&#039;&#039; &#039;&#039;&#039;) &amp;amp;nbsp;or import (&#039;&#039;&#039;&#039;&#039;enml&#039;&#039; &#039;&#039;&#039;) limits, which can further constrain energy exports and imports.&amp;amp;nbsp; An export constraint may also affect the production of oil and gas as described in the next section.&lt;br /&gt;
&lt;br /&gt;
As it is unlikely that the sums of these values of ENX and ENM across countries will be equal, which is necessary for trade to balance.&amp;amp;nbsp; To address this, IFs computes actual world energy trade (WET) as the average of the global sums of exports and imports.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WET=\frac{\sum_rENX_r+\sum_rENM_r}{2}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
and recomputes energy exports and imports, as:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENX_r=WET*\frac{ENX_r}{\sum_rENX_r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENM_r=WET*\frac{ENM_r}{\sum_rENM_r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
This maintains each country’s share of total global energy exports and imports.&lt;br /&gt;
&lt;br /&gt;
IFs can now update the moving average export (XKAVE) and import (MKAVE) propensities for the next time step.&amp;amp;nbsp; This requires historic weights for exports (&#039;&#039;&#039;&#039;&#039;xhw&#039;&#039; &#039;&#039;&#039;) and imports (&#039;&#039;&#039;&#039;&#039;mhw&#039;&#039; &#039;&#039;&#039;), yielding the equations:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;XKAVE_{r,t+1}=XKAVE_r*\mathbf{xhw}+(1-\mathbf{xhw})*\frac{ENX_r}{exportbase_r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;MKAVE_{r,t+1}=MKAVE_r*\mathbf{mhw}+(1-\mathbf{mhw})*\frac{ENM_r}{smoothpendem_r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
A further adjustment is made related to the import propensity, MKAVE, related to the difference between this propensity and a target level, ImportTarget, and the change in this difference since the previous year. &amp;amp;nbsp;This target starts at the level of MKAVE in the first year and gradually declines to 0 over a 150 year period.&amp;amp;nbsp; As in many other situations in IFs, this process makes use of the ADJUSTR function to determine the adjustment factor.&amp;amp;nbsp; The value of mulmlev is not allowed to exceed 1, so its effect can only be to reduce the value of MKAVE.&lt;br /&gt;
&lt;br /&gt;
Finally, XKAVE and MKAVE are checked to make sure that they do not exceed their maximum values, XKAVMAX and MKAVMAX, respectively.&lt;br /&gt;
&lt;br /&gt;
[1] The previous year’s values of WEP and CarTaxEnPriAdd are used as the current year’s values are not calculated until later in the model sequence.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Domestic Energy Stocks&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;IFs sets a target for energy stocks in each country as a fraction of a domestic stock base, StBase, which was defined earlier as the sum of a moving average of energy demand, smoothpendem, and a moving average of the production of oil, gas, coal, and hydro, smoothentot.&amp;amp;nbsp; This fraction is defined by the parameter &#039;&#039;&#039;&#039;&#039;dstlen&#039;&#039; &#039;&#039;&#039;.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;Stocks are initialized in the first year as &#039;&#039;&#039;&#039;&#039;dstlen&#039;&#039; &#039;&#039;&#039;multiplied by the initial domestic stock base, which is the sum of production of all energy types and an estimated value of apparent energy demand.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENST_{r,t=1}=\mathbf{dstlen}*(\sum_cENP_{r,e,t=1}+ENDEMEst_{r,t=1})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*e includes all energy types&lt;br /&gt;
*ENDEMEst is calculated as:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENDEMEst_r=(1-\mathbf{dstlen}*AVEPR_r)*\sum_eENP_{r,e,t=1}+ENM_{r,t=1}-ENX_{r,t=1})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*e includes all energy types&lt;br /&gt;
*AVEPR is a weighted average energy production growth rate&lt;br /&gt;
&lt;br /&gt;
In future years, IFs begins by summing the moving average energy demand, smoothpendem, across countries, storing this value as WENDEM and the same for moving average energy production from oil, gas, coal, and hydro, smoothentot, which it stores as WorldEnp.&amp;amp;nbsp; It also sums the moving average energy demand just for countries that have low propensity for exports, XKAVE &amp;lt; 0.2, and stores this value as WEnDemIm.&lt;br /&gt;
&lt;br /&gt;
At this point, IFs adjusts energy production by multiplying by a capacity utilization factor, CPUTF, which is assumed to be the same for all energy types in a country.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENP_{r,e}=ENP_{r,e}*CPUTF_r&amp;lt;/math&amp;gt; [1]&lt;br /&gt;
&lt;br /&gt;
The value of CPUTF is initialized to 1 in the first year.&amp;amp;nbsp; How it changes in time is described in the next section after the description of the calculation of the domestic price index.&lt;br /&gt;
&lt;br /&gt;
An initial estimate of energy stocks, ENST, is then calculated as the previous year’s stocks augmented by production and imports and reduced by use and exports&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENST_r=ENST_{r,t-1}+-ENDEM_r-ENX_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
If after this calculation, there are excess stocks, i.e., ENST &amp;gt; &#039;&#039;&#039;&#039;&#039;dstlen&#039;&#039; &#039;&#039;&#039; * StBase, and there is an export constraint, given by &#039;&#039;&#039;&#039;&#039;enxl&#039;&#039; &#039;&#039;&#039;, adjustments are made to the production of oil and gas&amp;lt;sup&amp;gt;[2]&amp;lt;/sup&amp;gt;, and, in turn, to energy stocks.&amp;amp;nbsp; The total reduction in oil and gas production is given as the amount of excess stocks, with a maximum reduction being the total amount of oil and gas production.&amp;amp;nbsp; This total amount of reduced production is then shared proportionately between oil and gas.&amp;amp;nbsp; The total reduction is also removed from ENST.&lt;br /&gt;
&lt;br /&gt;
Later, after the determination of prices, ENST is modified to: 1) ensure that they are not less than zero and 2) to account for any global shortfalls.&amp;amp;nbsp; These modifications are described in the next section.&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;div id=&amp;quot;ftn1&amp;quot;&amp;gt;&lt;br /&gt;
[1]&amp;amp;nbsp;This is the first of the two adjustments to energy production noted at the end of the [[Energy#Energy_Supply|Energy Supply]] section.&lt;br /&gt;
&lt;br /&gt;
[2] This is the second of the two adjustments to energy production noted at the end of the [[Energy#Energy_Supply|Energy Supply]] section.&lt;br /&gt;
&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Energy Prices and Final Adjustments to Domestic Energy Stocks and Capacity Utilization&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;IFs keeps track of separate domestic, ENPRI, and world, WEP, energy price indices, that apply to all forms of energy.&amp;amp;nbsp; These are initialized to a value of 100 in the first year.&amp;amp;nbsp; It also tracks the world energy price in terms of dollars per BBOE, WEPBYEAR, which is initialized as a global parameter.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;A number of pieces are needed for the calculation of energy prices.&amp;amp;nbsp; These include a world stock base, wstbase, world energy stocks, wenst, world energy production by energy type, WENP, world energy capital, WorldKen, and a global capital output ratio, wkenenpr.&amp;amp;nbsp; These are calculated as follows:&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;wstkbase=\sum_rStBase_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;wenstks=\sum_r(ENST_r-ENSHO_{r,t-1})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WENP_e=\sum_rENP_{r,e}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WorldKen=\sum_r\sum_e(ken_e*\frac{CPUTF_r}{MAX(5,\mathbf{lke_e})})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;wkenenpr=\frac{WorldKen}{WorldEnp}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*ENSHO is domestic energy shortage (described below)&lt;br /&gt;
*ken is capital for each energy type&lt;br /&gt;
*&#039;&#039;&#039;&#039;&#039;lke&#039;&#039; &#039;&#039;&#039; is the average lifetime of capital for each energy type&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;In cases when at least one country has an exogenous restriction on the production of oil, i.e., enpm(oil) &amp;lt; 1 for at least one country, a few additional variables are calculated:&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GlobalShortFall=\sum_r\sum_eMax(0,ENP_{r,e,t-1}-1.05*ENP_{r,e,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WorldEnProd=\sum_eWENP_e&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ShortFallSub=GlobalShortFall*MIN(10,\frac{WorldEnProd}{WENP(oil)})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;Otherwise these three variables all take on a value of 0.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;These values are used to calculate an adjustment factor driven by global energy stocks that affects domestic energy prices.&amp;amp;nbsp; The effect in the current year, wmul, is calculated using the ADJSTR function, which looks at the difference between world energy stocks, wenstks and the desired level, given by &#039;&#039;&#039;&#039;&#039;dstlen&#039;&#039; &#039;&#039;&#039; * wstbase, and the change in world energy stocks from the previous year.&amp;amp;nbsp; The presence of an exogenous restriction on the production of oil has two effects on the calculation of wmul.&amp;amp;nbsp; First, the value of ShortFallSub affects the two differences that feed into the ADJSTR function.&amp;amp;nbsp; Second, the elasticities applied in the ADJSTR function are tripled.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;The adjustment factor calculated in the current year is not applied directly to the calculation of domestic energy prices.&amp;amp;nbsp; Rather, a cumulative value, cumwmul, is calculated as:&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;cumwmul_t=cumwmul_{t-1}*(1+(wmul-1)*\mathbf{eprohw})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;Other factors affect the domestic energy price index – domestic energy stocks, possible cartel price premiums, &#039;&#039;&#039;&#039;&#039;encartpp&#039;&#039; &#039;&#039;&#039;, the first year value of the world energy price index, IWEP, changes in the global capita output ratio from the first year, whether the user has set a global energy price override. &#039;&#039;&#039;&#039;&#039;enprixi&#039;&#039;, &#039;&#039;&#039;and whether there are any restriction on oil production.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;The domestic energy stocks affect a country-specific “markup” factor, MarkUpEn.&amp;amp;nbsp; This starts at a value of 1 and changes as a function of the value of mul, which is calculated using the ADJSTR function.&amp;amp;nbsp; Here the differences are those between domestic energy stocks and desired stocks, given as &#039;&#039;&#039;&#039;&#039;dstlen&#039;&#039; &#039;&#039;&#039; * StBase, and the changes in energy stocks from the previous year.&amp;amp;nbsp; Shortages from the previous year are also taken into account.&amp;amp;nbsp; The user can also control the elasticities used in the ADJSTR function with the parameters &#039;&#039;&#039;&#039;&#039;epra&#039;&#039; &#039;&#039;&#039; and &#039;&#039;&#039;&#039;&#039;eprafs&#039;&#039; &#039;&#039;&#039;.&amp;amp;nbsp; This markup evolves over time as&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;MarkUpEn_{r,t}=MarkUpEn_{r,t-1}*mu&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;The domestic energy price index, ENPRI, is first calculated as:&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENPRI_r=\mathbf{X}*mul_r*cumwmul+\mathbf{encartpp}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;&#039;X&#039;&#039;&#039; = &#039;&#039;&#039;&#039;&#039;enprixi&#039;&#039;, &#039;&#039;&#039;when this parameter is set to a value greater than 1 and IWEP otherwise&lt;br /&gt;
&lt;br /&gt;
It is then recomputed as:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENPRI_r=MIN(ENPRI_r,ENPRI_{r,t-1}+\mathbf{encartpp}_t-\mathbf{encartpp}_{t-1}+\mathbf{X})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;&#039;X&#039;&#039;&#039; is 100 whenthere is a restriction on oil production in at least one country and 20 otherwise&lt;br /&gt;
&lt;br /&gt;
Furthermore, ENPRI is not allowed to fall by more than 10 in a given year.&lt;br /&gt;
&lt;br /&gt;
It is possible for the user to override this price calculation altogether.&amp;amp;nbsp; Any positive value of the exogenous country-specific energy price specification (&#039;&#039;&#039;&#039;&#039;enprix&#039;&#039; &#039;&#039;&#039;) will do so.&lt;br /&gt;
&lt;br /&gt;
It is only now that a country’s energy stocks and shortages are finalized for the current year.&amp;amp;nbsp; If ENST is less than 0, then a shortage is recorded as ENSHO = -ENST and ENST is set to 0.&amp;amp;nbsp; In addition, for countries that have a low propensity for exports, XKAVE &amp;lt; 0.2, a share of any global shortfall is added to their shortage, with the share determined by the country’s share of moving average energy demand among those countries:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENSHO_r=ENSHO_r+GlobalShortFall*\frac{smoothpendem_r}{WEnDemIm}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The energy shortage enters the Economic model in the calculation of gross sectoral production.&lt;br /&gt;
&lt;br /&gt;
The same differences in domestic stock from their target level and their change since the previous year, taking into account shortages from the previous year, are used to update the value of capacity utilization in energy, CPUTF, which was introduced earlier.&amp;amp;nbsp; The multiplier affecting CPUTF, Mul, is calculated using the ADJSTR function, with elasticities given by &#039;&#039;&#039;&#039;&#039;elenpst&#039;&#039; &#039;&#039;&#039; and &#039;&#039;&#039;&#039;&#039;elenpst2&#039;&#039; &#039;&#039;&#039;.&amp;amp;nbsp; In addition, the capacity utilization is smoothed over time.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CPUTF_{r,t}=0.5*CPUTF_{r,t-1}+0.5*Mul&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
This value is further assumed to converge to a value of 1 over a period of 100 years and is bound to always have a value between 0.2 and 2.&lt;br /&gt;
&lt;br /&gt;
This still leaves the need to calculate the world energy price. &amp;amp;nbsp;IFs actually tracks a world price including carbon taxes, WEP, and a world price ignoring carbon taxes, WEPNoTax.&amp;amp;nbsp; Carbon taxes are ignored in cases where the energy price is set exogenously using &#039;&#039;&#039;&#039;&#039;enprix&#039;&#039; &#039;&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
In both cases, the world energy price is a weighted average of domestic energy prices:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WEP=\frac{TENP}{TENPRI}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WEPNoTax=\frac{TENP}{TENPRINoTax}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;TENP=\sum_r\sum_eENP_{r,e}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;TENPRINoTax=\sum_r\sum_e(ENPRI_r*ENP_{r,e})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;TENPRI=\sum_r\sum_e((ENPRI_r+CarTaxEnPriAdd_r*\frac{WEP_{t=1}}{WEPBYEAR_{t=1}})*ENP_{r,e})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*WEP and WEPBYEAR convert CarTaxEnPriAdd from $/BBOE to an index value&lt;br /&gt;
*the term with CarTaxEnPriAdd is ignored in countries with exogenous energy prices in a given year&lt;br /&gt;
*CarTaxEnPriAdd is&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Finally, the value of WEPBYEAR is computed as:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WEPBYEAR=WEPBYEAR_{t=1}*\frac{WEP}{WEP_{t=1}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Energy Investment&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Investment in energy is relatively complex in IFs, because changes in investment are the key factor that allows us to clear the energy market in the long term.&amp;amp;nbsp; It is also different and perhaps slightly more complex in IFs than investment in agriculture.&amp;amp;nbsp; Whereas the latter involves computing a single investment need for agricultural capital, and subsequently dividing it between land and capital, in energy a separate demand or need is calculated for each energy type, based on profit levels specific to each energy type.&lt;br /&gt;
&lt;br /&gt;
We begin by calculating a total energy investment need (TINEED) to take to the economic model and place into the competition for investment among sectors.&amp;amp;nbsp; This investment need is a function of energy demand, adjusted by a number of factors, some global and some country-specific. To begin with, TINEED is calculated as&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;TINEED_r=ENDEM_r*mulendem*\frac{wkenenpri_t}{wkenenpri_{t-1}}*mulkenenpr*mulwst*mulstocks^{0.5}*mulrprof_r*mulrenew_r*sendeminvr_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*mulendem is the ratio of global energy demand per unit GDP in the current year to that in the previous year&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;mulkenenpr=\frac{WENDEM_t/WGDP_t}{WENDEM_{t-1}/WGDP_{t-1}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
*wkenenpri is the ratio of global energy capital to global energy production&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;wkenenpr=\frac{WorldKen}{WorldEnp}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
*mulkenenpr is the ratio of wkenenpr in the current year to that in the previous year&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;mulkenenpr=\frac{wkenenpr_t}{wkenenpr_{t-1}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
*mulwst and mulstocks are factors related to global energy stocks. mulwst is calculated using the ADJSTR function, where: the first order difference is that between global energy stocks, wenstks, and desired global energy stocks, DesStocks = &#039;&#039;&#039;&#039;&#039;dstlen&#039;&#039; &#039;&#039;&#039; * wstbase; the second order difference is between the level of world energy stocks in the current year and those in the past year; and the elasticities are given by the parameters &#039;&#039;&#039;&#039;&#039;elenpr&#039;&#039; &#039;&#039;&#039; and &#039;&#039;&#039;&#039;&#039;elenpr2&#039;&#039; &#039;&#039;&#039;. mulstocks is also related to global energy stocks, but is more directly related to the desired level of global energy stocks:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;mulstocks=\frac{DesStocks}{MAX(0.5*DesStocks,MIN(4*DesStocks,enstks))}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Note that mulstocks will always take on a value between ¼ and 4.&lt;br /&gt;
&lt;br /&gt;
*mulrprof is a function of the expected level of profits in the energy sector as a whole in a country, EPROFITR.&amp;amp;nbsp; Energy profits are calculated as the ratio of returns, EnReturn, to costs, ProdCosts.&amp;amp;nbsp; EPROFITR is actually a moving average of these profits relative to those in the base year, with a historical weighting factor controlled by the parameter &#039;&#039;&#039;&#039;&#039;eprohw&#039;&#039; &#039;&#039;&#039;.&amp;amp;nbsp; In full, we have:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EnReturn_r=WEPNoTax*\sum_eENP_{r,e}&amp;lt;/math&amp;gt; [1]&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ProdCost_r=\sum_e\frac{ken_{e,r}}{MAX(5,\mathbf{lke_e})}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EnReturn_r=\frac{EnReturn_r}{ProdCost_r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EPROFIT_{r,t}=\mathbf{eprohw}*EPROFIT_{r,t-1}+(1-\mathbf{eprohw})*\frac{EnReturn_{r,t}}{EnReturn_{r,t=1}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
We can now calculate mulrprof using the ADJSTR function.&amp;amp;nbsp; The first order difference is between the current value of EPROFITR and a target value of 1; the second order difference is the change in the value of EPROFITR from the previous year; the elasticities applied to these differences are given by the parameters &#039;&#039;&#039;&#039;&#039;eleniprof&#039;&#039; &#039;&#039;&#039; and &#039;&#039;&#039;&#039;&#039;eleniprof2&#039;&#039; &#039;&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
*mulrenew is a function of the share of other renewables in the energy mix in a country.&amp;amp;nbsp; It is assigned a value of 1 unless the production of energy from renewables exceeds 70% of total energy demand.&amp;amp;nbsp; If so, we have:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;mulrenew_r=MAX(0.5,1-(\frac{ENP_{r,renew}}{ENDEM_r}-0.7)*1)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Given these conditions, mulrenew can take on values between 0.5 and 1, with larger values associated with larger amounts of renewable production.&lt;br /&gt;
&lt;br /&gt;
*sendeminvr is a moving average of the ratio of investment need to energy demand in a country, with an accounting for changes in the global capital production ratio since the first year and is updated as&amp;lt;sup&amp;gt;[2]&amp;lt;/sup&amp;gt;:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;sendeminvr_{r,t+1}=0.95*sendeminvr_{r,t}+0.05*\frac{TINEED_{r,t}}{ENDEM_{r,t=1}}*\frac{wkenenpr_{t=1}}{wkenenpr_t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
After this initial calculation, two further adjustments are made to TINEED.&amp;amp;nbsp; The first is a reduction related to a possible reduction of inventory, invreduc, carried over from the previous year.&amp;amp;nbsp; The calculation of invreduc is described later in this section, where we look at reductions in investment in specific energy types due to resource constraints or other factors. The effect on TINEED is given as:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;TINEED_r=TINEED_r-MIN(0.7*invreduc_{r,t-1},0.6*TINEED_r)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Thus, the reduction in TINEED can be no more than 60 percent.&lt;br /&gt;
&lt;br /&gt;
Finally, the user can adjust TINEED with the use of the multiplier &#039;&#039;&#039;&#039;&#039;eninvm&#039;&#039; &#039;&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
Before this total investment need, TINEED, is passed to the Economic model, there is a chance that it may need to be further reduced.&amp;amp;nbsp; This depends on the calculation of a bound, TINeedBound.&amp;amp;nbsp; TINeedBound arises from a bottom-up calculation of the investment needs for each energy type individually, ineed.&amp;amp;nbsp; These depend upon the profits for each energy type and any possible bounds on production related to reserves and other factors.&lt;br /&gt;
&lt;br /&gt;
As with the estimate of total profits to energy, the returns by energy type depend upon production and costs.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EnReturnS_{r,e}=\frac{ENP_{r,e}}{EnCost_{r,e}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
For the non-fossil fuel energy types – hydro, nuclear, and other renewable – EnCost is based solely on capital depreciation&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EnCost_{r,e}=\frac{ken_{r,e}}{\mathbf{lke_e}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*e = hydro, nuclear, renew&lt;br /&gt;
&lt;br /&gt;
For the fossil fuel energy types – oil, gas, and coal – we must also consider any possible carbon taxes. EnCost is calculated as&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EnCost_{r,e}=\frac{ken_{r,e}}{\mathbf{lke_e}}+ENP_{r,e}*\mathbf{carfuel}_e*\mathbf{carbtax}_r+MAX(-0.5*\frac{ken_{r,e}}{\mathbf{lke_e}},ENP_{r,e}*(\mathbf{carfuel}_e-AvgCarFuel)*emtax_r)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*e = oil, coal, gas&lt;br /&gt;
*&#039;&#039;&#039;&#039;&#039;carfuel&#039;&#039; &#039;&#039;&#039; is the carbon content of the fuel in tons per BBOE&lt;br /&gt;
*AvgCarFuel is the unweighted arithmetic average of the carbon content of oil, gas, and coal&lt;br /&gt;
*&#039;&#039;&#039;&#039;&#039;carbtax&#039;&#039; &#039;&#039;&#039; is an exogenously specified country-specific carbon tax in $ per BBOE&lt;br /&gt;
*emtax is the number of years since the first year plus one multiplied by 2&lt;br /&gt;
&lt;br /&gt;
The change in eprofitrs from the first year is then calculated as:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;eprofitrs_{r,e}=\frac{EnReturnS_{r,e,t}}{EnReturnS_{r,e,t=1}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
An average return, avgreturn, is calculated as the weighted sum of the individual returns:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;avgreturn_r=\sum_e(ENP_{r,e}*EnReturnS_{r,e})smoothentot_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Investment need by energy type, ineed, grows in proportion to capital and as a function of relative profits.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ineed_{r,e,t}=ineed_{r,e,t=1}*\frac{ken_{r,e,t}}{ken_{r,e,t=1}}*eprofitrs^{elass_{r,e}}_{r,e}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;&#039;&#039;&#039;elass&#039;&#039; &#039;&#039;&#039; are country and energy-specific user controlled parameters&lt;br /&gt;
&lt;br /&gt;
At this point, ineed is checked to make sure that it does not fall by more than 20% or increase by more than 40% in any single year.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Also, if the user has set an exogenous target for production growth, i.e., &#039;&#039;&#039;&#039;&#039;eprodr&#039;&#039; &#039;&#039;&#039; &amp;gt; 0, all of the above is overridden and ineed is calculated as:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ineed_{r,e}=\frac{ken_{r,e}*(1+\mathbf{enprodr}_e)}{\mathbf{lke}_e}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
These investment needs are checked to make sure that they do not exceed what the known reserve base can support.&amp;amp;nbsp; This applies only to oil, gas, coal, and hydro. An initial estimate of the maximum level of investment is given by:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;maxinv_{r,e}=(\frac{RESER_{r,e}}{\mathbf{prodtf}_{r,e}}-\frac{ken_{r,e}}{QE_{r,e}}+\frac{ENP_{r,e}}{\mathbf{lke}_e})*QE_{r,e}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*e = oil, gas, coal, or hydro&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
The first term in parentheses, when multiplied by QE, indicates the amount of capital that would be necessary in order to yield the maximum level of production given the lower bound of the reserve production ratio, &#039;&#039;&#039;&#039;&#039;prodtf&#039;&#039; &#039;&#039;&#039;. The second term is simply the current level of capital and the third term indicates the level of depreciation of existing capital.&amp;amp;nbsp; This implies that countries will not make investments beyond those that would give it the maximum possible level of production for a given energy type.&lt;br /&gt;
&lt;br /&gt;
At the same time, IFs assumes there is a minimum level of investment, which is basically 30% of the capital depreciated during the current year:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;mininv_{r,e}=0.3*\frac{ENP_{r,e}}{\mathbf{lke}_e}*QE_{r,e}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*e = oil, gas, coal, or hydro&lt;br /&gt;
&lt;br /&gt;
In cases where the current production of oil, gas, or coal already equals or exceeds the exogenously specified maximum for a country – &#039;&#039;&#039;&#039;&#039;enpoilmax&#039;&#039; &#039;&#039;&#039;, &#039;&#039;&#039;&#039;&#039;enpgasmax&#039;&#039; &#039;&#039;&#039;, or &#039;&#039;&#039;&#039;&#039;enpcoalmax&#039;&#039; &#039;&#039;&#039; – maxinv is set equal to mininv.&amp;amp;nbsp; This again avoids useless investment.&lt;br /&gt;
&lt;br /&gt;
A further constraint is placed on the maximum investment level in capital for hydro production.&amp;amp;nbsp; This is done by simply replacing RESER/&#039;&#039;&#039;&#039;&#039;prodtf&#039;&#039; &#039;&#039;&#039; in the calculation of maxinv with the value ENDEM * EnpHydroDemRI * 2, where EnpHydroDemRI is the ratio of energy produced by hydro in the base year to total energy demand in that year.&amp;amp;nbsp; In other words, the growth in energy production from hydro in the current year from the first year cannot exceed twice the growth in total energy demand over that period, even if reserves are available, and capital investments are restricted accordingly.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;maxHydroProd_{r,t}=2*\frac{ENDEM_{r,t}}{ENDEM_{r,t=1}}*ENP_{r,Hydro,t=1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The constraints placed on investment in nuclear energy differ somewhat from these other fuels. IFs does not have an explicit measure of reserves for nuclear.&amp;amp;nbsp; Rather, it is assumed that the growth in capital in nuclear energy cannot exceed 1 percent of existing capital plus whatever is required to account for depreciation:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;maxinv_{r,e}=(0.01*\frac{ken_{r,e}}{QE_{r,e}}+\frac{ENP_{r,e}}{\mathbf{lke}_e})*QE_{r,e}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*e = nuclear&lt;br /&gt;
&lt;br /&gt;
Also, the minimum level of investment for nuclear energy is assumed to be 50 percent of the capital depreciated in the current year, rather than 30 percent as with oil, gas, coal, and hydro.&lt;br /&gt;
&lt;br /&gt;
There is no limit to the investments in capital for other renewables.&lt;br /&gt;
&lt;br /&gt;
Given these restrictions, the investment needs for oil, gas, coal, hydro, and nuclear are updated so that mininv &amp;lt;= ineed &amp;lt;= maxinv.&amp;amp;nbsp; Any reductions from the previous estimates of ineed are summed across energy types to yield the value of invreduc, which will affect the estimate of TINEED in the following year as described earlier.&lt;br /&gt;
&lt;br /&gt;
The final estimates of ineed for each energy type are summed to yield TINeedBound.&amp;amp;nbsp; If TINEED is greater than TINEEDBOUND, then TINEED is recalculated as the average of the two:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;TINEED_r=0.5*(TINEED_r+TINeedBound_r)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
This value of TINEED is passed to the Economic model as IDS&amp;lt;sub&amp;gt;energy&amp;lt;/sub&amp;gt;,&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;IDS_{r,s=energy}=sidsf_r*TINEED_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*sidsf is an adjustment coefficient converting units of energy capital into monetary values. This gradually converges to a value of 1 after a number of years specified by the parameter &#039;&#039;&#039;&#039;&#039;enconv&#039;&#039; &#039;&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
In the Economic model, the desired investment in energy must compete with other sectors for investment (see more about linkages between the Energy and Economic models in section 3.7).&amp;amp;nbsp; Once these sectoral investments are determined, a new value for investments in the energy sector, IDS&amp;lt;sub&amp;gt;s=energy&amp;lt;/sub&amp;gt;, is passed back to the Energy model.&amp;amp;nbsp; The adjustment coefficient is then applied to yield:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;inen_r=\frac{IDS_{r,s=energy}}{sidsf_r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
In the meantime, the desired investment for each energy type can be modified with a country and energy-type specific parameter &#039;&#039;&#039;&#039;&#039;eninvtm&#039;&#039; &#039;&#039;&#039;, and a new value of TINEED is calculated as the sum of these new levels of desired investment.&amp;amp;nbsp; The amount of the available investment, inen, going to each energy type is then calculated as:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ineed_{r,e}=inen_r*\frac{ineed_{r,e}*\mathbf{eninvtm}_{r,e}}{TINEED_r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
i.e., all energy types receive the same proportional increase or decrease in investment.&lt;br /&gt;
&lt;br /&gt;
These investments are then translated into units of capital, KEN_Shr,&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;KENShr_{r,e}=ineed_{r,e}-\frac{ken_{r,e}}{\mathbf{lke}_e}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The new level of capital is determined as:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ken_{r,e,t+1}=(ken_{r,e,t}+KENShr_{r,e})*(1-CIVDM_r)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*CIVDM is an exogenous factor reflecting civilian damage from war&lt;br /&gt;
&lt;br /&gt;
Note that there is no guarantee that KEN_Shr is positive, so it is theoretically possible for ken to fall below 0; IFs checks to make sure that this does not happen.&lt;br /&gt;
&amp;lt;/div&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
&amp;lt;div id=&amp;quot;ftn1&amp;quot;&amp;gt;&lt;br /&gt;
[1]&amp;amp;nbsp;World energy price is used to provide stability. The no tax world energy price is used as taxes do not contribute to returns.&lt;br /&gt;
&lt;br /&gt;
[2] Note the careful use of the time subscripts. sendeminvr is not updated until after the computation of the initial value of TINEED, so the initial calculation of TINEED needs to use the previous year’s value of sendeminvr. Furthermore, the updating of sendeminvr occurs after TINEED has been adjusted to reflect any inventory reductions, but before the investment multiplier, &#039;&#039;&#039;&#039;&#039;eninvm&#039;&#039; &#039;&#039;&#039;, is applied.&lt;br /&gt;
&amp;lt;/div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Economic Linkages&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The economic model and the two physical models have many variables in common.&amp;amp;nbsp; As in the agricultural model, IFs generally uses the values in the physical model to override those in the economic model.&amp;amp;nbsp; To do so, it computes coefficients in the first year that serve to adjust the physical values subsequently. The adjustment coefficients serve double duty - they translate from physical terms to constant monetary ones, and they adjust for discrepancies in initial empirical values between the two models.&lt;br /&gt;
&lt;br /&gt;
[[Energy#Energy_Investment|The Energy Investment section]] already described how desired investment, TINEED, is passed to the Economic model using the adjustment coefficient sidsf.&amp;amp;nbsp; The adjustment coefficient, ZSR is used to convert production:&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ZS_{r,s=2}=ZSR_r*WEPBYear_{r,t=1}*\sum^EENP_{r,e}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ZSRI_r=\frac{ZS_{r,s=2,t=1}}{WEPBYear_{r,t=1}*\sum^EENP_{r,e,t=1}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
ZSR is a convergence of ZSRI to a value of 1 in 30 years and WEPBYear converts the energy units, which are in BBOE to dollars.&lt;br /&gt;
&lt;br /&gt;
The adjustment coefficient SCSF is used to convert consumption:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CS_{r,s=2}=SCSF_r*ENDEM_r*0.6&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;SCSF_r=\frac{CS_{r,s=2,t=1}}{ENDEM_{r,t=1}*0.6}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Note that this assumes that consumer make up a constant 60 percent of consumption of total primary energy.&amp;amp;nbsp; Also SCSF remains constant over time.&lt;br /&gt;
&lt;br /&gt;
For stocks, imports, and exports, WEBPBYear serves as the adjustment coefficient&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ST_{r,s=2}=WEPBYear_{r,t=1}*ENST_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;XS_{r,s=2}=WEPBYear_{r,t=1_r}*ENX_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;MS_{r,s=2}=WEPBYear_{r,t=1}*ENM_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Finally, the indexed price (with a base of 1) in the energy sector of the economic submodel (PRI) is simply the ratio of current to initial regional energy price (ENPRI) time the value of PRI in the first year.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;PRI_{r,s=2}=PRI_{r,s=2,t=1}*\frac{ENPRI_r}{ENPRI_{r,t=1}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Resources and Reserves: Capital-to-Output Ratios and Discoveries&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== Capital-to-Output Ratios ===&lt;br /&gt;
&lt;br /&gt;
Resource base is important in selected energy categories of IFs: conventional oil, natural gas, coal, hydroelectric power, and unconventional oil.&amp;amp;nbsp; Resources are not important in the nuclear category, which represents an undefined mixture of burner, breeder and fusion power.&lt;br /&gt;
&lt;br /&gt;
Resource costs, as represented by the capital required to exploit them, increase as resource availability in the resource-constrained categories decreases.&amp;amp;nbsp; The capital-to-output ratio captures the increased cost.&amp;amp;nbsp; Kalymon (1975) took a similar approach.&lt;br /&gt;
&lt;br /&gt;
More specifically, the capital-to-output ratio (QE) increases in inverse proportion to the remaining resource base (as the base is cut in half, costs double&#039;&#039;&#039;; &#039;&#039;&#039;as it is cut to one fourth, costs quadruple).&amp;amp;nbsp; The model multiplies the initial capital output ratio by the initial resource base (RESOR) times a multiplier (RESORM) by which a model user can exogenously increase or decrease model assumptions.&amp;amp;nbsp; It then divides that product by initial resources minus cumulative production to date (CUMPR).&lt;br /&gt;
&lt;br /&gt;
Total available resources by energy type, ResorTot, are calculated as:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ResorTot_{r,e}=\mathbf{resorm}_{r,e}*\mathbf{resor}_{r,e}+\mathbf{resorunconm}_{r,e}*\mathbf{resoruncon}_{r,e}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*&#039;&#039;&#039;&#039;&#039;resor&#039;&#039; &#039;&#039;&#039; and &#039;&#039;&#039;&#039;&#039;resoruncon&#039;&#039; &#039;&#039;&#039; are exogenously assumed levels of the ultimate amount of conventional and unconventional forms of each energy type.&amp;amp;nbsp; There is no assumption about conventional resources for nuclear and only oil and gas include unconventional resources&lt;br /&gt;
*&#039;&#039;&#039;&#039;&#039;resorm&#039;&#039; &#039;&#039;&#039; and &#039;&#039;&#039;&#039;&#039;resorunconm&#039;&#039; &#039;&#039;&#039; are multipliers that can be used to change the amount of assumed ultimate resources by energy type&lt;br /&gt;
&lt;br /&gt;
All energy types begin with basic capital-to-output ratios, BQE and BQEUC.&amp;amp;nbsp; These are initially set equal to the same values of QE and QEUNCON, which are derived in the pre-processor, and then evolved according to exogenous assumptions about technological advance for each energy type:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;BQE_{r,e,t}=BQE_{r,e,t-1}*(1-\mathbf{etechadv}_e)&amp;lt;/math&amp;gt; [1]&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;BQEUNCON_{r,e,t}=BQEUNCON_{r,e,t-1}*(1-\mathbf{etechadvuncon}_e)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Recall that technological improvements result in declining amounts of capital required for each unit of energy produced.&lt;br /&gt;
&lt;br /&gt;
The initial translation of this basic capital-to-output ratio to the value actually used to determine energy production varies by energy type.&lt;br /&gt;
&lt;br /&gt;
This is most straightforward for nuclear and unconventional energy, which do not take into account remaining resources:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;QE_{r,e,t+1}=BQE_{r,e,t}*\mathbf{qem_{r,e}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*e is nuclear&lt;br /&gt;
*&#039;&#039;&#039;&#039;&#039;qem&#039;&#039; &#039;&#039;&#039; is an exogenous multiplier&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;QEUC_{r,e,t+1}=BQEUC_{r,e,t}*\mathbf{qeunconm_{r,e}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*e is oil or gas&lt;br /&gt;
*&#039;&#039;&#039;&#039;&#039;qeunconm&#039;&#039; &#039;&#039;&#039; is an exogenous multiplier&lt;br /&gt;
&lt;br /&gt;
For hydro and other renewables, QE depends upon the remaining resource, which is defined as the difference between the total resource available and a moving average of the difference in production vis-à-vis production in the first year. &amp;amp;nbsp;In other words, it is not cumulative production that is important, but rather the portion of resources used annually.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;QE_{r,e,t+1}=BQE_{r,e,t}*\frac{ResorTot_{r,e}}{resorrem_{r,e}}*\mathbf{qem_{r,e}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;resorrem_{r,e}=ResorTot_{r,e}-ENPGR_{r,e}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENPGR_{r,e}=SmoothENP_{r,e}-ENP_{r,e,t=1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;SmoothENP_{r,e,t}=0.8*SmoothENP_{r,e,t-1}+0.2*ENP_{r,e}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
*e = hydro or renew&lt;br /&gt;
&lt;br /&gt;
For oil, gas, and coal, the logic is similar, but the definition of remaining resources is somewhat different:&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;resorrem_{r,e}=MAX(ResorTot_{r,e}-CUMPR_{r,e},MaxFac_{r,e})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CUMPR_{r,e,t}=CUMPR_{r,e,t-1}+ENP_{r,e}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;MaxFac_{r,e}=0.1*ResorTot_{r,e}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Furthermore, the capital-to-output ratio is calculated as a moving average&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CompQE_{r,e}=BQE_{r,e}*(\frac{ResorTot_{r,e}}{resorrem_{r,e}})^{0.4}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;QE_{r,e,t+1}=(0.8*QE_{r,e,t}+0.2*CompQE_{r,e})*\mathbf{qem_{r,e}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*e is oil, gas, or coal&lt;br /&gt;
&lt;br /&gt;
=== Discoveries ===&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;Energy reserves decrease with production and increase with discoveries, the latter of which are limited by remaining resources and other factors. &amp;amp;nbsp;This only applies to oil, gas, and coal.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;RESER_{r,e,t+1}=RESER_{r,e,t}+rd_{r,e}-ENP_{r,e}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The rate of discovery, rd, is initially computed as a function of a number of factors related to global energy prices, remaining resources, global and domestic production, and several exogenous assumptions&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;rd_{r,e}=rdiaug_e*wepterm*reterm_{r,e}*\mathbf{rdm_{r,e}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&amp;amp;nbsp;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*e = oil, gas, coal&lt;br /&gt;
*&#039;&#039;&#039;&#039;&#039;rdm&#039;&#039; &#039;&#039;&#039; is a country and energy-specific exogenous multiplier&lt;br /&gt;
*rdi_aug is an energy-specific factor driven entirely by exogenous assumptions about initial rates of discovery, &#039;&#039;&#039;&#039;&#039;rdi&#039;&#039; &#039;&#039;&#039;, and annual increments, &#039;&#039;&#039;&#039;&#039;rdinr&#039;&#039; &#039;&#039;&#039;:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;rdiaug_e=\mathbf{rdi}_e+\mathbf{rdinr}_{r,e}*(t-firstyear)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
*wepterm is a global factor driven by the growth in world energy prices from the first year and an exogenously defined elasticity, &#039;&#039;&#039;&#039;&#039;elasdi&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;wepterm=1+\frac{WEP_t-WEP_{t=1}}{WEP_{t=1}}*\mathbf{elasdi}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
*reterm is a country and energy-specific factor representing an average of a country’s remaining resources as a share of original resources and its share of current production&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;reterm_{r,e}=0.5*(\frac{ResorTot_{r,e}-CUMPR_{r,e}-RESER_{r,e}}{\sum_e(ResorTot_{r,e,t=1}-RESER_{r,e,t=1})}+\frac{ENP_{r,e}}{WENP_e})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
A further assumption is that the rate of discovery cannot exceed 4 percent of the remaining resources in a country, where remaining resources are specified as:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;resorrem_{r,e}=ResorTot_{r,e}-CUMPR_{r,e}-RESER_{r,e}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;where&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*e = oil, gas, coal&lt;br /&gt;
*For oil the amount of unconventional oil in ResorTot is also affected by the parameter &#039;&#039;&#039;&#039;&#039;enresunce&#039;&#039; &#039;&#039;&#039;[2]&lt;br /&gt;
&amp;lt;div&amp;gt;[1] There used to be an additional impact of ICT broadband that would further reduce the BQE for other renewables, but that is currently not active in the model.&amp;amp;nbsp;&amp;lt;div id=&amp;quot;ftn1&amp;quot;&amp;gt;&lt;br /&gt;
[2] This only affects Canada, which has a value of &#039;&#039;&#039;&#039;&#039;enresunce&#039;&#039; &#039;&#039;&#039; = 0.3. Why this is not included in the QE calculations is unclear.&lt;br /&gt;
&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Energy Indicators&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Among useful energy or energy-related indicators is the ratio (ENRGDP) of energy demand (ENDEM) to gross domestic product (GDP).&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;ENRGDP_r=\frac{ENDEM_r}{GDP_r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Global production of energy by energy type (WENP) is the sum of regional productions (ENP).&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WENP_e=\sum^RENP_{r,e}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Global energy production is the basis for examining the build-up of carbon dioxide and Climate Change, as described in the documentation of the Environmental model.&lt;br /&gt;
&lt;br /&gt;
The ratio of oil and gas production globally to total energy production (OILGPR) helps trace the transition to other fuels.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;OILGPR=\frac{WENP_{e=1}+WENP_{e=2}}{\sum^EWENP_e}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Global energy reserves (WRESER) and global resources (WRESOR) are sums by energy type across regions, the latter taking into account any resource multiplier (RESORM) that a user specifies to modify basic model resource estimates.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WRESER_e=\sum^RRESER_{r,e}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;WRESOR_e=\sum^R(RESOR_{r,e}*RESORM_e)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Energy Bibliography&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
Kalymon, Basil A. 1975. &amp;quot;Economic Incentives in OPEC Oil Pricing Policy.&amp;quot; &#039;&#039;Journal of Development Economics&#039;&#039; 2: 337-362.&lt;br /&gt;
&lt;br /&gt;
Naill, Roger F. 1977.&#039;&#039;Managing the Energy Transition.&#039;&#039; Vols. 1 and 2. Cambridge, Mass: Ballinger Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Stanford University. 1978. &#039;&#039;Stanford Pilot Energy/Economic Model.&#039;&#039; Stanford: Department of Research, Interim Report, Vol. 1.&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8313</id>
		<title>Introduction to IFs</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8313"/>
		<updated>2017-09-07T21:42:20Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Purposes&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;International Futures (IFs) is a tool for thinking about long-term global trends and planning more strategically for the&amp;amp;nbsp;future.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
IFs can help you:&lt;br /&gt;
&lt;br /&gt;
*Understand&amp;amp;nbsp;the state of major&amp;amp;nbsp;global systems&lt;br /&gt;
*Explore&amp;amp;nbsp;long-term trends and consider where they might take&amp;amp;nbsp;us&lt;br /&gt;
*Learn about the dynamic&amp;amp;nbsp;interactions between global systems&lt;br /&gt;
*Clarify long-term organizational goals/priorities&lt;br /&gt;
*Develop alternative scenarios (if-then statements) about the future&lt;br /&gt;
*Investigate how different groups (households, firms or governments) can shape the future&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;IFs development and analysis depend&amp;amp;nbsp;on core, underlying assumptions, including the following.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*Global issues are becoming&amp;amp;nbsp;more significant as the scope of human interaction and human impact on the broader environment grow.&lt;br /&gt;
*Goals and priorities for human systems are becoming clearer and are more frequently and consistently communicated.&lt;br /&gt;
*Understanding of the dynamics of human systems is improving&amp;amp;nbsp;rapidly.&lt;br /&gt;
*The domain of human choice and action is broadening.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;What issues can you&amp;amp;nbsp;investigate with IFs?&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*[[Environment]]: Atmospheric carbon dioxide levels, world forest area, fossil fuel usage&lt;br /&gt;
*[[Socio-Political|Socio-Political Change]]: Life expectancy, literacy rate, democracy level, status of women, value change&lt;br /&gt;
*[[Population|Demographics]]: Population levels and growth, fertility, mortality, migration&lt;br /&gt;
*[[Agriculture|Food and Agriculture]]: Land use and production levels, calorie availability, malnutrition rates&lt;br /&gt;
*[[Energy]]: Resource and production levels, demand patterns, renewable energy share&lt;br /&gt;
*[[Economics]]: Sectoral production, consumption, and trade patterns and structural change&lt;br /&gt;
*[[Interstate_Politics_(IP)|Geopolitics]]: Country and regional power levels&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Visual Representation&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
[[File:IFsOverviewChart.jpg|frame|center|Visual representation of IFs structure]]&lt;br /&gt;
&lt;br /&gt;
Among the philosophical premises of the International Futures (IFs) project is that the model cannot be a &amp;quot;black box&amp;quot; to users and be truly useful. Model users must be able to examine the structures of IFs in order (1) to have confidence in them, and (2) learn from them.&lt;br /&gt;
&lt;br /&gt;
There is available (see topics under Understanding the Mode in the contents of this help system):&lt;br /&gt;
&lt;br /&gt;
*[[Understand_IFs#Dominant_Relations|Dominant Relations]] of the model structure&lt;br /&gt;
*[[Understand_IFs#2.2|Structure-Based and Agent-Class Driven Modeling]]&lt;br /&gt;
*[[Understand_IFs#Equation_Notation|Equation Notation]]&lt;br /&gt;
*[[Introduction_to_IFs#IFs_Bibliography|IFs Bibliography]] of data and data sources&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Quick Survey&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;population&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents 22 age-sex cohorts to age 100+&lt;br /&gt;
*calculates change in fertility and mortality rates in response to income, income distribution, and analysis multipliers&lt;br /&gt;
*computes average life expectancy at birth, literacy rate, and overall measures of human development (HDI) and physical quality of life&lt;br /&gt;
*represents migration and HIV/AIDS&lt;br /&gt;
*includes a newly developing submodel of formal education across primary, secondary, and tertiary levels&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;economic&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents the economy in six sectors: agriculture, materials, energy, industry, services, and ICT (other sectors could be configured, using raw data from the GTAP project)&lt;br /&gt;
*computes and uses input-output matrices that change dynamically with development level&lt;br /&gt;
*is a general equilibrium-seeking model that does not assume exact equilibrium will exist in any given year; rather it uses inventories as buffer stocks and to provide price signals so that the model chases equilibrium over time&lt;br /&gt;
*contains an endogenous production function that represents contributions to growth in multifactor productivity from R&amp;amp;D, education, worker health, economic policies (&amp;quot;freedom&amp;quot;), and energy prices (the &amp;quot;quality&amp;quot; of capital)&lt;br /&gt;
*uses a Linear Expenditure System to represent changing consumption patterns&lt;br /&gt;
*utilizes a &amp;quot;pooled&amp;quot; rather than the bilateral trade approach for international trade&lt;br /&gt;
*is being imbedded during 2002 in a social accounting matrix (SAM) envelope that will tie economic production and consumption to intra-actor financial flows&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;agricultural&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents production, consumption and trade of crops and meat; it also carries ocean fish catch and aquaculture in less detail&lt;br /&gt;
*maintains land use in crop, grazing, forest, urban, and &amp;quot;other&amp;quot; categories&lt;br /&gt;
*represents demand for food, for livestock feed, and for industrial use of agricultural products&lt;br /&gt;
*is a partial equilibrium model in which food stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the agricultural sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;energy&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*portrays production of six energy types: oil, gas, coal, nuclear, hydroelectric, and other renewable&lt;br /&gt;
*represents consumption and trade of energy in the aggregate&lt;br /&gt;
*represents known reserves and ultimate resources of the fossil fuels&lt;br /&gt;
*portrays changing capital costs of each energy type with technological change as well as with draw-downs of resources&lt;br /&gt;
*is a partial equilibrium model in which energy stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the energy sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The two &#039;&#039;&#039;socio-political&#039;&#039;&#039; sub-modules:&lt;br /&gt;
&lt;br /&gt;
Within countries or geographic groupings&lt;br /&gt;
&lt;br /&gt;
*represents fiscal policy through taxing and spending decisions&lt;br /&gt;
*shows six categories of government spending: military, health, education, R&amp;amp;D, foreign aid, and a residual category&lt;br /&gt;
*represents changes in social conditions of individuals (like fertility rates or literacy levels), attitudes of individuals (such as the level of materialism/postmaterialism of a society from the World Value Survey), and the social organization of people (such as the status of women)&lt;br /&gt;
*represents the evolution of democracy&lt;br /&gt;
*represents the prospects for state instability or failure&lt;br /&gt;
&lt;br /&gt;
Between countries or groupings of countries&lt;br /&gt;
&lt;br /&gt;
*traces changes in power balances across states and regions&lt;br /&gt;
*allows exploration of changes in the level of interstate threat&lt;br /&gt;
*represents possible action-reaction processes and arms races with associated potential for conflict among countries&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;environmental&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows tracking of remaining resources of fossil fuels, of the area of forested land, of water usage, and of atmospheric carbon dioxide emissions&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;technology&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows changes in assumptions about rates of technological advance in agriculture, energy, and the broader economy&lt;br /&gt;
*explicitly represents the extent of electronic networking of individuals in societies&lt;br /&gt;
*is tied to the governmental spending model with respect to R&amp;amp;D spending&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Background&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
International Futures (IFs) has evolved since 1980 through three &amp;quot;generations,&amp;quot; with a fourth generation now taking form.&lt;br /&gt;
&lt;br /&gt;
The first generation had deep roots in the world models of the 1970s, including those of the Club of Rome. In particular, IFs drew on the Mesarovic-Pestel or World Integrated Model (Mesarovic and Pestel 1974). The author of IFs had contributed to that project, including the construction of the energy submodel. IFs consciously also drew on the Leontief World Model (Leontief et al. 1977), the Bariloche Foundation’s world model (Herrera et al. 1976), and Systems Analysis Research Unit Model (SARU 1977), following comparative analysis of those models by Hughes (1980). That generation was written in FORTRAN and available for use on main-frame computers through CONDUIT, an educational software distribution center at the University of Iowa. Although the primary use of that and subsequent generations was by students, IFs has always had some policy analysis capability that has appealed to specialists. For example, the U.S. Foreign Service Institute used the first generation of IFs in a mid-career training program.&lt;br /&gt;
&lt;br /&gt;
The second generation of International Futures moved to early microcomputers in 1985, using the DOS platform. It was a very simplified version of the original IFs without regional or country differentiation.&lt;br /&gt;
&lt;br /&gt;
The third generation, first available in 1993, became a full-scale microcomputer model. The third generation improved earlier representations of demographic, energy, and food systems, but added new environmental and socio-political content. It built upon the collaboration of the author with the GLOBUS project, and it adopted the economic submodel of GLOBUS (developed by the author). GLOBUS had been created with the inspiration of Karl Deutsch and under the leadership of Stuart Bremer (1987) at the Wissenschaftszentrum in Berlin.&lt;br /&gt;
&lt;br /&gt;
The third generation has produced three editions/major releases of IFs, each accompanied by a book also called International Futures (Hughes 1993, 1996, 1999). The second edition moved to a Visual Basic platform that allowed a much improved menu-driven interface, running under Windows. The third edition incorporated an early global mapping capability and an initial ability to do cross-sectional and longitudinal data analysis.&lt;br /&gt;
&lt;br /&gt;
The fourth generation has been taking shape since early 2000. It has been heavily influenced by the usage of the model in an increasingly policy-analysis mode by several important organizations. First, General Motors commissioned a specialized version of IFs named CoVaTrA (Consumer Values Trends Analysis) with a need for updated and extended demographic modeling and representation of value change. An alliance was established with the World Values Survey, directed by Ronald Inglehart, to create that version. Second, the Strategic Assessments Group of the Central Intelligence Agency commissioned a specialized version named IFs for SAG. The work involved in preparing that greatly extended and enhanced the socio-political representations of the model, both domestic and international. Third, the European Commission sponsored a project named TERRA which has led to a specialized version named IFs for TERRA. IFs for TERRA work led to enhancements across the model, including improved representation of economic sectors, updated IO matrices and a basic Social Accounting Matrix, GINI and Lorenz curves, and preparing for extended environmental impact representation (drawing upon the Advanced Sustainability Analysis framework of the Finland Futures Research Center).&lt;br /&gt;
&lt;br /&gt;
Throughout this emergence of a fourth generation IFs (incorporating all of the above elements for additional users) there has been also a heavy emphasis on enhanced usability. Ideas from Robert Pestel in the TERRA project led to the creation of a new tree-structure for scenario creation and management. Ideas from Ronald Inglehart led to the development of the Guided Use structure and a somewhat more game-like character within that structure. Inglehart also help arrange funding to support the programming of Guided Use through the European Union Center of the University of Michigan.&lt;br /&gt;
&lt;br /&gt;
The fifth version of IFs is currently in use and represents broad strides to improving the model and its usability. It is the first version of this software to be placed online due to the help of the National Intelligence Council ([http://www.ifs.du.edu http://www.ifs.du.edu]). Also, usability has been increased as Packaged Displays and Flex Packaged Displays were introduced that allowed for the creation of very specific lists of countries/regions, groups or Glists. A new education model has also been incorporated into the broader IFs model. New scenarios were created for UNEP (focusing on environmental change) and Pardee (focusing on poverty). Finally, one of the largest changes made was incorporating 182 countries into the Base-Case scenario used by IFs. Previous versions of IFs used broader regions to forecast global trends. This change also did away with the Student and Professional versions.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Geographic Representation of the World&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
186 countries underpin the functioning of IFs and these countries can be displayed separately or as parts of larger groups that users can determine.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;Below is a visual representation of how different entities are organized into Countries/Regions, Groups or Glists:&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
[[File:Geo 186.png|frame|right|Visual representation of IF&#039;s definition of regions/countries/groups/glists]]&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;*Note: In older versions of IFs, Regions were used as intermediaries between Countries and Groups. In the future, they, or some similarly named unit, will be a sub-unit of Countries. Regions, acting as a sub-unit of Countries, are currently not a feature of IFs. See the image located at the bottom of this Help topic.&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
When using IFs, there are many occasions where the user is asked whether or not they would like to display their results as a product of single countries, or larger groups. This is typically a toggle switch that moves between Country/Region and Groups, however, it might be a three-way-toggle that includes Country/Region, Group and Glist.&lt;br /&gt;
&lt;br /&gt;
Countries/Regions are currently the smallest geographical unit that users can represent. The ability to split countries down into smaller regions, or states, is under development. There are 186 different countries/regions that users can display.&lt;br /&gt;
&lt;br /&gt;
Groups are variably organized geographically or by memberships in international institutions/regimes. You can find out who is represented in each group and add or delete members by exploring the [[Extended_Features#Manage_Groups/Regions|Managing Regionalization]] function.&lt;br /&gt;
&lt;br /&gt;
Glists merge both Groups and Countries/Regions. These lists are mostly geographically bound. In the future, the Glist distinction will become more important as some users may want to place, for example, both the Indian state of Kerala in a Glist with Sri Lanka and Nepal.&lt;br /&gt;
&lt;br /&gt;
Users may also want to [[Extended_Features#Change_Grouping/Regionalization|create]] their own groups or [[Extended_Features#Identify_Groups_or_Country/Region_Members|explore]] what countries are members of what groups.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Time Horizon&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Future Forecasts.&#039;&#039;&#039; IFs begins computation with data from 2000 and can dynamically calculate values for all variables annually through 2100.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Historical Analysis and &amp;quot;Forecasts.&amp;quot;&#039;&#039;&#039; IFs also includes an extensive and growing historical data base starting in 1960. The data basis allows analysis of relationships among variables across countries and across time.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Instructional Use&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The standard modes for using IFs in a classroom are:&lt;br /&gt;
&lt;br /&gt;
1. Assigning class members to an issue area or topic. Consider identifying specific questions for them to address.&lt;br /&gt;
&lt;br /&gt;
2. Assigning class members to a country/geographic region. Again, specificity helps.&lt;br /&gt;
&lt;br /&gt;
Most often, students will work independently or in groups on projects and share information after completing them. It is possible, however, to have students work interactively, by assigning them topics or regions, letting them begin work, and then have the interacting groups (or individuals) create a collective model run with the changes that each group proposes by topic or region. That process, although more difficult to organize, allows the class as whole to investigate the interaction of their topics or regions (and to share learning about model use).&lt;br /&gt;
&lt;br /&gt;
There is a&amp;amp;nbsp;[http://portfolio.du.edu/bhughes web site]&amp;amp;nbsp;available in support of the educational use of IFs. You will find syllabi at that site. There are several [[Introduction_to_IFs#Publications_on_IFs|publications]] on IFs, including a book structured specifically for educational use.&lt;br /&gt;
&lt;br /&gt;
Donald Borock has described his classroom use of IFs in print. Borock, Donald. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot; Advances in Social Science and Computers 4, 103-127.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Acknowledgements&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The author gratefully recognizes critical contributions in the forms of:&lt;br /&gt;
&lt;br /&gt;
:1. Testing and suggestions for development of IFs in one or more of multiple generations. By Donald Borock, Richard Chadwick, William Dixon, Dale Rothman, Phil Schrodt, Douglas Stuart, Donald Sylvan, Jonathan Wilkenfeld, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
:2. Computer assistance across many releases. By Michael Niemann, Terrance Peet-Lukes, Douglas McClure, Mohammod Irfan, and Jose Solorzano.&lt;br /&gt;
&lt;br /&gt;
:3. Data gathering and general assistance. By James Chung, Padma Padula, Shannon Brady, David Horan, Michael Ferrier, Kay Drucker, Warren Christopher, and Anwar Hossain.&lt;br /&gt;
&lt;br /&gt;
:4. Long-term encouragement and support. By Harold Guetzkow, Karl Deutsch, Richard Chadwick, Gerald Barney, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
:5. Association in related world modeling projects and projects building upon IFs. By Mihajlo Mesarovic, Aldo Barsotti, Juan Huerta, John Richardson, Thomas Shook, Patricia Strauch, and other members of the World Integrated Model (WIM) team. By Stuart Bremer, Peter Brecke, Thomas Cusack, Wolf Dieter-Eberwein, Brian Pollins, and Dale Smith of the GLOBUS modeling project. By Evan Hillebrand, Paul Herman, and others of the IFs for SAG project. By Rob Lempert and Steve Bankes at RAND, Santa Monica. By Robert Pestel, Jonathan Cave, Ronald Inglehart, Sergei Parinov, Pentti Malaska, and many others in the IFs for TERRA project.&lt;br /&gt;
&lt;br /&gt;
:6. Financial assistance (without responsibility for the form of the evolving product). By the National Science Foundation, the Cleveland Foundation, the Exxon Education Foundation, the Kettering Family Foundation, the Pacific Cultural Foundation, the United States Institute of Peace, General Motors, the Strategic Assessments Group of the Central Intelligence Agency, the European Commission (Information Society Technology) Programme, the European Union Center of the University of Michigan, the National Intelligence Council (for web conversion), and Frederick S. Pardee. &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Feedback&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
Feedback on how to improve IFs is always appreciated, especially if you find something that is not working. Compliments are also accepted. Please contact. To send the IFs team an e-mail, click on&amp;amp;nbsp;[mailto:pardee.center@du.edu Pardee Center]&amp;amp;nbsp;in stand-alone versions or on the web.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Support for IFs Use&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Publications on IFs&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
To obtain additional information about IFs and its use, consult:&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes and Evan E. Hillebrand, &#039;&#039;&#039;Exploring and Shaping International Futures.&#039;&#039;&#039; Boulder, CO: Paradigm Publishers, 2006. Specifically, see chapter 4.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &#039;&#039;&#039;International Futures: Choices in the Face of Uncertainty,&#039;&#039;&#039; 3rd ed. Boulder, CO: Westview Press, 1999. This volume is built around IFs and contains detailed suggestions for its use. Version 3.17 of IFs, which runs under Windows 95, is distributed with the third edition of the book. The second edition contained a version for Windows 3.1, and the first edition ran under DOS. Chapter 4 of the 2nd edition of IFs included Flow Charts of Worldviews , reproduced now in this Help system.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &#039;&#039;&#039;Continuity and Change in World Politics,&#039;&#039;&#039; 4th ed. Englewood Cliffs, N.J.: Prentice Hall, 2000. IFs can also usefully supplement this textbook on global politics.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &amp;quot;The International Futures (IFs) Modeling Project. 1999. &#039;&#039;&#039;Simulation and Gaming&#039;&#039;&#039; 30, No. 3 (September): 304-326.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;IFs Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Alcamo, Joseph, Rik Leemans and Eric Kreileman, eds. 1998.&amp;amp;nbsp;&#039;&#039;Global Change Scenarios of the 21st Century: Results from the IMAGE 2.1 Model&#039;&#039;. The Netherlands: Pergamon.&lt;br /&gt;
&lt;br /&gt;
Alcamo, Joseph. 1994.&amp;amp;nbsp;&#039;&#039;IMAGE 2.0: Integrated Modeling of Global Climate Change&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
&lt;br /&gt;
Alexandratos, Nikos, ed. 1995.&amp;amp;nbsp;&#039;&#039;World Agriculture: Towards 2010&#039;&#039;&amp;amp;nbsp;(An FAO Study). New York: FAO and John Wiley and Sons.&lt;br /&gt;
&lt;br /&gt;
Allen, R. G. D. 1968.&amp;amp;nbsp;&#039;&#039;Macro-Economic Theory: A Mathematical Treatment&#039;&#039;. New York: St. Martin&#039;s Press.&lt;br /&gt;
&lt;br /&gt;
Avery, Dennis. 1995. &amp;quot;Saving the Planet with Pesticides,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 50-82.&lt;br /&gt;
&lt;br /&gt;
Bailey, Ronald, ed. 1995.&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;. New York: The Free Press.&lt;br /&gt;
&lt;br /&gt;
Barbieri, Kathleen. 1996. &amp;quot;Economic Interdependence: A Path to Peace or a Source of Interstate Conflict?&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Peace Research&#039;&#039;&amp;amp;nbsp;33: 29-50.&lt;br /&gt;
&lt;br /&gt;
Barker, T.S. and A.W.A. Peterson, eds. 1987.&amp;amp;nbsp;&#039;&#039;The Cambridge Multisectoral Dynamic Model of the British Economy&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Barney, Gerald O., W. Brian Kreutzer, and Martha J. Garrett, eds. 1991.&amp;amp;nbsp;&#039;&#039;Managing a Nation&#039;&#039;, 2nd ed. Boulder: Westview Press.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. 1997.&amp;amp;nbsp;&#039;&#039;Determinants of Economic Growth: A Cross-Country Empirical Study&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. and Xavier Sala-i-Martin. 1999.&amp;amp;nbsp;&#039;&#039;Economic Growth&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
&lt;br /&gt;
Bennett, D. Scott, and Allan Stam. 2003.&amp;amp;nbsp;&#039;&#039;The Behavioral Origins of War: Cumulation and Limits to Knowledge in Understanding International Conflict&#039;&#039;. Ann Arbor: University of Michigan Press&lt;br /&gt;
&lt;br /&gt;
Birg, Herwig. 1995.&amp;amp;nbsp;&#039;&#039;World Population Projections for the 21st Century&#039;&#039;. Frankfurt: Campus Verlag.&lt;br /&gt;
&lt;br /&gt;
Borock, Donald M. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot;&amp;amp;nbsp;&#039;&#039;Advances in Social Science and Computers&#039;&#039;&amp;amp;nbsp;4, 103-127.&lt;br /&gt;
&lt;br /&gt;
Bos, Eduard, My T. Vu, Ernest Massiah, and Rodolfo A. Bulatao. 1994.&amp;amp;nbsp;&#039;&#039;World Population Projections 1994-95 Edition&#039;&#039;&amp;amp;nbsp;[editions are biannual] Baltimore: Johns Hopkins Press.&lt;br /&gt;
&lt;br /&gt;
Boulding, Elise and Kenneth E. Boulding. 1995.&amp;amp;nbsp;&#039;&#039;The Future: Images and Processes&#039;&#039;. Thousand Oaks, CA: Sage Publications.&lt;br /&gt;
&lt;br /&gt;
Brecke, Peter. 1993. &amp;quot;Integrated Global Models that Run on Personal Computers,&amp;quot;&amp;amp;nbsp;&#039;&#039;Simulation&#039;&#039;60 (2).&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. 1977.&amp;amp;nbsp;&#039;&#039;Simulated Worlds: A Computer Model of National Decision-Making&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A., ed. 1987.&amp;amp;nbsp;&#039;&#039;The GLOBUS Model: Computer Simulation of World-wide Political and Economic Developments&#039;&#039;. Boulder, CO: Westview.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. and Walter Gruhn. 1988.&amp;amp;nbsp;&#039;&#039;Micro GLOBUS: A Computer Model of Long-Term Global Political and Economic Processes&#039;&#039;. Berlin: edition sigma.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. and Barry B. Hughes. 1990.&amp;amp;nbsp;&#039;&#039;Disarmament and Development: A Design for the Future?&#039;&#039;&amp;amp;nbsp;Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Brockmeier, Martina and Channing Arndt (presentor). 2002. Social Accounting Matrices. Powerpoint presentation on GTAP and SAMs (June 21). Found on the web.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1981.&amp;amp;nbsp;&#039;&#039;Building a Sustainable Society&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1988. &amp;quot;Analyzing the Demographic Trap,&amp;quot; in&amp;amp;nbsp;&#039;&#039;State of the World 1987&#039;&#039;, eds. Lester R. Brown and others. New York: W.W. Norton, pp. 20-37.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1995.&amp;amp;nbsp;&#039;&#039;Who Will Feed China?&#039;&#039;&amp;amp;nbsp;New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1996.&amp;amp;nbsp;&#039;&#039;Tough Choices: Facing the Challenge of Food Scarcity&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., et al. 1996&amp;amp;nbsp;&#039;&#039;State of the World 1996&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., Nicholas Lenssen, and Hal Kane. 1995.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1995. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., Christopher Flavin, and Hal Kane. 1996.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1996. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Burkhardt, Helmut. 1995. &amp;quot;Priorities for a Sustainable Civilization,&amp;quot; unpublished conference paper. Department of Physics, Ryerson Polytechnic University, Toronto, Canada.&lt;br /&gt;
&lt;br /&gt;
Bussolo, Maurizio, Mohamed Chemingui and David O’Connor. 2002. A Multi-Region Social Accounting Matrix (1995) and Regional Environmental General Equilibrium Model for India (REGEMI). Paris: OECD Development Centre (February). Available at&amp;amp;nbsp;[http://www.oecd.org/dev/technics www.oecd.org/dev/technics].&lt;br /&gt;
&lt;br /&gt;
British Petroleum Company. 1995.&amp;amp;nbsp;&#039;&#039;BP Statistical Review of World Energy&#039;&#039;. London: British Petroleum Company.&lt;br /&gt;
&lt;br /&gt;
Central Intelligence Agency (CIA). 1991.&amp;amp;nbsp;&#039;&#039;Handbook of Economic Statistics, 1991&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
&lt;br /&gt;
Central Intelligence Agency (CIA). 1994.&#039;&#039;&amp;amp;nbsp;The World Factbook 1994&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
&lt;br /&gt;
Chang, Sheldon S. L. 1961.&amp;amp;nbsp;&#039;&#039;Synthesis of Optimum Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Chenery, Hollis and Moises Syrquin. 1975.&amp;amp;nbsp;&#039;&#039;Patterns of Development 1950-1970&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Cipolla, Carlo M. 1962.&amp;amp;nbsp;&#039;&#039;The Economic History of World Population&#039;&#039;. Baltimore: Penguin.&lt;br /&gt;
&lt;br /&gt;
Cook, Earl. 1976.&amp;amp;nbsp;&#039;&#039;Man, Energy, Society&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
&lt;br /&gt;
Committee on the Strategic Assessment of the U.S. Department of Energy’s Coal Program. 1995.&amp;amp;nbsp;&#039;&#039;Coal: Energy for the Future&#039;&#039;. Washington, D.C.: National Academy Press.&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1981.&amp;amp;nbsp;&#039;&#039;The Global 2000 Report to the President&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1981b.&amp;amp;nbsp;&#039;&#039;Environmental Trends&#039;&#039;. Washington, D.C. (July).&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1991.&amp;amp;nbsp;&#039;&#039;21st Annual Report&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Crescenzi, Mark J.C. and Andrew J. Enterline. 2001. &amp;quot;Time Remembered: A Dynamic Model of Interstate Interaction,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;45, no. 3 (September): 409-431.&lt;br /&gt;
&lt;br /&gt;
Crosson, Pierre, and Jock R. Anderson. 1992.&amp;amp;nbsp;&#039;&#039;Resources and Global Food Prospects&#039;&#039;. Washington, D.C.: The World Bank. World Bank Technical Paper Number 184.&lt;br /&gt;
&lt;br /&gt;
Cusack, Thomas R. and Richard J. Stoll. 1990.&amp;amp;nbsp;&#039;&#039;Exploring Realpolitik: Probing International Relations with Computer Simulatio&#039;&#039;n. Boulder: Lynne Rienner Publishers.&lt;br /&gt;
&lt;br /&gt;
Dargay, Joyce and Dermot Gately. 1999. &amp;quot;Income’s Effect on Car and Vehicle Ownership, Worldwide: 1960-2015,&amp;quot;&amp;amp;nbsp;&#039;&#039;Transportation Research Part A&#039;&#039;&amp;amp;nbsp;33: 101-138.&lt;br /&gt;
&lt;br /&gt;
Dall, P., Kaspar, F. and Alcamo, J. 1998. &amp;quot;Modeling World-wide Water Availability and Water Use Under the Influence of Climate Change,&amp;quot;&amp;amp;nbsp;&#039;&#039;Proceedings of the Second International Conference on Climate and Water&#039;&#039;, July 17-20, Espoo, Finland.&lt;br /&gt;
&lt;br /&gt;
Dimaranan, Betina V. and Robert A. McDougall, eds. 2002.&amp;amp;nbsp;&#039;&#039;Global Trade, Assistance, and Production: The GTAP 5 Data Base&#039;&#039;. Center for Global Trade Analysis, Purdue University. Available at [http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp].&lt;br /&gt;
&lt;br /&gt;
Dowlatabadi, H., and Morgan, M.G. 1993. &amp;quot;A Model Framework for Integrated Studies of the Climate Problem,&amp;quot;&amp;amp;nbsp;&#039;&#039;Energy Policy&#039;&#039;&amp;amp;nbsp;(March): 209-221.&lt;br /&gt;
&lt;br /&gt;
Duchin, Faye. 1998.&amp;amp;nbsp;&#039;&#039;Structural Economics: Measuring Change in Technology, Lifestyles, and the Environment&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
&lt;br /&gt;
Edwards, Stephen R. 1995. &amp;quot;Conserving Biodiversity,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 212-265.&lt;br /&gt;
&lt;br /&gt;
Edmonds, J., and Reilly, J.M. 1985.&amp;amp;nbsp;&#039;&#039;Global Energy: Assessing the Future&#039;&#039;. Oxford, UK: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Edmonds, J., Pitcher, H. Rosenberg, N., and Wigley, T. &amp;quot;Design for the Global Change Assessment Model.&amp;quot;&amp;amp;nbsp;&#039;&#039;Integrative Assessment of Mitigation, Impacts and Adaptation to Climate Change&#039;&#039;. Laxenburg, Austria.&lt;br /&gt;
&lt;br /&gt;
Ehrlich, Paul R. and Anne H. Ehrlich. 1972.&amp;amp;nbsp;&#039;&#039;Population, Resources, Environment&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
&lt;br /&gt;
Eicher, Carl. 1982. &amp;quot;Facing up to Africa&#039;s Food Crisis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Foreign Affairs&#039;&#039;&amp;amp;nbsp;61, no. 1 (Fall): 151-74.&lt;br /&gt;
&lt;br /&gt;
Eberstadt, Nicholas. 1995. &amp;quot;Population, Food, and Income,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 8-47.&lt;br /&gt;
&lt;br /&gt;
Esty, Daniel C., Jack A. Goldstone, Ted Robert Gurr, Barbara Harff, Marc Levy, Geoffrey D. Dabelko, Pamela T. Surko, and Alan N. Unger. 1998. State Failure Task Force Report: Phase II Findings. Volume provided courtesy of Ted Robert Gurr.&lt;br /&gt;
&lt;br /&gt;
Flavin, Christopher. 1996. &amp;quot;Facing Up to the Risks of Climate Change,&amp;quot; in Lester R. Brown and others, eds., State of the World 1996 (New York: W.W. Norton), pp. 21-39.&lt;br /&gt;
&lt;br /&gt;
Forrester, Jay W. 1968.&amp;amp;nbsp;&#039;&#039;Principles of Systems&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Gilpin, Robert. 1981.&amp;amp;nbsp;&#039;&#039;War and Change in World Politics&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Globerman, Steven. 2000 (May). Linkages Between Technological Change and Productivity Growth. Industry Canada Research Publications Program: Occasional Paper 23.&lt;br /&gt;
&lt;br /&gt;
Grant, Lindsey. 1982.&amp;amp;nbsp;&#039;&#039;The Cornucopian Fallacies&#039;&#039;. Washington, D.C.: Environmental Fund.&lt;br /&gt;
&lt;br /&gt;
Griffith, Rachel, Stephen Redding, and John Van Reenen. 2000.&amp;amp;nbsp;&#039;&#039;Mapping the Two Faces of R&amp;amp;D: Productivity Growth in a Panel of OECD Industries&#039;&#039;. Institute for Fiscal Studies (January)&lt;br /&gt;
&lt;br /&gt;
Gwartney, James and Robert Lawson with Dexter Samida. 2000.&amp;amp;nbsp;&#039;&#039;Economic Freedom of the World: 2000 Annual Report&#039;&#039;. Vancouver, B.C.: the Fraser Institute.&lt;br /&gt;
&lt;br /&gt;
Hammond, Allen. 1998.&amp;amp;nbsp;&#039;&#039;Which World? Scenarios for the 21st Century&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
&lt;br /&gt;
Harff, Barbara, with Ted Robert Gurr and Alan Unger. 1999. Preconditions of Genocide and Politicide: 1955-1998. Paper prepared for the State Failure Task Force and provided courtesy of Barbara Harff and Ted Gurr.&lt;br /&gt;
&lt;br /&gt;
Henderson, Hazel. 1996. &amp;quot;Changing Paradigms and Indicators: Implementing Equitable, Sustainable and Participatory Development,&amp;quot; in Jo Marie Griesgraber and Bernhard G. Gunter,&amp;amp;nbsp;&#039;&#039;Development: New Paradigms and Principles for the 21st Century&#039;&#039;. East Haven, CT: Pluto Press, pp. 103-136.&lt;br /&gt;
&lt;br /&gt;
Herrera, Amilcar O., et al. 1976.&#039;&#039;&amp;amp;nbsp;Catastrophe or New Society? A Latin American World Model&#039;&#039;. Ottawa: International Development Research Centre.&lt;br /&gt;
&lt;br /&gt;
Hoekstra, A.Y. 1998.&amp;amp;nbsp;&#039;&#039;Perspectives on Water: An Integrated Model-Based Exploration of the Future&#039;&#039;. Utrecht, the Netherlands: International Books.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1980.&amp;amp;nbsp;&#039;&#039;World Modeling&#039;&#039;. Lexington, Mass: Lexington Books.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1982.&amp;amp;nbsp;&#039;&#039;International Futures Simulation: User&#039;s Manual&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985a.&amp;amp;nbsp;&#039;&#039;International Futures Simulation&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985b. &amp;quot;World Models: The Bases of Difference,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;29, 77-101.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985c.&amp;amp;nbsp;&#039;&#039;World Futures: A Critical Analysis of Alternatives&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1987. &amp;quot;Domestic Economic Processes,&amp;quot; in Stuart A. Bremer, ed.,&amp;amp;nbsp;&#039;&#039;The Globus Model: Computer Simulation of Worldwide Political Economic Development&#039;&#039;&amp;amp;nbsp;(Frankfurt and Boulder: Campus and Westview), pp. 39-158.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1988. &amp;quot;International Futures: History and Status,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Science Microcomputer Review&#039;&#039;&amp;amp;nbsp;6, 43-48.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1999. &amp;quot;The International Futures (IFs) Modeling Project.&#039;&#039;&amp;amp;nbsp;Simulation and Gaming&#039;&#039;&amp;amp;nbsp;Vol 30, No. 3 (September): 304-326.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1999.&amp;amp;nbsp;&#039;&#039;International Futures&#039;&#039;, 3rd edition Boulder: Westview Press, 1999.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2000.&amp;amp;nbsp;&#039;&#039;Continuity and Change in World Politics&#039;&#039;. Englewood Cliffs, N.J.: Prentice-Hall, fourth edition.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2001. &amp;quot;Global Social Transformation: The Sweet Spot, the Steady Slog, and the Systemic Shift,&amp;quot;&amp;amp;nbsp;&#039;&#039;Economic Development and Cultural Change&#039;&#039;&amp;amp;nbsp;49, No. 2 (January): 423-458.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2002.&amp;amp;nbsp;&#039;&#039;Theats and Opportunities Analysis&#039;&#039;. Living document prepared for the Strategic Assessments Group, Office of Transnational Issues, Central Intelligence Agency, August 2002.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. and Anwar Hossain. 2003. Long-Term Socio-Economic Modeling: With Universal, Globally-Integrated Social Accounting Matrices (SAMs) in a General Equilibrium Model Structure. IFs Project Living Document, University of Denver.&lt;br /&gt;
&lt;br /&gt;
Huth, Paul. 1996.&amp;amp;nbsp;&#039;&#039;Standing Your Ground: Territorial Disputes and International Conflict&#039;&#039;. Ann Arbor, MI: University of Michigan Press.&lt;br /&gt;
&lt;br /&gt;
Inglehart, Ronald. 1997.&amp;amp;nbsp;&#039;&#039;Modernization and Postmodernization: Cultural, Economic, and Political Change in 43 Societies&#039;&#039;. Ewing, NJ: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1995.&amp;amp;nbsp;&#039;&#039;Oil, Gas, and Coal Supply Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996.&amp;amp;nbsp;&#039;&#039;World Energy Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996b.&amp;amp;nbsp;&#039;&#039;The Strategic Value of Fossil Fuels: Challenges and Responses&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;International Financial Statistics&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;World Economic Outlook&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
Intergovernmental Panel on Climate Change (IPCC). 1995. Several volumes by various working groups. Published by Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Jansen, Karel and Rob Vos, eds. 1997.&amp;amp;nbsp;&#039;&#039;External Finance and Adjustment: Failure and Success in the Developing World&#039;&#039;. London: Macmillan Press Ltd.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1998.&amp;amp;nbsp;&#039;&#039;Modeling Global Change: The Art of Integrated Assessment Modelling&#039;&#039;. Cheltenham, UK: Edward Elgar.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1996.&amp;amp;nbsp;&#039;&#039;Meeting Targets: Tools to Support Integrated Modelling of Global Change&#039;&#039;. Den Haag: CIP-Gegevens Koninklijke Bibliotheek.&lt;br /&gt;
&lt;br /&gt;
Jansson, Kurt, Michael Harris, Angela Penrose. 1987.&amp;amp;nbsp;&#039;&#039;The Ethiopian Famine&#039;&#039;. London: Zed Books Ltd.&lt;br /&gt;
&lt;br /&gt;
Jeffreys, Kent. 1995. &amp;quot;Rescuing the Oceans,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 296-338.&lt;br /&gt;
&lt;br /&gt;
Jones, Daniel M., Stuart A. Bremer, and J. David Singer. 1996. &amp;quot;Militarized Interstate Disputes, 1816-1992: Rationale, Coding Rules, and Empirical Patterns,&amp;quot;&amp;amp;nbsp;&#039;&#039;Conflict Management and Peace Science&#039;&#039;&amp;amp;nbsp;XV, No. 2: 163-215.&lt;br /&gt;
&lt;br /&gt;
Khan, Haider A. 1998.&amp;amp;nbsp;&#039;&#039;Technology, Development and Democracy&#039;&#039;. Northhampton, Mass: Edward Elgar Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Kahn, Herman, William Brown, and Leon Martel. 1976.&amp;amp;nbsp;&#039;&#039;The Next 200 Years&#039;&#039;. New York: William Morrow.&lt;br /&gt;
&lt;br /&gt;
Kalymon, Basil A. 1975. &amp;quot;Economic Incentives in OPEC Oil Pricing Policy.&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Development Economics&#039;&#039;&amp;amp;nbsp;2: 337-362.&lt;br /&gt;
&lt;br /&gt;
Kaplan, Robert. 1994. &amp;quot;The Coming Anarchy,&amp;quot;&amp;amp;nbsp;&#039;&#039;The Atlantic Monthly&#039;&#039;&amp;amp;nbsp;273 (February): .&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999a. &amp;quot;Aggregating Governance Indicators&amp;quot;. World Bank Policy Research Department Working Paper No. 2195.&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999b. &amp;quot;Governance Matters&amp;quot;. World Bank Policy Research Department Working Paper No. 2196.&lt;br /&gt;
&lt;br /&gt;
Keepin, B. and B. Wynne. 1984. &amp;quot;Technical Analysis of the IIASA Energy Scenarios,&amp;quot;&amp;amp;nbsp;&#039;&#039;Nature&#039;&#039;312: 691-695.&lt;br /&gt;
&lt;br /&gt;
Kehoe, Timothy J. 1996. Social Accounting Matrices and Applied General Equilibrium Models. Federal Reserve Bank of Minneapolis, Working Paper 563.&lt;br /&gt;
&lt;br /&gt;
Kennedy, Paul. 1993.&amp;amp;nbsp;&#039;&#039;Preparing for the Twenty-First Century&#039;&#039;. New York: Random House.&lt;br /&gt;
&lt;br /&gt;
Klein, Lawrence R. and Fu-chen Lo, eds. 1995.&amp;amp;nbsp;&#039;&#039;Modeling Global Change&#039;&#039;. Tokyo: United Nations University Press.&lt;br /&gt;
&lt;br /&gt;
Kornai, J. 1971.&amp;amp;nbsp;&#039;&#039;Anti-Equilibrium&#039;&#039;. Amsterdam: North Holland.&lt;br /&gt;
&lt;br /&gt;
Kwasnicki, Witold and Halina Kwasnicka. 1996. &amp;quot;Long-Term Diffusion Factors of Technological Development: An Evolutionary Model and Case Study,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 31-57.&lt;br /&gt;
&lt;br /&gt;
Leontief, Wassily, Anne Carter and Peter Petri. 1977.&amp;amp;nbsp;&#039;&#039;The Future of the World Economy&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander H., and Elizabeth R. Ducot. 1976. &amp;quot;AGRIMOD: A Simulation Model for the Analysis of U.S. Food Policies.&amp;quot; Paper delivered at Conference on Systems Analysis of Grain Reserves, Joint Annual Meeting of GRSA and TIMS, Philadelphia, Pa., March 31-April 2.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander, H., et al. 1977. Energy in Agriculture: On Modeling Inputs in AGRIMOD. Final Report to U.S. Department of Energy. Palo Alto: Systems Control, Inc., August, available through NTIS.&lt;br /&gt;
&lt;br /&gt;
Lichbach, Mark Irving. 1989. &amp;quot;An Evaluation of ‘Does Economic Inequality Breed Political Conflict?,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;, Vol 41 , No. 4 (July 1989): 431-470.&lt;br /&gt;
&lt;br /&gt;
Liverman, Dianne. 1983.&amp;amp;nbsp;&#039;&#039;The Use of Global Simulation Models in Assessing Climate Impacts on the World Food System&#039;&#039;. Dissertation, University of California, Los Angeles.&lt;br /&gt;
&lt;br /&gt;
Londregan, John B. and Keith T. Poole. 1996. &amp;quot;Does High Income Promote Democrary?&amp;quot;,&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 1 (October): 1-30.&lt;br /&gt;
&lt;br /&gt;
MacKenzie, James J. 1996. &amp;quot;Oil as a Finite Resource: When is Global Production Likely to Peak?&amp;quot; Paper of the World Resources Institute. Washington, D.C.: WRI.&lt;br /&gt;
&lt;br /&gt;
Maddison, Angus. 1995.&amp;amp;nbsp;&#039;&#039;Monitoring the World Economy 1820-1992&#039;&#039;. Paris: OECD.&lt;br /&gt;
&lt;br /&gt;
Malthus, Thomas. 1798.&amp;amp;nbsp;&#039;&#039;An Essay on the Principle of Population as It Affects the Future Improvement of Society&#039;&#039;. London (reprinted many times).&lt;br /&gt;
&lt;br /&gt;
Mansfield, Edward D. 1994.&amp;amp;nbsp;&#039;&#039;Power, Trade, and War&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Marchetti, Cesare, Perrin S. Meyer, and Jesse H. Ausubel. 1996. &amp;quot;Human Population Dynamics Revisited with the Logistic Model: How Much Can be Modeled and Predicted?,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 1-30.&lt;br /&gt;
&lt;br /&gt;
Martens, Pim and Jan Rotmans, eds. 1999.&amp;amp;nbsp;&#039;&#039;Climate Change: An Integrated Perspective&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
&lt;br /&gt;
Martens, W.J.M. 1997. &amp;quot;Health Impacts of Climate Change and Ozone Depletion: An Eco-Epidemiological Approach,&amp;quot; Maastricht, the Netherlands: Maastricht University.&lt;br /&gt;
&lt;br /&gt;
Mason, Andrew. 1997. &amp;quot;The Role of Population Change in the Asian Economic Miracle,&amp;quot; Honolulu, Hawaii: East-West Center, AsiaPacific Issues, No. 33 (October), 8 pages.&lt;br /&gt;
&lt;br /&gt;
McMahon, Walter W. 1997.&amp;amp;nbsp;&#039;&#039;Education and Development: Measuring the Social Benefits&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, Jorgen Randers, and William K. Behrens, III. 1972.&amp;amp;nbsp;&#039;&#039;Limits to Growth&#039;&#039;. New York: Universe Books.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, and Jorgen Randers. 1992.&amp;amp;nbsp;&#039;&#039;Beyond the Limits&#039;&#039;. Post Mills, Vermont: Chelsea Green Publishing Company.&lt;br /&gt;
&lt;br /&gt;
Meadows, Dennis L. et al. 1974.&amp;amp;nbsp;&#039;&#039;Dynamics of Growth in a Finite World&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Mesarovic, Mihajlo D. and Eduard Pestel. 1974.&amp;amp;nbsp;&#039;&#039;Mankind at the Turning Point&#039;&#039;. New York: E.P. Dutton &amp;amp; Co.&lt;br /&gt;
&lt;br /&gt;
Mishkin, Eli. And Ludwig Braun, ed. 1961.&amp;amp;nbsp;&#039;&#039;Adaptive Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Moore, Will H., Ronny Lindstrom, and Valerie O’Regan. 1996. &amp;quot;Land Reform, Political Violence and the Economic Inequality-Political Conflict Nexus: A Longitudinal Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Interactions&#039;&#039;&amp;amp;nbsp;21, No. 4: 335-363.&lt;br /&gt;
&lt;br /&gt;
Mori, Shunsuke and Masato Takahaashi, 1997. An Integrated Assessment Model for the Evaluation of New Energy Technologies and Food Production, accepted by&amp;amp;nbsp;&#039;&#039;International Journal of Global Energy Issues&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
Naill, Roger F. 1977.&amp;amp;nbsp;&#039;&#039;Managing the Energy Transition&#039;&#039;. Vols. 1 and 2. Cambridge, Mass: Ballinger Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1992. &amp;quot;The DICE Model: Background and Structure of a Dynamic Integrated Climate Economy,&amp;quot; New Haven: Yale University.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1979.&amp;amp;nbsp;&#039;&#039;The Efficient Use of Energy Resources&#039;&#039;. New Haven, CT: Yale University Press.&lt;br /&gt;
&lt;br /&gt;
Oneal, John R. and Bruce M. Russett. 1997. The Classical Liberals were Right: Democracy, Interdependence, and Conflict, 1950-1985.&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;41, no. 2 (June): 267-294.&lt;br /&gt;
&lt;br /&gt;
Pan, Xiaoming. 2000 (January). &amp;quot;Social and Ecological Accounting Matrix: an Empirical Study for China,&amp;quot; paper submitted for the Thirteenth International Conference on Input-Output Techniques, Macerata, Italy, August 21-25, 2000.&lt;br /&gt;
&lt;br /&gt;
Pesaran, M. Hashem and G. C. Harcourt. 1999. Life and Work of John Richard Nicholas Stone.&lt;br /&gt;
&lt;br /&gt;
Pirages, Dennis. 1989.&amp;amp;nbsp;&#039;&#039;Global Technopolitics&#039;&#039;. Pacific Grove, Calif: Brooks/Cole Publishing.&lt;br /&gt;
&lt;br /&gt;
Prinn, R. H.J., A. Sokolov, C. Wand, X. Xiao, Z. Yang, R. Eckhaus, P. Stone, D. Ellerman, J Melilo, J. Fitzmaurice, D. Kicklighter, and Y. Liu. 1996. &amp;quot;Integrated Global System Model for Climate Policy Analysis: Model Framework and Sensitivity Analysis.&amp;quot; Cambridge, Mass: Global Change Center, Massachusetts Institute of Technology.&lt;br /&gt;
&lt;br /&gt;
Przeworski, Adam and Fernando Limongi. 1997. &amp;quot;Modernization: Theories and Facts,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 2 (January): 155-183.&lt;br /&gt;
&lt;br /&gt;
Population Reference Bureau. 1996. World Population Data Sheet 1996. Washington, D.C.: Population Reference Bureau.&lt;br /&gt;
&lt;br /&gt;
Postel, Sandra. 1996.&amp;amp;nbsp;&#039;&#039;Dividing the Waters: Food Security, Ecosystem Health, and the New Politics of Scarcity&#039;&#039;. Worldwatch Paper 132. Washington, D.C.: Worldwatch Institute, September.&lt;br /&gt;
&lt;br /&gt;
Pyatt, G. and J.I. Round, eds. 1985.&amp;amp;nbsp;&#039;&#039;Social Accounting Matrices: A Basis for Planning&#039;&#039;. Washington, D.C.: The World Bank.&lt;br /&gt;
&lt;br /&gt;
Raskin, P., T. Banuri, G. Gallopín, P. Gutman, A. Hammond, R. Kates, and R. Swart. 2001. Great Transition:&amp;amp;nbsp;&#039;&#039;The Promise and Lure of the Times Ahead&#039;&#039;. Forthcoming.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1990.&amp;amp;nbsp;&#039;&#039;Global Politics&#039;&#039;, 4th edition. Boston: Houghton Mifflin.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1995.&amp;amp;nbsp;&#039;&#039;Democracy and International Conflict&#039;&#039;. Columbia: University of South Carolina Press.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee and J. David Singer. 1973. &amp;quot; Measuring the Concentration of Power in the International System,&amp;quot;&#039;&#039;&amp;amp;nbsp;Sociological Methods and Research&#039;&#039;&amp;amp;nbsp;1, no. 4: 403-436. Reprinted in&amp;amp;nbsp;&#039;&#039;Measuring the Correlates of War&#039;&#039;, edited by J. David Singer and Paul Diehl. Ann Arbor: University of Michigan Press, 1990.&lt;br /&gt;
&lt;br /&gt;
Rayner. S. 1992. &amp;quot;Cultural Theory and Risk Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Theory of Risk&#039;&#039;, ed. G. D. Preagor. Westport, USA.&lt;br /&gt;
&lt;br /&gt;
Repetto, Robert and Duncan Austin. 1997.&amp;amp;nbsp;&#039;&#039;The Costs of Climate Protection&#039;&#039;. Washington, D.C.: World Resources Institute.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis Fry. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Chicago: Quadrangle Books.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis F. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Pittsburgh: Boxwood Press.&lt;br /&gt;
&lt;br /&gt;
Romer, Paul M. 1994. &amp;quot;The Origins of Endogenous Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Economic Perspectives&#039;&#039;Vol 8, No. 1 (Winter): 3-22.&lt;br /&gt;
&lt;br /&gt;
Root T. and Stephen Schneider. 1995. &amp;quot;Ecology and Climate: Research Strategies and Implications,&amp;quot; Science 269 (52): 334-341.&lt;br /&gt;
&lt;br /&gt;
Rosegrant, Mark W., Mercedita Agcaoili-Sombilla, and Nicostrato D. Perez. 1995. &amp;quot;Global Food Projections to 2020: Implications for Investment.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 5.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1999. Integrated Assessment Models: Uncertainty, Quality and Use. Maastricht, the Netherlands: Maastricht University, International Centre for Integrative Studies (ICIS), Working Paper 199-E005.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and Burt de Vries, eds. 1997.&amp;amp;nbsp;&#039;&#039;Perspectives on Global Change: The Targets Approach&#039;&#039;. Cambridge, UK: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and M.B.A. van Asselt. 1996. &amp;quot;Integrated Assessment: A Growing Child on its Way to Maturity,&amp;quot;&amp;amp;nbsp;&#039;&#039;Climatic Change&#039;&#039;&amp;amp;nbsp;34 (3-4): 327-336.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1990.&amp;amp;nbsp;&#039;&#039;IMAGE: An Integrated Model to Assess the Greenhouse Effect&#039;&#039;. Dordrecht, the Netherlands: Kluwer Academics.&lt;br /&gt;
&lt;br /&gt;
Saaty, Thomas L. 1996. The Analytic Network Process: Decision Making with Dependence and Feedback. Pittsburgh: RWS Publications.&lt;br /&gt;
&lt;br /&gt;
Schafer, Andreas and David G. Victor. 1997. The Future Mobility of the World Population. Massachusetts Institute of Technology and International Institute for Applied Systems Analysis, Discussion Paper 97-6-4 (revision 2, September).&lt;br /&gt;
&lt;br /&gt;
Scheer, Sara J. and Satya Yadav. 1996. &amp;quot;Land Degradation in the Developing World: Implications for Food, Agriculture, and the Environment to 2020.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 14.&lt;br /&gt;
&lt;br /&gt;
Schneider, Stephen. 1997. &amp;quot;Integrated Assessment Modeling of Climate Change: Transparent Rational Tool for Policy Making or Opaque Screen Hiding Value-Laden Assumptions?&amp;quot;&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(4): 229-250.&lt;br /&gt;
&lt;br /&gt;
Schwartz, Peter. 1996.&#039;&#039;&amp;amp;nbsp;The Art of the Long View.&#039;&#039;&amp;amp;nbsp;New York: Doubleday.&lt;br /&gt;
&lt;br /&gt;
Sedjo, Roger A. 1995. &amp;quot;Forests: Conflicting Signals,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 178-209.&lt;br /&gt;
&lt;br /&gt;
Shane, Harold G. and Gary A. Sojka. 1990. &amp;quot;John Elfreth Watkins, Jr.: Forgotten Genius of Forecasting,&amp;quot; in Edward Cornish, ed.,&#039;&#039;&amp;amp;nbsp;The 1990s and Beyond&#039;&#039;. Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Shaw, Timothy W. and Clement E. Adibe. 1995-96. &amp;quot;Africa and Global Developments in the Twenty-First Century,&amp;quot; International Journal 51 (Winter): 1-26.&lt;br /&gt;
&lt;br /&gt;
Siegmann, Heinrich. 1985.&amp;amp;nbsp;&#039;&#039;Recent Developments in World Modeling&#039;&#039;. Berlin: Science Center.&lt;br /&gt;
&lt;br /&gt;
Simon, Julian. 1981.&amp;amp;nbsp;&#039;&#039;The Ultimate Resource&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Singer, J. David, Stuart Bremer, and John Stuckey. 1972. &amp;quot;Capability Distribution, Uncertainty, and Major Power Wars, 1820-1965.&amp;quot; In Bruce Russett, ed.,&amp;amp;nbsp;&#039;&#039;Peace, War, and Numbers.&#039;&#039;&amp;amp;nbsp;Beverly Hills: Sage.&lt;br /&gt;
&lt;br /&gt;
Sivard, Ruth Leger. 1993.&amp;amp;nbsp;&#039;&#039;World Military and Social Expenditures 1993.&#039;&#039;&amp;amp;nbsp;Washington, D.C. 20007: World Priorities, Box 25140.&lt;br /&gt;
&lt;br /&gt;
Solow, Robert M. 1956. &amp;quot;A Contribution to the Theory of Economic Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Quarterly Journal of Economics&#039;&#039;&amp;amp;nbsp;70, 1 (February): 65-94.&lt;br /&gt;
&lt;br /&gt;
Stanford University. 1978.&amp;amp;nbsp;&#039;&#039;Stanford Pilot Energy/Economic Model&#039;&#039;. Stanford: Department of Research, Interim Report, Vol. 1.&lt;br /&gt;
&lt;br /&gt;
Stockholm International Peace Research Institute (SIPRI). 1994.&amp;amp;nbsp;&#039;&#039;SIPRI Yearbook&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Stone, Richard. 1986. &amp;quot;The Accounts of Society,&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Applied Econometrics&#039;&#039;&amp;amp;nbsp;1, no. 1 (January): 5-28.&lt;br /&gt;
&lt;br /&gt;
Strategic Assessments Group (SAG), Office of Transnational Issues, Directorate of Intelligence. 2001 (February). The Global Economy in the Long Term. OTI IR 2001-013.&lt;br /&gt;
&lt;br /&gt;
Systems Analysis Research Unit (SARU). 1977.&amp;amp;nbsp;&#039;&#039;SARUM 76 Global Modeling Project&#039;&#039;. Departments of the Environment and Transport, 2 Marsham Street, London, 3WIP 3EB.&lt;br /&gt;
&lt;br /&gt;
Tammen, Ronald L, Jacek Kugler, Douglas Lemke, Allan C. Stam III, Carole Alsharabati, Mark Andrew Abdollahian, Brian Efird, and A.F.K. Organski. 2000. Power Transitions: Strategies for the 21st Century. New York: Chatham House Publishers.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1975. &amp;quot;Theoretical Foundations and Technical Implications.&amp;quot; in Charles Blitzer, Peter Clark and Lance Taylor, eds.,&amp;amp;nbsp;&#039;&#039;Economy-Wide Models and Development Planning.&#039;&#039;&amp;amp;nbsp;Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1979.&amp;amp;nbsp;&#039;&#039;Macro Models for Developing Countries&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Thirlwall, A. P. 1977.&amp;amp;nbsp;&#039;&#039;Growth and Development&#039;&#039;. New York: John Wiley and Sons.&lt;br /&gt;
&lt;br /&gt;
Thompson, M. 1997. Cultural Theory and Integrated Assessment.&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(3): 139-150.&lt;br /&gt;
&lt;br /&gt;
Thompson, M., R. Ellis and A. Wildavsky. 1990.&amp;amp;nbsp;&#039;&#039;Cultural Theory&#039;&#039;. Boulder, Co: Westview Press.&lt;br /&gt;
&lt;br /&gt;
Thorbecke, Erik. 2001. &amp;quot;The Social Accounting Matrix: Deterministic or Stochastic Concept?&amp;quot;, paper prepared for a conference in honor of Graham Pyatt&#039;s retirement, at the Institute of Social Studies, The Hague, Netherlands (November 29 and 30). Available at [http://people.cornell.edu/pages/et17/etpapers.html http://people.cornell.edu/pages/et17/etpapers.html].&lt;br /&gt;
&lt;br /&gt;
United Nations, Department of Economic and Social Affairs. 1956.&amp;amp;nbsp;&#039;&#039;Methods of Population Projections by Sex and Age&#039;&#039;. New York: United Nations, ST/SOA Series A.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1992.&amp;amp;nbsp;&#039;&#039;Long-Range World Population Projections. Two Centuries of Population Growth: 1950-2150&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1993.&amp;amp;nbsp;&#039;&#039;World Population Prospects - the 1992 Revision&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations Development Program (UNDP). 1995.&amp;amp;nbsp;&#039;&#039;Human Development Report&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1992.&amp;amp;nbsp;&#039;&#039;Production Yearbook.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1995.&#039;&#039;&amp;amp;nbsp;World Agriculture: Towards 2010.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 1999. The World at Six Billion New York: UN.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2000. Replacement Migration: Is it a Solution to Declining and Ageing Populations? New York: UN.&lt;br /&gt;
&lt;br /&gt;
United States Arms Control and Disarmament Agency (ACDA). 1995.&amp;amp;nbsp;&#039;&#039;World Military Expenditures and Arms Transfers 1995&#039;&#039;. Washington, D.C.: Arms Control and Disarmament Agency.&lt;br /&gt;
&lt;br /&gt;
United States Bureau of the Census. 1991.&amp;amp;nbsp;&#039;&#039;World Population Profile: 1991&#039;&#039;. Report WP/91 Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Walters, Robert S. and David H. Blake. 1992.&amp;amp;nbsp;&#039;&#039;The Politics of Global Economic Relations&#039;&#039;, 4th edition. Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Waltz, Kenneth N. 1959. Man, the State, and War: A Theoretical Analysis. New York: Columbia University Press.&lt;br /&gt;
&lt;br /&gt;
Watkins, John Elfreth, Jr. 1990. &amp;quot;What May Happen in the Next Hundred Years,&amp;quot; in Edward Cornish, ed.,&amp;amp;nbsp;&#039;&#039;The 1990s and Beyond.&#039;&#039;&amp;amp;nbsp;Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Wildavsky, Aaron, and Ellen Tenenbaum. 1981.&amp;amp;nbsp;&#039;&#039;The Politics of Mistrust&#039;&#039;. Beverly Hills: Sage Publications.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1991b.&amp;amp;nbsp;&#039;&#039;World Tables 1991&#039;&#039;. New York: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1995&amp;amp;nbsp;&#039;&#039;World Development Report 1995&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
World Energy Council (WEC) Commission. 1993.&amp;amp;nbsp;&#039;&#039;Energy for Tomorrow’s World.&#039;&#039;&amp;amp;nbsp;New York: St. Martin’s Press.&lt;br /&gt;
&lt;br /&gt;
World Resources Institute (WRI). 1994.&amp;amp;nbsp;&#039;&#039;World Resources 1994-95.&#039;&#039;&amp;amp;nbsp;New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Wortman, Sterling and Ralph W. Cummings, Jr. 1978.&#039;&#039;&amp;amp;nbsp;To Feed This World&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Zinnes, Dina A. and John W. Gillespie, eds. 1976.&amp;amp;nbsp;&#039;&#039;Mathematical Models in International Relations&#039;&#039;&amp;amp;nbsp;(New York: Preaeger).&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Energy Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Kalymon, Basil A. 1975. &amp;quot;Economic Incentives in OPEC Oil Pricing Policy.&amp;quot; &#039;&#039;Journal of Development Economics&#039;&#039; 2: 337-362.&lt;br /&gt;
&lt;br /&gt;
Naill, Roger F. 1977.&#039;&#039;Managing the Energy Transition.&#039;&#039; Vols. 1 and 2. Cambridge, Mass: Ballinger Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Stanford University. 1978. &#039;&#039;Stanford Pilot Energy/Economic Model.&#039;&#039; Stanford: Department of Research, Interim Report, Vol. 1.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Governance Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. and Jong-Wha Lee. 2001. &amp;quot;International Data on Educational Attainment: Updates and Implications,&amp;quot;&amp;amp;nbsp;&#039;&#039;Oxford Economic Papers&#039;&#039;&amp;amp;nbsp;53(3): 541-563.&lt;br /&gt;
&lt;br /&gt;
Cilliers, Jakkie, Barry Hughes, and Jonathan Moyer. 2011.&amp;amp;nbsp;&#039;&#039;African Futures 2050: The Next 40 Years&#039;&#039;. Pretoria, South Africa and Denver, Colorado: Institute for Security Studies and Frederick S. Pardee Center for International Futures.&lt;br /&gt;
&lt;br /&gt;
Correlates of War Project. 2011. “State System Membership List, v2011.” Online,&amp;amp;nbsp;[http://correlatesofwar.org/ http://correlatesofwar.org&amp;amp;nbsp;].&lt;br /&gt;
&lt;br /&gt;
Diamond, Larry. 1992. “Economic Development and Democracy Reconsidered.”&amp;amp;nbsp;&#039;&#039;American Behavioral Scientist&#039;&#039;&amp;amp;nbsp;35(4/5): 450-499.&lt;br /&gt;
&lt;br /&gt;
Diehl, Paul F., ed. 1999.&amp;amp;nbsp;&#039;&#039;A Roadmap to War: Territorial Dimensions of International Conflict&#039;&#039;, 1&amp;lt;sup&amp;gt;st&amp;lt;/sup&amp;gt;&amp;amp;nbsp;ed. Nashville: Vanderbilt University Press.&lt;br /&gt;
&lt;br /&gt;
Easton, David. 1965.&amp;amp;nbsp;&#039;&#039;A Framework for Political Analysis&#039;&#039;. Englewood Cliffs, New Jersey: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Esty, Daniel C., Jack A. Goldstone, Ted Robert Gurr, Barbara Harff, Marc Levy, Geoffrey D. Dabelko, Pamela Surko, and Alan N. Unger. 1998. “State Failure Task Force Report: Phase II Findings.” Study Commissioned by the Central Intelligence Agency and George Mason University School of Public Policy. Political Instability Task Force, Arlington VA.&lt;br /&gt;
&lt;br /&gt;
Freedom House, Inc. 2009.&amp;amp;nbsp;&#039;&#039;Freedom in the World 2009: The Annual Survey of Political Rights and Civil Liberties&#039;&#039;. Washington, DC: Freedom House, Inc.\&lt;br /&gt;
&lt;br /&gt;
Goldstone, Jack A. 2010. “The New Population Bomb”&amp;amp;nbsp;&#039;&#039;Foreign Affairs&#039;&#039;&amp;amp;nbsp;(January/February): 31-43.&lt;br /&gt;
&lt;br /&gt;
Goldstone, Jack A., Robert H. Bates, David L. Epstein, Ted Robert Gurr, Michael B. Lustik, Monty G. Marshall, Jay Ulfelder, and Mark Woodward. 2010. “A Global Model for Forecasting Political Instability.”&amp;amp;nbsp;&#039;&#039;American Journal of Political Science&#039;&#039;&amp;amp;nbsp;54(1): 190-208. doi: 10.1111/j.1540-5907.2009.00426.x.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2001. “Global Social Transformation: The Sweet Spot, the Steady Slog, and the Systemic Shift.”&amp;amp;nbsp;&#039;&#039;Economic Development and Cultural Change&#039;&#039;&amp;amp;nbsp;49(2): 423-458. doi: 10.1086/452510.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2002. &amp;quot;Threats and Opportunities Analysis,&amp;quot; working document prepared for the Strategic Assessments Group, Office of Transnational Issues, Central Intelligence Agency.&amp;amp;nbsp; Available on the IFs project web site at&amp;amp;nbsp;[http://www.ifs.du.edu/ www.ifs.du.edu].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., and Anwar Hossain. 2003. “Long-Term Socio-Economic Modeling: With Universal, Globally-Integrated Social Accounting Matrices (SAMs) in a General Equilibrium Model Structure.” Working Paper, University of Denver, Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/assets/documents/economyandsamdocument46.pdf http://www.ifs.du.edu/assets/documents/economyandsamdocument46.pdf]&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., Devin Joshi, Jonathan Moyer, Timothy Sisk and José Roberto Solórzano. 2014.&amp;amp;nbsp;&#039;&#039;Strengthening Governance Globally.&amp;amp;nbsp;&#039;&#039;vol. 5, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Huntington, Samuel P. 1991.&amp;amp;nbsp;&#039;&#039;The Third Wave: Democratization in the Late Twentieth Century&#039;&#039;. Norman, OK: University of Oklahoma.&lt;br /&gt;
&lt;br /&gt;
Inglehart, Ronald. 1997.&amp;amp;nbsp;&amp;amp;nbsp;&#039;&#039;Modernization and Postmodernization&#039;&#039;.&amp;amp;nbsp; Princeton: PrincetonUniversity Press.&lt;br /&gt;
&lt;br /&gt;
Joshi, Devin. 2011a. “Good Governance, State Capacity, and the Millennium Development Goals.”&amp;amp;nbsp;&#039;&#039;Perspectives on Global Development and Technology&amp;amp;nbsp;&#039;&#039;10(2): 339-360. doi: 10.1163/156914911X5824.68.&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi. 2010. “The Worldwide Governance Indicators: Methodology and Analytical Issues.” World Bank Policy Research Working Paper no. 5430. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Marshall, Monty G. and Benjamin R. Cole. 2008. “Global Report on Conflict, Governance and State Fragility 2008.”&amp;amp;nbsp;&#039;&#039;Foreign Policy Bulletin&#039;&#039;&amp;amp;nbsp;18: 3-21. doi: 10.1017/S1052703608000014.&lt;br /&gt;
&lt;br /&gt;
Marshall, Monty G., and Benjamin R. Cole. 2009. “Global Report 2009: Conflict, Governance, and State Fragility.” Vienna, VA.: Center for Systemic Peace and Center for Global Policy.&lt;br /&gt;
&lt;br /&gt;
Marshall, Monty G., and Benjamin R. Cole. 2011. &amp;quot;Global Report 2011: Conflict, Governance, and State Fragility.&amp;quot; Vienna, VA. Center for Systemic Peace.&lt;br /&gt;
&lt;br /&gt;
Marshall, Monty G., and Keith Jaggers. 2011. “Polity IV Project: Political Regime Characteristics and Transitions 1800-2010.”&amp;amp;nbsp;[http://www.systemicpeace.org/polity/polity4.htm http://www.systemicpeace.org/polity/polity4.htm]&amp;amp;nbsp;[accessed December 22 2012]&lt;br /&gt;
&lt;br /&gt;
Mauro, Paolo. 1995. “Corruption and Growth.”&amp;amp;nbsp;&#039;&#039;The Quarterly Journal of Economics&#039;&#039;&amp;amp;nbsp;110(3) (August): 681-712.&lt;br /&gt;
&lt;br /&gt;
Migdal, Joel. 1988.&amp;amp;nbsp;&#039;&#039;Strong Societies and Weak Sates: State-Society Relations and State Capabilities in the&amp;amp;nbsp;Third World&#039;&#039;. Princeton: Princeton University Press&lt;br /&gt;
&lt;br /&gt;
Mo, Pak Hung. 2001. “Corruption and Economic Growth.”&amp;amp;nbsp;&#039;&#039;Journal of Comparative Economics&amp;amp;nbsp;&#039;&#039;29(1) (March): 66-79. doi:10.1006/jcec.2000.1703.&lt;br /&gt;
&lt;br /&gt;
North, Douglass C., John Joseph Wallis, and Barry R. Weingast. 2009.&amp;amp;nbsp;&#039;&#039;Violence and Social Orders: A Conceptual Framework for Interpreting Recorded Human History&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Pierson, Paul. 2004.&amp;amp;nbsp;&#039;&#039;Politics in Time: History, Institutions, and Social Analysis&#039;&#039;. Princeton, NJ: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Rice, Susan E., and Stewart Patrick. 2008.&amp;amp;nbsp;&#039;&#039;Index of State Weakness in the Developing World.&#039;&#039;&amp;amp;nbsp;Washington, DC: The Brookings Institution.&lt;br /&gt;
&lt;br /&gt;
Shihata, Ibrahim F. I. 1996. “Corruption - A General Review with an Emphasis on the Role of the World Bank.”&amp;amp;nbsp;&#039;&#039;Dickinson Journal of International Law&#039;&#039;&amp;amp;nbsp;15: 451.&lt;br /&gt;
&lt;br /&gt;
Tanzi, Vito. 1998. “Corruption Around the World: Causes, Consequences, Scope, and Cures.” Staff Papers - International Monetary Fund 45(4) (December): 559-594.&lt;br /&gt;
&lt;br /&gt;
Urdal, H. 2004. “The devil in the demographics: the effect of youth bulges on domestic armed conflict, 1950-2000.” Social Development Papers: Conflict and Reconstruction Paper 14.&lt;br /&gt;
&lt;br /&gt;
Ware, H. 2004. “Pacific instability and youth bulges: the devil in the demography and the economy.” Paper delivered at the 12th Biennial Conference of the Australian Population Association, 15-17.&lt;br /&gt;
&lt;br /&gt;
Wagner, Adolph. 1892.&amp;amp;nbsp;&#039;&#039;Grundlegung der Politischen Ökonomie&#039;&#039;. Leipzig: C.F. Winter Publishing Firm.&lt;br /&gt;
&lt;br /&gt;
World Bank. 2011.&amp;amp;nbsp;&#039;&#039;World Development Indicators 2011.&#039;&#039;&amp;amp;nbsp;Washington, DC: World Bank. Available at&amp;amp;nbsp;[http://data.worldbank.org/data-catalog/world-development-indicators http://data.worldbank.org/data-catalog/world-development-indicators].&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Health Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Adams 1987.&amp;amp;nbsp;[http://www.geog.ucl.ac.uk/~jadams/PDFs/smeed&#039;s%20law.pdf &amp;quot;Smeed&#039;s Law: some further thoughts.&amp;quot;]&amp;amp;nbsp;&#039;&#039;Traffic Engineering and Control&#039;&#039;&amp;amp;nbsp;(Feb) 70-73.&lt;br /&gt;
&lt;br /&gt;
Alsan, Marcella, David E. Bloom, and David Canning. 2006. “The Effects of Population Health on Foreign Direct Investment Inflows to Low- and Middle-Income Countries,”&amp;amp;nbsp;&#039;&#039;World Development&#039;&#039;&amp;amp;nbsp;34(4): 613-630.&lt;br /&gt;
&lt;br /&gt;
Anand, Sudhir and Martin Ravallion. 1993. “Human development in poor countries: on the role of private incomes and public services,”&amp;amp;nbsp;&#039;&#039;Journal of Economic Perspectives&#039;&#039;&amp;amp;nbsp;7(1): 133–150.&lt;br /&gt;
&lt;br /&gt;
Ashraf, Quamrul H., Ashley Lester, and David N. Weil. 2008. “When Does Improving Health Raise GDP?”&amp;amp;nbsp; NBER Working Paper No. 14449. National Bureau of Economic Research, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
Bidani, Benu and Martin Ravallion. 1997. “Decomposing social indicators using distributional data.”&amp;amp;nbsp;&#039;&#039;Journal of Econometrics&#039;&#039;&amp;amp;nbsp;77: 125–139.&lt;br /&gt;
&lt;br /&gt;
Bloom, David E., and David Canning. 2004. “Global Demographic Change: Dimensions and Economic Significance.” NBER Working Paper No. 10817.&amp;amp;nbsp; National Bureau of Economic Research, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
Blössner, Monika, and Mercedes de Onis. 2005.&amp;amp;nbsp;&#039;&#039;Malnutrition: quantifying the health impact at national and local levels.&#039;&#039;&amp;amp;nbsp;Geneva, World Health Organization. (WHO Environmental Burden of Disease Series, No. 12).&lt;br /&gt;
&lt;br /&gt;
Dargay, Gately, and Sommer 2007. “Vehicle Ownership and Income Growth, Worldwide: 1960-2030”. Joyce Dargay, Dermot Gately and Martin Sommer, January 2007.&lt;br /&gt;
&lt;br /&gt;
Deaton, Angus, and Christina Paxson. 2000 (May). “Growth and Savings Among Individuals and Households.”&amp;amp;nbsp;&#039;&#039;The Review of Economics and Statistics&#039;&#039;&amp;amp;nbsp;82(2): 212-225.&lt;br /&gt;
&lt;br /&gt;
Desai, Manish A., Sumi Mehta, and Kirk R. Smith. 2004. “Indoor smoke from solid fuels: Assessing the environmental burden of disease.”WHOEnvironmental Burden of Disease Series No. 4&#039;&#039;.&amp;amp;nbsp;&#039;&#039;Annette Pruss-Üstun, Diamid Campbell-Lendrum, Carlos Corvalán, and Alistair Woodward, series eds. World Health Organization, Geneva.&lt;br /&gt;
&lt;br /&gt;
Ezzati, Majid and Alan D. Lopez. 2004. “Smoking and oral tobacco use.” In Majid Ezzati, Alan D. Lopez, Anthony Rodgers, and Cristopher J.L. Murray, eds.,&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors&#039;&#039;. Geneva: World Health Organization, 883-957.&amp;amp;nbsp; Retrieved 4 Feb 2009, from&amp;amp;nbsp;[http://www.who.int/publications/cra/chapters/volume1/part4/en/index.html http://www.who.int/publications/cra/chapters/volume1/part4/en/index.html].&lt;br /&gt;
&lt;br /&gt;
Ezzati, Majid, Alan D. Lopez, Anthony Rodgers, Christopher J.L. Murray, eds. 2004.&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors&#039;&#039;. Geneva: World Health Organization.&lt;br /&gt;
&lt;br /&gt;
Fernández-Villaverde, Jesús, and Dirk Kruegger. 2004 (September 14). “Consumption over the Life Cycle: Facts from Consumer Expenditure Survey Data,” unpublished manuscript, University of Pennsylvania and University of Frankfort.&amp;amp;nbsp;&amp;amp;nbsp;[http://www.dklevine.com/archive/refs4506439000000000304.pdf http://www.dklevine.com/archive/refs4506439000000000304.pdf]&lt;br /&gt;
&lt;br /&gt;
Fernández-Villaverde, Jesús, and Dirk Kruegger. 2005 (December 19). “Consumption over the Life Cycle: How Important are Consumer Durables?,” unpublished manuscript, University of Pennsylvania and Goethe University.&amp;amp;nbsp;&amp;amp;nbsp;[http://journals.cambridge.org/action/displayAbstract?fromPage=online&amp;amp;aid=8466457 http://journals.cambridge.org/action/displayAbstract?fromPage=online&amp;amp;aid=8466457]&lt;br /&gt;
&lt;br /&gt;
Gakidou, Emmanuela, Shefali Oza, Cecilia Vidal Fuertes, Amy Y. Li, Diana K. Lee, Angelica Sousa, Margaret C. Hogan, Stephen Vander Hoorn, and Majid Ezzati. 2007.” Improving Child Survival Through Environmental and Nutritional Interventions: The Importance of Targeting Interventions Toward the Poor.”&amp;amp;nbsp;&#039;&#039;Journal of the American Medical Association&#039;&#039;&amp;amp;nbsp;298(16): 1876-1887.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. and Hillebrand, Evan E. 2006. “Exploring and shaping International Futures”. Boulder, CO: Paradigm Publishers.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., Randall Kuhn, Cecilia Peterson, Dale Rothman, and Jose Solorzano. 2011.&amp;amp;nbsp;&amp;amp;nbsp;&#039;&#039;Improving Global Health: Patterns of Potential Human Progress, Volume 3&#039;&#039;.&amp;amp;nbsp; Paradigm Publishing and Oxford India.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2005.&amp;amp;nbsp; “Productivity in IFs.” Pardee Center for International Futures Working Paper, University of Denver, Denver, CO.&amp;amp;nbsp;&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx http://www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
James, W. Philip T., Rachel Jackson-Leach , Cliona Ni Mhurchu, Eleni Kalamara, Maryam Shayeghi, Neville J. Rigby, Chizuru Nishida, and Anthony Rodgers. 2004.&amp;amp;nbsp; “Overweight and obesity (high body mass index).” In Majid Ezzati, Alan D. Lopez, Anthony Rodgers and Christopher J.L. Murray, eds.,&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors.&#039;&#039;&amp;amp;nbsp;Geneva: World Health Organization, 959-1108.&lt;br /&gt;
&lt;br /&gt;
Jamison, Dean T., Jia Wang, Kenneth Hill, and Juan-Luis Londono. 1996. “Income, Mortality and Fertility in Latin America: Country-Level Performance, 1960 - 90.”&amp;amp;nbsp;&#039;&#039;Analisis Economico&#039;&#039;11(2): 219-261.&lt;br /&gt;
&lt;br /&gt;
Kelly, Christopher, Nora Pashayan, Sreetharan Munisamy, and Joshn W. Powles. 2009.&amp;amp;nbsp; “Mortality attributable to excess adiposity in England and Wales in 2003 and 2015: explorations with a spreadsheet implementation of the Comparative Risk Assessment mentodology.”&amp;amp;nbsp;&#039;&#039;Population Health Metrics&#039;&#039;&amp;amp;nbsp;7(11): 1-7.&lt;br /&gt;
&lt;br /&gt;
Lopez, Alan D., Neil E. Collishaw, and Tapani Piha. 1994. “A descriptive model of the cigarette epidemic in developed countries.”&amp;amp;nbsp;&#039;&#039;Tobacco Control&#039;&#039;&amp;amp;nbsp;3(3): 242-247. &amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Mathers, Colin D., and Dejan Loncar. 2005. &amp;quot;Updated Projections of Global Mortality and Burden of Disease, 2002-2030: Data Sources, Methods and Results.&amp;quot; Evidence and Information for Policy Working Paper. World Health Organization, Geneva.&lt;br /&gt;
&lt;br /&gt;
Mathers, Colin D., and Dejan Loncar. 2006. &amp;quot;Projections of Global Mortality and Burden of Disease from 2002 to 2030.&amp;quot;&amp;amp;nbsp;&#039;&#039;PLoS Medicine&#039;&#039;&amp;amp;nbsp;3(11): e442, 2011-2030.&amp;amp;nbsp; Retrieved 13 March 2009. doi:10.1371/journal.pmed.0030442.&lt;br /&gt;
&lt;br /&gt;
Mathers, Colin D., and Dejan Loncar. 2006b. “New projections of global mortality and burden of disease from 2002 to 2030.” Protocol S1. Technical Appendix to Mathers and Loncar 2006.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Mathers, Colin D., and Dejan Loncar. 2006c. “Results of Regressions of Age–Sex-Specific Mortality for Detailed Causes on the Respective Cause Cluster Based on the Full Country Panel Dataset, 1950–2002.” Technical Appendix to Mathers and Loncar 2006.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Nixon, John, and Philippe Ulmann. 2006. “The Relationship Between Health Care Expenditure and Health Outcomes: Evidence and caveats for a Causal Link.”&amp;amp;nbsp;&#039;&#039;European Journal of Health Economics&#039;&#039;&amp;amp;nbsp;7: 7-18.&lt;br /&gt;
&lt;br /&gt;
Peto, Richard, Jillian Boreham, Alan D. Lopez, Michael Thun, and Clark Heath, Jr. 1992. “Mortality from Tobacco in Developed Countries: Indirect Estimation from National Vital Statistics.”&amp;amp;nbsp;&#039;&#039;Lancet&amp;amp;nbsp;&#039;&#039;339(8804): 1268–1278. doi:10.1016/0140- 6736(92)91600-D.&lt;br /&gt;
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Ploeg, Martine, Katja K. H. Aben, and Lambertus A. Kiemeney. 2009. “The Present and Future Burden of Urinary Bladder Cancer in the World.”&amp;amp;nbsp;&#039;&#039;World Journal of Urology&#039;&#039;&amp;amp;nbsp;27(3): 289-293. doi:[http://dx.doi.org/10.1007/s00345-009-0383-3 &amp;amp;nbsp;10.1007/s00345-009-0383-3&amp;amp;nbsp;]. &amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Shibuya, Kenji, Mie Inoue, and Alan D. Lopez. 2005. “Statistical Modeling and Projections of Lung Cancer Mortality in 4 Industrialized Countries.”&amp;amp;nbsp;&#039;&#039;International Journal of Cancer&#039;&#039;&amp;amp;nbsp;117(3): 476-485. doi:[http://dx.doi.org/10.1002/ijc.21078 &amp;amp;nbsp;10.1002/ijc.21078&amp;amp;nbsp;]. &amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Smeed, RJ 1949. &amp;quot;Some statistical aspects of road safety research&amp;quot;.&amp;amp;nbsp;[http://en.wikipedia.org/wiki/Royal_Statistical_Society &#039;&#039;Royal Statistical Society&#039;&#039;], Journal (A) CXII (Part I, series 4). 1-24.&lt;br /&gt;
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Smith, Lisa C. and Lawrence Haddad. 2000. “Explaining Child Malnutrition in Developing Countries: A Cross-Sectional Analysis.” Washington, D.C.: International Food Policy Research Institute.&lt;br /&gt;
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Soares, Rodrigo R. 2007. “On the Determinants of Mortality Reductions in the Developing World.”&amp;amp;nbsp;&#039;&#039;Population and Development Review&amp;amp;nbsp;&#039;&#039;33(2): 247-287.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2003.&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;World Population Prospects: The 2002 Revision, Highlight.&#039;&#039;&amp;amp;nbsp; New York:&amp;amp;nbsp; United Nations. Department of Economics and Social Affairs.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2009.&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;World Population Prospects: The 2008 Revision, Highlights.&#039;&#039;&amp;amp;nbsp; New York:&amp;amp;nbsp; United Nations. Department of Economics and Social Affairs.&lt;br /&gt;
&lt;br /&gt;
Wagstaff, Adam. 2002. “Inequalities in Health in Developing Countries: Swimming Against the Tide?” Unpublished Manuscript&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Infrastructure Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Agénor, Pierre-Richard, Mustapha Kamel Nabli, and Tarik M. Yousef. 2007. “Public Infrastructure and Private Investment in the Middle East and North Africa.” In Mustapha Kamel Nabli, ed.,. Breaking the Barriers to Higher Economic Growth: Better Governance and Deeper Reforms in the Middle East and North Africa. Washington, DC: World Bank Publications, 399–422.&lt;br /&gt;
&lt;br /&gt;
Asian Development Bank, Japan Bank for International Cooperation, and World Bank. 2005.&amp;amp;nbsp;&#039;&#039;Connecting East Asia: A New Framework for Infrastructure&#039;&#039;. Tokyo: Asian Development Bank, Japan Bank for International Cooperation, and World Bank.&amp;amp;nbsp;[http://siteresources.worldbank.org/INTEASTASIAPACIFIC/Resources/Connecting-East-Asia.pdf http://siteresources.worldbank.org/INTEASTASIAPACIFIC/Resources/Connecting-East-Asia.pdf].&lt;br /&gt;
&lt;br /&gt;
Bhattacharyay, Biswa Nath. 2010. “Estimating Demand for Infrastructure in Energy, Transport, Telecommunications, Water and Sanitation in Asia and the Pacific: 2010-2020”. Working Paper no. 248. Asian Development Bank Institute, Tokyo.&amp;amp;nbsp;[http://www.adbi.org/working-paper/2010/09/09/4062.infrastructure.demand.asia.pacific/ http://www.adbi.org/working-paper/2010/09/09/4062.infrastructure.demand.asia.pacific/].&lt;br /&gt;
&lt;br /&gt;
Bruinsma, Jelle. 2011. “The Resources Outlook: By How Much Do Land, Water and Crop Yields Need to Increase by 2050?” In Piero Conforti, ed.,.&amp;amp;nbsp;&#039;&#039;Looking Ahead in World Food and Agriculture: Perspectives to 2050&#039;&#039;. Rome: Food and Agriculture Organization of the United Nations (FAO), 233–275.&amp;amp;nbsp;[http://www.fao.org/docrep/014/i2280e/i2280e.pdf http://www.fao.org/docrep/014/i2280e/i2280e.pdf].&lt;br /&gt;
&lt;br /&gt;
Calderón, César, and Luis Servén. 2010a. “Infrastructure and Economic Development in Sub-Saharan Africa.”&amp;amp;nbsp;&#039;&#039;Journal of African Economies&#039;&#039;&amp;amp;nbsp;19(Supplement 1): i13–i87. doi:10.1093/jae/ejp022.&amp;amp;nbsp;[http://jae.oxfordjournals.org/cgi/content/abstract/19/suppl_1/i13 http://jae.oxfordjournals.org/cgi/content/abstract/19/suppl_1/i13].&lt;br /&gt;
&lt;br /&gt;
Calderón, César, and Luis Servén. 2010b. “Infrastructure in Latin America”. World Bank Policy Research Working Paper. Report Number 5317. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Canning, David. 1998. “A Database of World Stocks of Infrastructure, 1950-1995.”&amp;amp;nbsp;&#039;&#039;The World Bank Economic Review&#039;&#039;&amp;amp;nbsp;12(3): 529–548.&lt;br /&gt;
&lt;br /&gt;
Canning, David, and Mansour Farahani. 2007. “A Database of World Stocks of Infrastructure: Update 1950-2005”. Harvard School of Public Health, Boston, MA.&amp;amp;nbsp;[http://www.hsph.harvard.edu/faculty/david-canning/data-sets/ http://www.hsph.harvard.edu/faculty/david-canning/data-sets/].&lt;br /&gt;
&lt;br /&gt;
Cavallo, Eduardo Alfredo, and Christian Daude. 2008. “Public Investment in Developing Countries: A Blessing or a Curse?” RES Working Paper #4597. Inter-American Development Bank (IADB) - Research Department, OECD, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Chatterton, Isabe, and Olga S. Puerto. 2006.&amp;amp;nbsp;&#039;&#039;Estimation of Infrastructure Investment Needs in the South Asia Region: Executive Summary&#039;&#039;. Washington, DC: World Bank.&amp;amp;nbsp;[http://siteresources.worldbank.org/INTSARREGTOPTRANSPORT/Resources/Inf_Investment_Needs_IC_version4.pdf http://siteresources.worldbank.org/INTSARREGTOPTRANSPORT/Resources/Inf_Investment_Needs_IC_version4.pdf].&lt;br /&gt;
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Congressional Budget Office. 2010.&amp;amp;nbsp;&#039;&#039;Public Spending on Transportation and Water Infrastructure&#039;&#039;. Washington, DC: Congressional Budget Office.&amp;amp;nbsp;[http://www.cbo.gov/publication/21902 http://www.cbo.gov/publication/21902].&lt;br /&gt;
&lt;br /&gt;
Estache, Antonio, and Ana Goicoechea. 2005. “A Research Database on Infrastructure Economic Performance”. Policy Research Working Paper no. 3643. World Bank, Washington, DC.&amp;amp;nbsp;[http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2005/06/16/000016406_20050616100502/Rendered/PDF/wps3643.pdf http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2005/06/16/000016406_20050616100502/Rendered/PDF/wps3643.pdf].&lt;br /&gt;
&lt;br /&gt;
Ezzati, Majid, Alan D. Lopez, Anthony Rodgers, and Christopher J. L. Murray, eds. 2004.&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors&#039;&#039;. Geneva, Switzerland: World Health Organization (WHO).&lt;br /&gt;
&lt;br /&gt;
Fay, Marianne. 2001. “Financing the Future: Infrastructure Needs in Latin America, 2000-05”. Policy Research Working Paper no. 2545. World Bank, Washington, DC.&amp;amp;nbsp;[http://elibrary.worldbank.org/docserver/download/2545.pdf?expires=1375200693&amp;amp;id=id&amp;amp;accname=guest&amp;amp;checksum=DB7E5FB146EE28C93511B5ADAB2FD3CB http://elibrary.worldbank.org/docserver/download/2545.pdf?expires=1375200693&amp;amp;id=id&amp;amp;accname=guest&amp;amp;checksum=DB7E5FB146EE28C93511B5ADAB2FD3CB].&lt;br /&gt;
&lt;br /&gt;
Fay, Marianne, and Tito Yepes. 2003. “Investing in Infrastructure: What Is Needed from 2000 to 2010?” Policy Research Working Paper no. 3102. World Bank, Washington, DC. RePEc.&amp;amp;nbsp;[http://ideas.repec.org/p/wbk/wbrwps/3102.html http://ideas.repec.org/p/wbk/wbrwps/3102.html].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2007. “Forecasting Global Economic Growth with Endogenous Multifactor Productivity: The International Futures (IFs) Approach”. Pardee Center for International Futures Working Paper, University of Denver. Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., Devin Joshi, Jonathan Moyer, Timothy Sisk and José Roberto Solórzano. 2014. Strengthening Governance Globally. vol. 5, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Hughes, Gordon, Paul Chinowsky, and Ken Strzepek. 2009. “The Costs of Adapting to Climate Change for Infrastructure”. Economics of Adaptation to Climate Change Discussion Paper no. 2. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
International Transport Forum, and Organisation for Economic Cooperation and Development (OECD). 2011. “Trends in Transport Infrastructure Investment 1995-2009”. Paris.&lt;br /&gt;
&lt;br /&gt;
Kohli, Harpaul Alberto, and Phillip Basil. 2011. “Requirements for Infrastructure Investment in Latin America Under Alternate Growth Scenarios.”&amp;amp;nbsp;&#039;&#039;Global Journal of Emerging Market Economies&#039;&#039;&amp;amp;nbsp;3(1): 59 –110. doi:10.1177/097491011000300103.&amp;amp;nbsp;[http://eme.sagepub.com/content/3/1/59.abstract http://eme.sagepub.com/content/3/1/59.abstract].&lt;br /&gt;
&lt;br /&gt;
Kim, M. Julie, and Rita Nangia. 2010. “Infrastructure Development in India and China—A Comparative Analysis.” In William Ascher and Corinne Krupp, eds.,.&amp;amp;nbsp;&#039;&#039;Physical Infrastructure Development: Balancing The Growth, Equity, and Environmental Imperatives&#039;&#039;. New York, NY: Palgrave Macmillan, 97–140.&lt;br /&gt;
&lt;br /&gt;
Lora, Eduardo A. 2007.&amp;amp;nbsp;&#039;&#039;Public Investment in Infrastructure in Latin America: Is Debt the Culprit?&#039;&#039;&amp;amp;nbsp;Inter-American Development Bank Working Paper. Washington, DC: Inter-American Development Bank (IADB) - Research Department.&lt;br /&gt;
&lt;br /&gt;
Nelson, Gerald C., Mark W. Rosegrant, Amanda Palazzo, Ian Gray, Christina Ingersoll, Richard Robertson, Simla Tokgoz, Tingju Zhu, Timothy B. Sulser, Claudia Ringler, Siwa Msangi, and Liangzhi You. 2010.&amp;amp;nbsp;&#039;&#039;Food Security, Farming, and Climate Change to 2050: Scenarios, Results, Policy Options&#039;&#039;. Washington, DC: International Food Policy Research Institute.&amp;amp;nbsp;[http://www.ifpri.org/publication/food-security-farming-and-climate-change-2050 http://www.ifpri.org/publication/food-security-farming-and-climate-change-2050].&lt;br /&gt;
&lt;br /&gt;
Organisation for Economic Co-operation and Development. 2006.&amp;amp;nbsp;&#039;&#039;Infrastructure to 2030 Volume 1: Telecom, Land Transport, Water and Electricity&#039;&#039;. Infrastructure to 2030. Paris: Organisation for Economic Cooperation and Development.&lt;br /&gt;
&lt;br /&gt;
Organisation for Economic Co-operation and Development. 2009.&amp;amp;nbsp;&#039;&#039;Going for Growth: Economic Policy Reforms&#039;&#039;. Paris: Organisation for Economic Cooperation and Development (OECD).&lt;br /&gt;
&lt;br /&gt;
Qiang, Christine Zhen-Wei, Carlo M. Rossotto, and Kaoru Kimura. 2009. “Economic Impacts of Broadband.” In World Bank, ed.,.&amp;amp;nbsp;&#039;&#039;2009 Information and Communications for Development: Extending Reach and Increasing Impact&#039;&#039;. Washington, DC: World Bank, 35–50.&lt;br /&gt;
&lt;br /&gt;
Rothman, Dale S. Mohammod T. Irfan, Eli Margolese-Malin, Barry B. Hughes, Jonathan Moyer, and Janet Dickson. 2013.&amp;amp;nbsp;&#039;&#039;Building Global Infrastructure.&amp;amp;nbsp;&#039;&#039;vol. 4, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press. Stambrook, David. 2006. “Key Factors Driving the Future Demand for Surface Transport Infrastructure and Services.” In Organisation for Economic Cooperation and Development (OECD), ed.,.&amp;amp;nbsp;&#039;&#039;Infrastructure to 2030 Volume 1: Telecom, Land Transport, Water and Electricity&#039;&#039;. Infrastructure to 2030. Paris: Organisation for Economic Cooperation and Development (OECD), 185–239.&lt;br /&gt;
&lt;br /&gt;
World Health Organization, and UNICEF. 2013.&amp;amp;nbsp;&#039;&#039;Progress on Sanitation and Drinking-Water - 2013 Update&#039;&#039;. Geneva: World Health Organization.&lt;br /&gt;
&lt;br /&gt;
Yepes, Tito. 2008. “Investment Needs for Infrastructure in Developing Countries 2008-15”. Draft. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Yepes, Tito. 2005.&amp;amp;nbsp;&#039;&#039;Expenditure on Infrastructure in East Asia Region, 2006–2010&#039;&#039;. East Asia Pacific Infrastructure Flagship Study. Manila: Asian Development Bank (ADB), Japan Bank for International Cooperation (JBIC), World Bank.&lt;br /&gt;
&lt;br /&gt;
You, Liangzhi, Claudia Ringler, Ulrike Wood-Sichra, Richard Robertson, Stanley Wood, Tingju Zhu, Gerald Nelson, Zhe Guo, and Yan Sun. 2011. “What Is the Irrigation Potential for Africa? A Combined Biophysical and Socioeconomic Approach.”&amp;amp;nbsp;&#039;&#039;Food Policy&#039;&#039;&amp;amp;nbsp;36(6): 770–782. doi:10.1016/j.foodpol.2011.09.001.&amp;amp;nbsp;[http://www.sciencedirect.com/science/article/pii/S030691921100114X http://www.sciencedirect.com/science/article/pii/S030691921100114X].&lt;br /&gt;
&lt;br /&gt;
== [[Development_Mode_Features|Development Mode Features]] ==&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Education&amp;diff=8312</id>
		<title>Education</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Education&amp;diff=8312"/>
		<updated>2017-09-07T21:41:05Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;=== Overview ===&lt;br /&gt;
&lt;br /&gt;
The most recent and complete education model documentation is available on Pardee&#039;s [http://pardee.du.edu/ifs-education-model-documentation website]. Although the text in this interactive system is, for some IFs models, often significantly out of date, you may still find the basic description useful to you.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;The education model of IFs simulates patterns of educational participation and attainment in 186 countries over a long time horizon under alternative assumptions about uncertainties and interventions (Irfan 2008).&amp;amp;nbsp; Its purpose is to serve as a generalized thinking and analysis tool for educational futures within a broader human development context.&amp;amp;nbsp;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;The model forecasts gender- and country-specific access, participation and progression rates at levels of formal education starting from elementary through lower and upper secondary to tertiary. The model also forecasts costs and public spending by level of education. Dropout, completion and transition to the next level of schooling are all mapped onto corresponding age cohorts thus allowing the model to forecast educational attainment for the entire population at any point in time within the forecast horizon.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;From simple accounting of the grade progressions to complex budget balancing and budget impact algorithm, the model draws upon the extant understanding and standards (e.g., UNESCO&#039;s ISCED classification explained later) about national systems of education around the world. One difference between other attempts at forecasting educational participation and attainment (e.g, McMahon 1999; Bruns, Mingat and Rakotomalala 2003; Wils and O’Connor 2003; Delamonica, Mehrotra and Vandemoortele. 2001; Cuaresma and Lutz 2007) and our forecasting, is the embedding of education within an integrated model in which demographic and economic variables interact with education, in both directions, as the model runs.&amp;amp;nbsp;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;In the figure below we display the major variables and components that directly determine education demand, supply, and flows in the IFs system.&amp;amp;nbsp; We emphasize again the inter-connectedness of the components and their relationship to the broader human development system.&amp;amp;nbsp; For example, during each year of simulation, the IFs cohort-specific demographic model provides the school age population to the education model.&amp;amp;nbsp; In turn, the education model feeds its calculations of education attainment to the population model’s determination of women’s fertility.&amp;amp;nbsp; Similarly, the broader economic and socio-political systems provide funding for education, and levels of educational attainment affect economic productivity and growth, and therefore also education spending.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;[[File:EduOverview.png|frame|center|Visual representation of education demand, supply, and flows in the IFs system]]&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Structure and Agent System: Education&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot; border=&amp;quot;0&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width: 50%&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;System/Subsystem&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;National Education System&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Organizing Structure&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Various Levels of Education; Age Cohorts&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Stocks&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Educational Attainment; Enrollment&amp;lt;/div&amp;gt;&lt;br /&gt;
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| style=&amp;quot;text-align: left&amp;quot; valign=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Flows&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Intake; Graduation; Transition; Spending&amp;lt;/div&amp;gt;&lt;br /&gt;
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| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Key Aggregate&amp;amp;nbsp;&#039;&#039;&#039; &#039;&#039;&#039;Relationships&amp;amp;nbsp;&#039;&#039;&#039;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;(illustrative, not comprehensive)&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Demand for and achievement in education changes with income, societal change&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Public spending available for education rises with income level&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Cost of schooling rises with income level&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Lack (surplus) of public spending in education hurts (helps) educational access and progression&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;More education helps economic growth and reduces fertility&amp;lt;/div&amp;gt;&lt;br /&gt;
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| style=&amp;quot;text-align: left&amp;quot; valign=&amp;quot;center&amp;quot; | &amp;lt;div style=&amp;quot;text-align: left&amp;quot;&amp;gt;&#039;&#039;&#039;Key Agent-Class Behavior&amp;amp;nbsp;&#039;&#039;&#039; &#039;&#039;&#039;Relationships&#039;&#039;&#039;&amp;lt;/div&amp;gt;&amp;lt;div style=&amp;quot;text-align: left&amp;quot;&amp;gt;(illustrative, not comprehensive)&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Families send children to school; Government revenue and expenditure in education&amp;lt;/div&amp;gt;&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Education Model Coverage&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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UNESCO has developed a standard classification system for national education systems called International Standard Classification of Education, ISCED. ISCED 1997 uses a numbering system to identify the sequential levels of educational systems—namely, pre-primary, primary, lower secondary, upper secondary, post-secondary non tertiary and tertiary—which are characterized by curricula of increasing difficulty and specialization as the students move up the levels. IFs education model covers&amp;amp;nbsp; primary (ISCED level 1), lower secondary (ISCED level 2), upper secondary (ISCED level 3), and tertiary education (ISCED levels 5A, 5B and 6).&lt;br /&gt;
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The model covers 186 countries that can be grouped into any number of flexible country groupings, e.g., UNESCO regions, like any other sub-module of IFs. Country specific entrance age and school-cycle length [[Education#Sources_of_Education_Data|data are collected]] and used in IFs to represent national education systems as closely as possible. For all of these levels, IFs forecast variables representing student flow rates, e.g., intake, persistence, completion and graduation, and stocks, e.g., enrolment, with the girls and the boys handled separately within each country.&lt;br /&gt;
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One important distinction among the flow rates is a gross rate versus a net rate for the same flow. Gross rates include all pupils whereas net rates include pupils who enter the school at the right age, given the statutory entrance age in the country and proceed without any repetition. The IFs education model forecasts both net and gross rates for primary education. For other levels we forecast gross rates only. It would be useful to look at the net rates at least for lower secondary, as the catch up continues up to that level. However, we could not obtain net rate data for lower secondary.&amp;amp;nbsp;&lt;br /&gt;
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Additionally, for lower and upper secondary, the IFs model covers both general and vocational curriculum and forecasts the vocational share of total enrolment, EDSECLOWRVOC (for lower secondary) and EDSECUPPRVOC (for upper secondary). Like all other participation variables, these two are also disaggregated by gender.&lt;br /&gt;
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The output of the national education system, i.e., school completion and partial completion of the young people, is added to the [http://www.du.edu/ifs/help/understand/education/flowcharts/attainment.html educational attainment] of the adults in the population. IFs forecasts four categories of attainment - portion with no education, completed primary education, completed secondary education and completed tertiary education - separately for men and women above fifteen years of age by five year cohorts as well as an aggregate over all adult cohorts. Model software contains so-called &amp;quot;Education Pyramid&amp;quot; or a display of educational attainments mapped over five year age cohorts as is usually done for population pyramids.&lt;br /&gt;
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Another aggregate measure of educational attainment that we forecast is the average years of education of the adults. We have several measures, EDYEARSAG15, average years of education for all adults aged 15 and above, EDYRSAG25, average years of education for those 25 and older, EDYRSAG15TO24, average years of education for the youngest of the adults aged between fifteen years to twenty four.&lt;br /&gt;
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IFs education model also covers [[Education#Education_Financial_Flow|financing of education]]. The model forecast per student public expenditure as a share of per capita income. The model also forecast total public spending in education and the share of that spending that goes to each level of education.&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;What the Model Does Not Cover&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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ISCED level 0, pre-primary, and level 4, post-secondary pre tertiary, are not common across all countries and are thus excluded from IFs education model.&lt;br /&gt;
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On the financing side, the model does not include private spending in education, a significant share of spending especially for tertiary education in many countries and even for secondary education in some countries. Scarcity of good data and lack of any pattern in the historical unfolding precludes modelling private spending in education.&lt;br /&gt;
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Quality of national education system can also vary across countries and over time. The IFs education model does not forecast any explicit indicator of education quality. However, the survival and graduation rates that the model forecasts for all levels of education are implicit indicators of system quality.&amp;amp;nbsp; At this point IFs does not forecast any indicator of cognitive quality of learners. However, the IFs database does have data on cognitive quality.&lt;br /&gt;
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The IFs education model does not cover private spending in education.&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Sources of Education Data&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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UNESCO is the UN agency charged with collecting and maintaining education-related data from across the world. UNICEF collects some education data through their MICS survey. USAID also collects education data as a part of its Demographic and Household Surveys (DHS). OECD collects better data especially on tertiary education for its members as well as few other countries.&lt;br /&gt;
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We collected our [[Education#Education_Student_Flow|student flows]] and per student cost data from UNESCO Institute for Statistics&#039; (UIS) [http://stats.uis.unesco.org/unesco/tableviewer/document.aspx?ReportId=143 web data repository]. (Accessed on 05/17/2013)&lt;br /&gt;
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For [[Education#Education_Attainment|educational attainment]] data we use estimates by Robert Barro and Jong Wha Lee (2000). They &amp;amp;nbsp;have published their estimates of human capital stock (i.e., the educational attainment of adults) at the website of the Center for International Development of Harvard University. In 2001, Daniel Cohen and Marcelo Soto presented a paper providing another human capital dataset for a total of ninety-five countries. We collect that data as well in our database.&lt;br /&gt;
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When needed we also calculated our own series using underlying data from UNESCO. For example, we calculate an adjusted net intake rate for primary using the age specific intake rates that UNESCO report. We also calculated survival rates in lower and upper secondary (EDSECLOWRSUR, EDSECUPPRSUR) using a reconstructed cohort simulation method from grade-wise enrollment data for two consecutive years. The transition rate from lower to upper secondary is also calculated using grade data.&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Reconciliation of Flow Rates&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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Incongruities among the base year primary flow rates (intake, survival, and enrollment) can arise either from reported data values that, in combination, do not make sense, or from the use of “stand-alone” cross-sectional estimations used in the [http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf IFs pre-processor] to fill missing data.&amp;amp;nbsp; Such incongruities might arise among flow rates within a single level of education (e.g., primary intake, survival, and enrollment rates that are incompatible) or between flow rates across two levels of education (e.g., primary completion rate and lower secondary intake rate).&lt;br /&gt;
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The IFs education model uses algorithms to reconcile incongruent flow values.&amp;amp;nbsp; They work by (1) analyzing incongruities; (2) applying protocols that identify and retain the data or estimations that are probably of higher quality; and (3) substituting recomputed values for the data or estimations that are probably of lesser quality.&amp;amp;nbsp; For example, at the primary level, data on enrollment rates are more extensive and more straight-forward than either intake or survival data; in turn, intake rates have fewer missing values and are arguably more reliable measures than survival rates.&amp;amp;nbsp; The IFs pre-processor reconciles student flow data for Primary by using an algorithm that assumes enrollment numbers to be more reliable than the entrance data and entrance data to be more reliable than survival data.&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Variable Naming Convention&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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All education model variable names start with a two-letter prefix of &#039;ED&#039; followed, in most cases, by the three letter level indicator - PRI for primary, SEC for secondary, TER for tertiary. Secondary is further subdivided into SECLOWR for lower secondary and SECUPPR for upper secondary. Parameters in the model, which are named using lowercase letters like those in other IFs modules, also follow a similar naming convention.&lt;br /&gt;
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= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Dominant Relations: Education&amp;lt;/span&amp;gt; =&lt;br /&gt;
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&amp;lt;span&amp;gt;The dominant relationships in the model are those that determine various educational flow rates, e.g., intake rate for primary (EDPRIINT) or tertiary (EDTERINT), or survival rates in primary (EDPRISUR) or lower secondary (EDSECLOWRSUR). These rates are functions of per capita income. Non-income drivers of education are represented by upward shifts in these functions. These rates follow an S-shaped path in most cases. The flows interact with a stocks and flows structure to derive major stocks like enrollment, for the young, and attainment, for the adult.&amp;lt;/span&amp;gt;&lt;br /&gt;
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On the financing side, the major dynamic is&amp;amp;nbsp; in the cost of education, e.g., cost per student in primary, EDEXPERPRI, the bulk of which is teachers&#039; salary and which thus goes up with rising income.&lt;br /&gt;
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&amp;lt;span&amp;gt;Public spending allocation in education, GDS(Educ) is a function of national income per capita that proxies level of economic development. Demand for educational spending -&amp;amp;nbsp; determined by initial projections of enrollment and of per student cost - and total availability of public funds affect the base allocation derived from function.&amp;lt;/span&amp;gt;&lt;br /&gt;
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For diagrams see: [[Education#Education_Student_Flow|Student Flow Charts]]; [[Education#Education_Financial_Flow|Budget Flow Charts]]; [[Education#Education_Attainment|Attainment Flow Charts]]&lt;br /&gt;
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For equations see: [[Education#Equations:_Student_Flow|Student Flow Equations]]; [[Education#Equations:_Budget_Flow|Budget Flow Equations]]; [[Education#Equations:_Attainment|Attainment Equations]]&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Key dynamics are directly linked to the dominant relations&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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*Intake, survival and transition rates are functions of per capita income (GDPPCP). These functions shift upward over time representing the non-income drivers of education.&lt;br /&gt;
*Each year flow rates are used to update major stocks like enrollment, for the young, and attainment, for the adult.&lt;br /&gt;
*Per student expenditure at all levels of education is a function of per capita income.&lt;br /&gt;
*Deficit or surplus in public spending on education, GDS(Educ) affects intake, transition and survival rates at all levels of education.&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Education: Selected Added Value&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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&amp;lt;span&amp;gt;IFs Education model is an integrated model. The education system in the model is interlinked with demographic, economic and socio-political systems with mutual feedback within and across theses systems. Schooling of the young is linked to education of the population as whole in this model.&amp;lt;/span&amp;gt;&lt;br /&gt;
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&amp;lt;span&amp;gt;The model is well suited for scenario analysis with representation of policy levers for entrance into and survival at various levels of schooling. Girls and boys are represented separately in this model.&amp;lt;/span&amp;gt;&lt;br /&gt;
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&amp;lt;span&amp;gt;The education budget is also endogenous to the model with income driven dynamics in cost per student for each level of education. Budget availability affect enrollment. Educational attainment raises income and affordability of education at individual and national level.&amp;lt;/span&amp;gt;&lt;br /&gt;
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= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Education Flow Charts&amp;lt;/span&amp;gt; =&lt;br /&gt;
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=== Overview ===&lt;br /&gt;
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For each country, the IFs education model represents a multilevel formal education system that starts at primary and ends at tertiary.&amp;amp;nbsp;[[Education#Education_Student_Flow|Student flows]], i.e., entry into and progression through the system are determined by forecasts on intake and persistence (or survival) rates superimposed on the population of the corresponding age cohorts obtained from IFs population forecasts. Students at all levels are disaggregated by gender. Secondary education is further divided into lower and upper secondary, and then further into general and vocational according to the curricula that are followed.&lt;br /&gt;
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The model represents the dynamics in [[Education#Education_Financial_Flow|education financing]] through per student costs for each level of education and a total public spending in education. Policy levers are available for changing both spending and cost.&lt;br /&gt;
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School completion (or dropout) in the education model is carried forward as the [Education#Education Attainment|attainment]] of the overall population. As a result, the education model forecasts population structures by age, sex, and attained education, i.e., years and levels of completed education.&lt;br /&gt;
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The major agents represented in the education system of the model are households,—represented by the parents who decide which of their boys and girls will go to school—and governments that direct resources into and across the educational system.&amp;amp;nbsp; The major flows within the model are student and budgetary, while the major stock is that of educational attainment embedded in a population. Other than the budgetary variables, all the flows and stocks are gender disaggregated.&lt;br /&gt;
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The education model has forward and backward linkages with other parts of the IFs model. During each year of simulation, the IFs cohort-specific [[Population#Structure_and_Agent_System:_Demographic|demographic model]] provides the school age population to the education model.&amp;amp;nbsp; In turn, the education model feeds its calculations of education attainment to the population model’s determination of women’s fertility.&amp;amp;nbsp; Similarly, the broader economic and socio-political systems provide funding for education, and levels of educational attainment affect [[Economics#Multifactor_Productivity|economic productivity and growth]], and therefore also education spending.&amp;amp;nbsp;&lt;br /&gt;
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The figure below shows the major variables and components that directly determine education demand, supply, and flows in the IFs system.&amp;amp;nbsp; The diagram attempts to emphasize on the inter-connectedness of the education model components and their relationship to the broader human development system.&lt;br /&gt;
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[[File:Overvieweducation flow.png|frame|center|Visual representation of education demand, supply, and flows in the IFs system]]&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Education Student Flow&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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IFs education model simulates grade-by-grade student flow for each level of education that the model covers. Grade-by-grade student flow model combine the effects of grade-specific dropout, repetition and reentry into an average cohort-specific &#039;&#039;grade-to-grade flow rate&#039;&#039;, calculated from the survival rate for the cohort. Each year the number of new entrants is determined by the forecasts of the intake rate and the entrance age population. In successive years, these entrants are moved to the next higher grades, one grade each year, using the &#039;&#039;grade-to-grade flow rate&#039;&#039;. The simulated grade-wise enrollments are then used to determine the total enrollment at the particular level of education. Student flow at a particular level of education, e.g., primary, is culminated with rates of completion and transition by some to the next level, e.g., lower secondary.&lt;br /&gt;
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The figure below shows details of the student flow for primary (or, elementary) level. This is illustrative of the student flow at other levels of education. We model both net and gross enrollment rates for primary. The model tracks the pool of potential students who are above the entrance age (as a result of never enrolling or of having dropped out), and brings back some of those students, marked as late/reentrant in the figure, (dependent on initial conditions with respect to gross versus net intake) for the dynamic calculation of total gross enrollments.&lt;br /&gt;
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A generally similar grade-flow methodology models lower and upper secondary level student flows. We use country-specific entrance ages and durations at each level. As the historical data available does not allow estimating a rate of transition from upper secondary to tertiary, the tertiary education model calculates a tertiary intake rate from tertiary enrollment and graduation rate data using an algorithm which derives a tertiary intake with a lower bound slightly below the upper secondary graduation rate in the previous year.[[File:Educationstudentflow.png|frame|center|Student flow for primary (or, elementary) level.]]&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Education Financial Flow&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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In addition to [[Education#Education_Student_Flow|student flows]], and interacting closely with them, the IFs education model also tracks financing of education. Because of the scarcity of private funding data, IFs specifically represents public funding only, and our formulations of public funding implicitly assume that the public/private funding mix will not change over time.&lt;br /&gt;
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The accounting of educational finance is composed of two major components, per student cost and the total number of projected students, the latter of the two is discussed in the [[Education#Education_Student_Flow|student flows]] section. Spending per student at all levels of education is driven by average income. Given forecasts of spending per student by level of education and given initial enrollments forecasts by level, an estimate of the total education funding demanded is obtained by summing across education levels the products of spending per student and student numbers.&lt;br /&gt;
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The funding needs are sent to the IFs [[Socio-Political#Structure_and_Agent_System:_Socio-Political|sociopolitical model]] where educational spending is initially determined from the patterns in such spending regressed against the level of economic development of the countries. A priority parameter (&#039;&#039;&#039;edbudgon&#039;&#039;&#039;) is then used to prioritize spending needs over spending patterns. This parameter can be changed by model user within a range of values going from zero to one&amp;amp;nbsp; with the zero value awarding maximum priority to fund demands. Finally, total government consumption spending (GOVCON) is distributed among education and other social spending sectors, namely infrastructure, health, public R&amp;amp;D, defense and an &amp;quot;other&amp;quot; category, using a normalization algorithm.&lt;br /&gt;
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Government spending is then taken back to the education module and compared against fund needs. Budget impact, calculated as a ratio of the demanded and allocated funds, makes an impact on the initial projection of student flow rates (intake, survival, and transition). The positive (upward) side of the budget impact is non-linear with the maximum boost to growth occurring when a flow rate is at or near its mid-point or within the range of the inflection points of an assumed S-shaped path, to be precise. Impact of deficit is more or less linear except at impact ratios close to 1, whence the downward impact is dampened. Final student flow rates are used to calculate final enrollment numbers using population forecasts for relevant age cohorts. Finally, cost per students are adjusted to reflect final enrollments and fund availability.&lt;br /&gt;
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[[File:Edfinancialflows.png|frame|center|Visual representation of the education financial flow]]&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Education Attainment&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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The algorithm for the tracking of education attainment is very straight-forward.&amp;amp;nbsp; The model maintains the structure of the population not only by age and sex categories, but also by years and levels of completed education.&amp;amp;nbsp; In each year of the model’s run, the youngest adults pick up the appropriate total years of education and specific levels of completed education.&amp;amp;nbsp; The model advances each cohort in 1-year time steps after subtracting deaths. In addition to cohort attainment, the model also calculates overall attainment of adults (15+ and 25+) as average years of education&amp;amp;nbsp; (EDYRSAG15, EDYRSAG25) and as share of people 15+ with a certain level of education completed (EDPRIPER, EDSECPER, EDTERPER).&lt;br /&gt;
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One limitation of our model is that it does not represent differential mortality rates associated with different levels of education attainment (generally lower for the more educated).&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt;&amp;amp;nbsp;&amp;lt;/sup&amp;gt;This leads, other things equal, to a modest underestimate of adult education attainment, growing with the length of the forecast horizon.&amp;amp;nbsp; The averaging method that IFs uses to advance adults through the age/sex/education categories also slightly misrepresents the level of education attainment in each 5-year category.&lt;br /&gt;
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[[File:Edattainment.png|frame|center|Visual representation of education attainment]] &amp;lt;span style=&amp;quot;color: #990000&amp;quot; data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;1]&amp;lt;/span&amp;gt;&amp;amp;nbsp;The multi-state demographic method developed and utilized by IIASA does include education-specific mortality rates.&lt;br /&gt;
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= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Education Equations&amp;lt;/span&amp;gt; =&lt;br /&gt;
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=== &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
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&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;The IFs education model represent two types of educational stocks, [[Education#Equations:_Student_Flow|stocks of pupils]]&amp;amp;nbsp;&amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;and stocks of adults with a certain level of [[Education#Equations:Attainment|educational attainment]] &amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;. &amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;These stocks are initialized with historical data. The simulation model then recalculates the stock each year from its level the previous year and the net annual change resulting from inflows and outflows.&amp;lt;/span&amp;gt;&lt;br /&gt;
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&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;The core dynamics of the model is in these [[Education#Equations:_Student_Flow|flow rates]]&amp;lt;/span&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;. These&amp;amp;nbsp;&amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;flow&amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;rates are expressed as a percentage of age-appropriate population and thus have a theoretical range of zero to one hundred percent. Growing systems with a saturation point usually follow a sigmoid (S-shaped) trajectory with low growth rates at the two ends as the system begins to expand and as it approaches saturation. Maximum growth in such a system occurs at an inflection point, usually at the middle of the range or slightly above it, at which growth rate reverses direction. Some researchers (Clemens 2004; Wils and O’Connor 2003) have identified sigmoid trends in educational expansion by analyzing enrollment rates at elementary and secondary level. The IFs education model is not exactly a trend extrapolation; it is rather a forecast based on fundamental drivers, for example, income level. Educational rates in our model are driven by income level, a systemic shift algorithm and a [[Education#Equations:_Budget_Flow|budget impact]]&amp;amp;nbsp;&amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;resulting from the availability of public fund. However, there are growth rate parameters for most of the flows that allow model user to simulate desired growth that follows a sigmoid-trajectory. Another area that makes use of a sigmoid growth rate algorithm is the boost in flow rates as a result of budget surplus.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;Intake (or transition), survival, enrollment and completion are some of the rates that IFs model forecast. Rate forecasts [[Education#Structure_and_Agent_System:_Education|cover]]&amp;amp;nbsp;elementary&amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;, lower secondary, upper secondary and tertiary levels of education with separate equations for boys and girls for each of the rate variables. All of these rates are required to calculate pupil stocks while completion rate and dropout rate (reciprocal of survival rate) are used to determine educational attainment of adults.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;On the financial side of education, IFs forecast cost per student for each level. These per student costs are multiplied with enrollments to calculate fund demand. Budget allocation calculated in IFs [[Socio-Political#Structure_and_Agent_System:_Socio-Political|socio-political module]] &amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;is&amp;amp;nbsp;&amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;sent back to&amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;education model to calculate final enrollments and cost per student as a result of fund shortage or surplus.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;The population module provides cohort population to the education model. The [[Economics#Dominant_Relations:_Economics|economic model]] provides&amp;amp;nbsp;&amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;per capita income and the socio-political model provides budget allocation. Educational attainment of adults calculated by the education module affects [[Population#Fertility_Detail|fertility]] and [[Population#Mortality_Detail|mortality]] in the [[Population#Structure_and_Agent_System:_Demographic|population]] and&amp;lt;/span&amp;gt;&amp;amp;nbsp;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;[[Health#Structure_and_Agent_System:_Health|health]] modules, affects productivity in the economic module and affects other socio-political outcomes like [[Governance#Inclusiveness|governance and democracy]] levels&amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Equations: Student Flow&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== Econometric Models for Core Inflow and Outflow ===&lt;br /&gt;
&lt;br /&gt;
Enrollments at various levels of education - EDPRIENRN, EPRIENRG, EDSECLOWENRG, EDSECUPPRENRG, EDTERENRG - are initialized with historical data for the beginning year of the model. Net change in enrollment at each time step is [[Education#Education_Student_Flow|determined by inflows]] (intake or transition) and outflows (dropout or completion). Entrance to the school system (EDPRIINT, EDTERINT), transition from the lower level (EDSECLOWRTRAN, EDSECUPPRTRAN) - and outflows - completion (EDPRICR), dropout or it&#039;s reciprocal, survival (EDPRISUR) - are some of these rates that are forecast by the model.&lt;br /&gt;
&lt;br /&gt;
The educational flow rates are best explained by per capita income that serves as a proxy for the families&#039; opportunity cost of sending children to school. For each of these rates, separate regression equations for boys and girls are estimated from historical data for the most recent year. These regression equations, which are updated with most recent data as the model is rebased with new data every five years, are usually logarithmic in form. The following figure shows such a regression plot for net intake rate in elementary against per capita income in PPP dollars.&lt;br /&gt;
&lt;br /&gt;
In each of the forecast years, values of the educational flow rates [[File:EdcrosssectionalGDP.png|frame|right|Example of an econometric models for core inflow and outflow]]are first determined from these regression equations. Independent variables used in the regression equations are endogenous to the IFS model. For example, per capita income, GDPPCP, forecast by the IFs&amp;amp;nbsp;[[Economics#Dominant_Relations:_Economics|economic model]]&amp;amp;nbsp;drives many of the educational flow rates. The following equation shows the calculation of one such student flow rate (CalEdPriInt) from the log model of net primary intake rate shown in the earlier figure.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CalEdPriInt_{g=1,r,t}=77.347+9.6372lnGDPPCP_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
While all countries are expected to follow the regression curve in the long run, the residuals in the base year make it difficult to generate a smooth path with a continuous transition from historical data to regression estimation. We handle this by adjusting regression forecast for country differences using an algorithm that we call &amp;quot;shift factor&amp;quot; algorithm. In the first year of the model run we calculate a shift factor (EDPriIntNShift) as the difference (or ratio) between historical data on net primary intake rate (EDPRIINTN) and regression prediction for the first year for all countries. As the model runs in subsequent years, these shift factors (or initial ratios) converge to zero or one if it is a ratio (code routine ConvergeOverTime in the equation below) making the country forecast merge with the global function gradually. The period of convergence for the shift factor (PriIntN_Shift_Time) is determined through trial and error in each case.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EdPriIntNShift_{g,r,t=1}=EDPRIINTN_{g,r,t=1}-CalEdPriInt_{g,r,t=1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDPRIINTN_{g,r,t}=CalEdPriInt_{g,r,t}+ConvergeOverTime(EdPriIntNShift_{g,r,t=1},0,PriIntNShiftTime)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The base forecast on flow rates resulting from of this regression model with country shift is used to calculate the demand for funds. These base flow rates might change as a result of budget impact based on the availability or shortage of education budget explained in the [[Education#Equations:_Budget_Flow|budget flow section]].&lt;br /&gt;
&lt;br /&gt;
=== Systemic Shift ===&lt;br /&gt;
&lt;br /&gt;
Access and participation in education increases with socio-economic developments that bring changes to people&#039;s perception about the value of education. This upward shifts are clearly visible in cross-sectional regression done over two adequately apart points in time. The next figure illustrates such shift by plotting net intake rate for boys at the elementary level against GDP per capita (PPP dollars) for two points in time, 1992 and 2000.[[File:EdGDPnetintake.png|frame|right|Net intake rate for boys at the elementary level against GDP per capita (PPP dollars)]]&lt;br /&gt;
&lt;br /&gt;
IFs education model introduces an algorithm to represent this shift in the regression functions. This &amp;quot;systemic shift&amp;quot; algorithm starts with two regression functions about 10 to 15 years apart. An additive factor to the flow rate is estimated each year by calculating the flow rate (CalEdPriInt1 and CalEdPriInt2 in the equations below) progress required to shift from one function, e.g., &amp;amp;nbsp;&amp;amp;nbsp;to the other, s, &amp;amp;nbsp;in a certain number of years (SS_Denom), as shown below. This systemic shift factor (CalEdPriIntFac) is then added to the flow rate (EDPRIINTN in this case) for a particular year (t) calculated from regression and country shift as described in the previous section.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CalEdPriInt1_{g,r,t}=f_1(GDPPCP_{g,r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CalEdPriInt2_{g,r,t}=f_2(GDPPCP_{g,r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CalEdPriIntFac_{g,r,t}=\frac{t-1}{SSDenom}*(CalEdPriInt2_{g,r,t}-CalEdPriInt1_{g,r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDPRIINTN_{g,r,t}=EDPRIINTN_{g,r,t}+CalEdPriIntFac_{g,r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
As said earlier, [[Education#Education_Student_Flow|Student flow]] rates are expressed as a percentage of underlying stocks like the number of school age children or number of pupils at a certain grade level. The flow-rate dynamics work in conjunction with population dynamics (modeled inside IFs [[Population#Structure_and_Agent_System:_Demographic|population module]]) to forecast enrollment totals.&lt;br /&gt;
&lt;br /&gt;
=== Grade Flow Algorithm ===&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
Once the core inflow (intake or transition) and outflow (survival or completion) are determined, enrollment is calculated from grade-flows. Our grade-by-grade student flow model therefore uses some simplifying assumptions in its calculations and forecasts. We combine the effects of grade-specific dropout, repetition and reentry into an average cohort-specific grade-to-grade dropout rate, calculated from the survival rate (EDPRISUR for primary) of the entering cohort over the entire duration of the level (&#039;&#039;&#039;EDPRILEN&amp;amp;nbsp;&#039;&#039;&#039;for primary). Each year the number of new entrants is determined by the forecasts of the intake rate (EDPRIINT) and the entrance age population. In successive years, these entrants are moved to the next higher grades, one grade each year, subtracting the grade-to-grade dropout rate (DropoutRate). The simulated grade-wise enrollments (GradeStudents with Gcount as a subscript for grade level) are then used to determine the total enrollment at the particular level of education (EDPRIENRG for Primary).&lt;br /&gt;
&lt;br /&gt;
There are some obvious limitations of this simplified approach. While our model effectively includes repeaters, we represent them implicitly (by including them in our grade progression) rather than representing them explicitly as a separate category.&amp;amp;nbsp; Moreover, by setting first grade enrollments to school entrants, we exclude repeating students from the first grade total.&amp;amp;nbsp; On the other hand, the assumption of the same grade-to-grade flow rate across all grades might somewhat over-state enrollment in a typical low-education country, where first grade drop-out rates are typically higher than the drop-out rates in subsequent grades.&amp;amp;nbsp; Since our objective is to forecast enrollment, attainment and associated costs by level rather than by grade, however, we do not lose much information by accounting for the approximate number of school places occupied by the cohorts as they proceed and focusing on accurate representation of total enrollment.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;DropoutRate_{g,r,t}=1-(\frac{EDPRISUR_{g,r,t}}{100})^{\frac{1}{\mathbf{EDPRILEN}_r-1}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GradeStudents_{GCount=1,g,r,t}=EDPRIINT_{g,r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GradeStudents_{Gcount,g,r,t}=GradeStudents_{Gcount-1,g,r,t}*(1-DropoutRate_{g,r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDPRIENRG_{g,r,t}=\sum^\mathbf{EDPRILEN}_{Gcount=1}GradeStudents_{Gcount,g,r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&amp;lt;/div&amp;gt;&lt;br /&gt;
=== Gross and Net ===&lt;br /&gt;
&lt;br /&gt;
Countries with a low rate of schooling, especially those that are catching up, usually have a large number of over-age students. Enrollment and entrance rates that count students of all ages are called gross rates in contrast to the net rate that only takes the of-age students in the numerator of the rate calculation expression. UNESCO report net and gross rates separately for entrance and participation in elementary. IFs education model forecasts both net and gross rate in primary education. An overage pool (PoolPrimary) is estimated at the model base year using net and gross intake rate data. Of-age non-entrants continue to add to the pool (PoolInflow). The pool is exhausted using a rate (PcntBack) determined by the gross and net intake rate differential at the base year. The over-age entrants (cOverAgeIntk_Pri) gleaned from the pool are added to the net intake rate (EDPRIINTN) to calculate the gross intake rate (EDPRIINT).&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;PoolPrimary_{r,g,t=1}=f(EDPRIINTN_{g,r,t=1},EDPRIINT_{g,r,t=1})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;PcntBack_{r,g}=f(PoolPrimary_{r,g,t=1},EDPRIINTN_{g,r,t=1},EDPRIINT_{g,r,t=1})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;PoolInflow=f(EDPRIINTN_{g,r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;cOverAgeIntkPri=f(EDPRIINTN_{g,r,t},PoolPrimary_{g,r,t},PcntBack_{r,g})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;PoolPrimary_{r,g,t}=PoolPrimary_{r,g,t-1}+PoolInflow-cOverAgeIntkPri&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDPRIINT_{g,r,t}=EDPRIINTN_{g,r,t}+cOverAgeIntkPri&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
=== Vocational Education ===&lt;br /&gt;
&lt;br /&gt;
IFs education model forecasts vocational education at lower and upper secondary levels. The variables of interest are vocational shares of total enrollment in lower secondary (EDSECLOWRVOC) and the same in upper secondary (EDSECUPPRVOC). Country specific vocational participation data collected from UNESCO Institute for Statistics do not show any common trend in provision or attainment of vocational education across the world. International Futures model initialize vocational shares with UNESCO data, assumes the shares to be zero when no data is available and projects the shares to be constant over the entire forecasting horizon.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
IFs also provides two scenario intervention parameters for lower (&#039;&#039;edseclowrvocadd) &#039;&#039;and upper secondary (&#039;&#039;edsecupprvocadd&#039;&#039;) vocational shares. These parameters are additive with a model base case value of zero. They can be set to negative or positive values to raise or lower the percentage share of vocational in total enrollment. Changed vocational shares are bound to an upper limit of seventy percent. This upper bound is deduced from the upper secondary vocational share in Germany, which at about 67% is the largest among all vocational shares for which we have data. Changes to the vocational share through the additive parameters will also result in changes in the total enrollment, e.g., EDSECLOWRTOT for lower secondary, which is calculated using general (non-vocational) enrollment (EdSecTot_Gen) and vocational share, as shown in the equations below (for lower secondary).&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDSECLOWRVOCI_{g,r}=EDSECLOWRVOC_{g,r,t=1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDSECLOWRVOC_{g,r,t}=EDSECLOWRVOCI_{g,r}+edseclowrvocadd_{g,r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDSECLOWRTOT_{g,r,t}=\frac{EdSecTotGen_{g,r,t}}{1-\frac{EDSECLOWRVOC_{g,r,t}}{100}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Forecasts of &#039;&#039;EdSecTot_Gen&amp;lt;sub&amp;gt;g,r,t&amp;lt;/sub&amp;gt; &#039;&#039;&amp;amp;nbsp;is obtained in the full lower secondary model using transition rates from primary to lower secondary and survival rates of lower secondary.&lt;br /&gt;
&lt;br /&gt;
=== Science and Engineering Graduates in Tertiary ===&lt;br /&gt;
&lt;br /&gt;
Strength of STEM (Science, Technology, Engineering and Mathematics) programs is an important indicator of a country’s technological innovation capacities. IFs education model forecasts the share of science and engineering degrees (EDTERGRSCIEN) among all tertiary graduates in a country. Data for this variable is available through UNESCO Institute for Statistics. The forecast is based on a regression of science and engineering share on average per person income in constant international dollar (GDPPCP). There is an additive parameter (&#039;&#039;edterscienshradd&#039;&#039;), with a base case value of zero, that can be used to add to (or subtract from) the percentage share of science and engineering among tertiary graduates. This parameter does not have any effect on the total number of tertiary graduates (EDTERGRADS).&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDTERGRSCIEN_{r,t}=f(GDPPCP_{r,t})+edterscienshradd_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Equations: Budget Flow&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Resources required to maintain the projected student flows are determined by multiplying enrollment rates with per student cost forecasts. Availability of resources, as determined in the IFs socio-political model, affect flow rates and the final enrollment rate.&lt;br /&gt;
&lt;br /&gt;
Public expenditure per student (EDEXPERPRI) as a percentage of per capita income is first estimated (CalExpPerStud) using a regression equation. Country situations are added as a shift factor (EdExPerPriShift) that wears off over a period of time (&#039;&#039;&#039;edexppconv&#039;&#039;&#039;) in the same manner as those for student flow rates. The following group of equations show the calculation of per student expenditure in primary (EDEXPERPRI).&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CalExpPerStud_{r,t}=f(GDPPCP_{r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EdExpPerPriShift_{r,t=1}=EDEXPERPRI_{r,t=1}-CalExpPerStud_{r,t=1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDEXPERPRI_{r,t}=CalExpPerStud_{r,t=1}+ConvergeOverTime(EdExpPerPriShift_{r,t=1},0,\mathbf{edexppconv})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Total fund demand (EDBUDDEM, see calculation below) is passed to the IFs socio-political model where a detail government budget model distributes total government consumption among various public expenditure sectors. For education allocation, an initial estimate (gkcomp) is first made from a regression function of educational spending as a percentage of GDP over GDP per capita at PPP dollars (GDPPCP) as a country gets richer.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;gkcomp_{r,Educ,t}=f(GDPPCP_{r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Like several other functions discussed in this sub-module, country situation is reflected by estimating country ratio (gkri) between the predicted and historical value in the base year. This ratio converges to a value of one very slowly essentially maintaining the historic ratio. Public spending on education in billion dollars (GDS) is then calculated using the regression result, GDP and the multiplicative shift.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;gkri_{r,Educ}=GDS_{r,Educ,t=1}/GDP_{r,t=1}/gkcomp_{r,Educ,t=1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;gkshift_{r,t,Educ}=ConvergeOverTime(gkri_{r,Educ}, 200,1)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GDS_{r,Educ,t}=gkcomp_{r,Educ,t}*gkshift_{r,t,Educ}*GDP_{r,t}/100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
[[Socio-Political#Policy_Equations:_Government_Expenditures|Sociopolitical model]]&amp;amp;nbsp;also forecast public spending in other areas of social spending, i.e., military, health, R&amp;amp;D. Another public spending sector, [[Infrastructure#Determining_the_Actual_Funds_for_Infrastructure_Spending|infrastructure]]&amp;amp;nbsp;is calculated bottom-up, i.e., as an aggregation of demand for construction and maintenance of various types of infrastructure.&lt;br /&gt;
&lt;br /&gt;
Once all the spending shares are projected, a normalization algorithm is used to distribute the total available government consumption budget (GOVCON) among all sectors.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GTOT=\sum^{NGovExp}_{s=1}GDS_{r,s}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GDS_{r,s}=\frac{GDS_{r,s}}{GTOT}*GOVCON_{r,s}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Before normalization, a priority parameter allows setting aside all or part of fund demands for the ground up spending sectors, i.e., infrastructure and education. For education sector, the prioritization parameter (&#039;&#039;&#039;edbudgon&#039;&#039;&#039;) is used to set aside a certain portion of the projected education investment as shown in the equations below.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GDSSetAside=GDS_{r,Educ}*(1-\mathbf{edbudgon})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GDS_{r,Educ}=GDS_{r,Educ}-GDSSetAside&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Education allocation, GDS (Educ) calculated thus is taken back to the education model. A second normalization and prioritization is done within the education model to distribute total education allocation among different levels of education. This across level normalization uses the percentage share of each educational level in the total demand for education funding. First, total expenditure demand for all levels of education combined is determined by multiplying the total enrollments with per student costs. The following equation shows the calculation for Primary.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;BudDemPri_{r,t}=UDEDExpPerPri_{r,t}*GDPPCP_{r,t}*\sum^2_{g=1}UDEnrollCT_{r,t}/100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Fund demands for all levels are added up to get the total fund demand under no budget constraint. The prefixes UD here stands for budget unconstrained demand.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDBudDem_{r,t}=BudDemPri_{r,t}+BudDemSecLowr_{r,t}+BudDemSecUppr_{r,t}+BudDemTer_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Any surplus or deficit in educational allocation, calculated as the difference between education sector allocation in the government budget model and the total fund requirement for all levels of education combined, first undergoes an adjustment algorithm that boosts (in case of surplus) or reduces (in case of deficit) per student cost for those countries which are below or above the level they are supposed to be. Post this adjustment, allocation is distributed across all levels using a normalization process based on demand.&amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
A budget impact ratio &amp;amp;nbsp;is then calculated as the ratio of the fund demanded (CalcTotCost) and fund obtained (CalcTotSpend). This budget impact ratio (CalcBudgetImpact) &amp;amp;nbsp;increases or decreases the pre-budget (or demand side as we call it) projection of [[Education#Equations:_Student_Flow|student flow rates]] (intake, survival, and transition). The positive (upward) side of the budget impact is non-linear with the maximum boost to growth occurring when a flow rate is at or near its mid-point or within the range of the inflection points of an assumed S-shaped path, to be precise. Impact of deficit is more or less linear except at impact ratios close to 1, whence the downward impact is dampened. Final student flow rates are used to calculate final enrollment numbers using population forecasts for relevant age cohorts. Finally, cost per students are adjusted to reflect final enrollments and fund availability.&lt;br /&gt;
&lt;br /&gt;
Budget impacts uses a non-linear algorithm intended to generate an S-shaped growth rate. Final enrollment is then calculated from this final flow rates and any of the remaining budget is used to increase per student expenditure.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CalcTotCost=(EDEXPERPRI_r/100)*GDPPC_r*convtoexchange*\sum^2_{g=1}EDPRITOT_{r,g}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CalcTotSpend=GDS_{r,Educ}*GDSED_{r,Pri}/SpendCostRI&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
In the equations above, convtoexchange is a factor that converts monetary units from PPP to exchange rate dollars, SpendCostRI is a ratio calculated at the first year of the model to reconcile historical data on aggregate and bottom-up spending.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CalcBudgetImpact=CalcTotSpend/CalcTotCost&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDPRIINTN_{g,r,t}=f(EDPRIINTN_{g,r,t},CalcBudgetImpact)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDPRISUR_{g,r,t}=f(EDPRISUR_{g,r,t},CalcBudgetImpact)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== Equations: Attainment ==&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;There are two types of variables that keep track of educational attainment: average years of education of adults (EDYRSAG15, EDYRSAG15TO24 and EDYRSAG25) and percentage of adults with a certain level of education (EDPRIPER, EDSECPER, EDTERPER). Both groups forecast attainment by gender.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;The basis of calculation for both groups of variables is educational attainment by age cohort and gender as contained in intermediate model variables, EDPriPopPer &amp;lt;sub&amp;gt;r.g,c,t&amp;lt;/sub&amp;gt; ,&amp;amp;nbsp; EDSecPopPer&amp;lt;sub&amp;gt;r.g,c,t&amp;lt;/sub&amp;gt;, EdTerPopPer&amp;lt;sub&amp;gt;r.g,c,t&amp;lt;/sub&amp;gt; (where, r stands for country or region, g for gender, c for cohort and t for time).&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;We initialize attainments of the entire adult population (EDPRIPER, EDSECPER, EDTERPER) using historical data estimated by Barro and Lee (2000) and use a spread algorithm. The spread algorithm starts with the most recent data on school completion rate (EDPRICR for primary) which is considered as the average attainment of the graduating cohort. The algorithm then uses the differential between that completion rate and the attainment rate of the adults (EDPRIPER) to back calculates a delta reduction for each of the older cohorts (EdPriPopPer) such that averaging attainments over cohorts one can obtain average attainment for all adults (EDPRIPER).&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span&amp;gt;&amp;lt;math&amp;gt;EDPriPopPer_{c,g,r,t=1}=f(EDPRIPER_{r,g,t=1},EDPRICR_{r,g,t=1})&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;where, subscript c stand for five year age cohorts going from 1 to 21. Cohort 4, represents the 15 to 19 years and NC, total number of age cohorts.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;For subsequent forecast years, cohort educational attainment for each level of education is calculated by adding graduates from that level of education to the appropriate age cohort, advancing graduates from the younger cohort, and passing graduates to the older cohort.&amp;amp;nbsp;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span&amp;gt;&amp;lt;math&amp;gt;EDPriPopPer_{c=pc,g,r,t}=0.8*EDPriPopPer_{c=pc,g,r,t-1}+0.2*EDPRICR_{g,r,t}&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;where, pc stands for the five year age cohort where the primary graduates belong. For all other cohorts:&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span&amp;gt;&amp;lt;math&amp;gt;EdPriPopPer_{c,g,r,t}=0.8*EdPriPopPer_{c,g,r,t-1}+0.2*EdPriPopPer_{c-1,g,r,t-1}&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;Cohort attainments for secondary and tertiary education (EDSECPOPPER, EDTERPOPPER) are initialized and forecast in a similar fashion. An average years of education reflecting completion of levels is then calculated by from the cohort attainment, population and cohort length as shown in the next equation where&amp;amp;nbsp; &amp;amp;nbsp;AGEDST&amp;lt;sub&amp;gt;c,g,r,t&amp;lt;/sub&amp;gt; contains the population of five year age cohorts and &#039;&#039;&#039;EDPRILEN&#039;&#039;&#039; &amp;lt;sub&amp;gt;r,t&amp;lt;/sub&amp;gt; &amp;lt;/span&amp;gt; &amp;amp;nbsp;&amp;lt;span&amp;gt;&amp;amp;nbsp;is the duration of primary cycle in years.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span&amp;gt;&amp;lt;math&amp;gt;AvgYearsPriEdPop_{g,r,t}=\frac{\sum^{NCohorts}_{c=pc}\frac{EDPriPopPer_{c,g,r,t}}{100}*EDPRILEN_r*AGEDST_{c,g,r,t}}{\sum^{NCohorts}_{c=pc}AGEDST_{c,g,r,t}}&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;For those who dropout before completing a certain level we need to calculate the partial attainment and add that to the average years of education. The average of the partial years of education at a particular year is calculated from dropouts by level and grade as shown below. Calculation of the average of partial years resulting from dropouts in primary education is illustrated in the equations below. Partial years from current year dropouts at other levels of education are calculated in the same manner and all the partial years are averaged to an overall average. This new partial attainment is then added to the partial attainment of five year cohorts which are initialized and advanced in a similar manner as that used for cohort averages on completed attainment.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span&amp;gt;&amp;lt;math&amp;gt;DropoutRate_{g,r,t}=f(EDPRISUR_{g,r,t},\mathbf{EDPRILEN}_r)&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span&amp;gt;&amp;lt;math&amp;gt;GrStudents_{GCount,g,r,t}=f(EDPRIINT_{g,r,t},DropoutRate_{g,r,t},\mathbf{EDPRILEN}_r)&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span&amp;gt;&amp;lt;math&amp;gt;PartialPriPersYearsNew_{g,r,t}=\frac{(\sum^{EDPRILEN_r}_{GCount=2}GrStudents_{GCount,g,r,t}*DropoutRate_{g,r,t}*(GCount-1))*\mathbf{\sum^{EDPRILEN}_{c=EDPRISTART}}FAGEDST_{g,r,t}}{\mathbf{\sum^{EDPRILEN}_{c=EDPRISTART}}FAGEDST_{g,r,t}}&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;Here, &amp;amp;nbsp;EDPRISUR is the survival rate in primary education, EDPRISTART is the official entrance age for primary schooling, Gr_Students is the enrollment at a certain grade, GCount is the grade counter and FAGEDST is the population of the single year age cohort corresponding to the grade level.&amp;amp;nbsp;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;Overall attainment, i.e., average years of education are calculated by averaging the attainments and partial attainments of five year age cohorts as shown in the equation below. The suffixes on the variables EDYRSAG15, EDYRSAG15TO24 and EDYRSAG25 indicate the age thresholds at which or the age bracket over which attainment is averaged.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span&amp;gt;&amp;lt;math&amp;gt;EDYRSAG15_{g,r,t}=AvgYearsPriEdPop_{g,r,t}+AvgYearsSecEdPop_{g,r,t}+AvgYearsTerEdPop_{g,r,t}+PartialYearsEdPop_{g,r,t}&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;Attainments by level, i.e., EDPRIPER, EDSECPER and EDTERPER are also obtained by summing across the corresponding five year cohorts, i.e., EdPriPopPer etc.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span&amp;gt;&amp;lt;math&amp;gt;EDPRIPER_{g,r,t}=\frac{\sum^{NCohorts}_{c=4}EdPriPopPer_{c,g,r,t}*AGEDST_{c,g,r,t}}{\sum^{NCohorts}_{c=4}AGEDST_{c,g,r,t}}&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;Cohort attainments by level of education are also used in to build a specialized educational attainment display, commonly referred to as education pyramid in congruence with demographic pyramids used to display population by age cohorts stacked one on top of the other with the men and women cohorts put opposite to each other around a vertical axis. Education pyramid superimposes educational attainment on top of the demographic pyramid.&amp;amp;nbsp;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Knowledge Systems&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== Overview ===&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;Knowledge and innovation are important drivers of &amp;amp;nbsp;economic growth and human well-being. These activities also &amp;amp;nbsp;help societies address major social and environmental challenges. Education and research and a linear relationship between these and product development are no longer considered a good model of knowledge and innovation systems. However, the linear model was the first successful attempt (Bush, V, 1945) in conceptualizing the science, technology and innovation (STI) activities. One of the major contributions of these first models was the distinction between basic and applied researches and the identification of stakeholders and funding for each type as shown in the next figure.&amp;lt;/span&amp;gt;[[File:Edknowledge1.png|frame|right|Linear model of STI activities]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;The failure of the linear model to capture the intricacies and interactions involved in the innovation process and the broader role of the public and private institutions and individuals in facilitating creation and diffusion of knowledge prompted some experts to resort to rich qualitative description of so called “national systems of innovation” starting from late 1980s, early 1990s. Increased educational attainment, fast expansion of information and communication technologies, more sophisticated production technologies and an expansion in the exchange of goods, ideas and people over the last few decades tell of something broader than just innovation constrained within national boundaries. Recent literature (citation) use concepts like knowledge economy or knowledge society to describe the systemic nature and impact of knowledge-intensive activities.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;This new literature takes an evolutionary perspective and talks about a gradual unfolding of knowledge and innovation system (citation: Nelson, Freeman etc) within a country marked by a certain types of actors, institutions and organizations and the linkages across and within such components. Studies in this area range from more focused concepts of knowledge economy (citation: WB; OECD) to a broader knowledge society (citation: UNESCO; Bell), from a more qualitative innovation systems approach (citation: Nelson; Freeman) to a measurement focused innovation capacity approach (citation: GII Dutta, Archibucchi..). The complementarity of the components of such a system demands that the components be studied together. Accordingly, experts have come up with composite indices for assessing the knowledge and innovation capacities of countries around the world. Such indices give a good idea of the overall status of the innovation capacities of the country and the stage of knowledge society it is in. The components of the composite indices are categorized across four to five major dimensions (or, pillars, as some studies call these), for example, education and skills, information infrastructure, institutional regime, innovation activities (WB Knowledge Index etc).&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;International Futures (IFs) Knowledge module builds on other knowledge systems measurement approaches (cite WB KEI here) by designing a composite knowledge index (KNTOTALINDEX) comprised of five sub-indices containing a total of (x) components. The indices and the sub-indices are then forecast over the entire IFs’ horizon by combining the components which are themselves forecast through different modules of the integrated IFs model. To our knowledge, IFs is the only model capable of making such an organic forecast of the knowledge capacity of a country.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;IFs Knowledge Indices&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;The capacity of a society to tap from and add to the pool of existing knowledge, local and global, depends on&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
*skills and qualifications of people to assimilate existing and new knowledge,&lt;br /&gt;
*an innovation system to facilitate development or adoption of of new knowledge, processes and products&lt;br /&gt;
*a technological infrastructure to share, disseminate and regenerate knowledge and information within and across societies&lt;br /&gt;
*political and institutional environment conducive to the generation, diffusion and utilization of knowledge&lt;br /&gt;
*regulations that offer appropriate incentives towards and remove barriers from international transfer of knowledge&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;The above list of the driving dimensions of a knowledge system is exhaustive, to the best of our knowledge. The list has five dimensions contrasted to the four pillars identified by the WB KAM. However, World Bank includes tariff &amp;amp; non-tariff barriers, an indicator of international transfer, in their fourth pillar on economic and institutional environment.&amp;amp;nbsp;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;IFs now has five indices representing the five dimensions described above. The details of each of these indices, and a sixth one averaged from these five, will be described later. Suffice here to say that, the indices are calculated each of the forecast years by averaging the forecasted value of relevant IFs variables, normalized over a continuous interval going from 0 to 1. That is, IFs integrated simulation, first, forecasts a specific variable, e.g., adult literacy rate, it then converts the forecast to a normalized value lying between zero to one and then averages one or more of these normalized values to obtain an index along each of the dimensions of knowledge assessment. The table below compares IFs knowledge indices with those from World Bank.&amp;amp;nbsp;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%; border: 1px solid #cccccc&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | &#039;&#039;&#039;No.&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | &#039;&#039;&#039;Dimension/Pillar&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | &#039;&#039;&#039;World Bank Variables&#039;&#039;&#039;&amp;lt;br/&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | &#039;&#039;&#039;IFs Index&#039;&#039;&#039;&amp;lt;br/&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | &#039;&#039;&#039;IFs Variables&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | 1&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Human Capital&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Adult literacy rate; Secondary enrollment rate; Tertiary enrollment rate&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | KNHCINDEX&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Adult literacy rate; Adult secondary graduation rate&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | 2&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Innovation&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | R&amp;amp;D researchers, Patent count; Journal articles (all per million people)&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | KNINNOVINDEX&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Total R&amp;amp;D expenditure (% of GDP); Tertiary graduation rate in science and engineering&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | 3&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | ICT&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Telephones (land + mobile) per 1000 persons; Computers per 1000 persons; Internet users per 10000 persons&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | KNICTINDEX&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Telephone (fixed); Mobile phone; Personal Computers; Broadband&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | 4&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Economic and Institutional Regime&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; rowspan=&amp;quot;2&amp;quot; | Tariff and non-tariff barriers; Regulatory quality; Rule of law&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | KNENVINDEX&amp;lt;br/&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Freedom; Economic freedom; Government regulation quality&amp;lt;br/&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | 5&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | International Transfer of Knowledge&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | KNEXTINDEX&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Economic integration index&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | 6&amp;lt;br/&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Composite Index&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Knowledge Index, KI (from the first three) and Knowledge Economy Index, KEI (from all 4)&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | KNTOTALINDEX&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | &lt;br /&gt;
From all of the above&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
[[File:Edknowledge2.png|frame|center|IFs Knowledge Model]]&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Knowledge Systems Equations: Total Knowledge Index&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;The composite index (KNTOTALINDEX) consists of five sub-indices, of which the first four contains national actors and institutions only. The fifth one, international transfer index (KNEXTINDEX), attempts to capture the impact of global knowledge flows through a measure of the country’s openness to the international system. The first four sub-indices - human capital (KNHCINDEX), information infrastructure (KNICTINDEX), innovation systems (KNINNOVINDEX) and governance and business environment (KNENVINDEX) – will be described below. The external index (KNEXTINDEX) is given a somewhat lower weight in the total index than the other four sub-indices which are equally weighted to a total of 90% of the total index. KNEXTINDEX itself is constructed from two equally weighted components of international trade and foreign direct investment.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;KNTOTALINDEX_{r,t}=0.9*\frac{(KNHCINDEX_{r,t}+KNICTINDEX_{r,t}+KNINNOVINDEX_{r,t}+KNENVINDEX_{r,t})}{4}+0.1*(KNEXTINDEX_{r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Knowledge Systems Equations: Knowledge Sub-Indices&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
In this section we describe the calculation method for various IFs knowledge indices.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
=== Human capital Index: KNHCINDEX ===&lt;br /&gt;
&lt;br /&gt;
The purpose of this index is to capture the cross-country differences in the productive capacity of an average worker. We use two educational stock variables for the purpose. Differences in the rate of literacy, the sheer ability to read or write, make a big difference in productivity in more traditional type and/or informal activities. As the countries move gradually a more traditional agricultural economy to comparatively higher value added activities, e.g., assembling machineries or running a call center, secondary education become more important. The index is built through a combination of two sub-indices: literacy index, LitIndex and secondary attainment index, AdultSecPerIndex, weighted equally.&lt;br /&gt;
&lt;br /&gt;
This index could be improved by adding a measure of the quality of education and an indicator of the skill-base of the worker. Unfortunately, IFs forecasts on those two areas are limited or non-existent at this point. [Note: The sub-indices – LitIndex and AdultSecPerIndex – used for this and other knowledge indices are calculated only in the model code. They are not available for display.]&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;KNHCINDEX_{r,t}=(LitIndex_{r,t}+AdultSecPerIndex_{r,t})/2&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Literacy index, with a theoretical range of values from 0 to 1, is calculated by dividing literacy rate, LIT, which can range from 0 to 100, by 100.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;LitIndex_{r,t}=LIT_{r,t}/100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
For the sub-index on secondary attainment (percentage of adults with completed secondary education), we use a similar normalization algorithm like the literacy sub-index.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;AdultSecPerIndex_{r,t}=EDSECPER_{r,total,t}/100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
LIT and EDSECPER are forecast in the IFs [[Population|population]] and [[Education#Education|education]] modules.&lt;br /&gt;
&lt;br /&gt;
Because it excludes any measure of higher education which is included in the innovation sub-index (KNINNOVINDEX) described below, KNHCINDEX turns out to be very useful in showing the differences across developing countries. Even for richer countries, most of which achieved near universal secondary enrollment and universal literacy, the index shows significant variance coming from the secondary attainment differences among the elderly.&lt;br /&gt;
&lt;br /&gt;
[[File:Edknowledge3.png|frame|center|KNINNOVINDEX]]&lt;br /&gt;
&lt;br /&gt;
=== Innovation Index: KNINNOVINDEX ===&lt;br /&gt;
&lt;br /&gt;
This IFs knowledge sub-index measures the innovation capacity of a nation through its R&amp;amp;D inputs – resources and personnel. It comprises of a total R&amp;amp;D expenditure index and a tertiary science and engineering graduation index as shown in the equations below.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;KNINNOVINDEX_{r,t}=(RandDExpIndex_{r,t}+EdTerGrateIndex_{r,t})/2&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
For R&amp;amp;D expenditure, the highest spenders like Israel and Finland, spend close to or little over 4% of GDP and we use that number as a maximum to normalize all other countries in a zero to one range.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;RandDExpIndex_{r,t}=RANDDEXP_{r,t}/4&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
For science and engineering graduation rate, 25% is used as a maximum. The equations below show the calculation which uses tertiary graduation percentage, EDTERGRATE &amp;lt;sub&amp;gt;Total&amp;lt;/sub&amp;gt; and the share of total graduates that obtain a science or engineering degree, EDTERGRSCIEN, both of which are forecast in the IFs education model.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EdTerGrateIndex_{r,t}=EDTERGRATE_{r,total,t}*\frac{EEDTERGRSCIEN_{r,t}}{100}/25&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
=== ICT Index: KNICTINDEX ===&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;Information and communication technologies (ICT) have a very significant role in facilitating the creation and diffusion of knowledge. IFs knowledge sub-index on ICT is built from the diffusion rates of core ICT technologies mobile, landline, broadband and a personal computer access rate sub-index. The telephone lines (fixed lines) sub-index, unlike the other three, use the logarithm of telephone line access rates as the differences in impacts of plain old telephone system decreases at higher access rates. In fact, the gradual shift from a wired to a wireless line as a personal communication device, demands that we reconsider the inclusion of this component in the ICT index.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;KNICTINDEX_{r,t}=(ICTTelephoneIndex_{r,t}+ICTMobileIndex_{r,t}+ICTBroadIndex_{r,t}+ICTComputersIndex_{r,t})/4&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;ICTTelephoneIndex_{r,t}=log(INFRATELE_{r,t})/3&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;ICTMobileIndex_{r,t}=ICTMOBIL_{r,t}/100&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;ICTBroadIndex_{r,t}=ICTBROAD_{r,t}/100&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;ICTComputersIndex_{r,t}=ICTCOMPUTERS_{r,t}/100&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;Governance and Regulatory Environment: KNENVINDEX&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;The existence of economic and regulatory institutions and an effective governance of such institutions are important for generation, diffusion and utilization of knowledge. IFs knowledge sub-index representing these, KNENVINDEX, is calculated from three sub-indices which are themselves indices forecast by other IFs modules. These indices, one for economic freedom, a second one for overall freedom in the society and a third one on governance regulatory quality are each normalized to a 0 to 1 scale and averaged to get KNENVINDEX.&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;For the variables economic freedom, political freedom and governance regulation quality and average them to KNENVINDEX.&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;KNENVINDEX_{r,t}=(EconFreeIndex_{r,t}+FreeDomIndex_{r,t}+GovRegQualIndex_{r,t})/3&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;EconFreeIndex_{r,t}=ECONFREE_{r,t}/10&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;FreeDomIndex_{r,t}=FREEDOM_{r,t}/14&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;GovRegQualIndex_{r,t}=GOVREGQUAL_{r,t}/5&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;International Transfer Index: KNEXTINDEX&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;KNEXTINDEX attempts to represent cross-national knowledge flows, a major phenomenon in today’s globalized world. The more open a country is the more likely it is for her to learn from the global advancements in science, technology and other forms of knowledge. The sub-index that IFs calculates uses two indicators, trade and foreign direct investment (FDI). FDI indicator is given twice the weight given to trade volume.&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;KNEXTINDEX_{r,t}=(TradeIndex_{r,t}+2*InvIndex_{r,t})/2&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;TradeIndex_{r,t}=log\frac{XRPA_{r,t}+MRPA_{r,t}}{GDPPOT_{r,t}}/log1000&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;InvIndex_{r,t}=(log(\frac{XFDISTOCK_{r,t}+XFDISTOUT_{r,t}}{GDPPOT_{r,t}}))/log(500)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Education Bibliography&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
Archibugi, Daniele, and Alberto Coco. 2005. “Measuring Technological Capabilities at the Country Level: A Survey and a Menu for Choice.” Research Policy 34(2). Research Policy: 175–194.&lt;br /&gt;
&lt;br /&gt;
Bush, Vannevar. 1945. Science: The Endless Frontier. Washington: United States Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert and Jong-Wha Lee. 2010. &amp;quot;A New Data Set of Educational Attainment in the World, 1950-2010.&amp;quot; NBER Working Paper No. 15902. National Bureau of Economic Research, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert and Jong-Wha Lee. 2000. “International Data on Educational Attainment: Updates and Implications.” NBER Working Paper No. 7911. National Bureau of Economic Research, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
Bruns, Barbara, Alain Mingat, and Ramahatra Rakotomalala. 2003. Achieving Universal Primary Education by 2015: A Chance for Every Child. Washington, DC: World Bank.&lt;br /&gt;
&lt;br /&gt;
Chen, Derek H. C., and Carl J. Dahlman. 2005. The Knowledge Economy, the KAM Methodology and World Bank Operations. The World Bank, October 19.&lt;br /&gt;
&lt;br /&gt;
Clemens, Michael A. 2004. The Long Walk to School: International education goals in historical perspective. Econ WPA, March.&amp;amp;nbsp;[http://ideas.repec.org/p/wpa/wuwpdc/0403007.html http://ideas.repec.org/p/wpa/wuwpdc/0403007.html].&lt;br /&gt;
&lt;br /&gt;
Cohen, Daniel, and Marcelo Soto. 2001. “Growth and Human Capital: Good Data, Good Results.” Technical Paper 179.&amp;amp;nbsp; Paris: OECD.&lt;br /&gt;
&lt;br /&gt;
Cuaresma, Jesus Crespo, and Wolfgang Lutz. 2007 (April).&amp;amp;nbsp; “Human Capital, Age Structure and Economic Growth:&amp;amp;nbsp; Evidence from a New Dataset.” Interim Report IR-07-011. Laxenburg, Austria:&amp;amp;nbsp; International Institute for Applied Systems Analysis.&lt;br /&gt;
&lt;br /&gt;
Delamonica, Enrique, Santosh Mehrotra, and Jan Vandemoortele.&amp;amp;nbsp;2001 (August).&amp;amp;nbsp; “Is EFA Affordable? Estimating the Global Minimum Cost of ‘Education for All’”. Innocenti Working Paper No. 87.&amp;amp;nbsp; Florence: UNICEF Innocenti Research Centre.&amp;amp;nbsp;[http://www.unicef-irc.org/publications/pdf/iwp87.pdf http://www.unicef-irc.org/publications/pdf/iwp87.pdf].&lt;br /&gt;
&lt;br /&gt;
Dickson, Janet R., Barry B. Hughes, and Mohammod T. Irfan. 2010. Advancing Global Education. Vol 2, Patterns of Potential Human Progress series.&amp;amp;nbsp; Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.&amp;amp;nbsp;[http://www.ifs.du.edu/documents http://www.ifs.du.edu/documents].&lt;br /&gt;
&lt;br /&gt;
Dutta, Soumitra (Ed.). 2013. The Global Innovation Index 2013. The Local Dynamics of Innovation.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2004b (March).&amp;amp;nbsp; “International Futures (IFs): An Overview of Structural Design.” Pardee Center for International Futures Working Paper, Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx http://www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. and Evan E. Hillebrand. 2006.&amp;amp;nbsp;&amp;amp;nbsp;&#039;&#039;Exploring and Shaping International Futures&#039;&#039;.&amp;amp;nbsp; Boulder, Co:&amp;amp;nbsp; Paradigm Publishers.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. with Anwar Hossain and Mohammod T. Irfan. 2004 (May).&amp;amp;nbsp; “The Structure of IFs.” Pardee Center for International Futures Working Paper, Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx http://www.ifs.du.edu/documents/reports.aspx].&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Irfan, Mohammod T. 2008.&amp;amp;nbsp; “A Global Education Transition: Computer Simulation of Alternative Paths in Universal Basic Education,” Ph.D. dissertation presented to the Josef Korbel School of International Studies, University of Denver, Denver, Colorado.&amp;amp;nbsp;&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx http://www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
Juma, Calestous, and Lee Yee-Cheong. 2005. Innovation: Applying Knowledge in Development. London: Earthscan. (Available online at&amp;amp;nbsp;[http://www.unmillenniumproject.org/documents/Science-complete.pdf http://www.unmillenniumproject.org/documents/Science-complete.pdf&amp;amp;nbsp;])&lt;br /&gt;
&lt;br /&gt;
McMahon, Walter W. 1999 (first published in paperback in 2002).&amp;amp;nbsp; Education and Development: Measuring the Social Benefits. Oxford:&amp;amp;nbsp; Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Wils, Annababette and Raymond O&#039;Connor. 2003. “The causes and dynamics of the global education transition.” AED Working Paper. Washington, DC: Academy for Educational Development&lt;br /&gt;
&lt;br /&gt;
UNESCO. 2010. UNESCO Science Report 2010. The Current Status of Science around the World. UNESCO. Paris.&lt;br /&gt;
&lt;br /&gt;
World Bank. 2010. Innovation Policy: A Guide for Developing Countries. (Available online at&amp;amp;nbsp;[https://openknowledge.worldbank.org/bitstream/handle/10986/2460/548930PUB0EPI11C10Dislosed061312010.pdf?sequence=1 https://openknowledge.worldbank.org/bitstream/handle/10986/2460/548930PUB0EPI11C10Dislosed061312010.pdf?sequence=1])&lt;br /&gt;
&lt;br /&gt;
World Bank. 2007. Building Knowledge Economies: Advanced Strategies for Development. WBI Development Studies. Washington, D.C: World Bank. (Available online at&amp;amp;nbsp;[http://siteresources.worldbank.org/KFDLP/Resources/461197-1199907090464/BuildingKEbook.pdf http://siteresources.worldbank.org/KFDLP/Resources/461197-1199907090464/BuildingKEbook.pdf])&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Education&amp;diff=8311</id>
		<title>Education</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Education&amp;diff=8311"/>
		<updated>2017-09-07T21:40:41Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;=== Overview ===&lt;br /&gt;
&lt;br /&gt;
The most recent and complete education model documentation is available on Pardee&#039;s [http://pardee.du.edu/ifs-education-model-documentation website]. Although the text in this interactive system is, for some IFs models, often significantly out of date, you may still find the basic description useful to you.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;The education model of IFs simulates patterns of educational participation and attainment in 186 countries over a long time horizon under alternative assumptions about uncertainties and interventions (Irfan 2008).&amp;amp;nbsp; Its purpose is to serve as a generalized thinking and analysis tool for educational futures within a broader human development context.&amp;amp;nbsp;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;The model forecasts gender- and country-specific access, participation and progression rates at levels of formal education starting from elementary through lower and upper secondary to tertiary. The model also forecasts costs and public spending by level of education. Dropout, completion and transition to the next level of schooling are all mapped onto corresponding age cohorts thus allowing the model to forecast educational attainment for the entire population at any point in time within the forecast horizon.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;From simple accounting of the grade progressions to complex budget balancing and budget impact algorithm, the model draws upon the extant understanding and standards (e.g., UNESCO&#039;s ISCED classification explained later) about national systems of education around the world. One difference between other attempts at forecasting educational participation and attainment (e.g, McMahon 1999; Bruns, Mingat and Rakotomalala 2003; Wils and O’Connor 2003; Delamonica, Mehrotra and Vandemoortele. 2001; Cuaresma and Lutz 2007) and our forecasting, is the embedding of education within an integrated model in which demographic and economic variables interact with education, in both directions, as the model runs.&amp;amp;nbsp;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;In the figure below we display the major variables and components that directly determine education demand, supply, and flows in the IFs system.&amp;amp;nbsp; We emphasize again the inter-connectedness of the components and their relationship to the broader human development system.&amp;amp;nbsp; For example, during each year of simulation, the IFs cohort-specific demographic model provides the school age population to the education model.&amp;amp;nbsp; In turn, the education model feeds its calculations of education attainment to the population model’s determination of women’s fertility.&amp;amp;nbsp; Similarly, the broader economic and socio-political systems provide funding for education, and levels of educational attainment affect economic productivity and growth, and therefore also education spending.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;[[File:EduOverview.png|frame|center|Visual representation of education demand, supply, and flows in the IFs system]]&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Structure and Agent System: Education&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot; border=&amp;quot;0&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width: 50%&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;System/Subsystem&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;National Education System&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Organizing Structure&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Various Levels of Education; Age Cohorts&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Stocks&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Educational Attainment; Enrollment&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; valign=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Flows&#039;&#039;&#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Intake; Graduation; Transition; Spending&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;&#039;&#039;&#039;Key Aggregate&amp;amp;nbsp;&#039;&#039;&#039; &#039;&#039;&#039;Relationships&amp;amp;nbsp;&#039;&#039;&#039;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;(illustrative, not comprehensive)&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Demand for and achievement in education changes with income, societal change&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Public spending available for education rises with income level&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Cost of schooling rises with income level&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Lack (surplus) of public spending in education hurts (helps) educational access and progression&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;nbsp;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;More education helps economic growth and reduces fertility&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; valign=&amp;quot;center&amp;quot; | &amp;lt;div style=&amp;quot;text-align: left&amp;quot;&amp;gt;&#039;&#039;&#039;Key Agent-Class Behavior&amp;amp;nbsp;&#039;&#039;&#039; &#039;&#039;&#039;Relationships&#039;&#039;&#039;&amp;lt;/div&amp;gt;&amp;lt;div style=&amp;quot;text-align: left&amp;quot;&amp;gt;(illustrative, not comprehensive)&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Families send children to school; Government revenue and expenditure in education&amp;lt;/div&amp;gt;&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Education Model Coverage&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
UNESCO has developed a standard classification system for national education systems called International Standard Classification of Education, ISCED. ISCED 1997 uses a numbering system to identify the sequential levels of educational systems—namely, pre-primary, primary, lower secondary, upper secondary, post-secondary non tertiary and tertiary—which are characterized by curricula of increasing difficulty and specialization as the students move up the levels. IFs education model covers&amp;amp;nbsp; primary (ISCED level 1), lower secondary (ISCED level 2), upper secondary (ISCED level 3), and tertiary education (ISCED levels 5A, 5B and 6).&lt;br /&gt;
&lt;br /&gt;
The model covers 186 countries that can be grouped into any number of flexible country groupings, e.g., UNESCO regions, like any other sub-module of IFs. Country specific entrance age and school-cycle length [[Education#Sources_of_Education_Data|data are collected]] and used in IFs to represent national education systems as closely as possible. For all of these levels, IFs forecast variables representing student flow rates, e.g., intake, persistence, completion and graduation, and stocks, e.g., enrolment, with the girls and the boys handled separately within each country.&lt;br /&gt;
&lt;br /&gt;
One important distinction among the flow rates is a gross rate versus a net rate for the same flow. Gross rates include all pupils whereas net rates include pupils who enter the school at the right age, given the statutory entrance age in the country and proceed without any repetition. The IFs education model forecasts both net and gross rates for primary education. For other levels we forecast gross rates only. It would be useful to look at the net rates at least for lower secondary, as the catch up continues up to that level. However, we could not obtain net rate data for lower secondary.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Additionally, for lower and upper secondary, the IFs model covers both general and vocational curriculum and forecasts the vocational share of total enrolment, EDSECLOWRVOC (for lower secondary) and EDSECUPPRVOC (for upper secondary). Like all other participation variables, these two are also disaggregated by gender.&lt;br /&gt;
&lt;br /&gt;
The output of the national education system, i.e., school completion and partial completion of the young people, is added to the [http://www.du.edu/ifs/help/understand/education/flowcharts/attainment.html educational attainment] of the adults in the population. IFs forecasts four categories of attainment - portion with no education, completed primary education, completed secondary education and completed tertiary education - separately for men and women above fifteen years of age by five year cohorts as well as an aggregate over all adult cohorts. Model software contains so-called &amp;quot;Education Pyramid&amp;quot; or a display of educational attainments mapped over five year age cohorts as is usually done for population pyramids.&lt;br /&gt;
&lt;br /&gt;
Another aggregate measure of educational attainment that we forecast is the average years of education of the adults. We have several measures, EDYEARSAG15, average years of education for all adults aged 15 and above, EDYRSAG25, average years of education for those 25 and older, EDYRSAG15TO24, average years of education for the youngest of the adults aged between fifteen years to twenty four.&lt;br /&gt;
&lt;br /&gt;
IFs education model also covers [[Education#Education_Financial_Flow|financing of education]]. The model forecast per student public expenditure as a share of per capita income. The model also forecast total public spending in education and the share of that spending that goes to each level of education.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;What the Model Does Not Cover&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
ISCED level 0, pre-primary, and level 4, post-secondary pre tertiary, are not common across all countries and are thus excluded from IFs education model.&lt;br /&gt;
&lt;br /&gt;
On the financing side, the model does not include private spending in education, a significant share of spending especially for tertiary education in many countries and even for secondary education in some countries. Scarcity of good data and lack of any pattern in the historical unfolding precludes modelling private spending in education.&lt;br /&gt;
&lt;br /&gt;
Quality of national education system can also vary across countries and over time. The IFs education model does not forecast any explicit indicator of education quality. However, the survival and graduation rates that the model forecasts for all levels of education are implicit indicators of system quality.&amp;amp;nbsp; At this point IFs does not forecast any indicator of cognitive quality of learners. However, the IFs database does have data on cognitive quality.&lt;br /&gt;
&lt;br /&gt;
The IFs education model does not cover private spending in education.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Sources of Education Data&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
UNESCO is the UN agency charged with collecting and maintaining education-related data from across the world. UNICEF collects some education data through their MICS survey. USAID also collects education data as a part of its Demographic and Household Surveys (DHS). OECD collects better data especially on tertiary education for its members as well as few other countries.&lt;br /&gt;
&lt;br /&gt;
We collected our [[Education#Education_Student_Flow|student flows]] and per student cost data from UNESCO Institute for Statistics&#039; (UIS) [http://stats.uis.unesco.org/unesco/tableviewer/document.aspx?ReportId=143 web data repository]. (Accessed on 05/17/2013)&lt;br /&gt;
&lt;br /&gt;
For [[Education#Education_Attainment|educational attainment]] data we use estimates by Robert Barro and Jong Wha Lee (2000). They &amp;amp;nbsp;have published their estimates of human capital stock (i.e., the educational attainment of adults) at the website of the Center for International Development of Harvard University. In 2001, Daniel Cohen and Marcelo Soto presented a paper providing another human capital dataset for a total of ninety-five countries. We collect that data as well in our database.&lt;br /&gt;
&lt;br /&gt;
When needed we also calculated our own series using underlying data from UNESCO. For example, we calculate an adjusted net intake rate for primary using the age specific intake rates that UNESCO report. We also calculated survival rates in lower and upper secondary (EDSECLOWRSUR, EDSECUPPRSUR) using a reconstructed cohort simulation method from grade-wise enrollment data for two consecutive years. The transition rate from lower to upper secondary is also calculated using grade data.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Reconciliation of Flow Rates&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Incongruities among the base year primary flow rates (intake, survival, and enrollment) can arise either from reported data values that, in combination, do not make sense, or from the use of “stand-alone” cross-sectional estimations used in the [http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf IFs pre-processor] to fill missing data.&amp;amp;nbsp; Such incongruities might arise among flow rates within a single level of education (e.g., primary intake, survival, and enrollment rates that are incompatible) or between flow rates across two levels of education (e.g., primary completion rate and lower secondary intake rate).&lt;br /&gt;
&lt;br /&gt;
The IFs education model uses algorithms to reconcile incongruent flow values.&amp;amp;nbsp; They work by (1) analyzing incongruities; (2) applying protocols that identify and retain the data or estimations that are probably of higher quality; and (3) substituting recomputed values for the data or estimations that are probably of lesser quality.&amp;amp;nbsp; For example, at the primary level, data on enrollment rates are more extensive and more straight-forward than either intake or survival data; in turn, intake rates have fewer missing values and are arguably more reliable measures than survival rates.&amp;amp;nbsp; The IFs pre-processor reconciles student flow data for Primary by using an algorithm that assumes enrollment numbers to be more reliable than the entrance data and entrance data to be more reliable than survival data.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Variable Naming Convention&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
All education model variable names start with a two-letter prefix of &#039;ED&#039; followed, in most cases, by the three letter level indicator - PRI for primary, SEC for secondary, TER for tertiary. Secondary is further subdivided into SECLOWR for lower secondary and SECUPPR for upper secondary. Parameters in the model, which are named using lowercase letters like those in other IFs modules, also follow a similar naming convention.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Dominant Relations: Education&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;The dominant relationships in the model are those that determine various educational flow rates, e.g., intake rate for primary (EDPRIINT) or tertiary (EDTERINT), or survival rates in primary (EDPRISUR) or lower secondary (EDSECLOWRSUR). These rates are functions of per capita income. Non-income drivers of education are represented by upward shifts in these functions. These rates follow an S-shaped path in most cases. The flows interact with a stocks and flows structure to derive major stocks like enrollment, for the young, and attainment, for the adult.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
On the financing side, the major dynamic is&amp;amp;nbsp; in the cost of education, e.g., cost per student in primary, EDEXPERPRI, the bulk of which is teachers&#039; salary and which thus goes up with rising income.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;Public spending allocation in education, GDS(Educ) is a function of national income per capita that proxies level of economic development. Demand for educational spending -&amp;amp;nbsp; determined by initial projections of enrollment and of per student cost - and total availability of public funds affect the base allocation derived from function.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
For diagrams see: [[Education#Education_Student_Flow|Student Flow Charts]]; [[Education#Education_Financial_Flow|Budget Flow Charts]]; [[Education#Education_Attainment|Attainment Flow Charts]]&lt;br /&gt;
&lt;br /&gt;
For equations see: [[Education#Equations:_Student_Flow|Student Flow Equations]]; [[Education#Equations:_Budget_Flow|Budget Flow Equations]]; [[Education#Equations:_Attainment|Attainment Equations]]&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Key dynamics are directly linked to the dominant relations&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
*Intake, survival and transition rates are functions of per capita income (GDPPCP). These functions shift upward over time representing the non-income drivers of education.&lt;br /&gt;
*Each year flow rates are used to update major stocks like enrollment, for the young, and attainment, for the adult.&lt;br /&gt;
*Per student expenditure at all levels of education is a function of per capita income.&lt;br /&gt;
*Deficit or surplus in public spending on education, GDS(Educ) affects intake, transition and survival rates at all levels of education.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Education: Selected Added Value&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;IFs Education model is an integrated model. The education system in the model is interlinked with demographic, economic and socio-political systems with mutual feedback within and across theses systems. Schooling of the young is linked to education of the population as whole in this model.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;The model is well suited for scenario analysis with representation of policy levers for entrance into and survival at various levels of schooling. Girls and boys are represented separately in this model.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;The education budget is also endogenous to the model with income driven dynamics in cost per student for each level of education. Budget availability affect enrollment. Educational attainment raises income and affordability of education at individual and national level.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Education Flow Charts&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== Overview ===&lt;br /&gt;
&lt;br /&gt;
For each country, the IFs education model represents a multilevel formal education system that starts at primary and ends at tertiary.&amp;amp;nbsp;[[Education#Education_Student_Flow|Student flows]], i.e., entry into and progression through the system are determined by forecasts on intake and persistence (or survival) rates superimposed on the population of the corresponding age cohorts obtained from IFs population forecasts. Students at all levels are disaggregated by gender. Secondary education is further divided into lower and upper secondary, and then further into general and vocational according to the curricula that are followed.&lt;br /&gt;
&lt;br /&gt;
The model represents the dynamics in [[Education#Education_Financial_Flow|education financing]] through per student costs for each level of education and a total public spending in education. Policy levers are available for changing both spending and cost.&lt;br /&gt;
&lt;br /&gt;
School completion (or dropout) in the education model is carried forward as the [Education#Education Attainment|attainment]] of the overall population. As a result, the education model forecasts population structures by age, sex, and attained education, i.e., years and levels of completed education.&lt;br /&gt;
&lt;br /&gt;
The major agents represented in the education system of the model are households,—represented by the parents who decide which of their boys and girls will go to school—and governments that direct resources into and across the educational system.&amp;amp;nbsp; The major flows within the model are student and budgetary, while the major stock is that of educational attainment embedded in a population. Other than the budgetary variables, all the flows and stocks are gender disaggregated.&lt;br /&gt;
&lt;br /&gt;
The education model has forward and backward linkages with other parts of the IFs model. During each year of simulation, the IFs cohort-specific [[Population#Structure_and_Agent_System:_Demographic|demographic model]] provides the school age population to the education model.&amp;amp;nbsp; In turn, the education model feeds its calculations of education attainment to the population model’s determination of women’s fertility.&amp;amp;nbsp; Similarly, the broader economic and socio-political systems provide funding for education, and levels of educational attainment affect [[Economics#Multifactor_Productivity|economic productivity and growth]], and therefore also education spending.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
The figure below shows the major variables and components that directly determine education demand, supply, and flows in the IFs system.&amp;amp;nbsp; The diagram attempts to emphasize on the inter-connectedness of the education model components and their relationship to the broader human development system.&lt;br /&gt;
&lt;br /&gt;
[[File:Overvieweducation flow.png|frame|center|Visual representation of education demand, supply, and flows in the IFs system]]&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Education Student Flow&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
IFs education model simulates grade-by-grade student flow for each level of education that the model covers. Grade-by-grade student flow model combine the effects of grade-specific dropout, repetition and reentry into an average cohort-specific &#039;&#039;grade-to-grade flow rate&#039;&#039;, calculated from the survival rate for the cohort. Each year the number of new entrants is determined by the forecasts of the intake rate and the entrance age population. In successive years, these entrants are moved to the next higher grades, one grade each year, using the &#039;&#039;grade-to-grade flow rate&#039;&#039;. The simulated grade-wise enrollments are then used to determine the total enrollment at the particular level of education. Student flow at a particular level of education, e.g., primary, is culminated with rates of completion and transition by some to the next level, e.g., lower secondary.&lt;br /&gt;
&lt;br /&gt;
The figure below shows details of the student flow for primary (or, elementary) level. This is illustrative of the student flow at other levels of education. We model both net and gross enrollment rates for primary. The model tracks the pool of potential students who are above the entrance age (as a result of never enrolling or of having dropped out), and brings back some of those students, marked as late/reentrant in the figure, (dependent on initial conditions with respect to gross versus net intake) for the dynamic calculation of total gross enrollments.&lt;br /&gt;
&lt;br /&gt;
A generally similar grade-flow methodology models lower and upper secondary level student flows. We use country-specific entrance ages and durations at each level. As the historical data available does not allow estimating a rate of transition from upper secondary to tertiary, the tertiary education model calculates a tertiary intake rate from tertiary enrollment and graduation rate data using an algorithm which derives a tertiary intake with a lower bound slightly below the upper secondary graduation rate in the previous year.[[File:Educationstudentflow.png|frame|center|Student flow for primary (or, elementary) level.]]&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Education Financial Flow&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
In addition to [[Education#Education_Student_Flow|student flows]], and interacting closely with them, the IFs education model also tracks financing of education. Because of the scarcity of private funding data, IFs specifically represents public funding only, and our formulations of public funding implicitly assume that the public/private funding mix will not change over time.&lt;br /&gt;
&lt;br /&gt;
The accounting of educational finance is composed of two major components, per student cost and the total number of projected students, the latter of the two is discussed in the [[Education#Education_Student_Flow|student flows]] section. Spending per student at all levels of education is driven by average income. Given forecasts of spending per student by level of education and given initial enrollments forecasts by level, an estimate of the total education funding demanded is obtained by summing across education levels the products of spending per student and student numbers.&lt;br /&gt;
&lt;br /&gt;
The funding needs are sent to the IFs [[Socio-Political#Structure_and_Agent_System:_Socio-Political|sociopolitical model]] where educational spending is initially determined from the patterns in such spending regressed against the level of economic development of the countries. A priority parameter (&#039;&#039;&#039;edbudgon&#039;&#039;&#039;) is then used to prioritize spending needs over spending patterns. This parameter can be changed by model user within a range of values going from zero to one&amp;amp;nbsp; with the zero value awarding maximum priority to fund demands. Finally, total government consumption spending (GOVCON) is distributed among education and other social spending sectors, namely infrastructure, health, public R&amp;amp;D, defense and an &amp;quot;other&amp;quot; category, using a normalization algorithm.&lt;br /&gt;
&lt;br /&gt;
Government spending is then taken back to the education module and compared against fund needs. Budget impact, calculated as a ratio of the demanded and allocated funds, makes an impact on the initial projection of student flow rates (intake, survival, and transition). The positive (upward) side of the budget impact is non-linear with the maximum boost to growth occurring when a flow rate is at or near its mid-point or within the range of the inflection points of an assumed S-shaped path, to be precise. Impact of deficit is more or less linear except at impact ratios close to 1, whence the downward impact is dampened. Final student flow rates are used to calculate final enrollment numbers using population forecasts for relevant age cohorts. Finally, cost per students are adjusted to reflect final enrollments and fund availability.&lt;br /&gt;
&lt;br /&gt;
[[File:Edfinancialflows.png|frame|center|Visual representation of the education financial flow]]&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Education Attainment&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The algorithm for the tracking of education attainment is very straight-forward.&amp;amp;nbsp; The model maintains the structure of the population not only by age and sex categories, but also by years and levels of completed education.&amp;amp;nbsp; In each year of the model’s run, the youngest adults pick up the appropriate total years of education and specific levels of completed education.&amp;amp;nbsp; The model advances each cohort in 1-year time steps after subtracting deaths. In addition to cohort attainment, the model also calculates overall attainment of adults (15+ and 25+) as average years of education&amp;amp;nbsp; (EDYRSAG15, EDYRSAG25) and as share of people 15+ with a certain level of education completed (EDPRIPER, EDSECPER, EDTERPER).&lt;br /&gt;
&lt;br /&gt;
One limitation of our model is that it does not represent differential mortality rates associated with different levels of education attainment (generally lower for the more educated).&amp;lt;sup&amp;gt;&amp;lt;span style=&amp;quot;color: #990000&amp;quot;&amp;gt;[1]&amp;lt;/span&amp;gt;&amp;amp;nbsp;&amp;lt;/sup&amp;gt;This leads, other things equal, to a modest underestimate of adult education attainment, growing with the length of the forecast horizon.&amp;amp;nbsp; The averaging method that IFs uses to advance adults through the age/sex/education categories also slightly misrepresents the level of education attainment in each 5-year category.&lt;br /&gt;
&lt;br /&gt;
[[File:Edattainment.png|frame|center|Visual representation of education attainment]] &amp;lt;span style=&amp;quot;color: #990000&amp;quot; data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;1]&amp;lt;/span&amp;gt;&amp;amp;nbsp;The multi-state demographic method developed and utilized by IIASA does include education-specific mortality rates.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Education Equations&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;The IFs education model represent two types of educational stocks, [[Education#Equations:_Student_Flow|stocks of pupils]]&amp;amp;nbsp;&amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;and stocks of adults with a certain level of [[Education#Equations:Attainment|educational attainment]] &amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;. &amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;These stocks are initialized with historical data. The simulation model then recalculates the stock each year from its level the previous year and the net annual change resulting from inflows and outflows.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;The core dynamics of the model is in these [[Education#Equations:_Student_Flow|flow rates]]&amp;lt;/span&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;. These&amp;amp;nbsp;&amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;flow&amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;rates are expressed as a percentage of age-appropriate population and thus have a theoretical range of zero to one hundred percent. Growing systems with a saturation point usually follow a sigmoid (S-shaped) trajectory with low growth rates at the two ends as the system begins to expand and as it approaches saturation. Maximum growth in such a system occurs at an inflection point, usually at the middle of the range or slightly above it, at which growth rate reverses direction. Some researchers (Clemens 2004; Wils and O’Connor 2003) have identified sigmoid trends in educational expansion by analyzing enrollment rates at elementary and secondary level. The IFs education model is not exactly a trend extrapolation; it is rather a forecast based on fundamental drivers, for example, income level. Educational rates in our model are driven by income level, a systemic shift algorithm and a [[Education#Equations:_Budget_Flow|budget impact]]&amp;amp;nbsp;&amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;resulting from the availability of public fund. However, there are growth rate parameters for most of the flows that allow model user to simulate desired growth that follows a sigmoid-trajectory. Another area that makes use of a sigmoid growth rate algorithm is the boost in flow rates as a result of budget surplus.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;Intake (or transition), survival, enrollment and completion are some of the rates that IFs model forecast. Rate forecasts [[Education#Structure_and_Agent_System:_Education|cover]]&amp;amp;nbsp;elementary&amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;, lower secondary, upper secondary and tertiary levels of education with separate equations for boys and girls for each of the rate variables. All of these rates are required to calculate pupil stocks while completion rate and dropout rate (reciprocal of survival rate) are used to determine educational attainment of adults.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;On the financial side of education, IFs forecast cost per student for each level. These per student costs are multiplied with enrollments to calculate fund demand. Budget allocation calculated in IFs [[Socio-Political#Structure_and_Agent_System:_Socio-Political|socio-political module]] &amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;is&amp;amp;nbsp;&amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;sent back to&amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;education model to calculate final enrollments and cost per student as a result of fund shortage or surplus.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;The population module provides cohort population to the education model. The [[Economics#Dominant_Relations:_Economics|economic model]] provides&amp;amp;nbsp;&amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;per capita income and the socio-political model provides budget allocation. Educational attainment of adults calculated by the education module affects [[Population#Fertility_Detail|fertility]] and [[Population#Mortality_Detail|mortality]] in the [[Population#Structure_and_Agent_System:_Demographic|population]] and&amp;lt;/span&amp;gt;&amp;amp;nbsp;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;[[Health#Structure_and_Agent_System:_Health|health]] modules, affects productivity in the economic module and affects other socio-political outcomes like [[Governance#Inclusiveness|governance and democracy]] levels&amp;lt;/span&amp;gt; &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Equations: Student Flow&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== Econometric Models for Core Inflow and Outflow ===&lt;br /&gt;
&lt;br /&gt;
Enrollments at various levels of education - EDPRIENRN, EPRIENRG, EDSECLOWENRG, EDSECUPPRENRG, EDTERENRG - are initialized with historical data for the beginning year of the model. Net change in enrollment at each time step is [[Education#Education_Student_Flow|determined by inflows]] (intake or transition) and outflows (dropout or completion). Entrance to the school system (EDPRIINT, EDTERINT), transition from the lower level (EDSECLOWRTRAN, EDSECUPPRTRAN) - and outflows - completion (EDPRICR), dropout or it&#039;s reciprocal, survival (EDPRISUR) - are some of these rates that are forecast by the model.&lt;br /&gt;
&lt;br /&gt;
The educational flow rates are best explained by per capita income that serves as a proxy for the families&#039; opportunity cost of sending children to school. For each of these rates, separate regression equations for boys and girls are estimated from historical data for the most recent year. These regression equations, which are updated with most recent data as the model is rebased with new data every five years, are usually logarithmic in form. The following figure shows such a regression plot for net intake rate in elementary against per capita income in PPP dollars.&lt;br /&gt;
&lt;br /&gt;
In each of the forecast years, values of the educational flow rates [[File:EdcrosssectionalGDP.png|frame|right|Example of an econometric models for core inflow and outflow]]are first determined from these regression equations. Independent variables used in the regression equations are endogenous to the IFS model. For example, per capita income, GDPPCP, forecast by the IFs&amp;amp;nbsp;[[Economics#Dominant_Relations:_Economics|economic model]]&amp;amp;nbsp;drives many of the educational flow rates. The following equation shows the calculation of one such student flow rate (CalEdPriInt) from the log model of net primary intake rate shown in the earlier figure.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CalEdPriInt_{g=1,r,t}=77.347+9.6372lnGDPPCP_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
While all countries are expected to follow the regression curve in the long run, the residuals in the base year make it difficult to generate a smooth path with a continuous transition from historical data to regression estimation. We handle this by adjusting regression forecast for country differences using an algorithm that we call &amp;quot;shift factor&amp;quot; algorithm. In the first year of the model run we calculate a shift factor (EDPriIntNShift) as the difference (or ratio) between historical data on net primary intake rate (EDPRIINTN) and regression prediction for the first year for all countries. As the model runs in subsequent years, these shift factors (or initial ratios) converge to zero or one if it is a ratio (code routine ConvergeOverTime in the equation below) making the country forecast merge with the global function gradually. The period of convergence for the shift factor (PriIntN_Shift_Time) is determined through trial and error in each case.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EdPriIntNShift_{g,r,t=1}=EDPRIINTN_{g,r,t=1}-CalEdPriInt_{g,r,t=1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDPRIINTN_{g,r,t}=CalEdPriInt_{g,r,t}+ConvergeOverTime(EdPriIntNShift_{g,r,t=1},0,PriIntNShiftTime)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The base forecast on flow rates resulting from of this regression model with country shift is used to calculate the demand for funds. These base flow rates might change as a result of budget impact based on the availability or shortage of education budget explained in the [[Education#Equations:_Budget_Flow|budget flow section]].&lt;br /&gt;
&lt;br /&gt;
=== Systemic Shift ===&lt;br /&gt;
&lt;br /&gt;
Access and participation in education increases with socio-economic developments that bring changes to people&#039;s perception about the value of education. This upward shifts are clearly visible in cross-sectional regression done over two adequately apart points in time. The next figure illustrates such shift by plotting net intake rate for boys at the elementary level against GDP per capita (PPP dollars) for two points in time, 1992 and 2000.[[File:EdGDPnetintake.png|frame|right|Net intake rate for boys at the elementary level against GDP per capita (PPP dollars)]]&lt;br /&gt;
&lt;br /&gt;
IFs education model introduces an algorithm to represent this shift in the regression functions. This &amp;quot;systemic shift&amp;quot; algorithm starts with two regression functions about 10 to 15 years apart. An additive factor to the flow rate is estimated each year by calculating the flow rate (CalEdPriInt1 and CalEdPriInt2 in the equations below) progress required to shift from one function, e.g., &amp;amp;nbsp;&amp;amp;nbsp;to the other, s, &amp;amp;nbsp;in a certain number of years (SS_Denom), as shown below. This systemic shift factor (CalEdPriIntFac) is then added to the flow rate (EDPRIINTN in this case) for a particular year (t) calculated from regression and country shift as described in the previous section.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CalEdPriInt1_{g,r,t}=f_1(GDPPCP_{g,r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CalEdPriInt2_{g,r,t}=f_2(GDPPCP_{g,r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CalEdPriIntFac_{g,r,t}=\frac{t-1}{SSDenom}*(CalEdPriInt2_{g,r,t}-CalEdPriInt1_{g,r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDPRIINTN_{g,r,t}=EDPRIINTN_{g,r,t}+CalEdPriIntFac_{g,r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
As said earlier, [[Education#Education_Student_Flow|Student flow]] rates are expressed as a percentage of underlying stocks like the number of school age children or number of pupils at a certain grade level. The flow-rate dynamics work in conjunction with population dynamics (modeled inside IFs [[Population#Structure_and_Agent_System:_Demographic|population module]]) to forecast enrollment totals.&lt;br /&gt;
&lt;br /&gt;
=== Grade Flow Algorithm ===&lt;br /&gt;
&amp;lt;div&amp;gt;&lt;br /&gt;
Once the core inflow (intake or transition) and outflow (survival or completion) are determined, enrollment is calculated from grade-flows. Our grade-by-grade student flow model therefore uses some simplifying assumptions in its calculations and forecasts. We combine the effects of grade-specific dropout, repetition and reentry into an average cohort-specific grade-to-grade dropout rate, calculated from the survival rate (EDPRISUR for primary) of the entering cohort over the entire duration of the level (&#039;&#039;&#039;EDPRILEN&amp;amp;nbsp;&#039;&#039;&#039;for primary). Each year the number of new entrants is determined by the forecasts of the intake rate (EDPRIINT) and the entrance age population. In successive years, these entrants are moved to the next higher grades, one grade each year, subtracting the grade-to-grade dropout rate (DropoutRate). The simulated grade-wise enrollments (GradeStudents with Gcount as a subscript for grade level) are then used to determine the total enrollment at the particular level of education (EDPRIENRG for Primary).&lt;br /&gt;
&lt;br /&gt;
There are some obvious limitations of this simplified approach. While our model effectively includes repeaters, we represent them implicitly (by including them in our grade progression) rather than representing them explicitly as a separate category.&amp;amp;nbsp; Moreover, by setting first grade enrollments to school entrants, we exclude repeating students from the first grade total.&amp;amp;nbsp; On the other hand, the assumption of the same grade-to-grade flow rate across all grades might somewhat over-state enrollment in a typical low-education country, where first grade drop-out rates are typically higher than the drop-out rates in subsequent grades.&amp;amp;nbsp; Since our objective is to forecast enrollment, attainment and associated costs by level rather than by grade, however, we do not lose much information by accounting for the approximate number of school places occupied by the cohorts as they proceed and focusing on accurate representation of total enrollment.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;DropoutRate_{g,r,t}=1-(\frac{EDPRISUR_{g,r,t}}{100})^{\frac{1}{\mathbf{EDPRILEN}_r-1}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GradeStudents_{GCount=1,g,r,t}=EDPRIINT_{g,r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GradeStudents_{Gcount,g,r,t}=GradeStudents_{Gcount-1,g,r,t}*(1-DropoutRate_{g,r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDPRIENRG_{g,r,t}=\sum^\mathbf{EDPRILEN}_{Gcount=1}GradeStudents_{Gcount,g,r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&amp;lt;/div&amp;gt;&lt;br /&gt;
=== Gross and Net ===&lt;br /&gt;
&lt;br /&gt;
Countries with a low rate of schooling, especially those that are catching up, usually have a large number of over-age students. Enrollment and entrance rates that count students of all ages are called gross rates in contrast to the net rate that only takes the of-age students in the numerator of the rate calculation expression. UNESCO report net and gross rates separately for entrance and participation in elementary. IFs education model forecasts both net and gross rate in primary education. An overage pool (PoolPrimary) is estimated at the model base year using net and gross intake rate data. Of-age non-entrants continue to add to the pool (PoolInflow). The pool is exhausted using a rate (PcntBack) determined by the gross and net intake rate differential at the base year. The over-age entrants (cOverAgeIntk_Pri) gleaned from the pool are added to the net intake rate (EDPRIINTN) to calculate the gross intake rate (EDPRIINT).&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;PoolPrimary_{r,g,t=1}=f(EDPRIINTN_{g,r,t=1},EDPRIINT_{g,r,t=1})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;PcntBack_{r,g}=f(PoolPrimary_{r,g,t=1},EDPRIINTN_{g,r,t=1},EDPRIINT_{g,r,t=1})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;PoolInflow=f(EDPRIINTN_{g,r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;cOverAgeIntkPri=f(EDPRIINTN_{g,r,t},PoolPrimary_{g,r,t},PcntBack_{r,g})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;PoolPrimary_{r,g,t}=PoolPrimary_{r,g,t-1}+PoolInflow-cOverAgeIntkPri&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDPRIINT_{g,r,t}=EDPRIINTN_{g,r,t}+cOverAgeIntkPri&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
=== Vocational Education ===&lt;br /&gt;
&lt;br /&gt;
IFs education model forecasts vocational education at lower and upper secondary levels. The variables of interest are vocational shares of total enrollment in lower secondary (EDSECLOWRVOC) and the same in upper secondary (EDSECUPPRVOC). Country specific vocational participation data collected from UNESCO Institute for Statistics do not show any common trend in provision or attainment of vocational education across the world. International Futures model initialize vocational shares with UNESCO data, assumes the shares to be zero when no data is available and projects the shares to be constant over the entire forecasting horizon.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
IFs also provides two scenario intervention parameters for lower (&#039;&#039;edseclowrvocadd) &#039;&#039;and upper secondary (&#039;&#039;edsecupprvocadd&#039;&#039;) vocational shares. These parameters are additive with a model base case value of zero. They can be set to negative or positive values to raise or lower the percentage share of vocational in total enrollment. Changed vocational shares are bound to an upper limit of seventy percent. This upper bound is deduced from the upper secondary vocational share in Germany, which at about 67% is the largest among all vocational shares for which we have data. Changes to the vocational share through the additive parameters will also result in changes in the total enrollment, e.g., EDSECLOWRTOT for lower secondary, which is calculated using general (non-vocational) enrollment (EdSecTot_Gen) and vocational share, as shown in the equations below (for lower secondary).&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDSECLOWRVOCI_{g,r}=EDSECLOWRVOC_{g,r,t=1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDSECLOWRVOC_{g,r,t}=EDSECLOWRVOCI_{g,r}+edseclowrvocadd_{g,r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDSECLOWRTOT_{g,r,t}=\frac{EdSecTotGen_{g,r,t}}{1-\frac{EDSECLOWRVOC_{g,r,t}}{100}}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Forecasts of &#039;&#039;EdSecTot_Gen&amp;lt;sub&amp;gt;g,r,t&amp;lt;/sub&amp;gt; &#039;&#039;&amp;amp;nbsp;is obtained in the full lower secondary model using transition rates from primary to lower secondary and survival rates of lower secondary.&lt;br /&gt;
&lt;br /&gt;
=== Science and Engineering Graduates in Tertiary ===&lt;br /&gt;
&lt;br /&gt;
Strength of STEM (Science, Technology, Engineering and Mathematics) programs is an important indicator of a country’s technological innovation capacities. IFs education model forecasts the share of science and engineering degrees (EDTERGRSCIEN) among all tertiary graduates in a country. Data for this variable is available through UNESCO Institute for Statistics. The forecast is based on a regression of science and engineering share on average per person income in constant international dollar (GDPPCP). There is an additive parameter (&#039;&#039;edterscienshradd&#039;&#039;), with a base case value of zero, that can be used to add to (or subtract from) the percentage share of science and engineering among tertiary graduates. This parameter does not have any effect on the total number of tertiary graduates (EDTERGRADS).&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDTERGRSCIEN_{r,t}=f(GDPPCP_{r,t})+edterscienshradd_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Equations: Budget Flow&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Resources required to maintain the projected student flows are determined by multiplying enrollment rates with per student cost forecasts. Availability of resources, as determined in the IFs socio-political model, affect flow rates and the final enrollment rate.&lt;br /&gt;
&lt;br /&gt;
Public expenditure per student (EDEXPERPRI) as a percentage of per capita income is first estimated (CalExpPerStud) using a regression equation. Country situations are added as a shift factor (EdExPerPriShift) that wears off over a period of time (&#039;&#039;&#039;edexppconv&#039;&#039;&#039;) in the same manner as those for student flow rates. The following group of equations show the calculation of per student expenditure in primary (EDEXPERPRI).&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CalExpPerStud_{r,t}=f(GDPPCP_{r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EdExpPerPriShift_{r,t=1}=EDEXPERPRI_{r,t=1}-CalExpPerStud_{r,t=1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDEXPERPRI_{r,t}=CalExpPerStud_{r,t=1}+ConvergeOverTime(EdExpPerPriShift_{r,t=1},0,\mathbf{edexppconv})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Total fund demand (EDBUDDEM, see calculation below) is passed to the IFs socio-political model where a detail government budget model distributes total government consumption among various public expenditure sectors. For education allocation, an initial estimate (gkcomp) is first made from a regression function of educational spending as a percentage of GDP over GDP per capita at PPP dollars (GDPPCP) as a country gets richer.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;gkcomp_{r,Educ,t}=f(GDPPCP_{r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Like several other functions discussed in this sub-module, country situation is reflected by estimating country ratio (gkri) between the predicted and historical value in the base year. This ratio converges to a value of one very slowly essentially maintaining the historic ratio. Public spending on education in billion dollars (GDS) is then calculated using the regression result, GDP and the multiplicative shift.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;gkri_{r,Educ}=GDS_{r,Educ,t=1}/GDP_{r,t=1}/gkcomp_{r,Educ,t=1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;gkshift_{r,t,Educ}=ConvergeOverTime(gkri_{r,Educ}, 200,1)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GDS_{r,Educ,t}=gkcomp_{r,Educ,t}*gkshift_{r,t,Educ}*GDP_{r,t}/100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
[[Socio-Political#Policy_Equations:_Government_Expenditures|Sociopolitical model]]&amp;amp;nbsp;also forecast public spending in other areas of social spending, i.e., military, health, R&amp;amp;D. Another public spending sector, [[Infrastructure#Determining_the_Actual_Funds_for_Infrastructure_Spending|infrastructure]]&amp;amp;nbsp;is calculated bottom-up, i.e., as an aggregation of demand for construction and maintenance of various types of infrastructure.&lt;br /&gt;
&lt;br /&gt;
Once all the spending shares are projected, a normalization algorithm is used to distribute the total available government consumption budget (GOVCON) among all sectors.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GTOT=\sum^{NGovExp}_{s=1}GDS_{r,s}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GDS_{r,s}=\frac{GDS_{r,s}}{GTOT}*GOVCON_{r,s}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Before normalization, a priority parameter allows setting aside all or part of fund demands for the ground up spending sectors, i.e., infrastructure and education. For education sector, the prioritization parameter (&#039;&#039;&#039;edbudgon&#039;&#039;&#039;) is used to set aside a certain portion of the projected education investment as shown in the equations below.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GDSSetAside=GDS_{r,Educ}*(1-\mathbf{edbudgon})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;GDS_{r,Educ}=GDS_{r,Educ}-GDSSetAside&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Education allocation, GDS (Educ) calculated thus is taken back to the education model. A second normalization and prioritization is done within the education model to distribute total education allocation among different levels of education. This across level normalization uses the percentage share of each educational level in the total demand for education funding. First, total expenditure demand for all levels of education combined is determined by multiplying the total enrollments with per student costs. The following equation shows the calculation for Primary.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;BudDemPri_{r,t}=UDEDExpPerPri_{r,t}*GDPPCP_{r,t}*\sum^2_{g=1}UDEnrollCT_{r,t}/100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Fund demands for all levels are added up to get the total fund demand under no budget constraint. The prefixes UD here stands for budget unconstrained demand.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDBudDem_{r,t}=BudDemPri_{r,t}+BudDemSecLowr_{r,t}+BudDemSecUppr_{r,t}+BudDemTer_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Any surplus or deficit in educational allocation, calculated as the difference between education sector allocation in the government budget model and the total fund requirement for all levels of education combined, first undergoes an adjustment algorithm that boosts (in case of surplus) or reduces (in case of deficit) per student cost for those countries which are below or above the level they are supposed to be. Post this adjustment, allocation is distributed across all levels using a normalization process based on demand.&amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
A budget impact ratio &amp;amp;nbsp;is then calculated as the ratio of the fund demanded (CalcTotCost) and fund obtained (CalcTotSpend). This budget impact ratio (CalcBudgetImpact) &amp;amp;nbsp;increases or decreases the pre-budget (or demand side as we call it) projection of [[Education#Equations:_Student_Flow|student flow rates]] (intake, survival, and transition). The positive (upward) side of the budget impact is non-linear with the maximum boost to growth occurring when a flow rate is at or near its mid-point or within the range of the inflection points of an assumed S-shaped path, to be precise. Impact of deficit is more or less linear except at impact ratios close to 1, whence the downward impact is dampened. Final student flow rates are used to calculate final enrollment numbers using population forecasts for relevant age cohorts. Finally, cost per students are adjusted to reflect final enrollments and fund availability.&lt;br /&gt;
&lt;br /&gt;
Budget impacts uses a non-linear algorithm intended to generate an S-shaped growth rate. Final enrollment is then calculated from this final flow rates and any of the remaining budget is used to increase per student expenditure.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CalcTotCost=(EDEXPERPRI_r/100)*GDPPC_r*convtoexchange*\sum^2_{g=1}EDPRITOT_{r,g}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CalcTotSpend=GDS_{r,Educ}*GDSED_{r,Pri}/SpendCostRI&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
In the equations above, convtoexchange is a factor that converts monetary units from PPP to exchange rate dollars, SpendCostRI is a ratio calculated at the first year of the model to reconcile historical data on aggregate and bottom-up spending.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;CalcBudgetImpact=CalcTotSpend/CalcTotCost&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDPRIINTN_{g,r,t}=f(EDPRIINTN_{g,r,t},CalcBudgetImpact)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EDPRISUR_{g,r,t}=f(EDPRISUR_{g,r,t},CalcBudgetImpact)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== Equations: Attainment ==&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;There are two types of variables that keep track of educational attainment: average years of education of adults (EDYRSAG15, EDYRSAG15TO24 and EDYRSAG25) and percentage of adults with a certain level of education (EDPRIPER, EDSECPER, EDTERPER). Both groups forecast attainment by gender.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;The basis of calculation for both groups of variables is educational attainment by age cohort and gender as contained in intermediate model variables, EDPriPopPer &amp;lt;sub&amp;gt;r.g,c,t&amp;lt;/sub&amp;gt; ,&amp;amp;nbsp; EDSecPopPer&amp;lt;sub&amp;gt;r.g,c,t&amp;lt;/sub&amp;gt;, EdTerPopPer&amp;lt;sub&amp;gt;r.g,c,t&amp;lt;/sub&amp;gt; (where, r stands for country or region, g for gender, c for cohort and t for time).&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;We initialize attainments of the entire adult population (EDPRIPER, EDSECPER, EDTERPER) using historical data estimated by Barro and Lee (2000) and use a spread algorithm. The spread algorithm starts with the most recent data on school completion rate (EDPRICR for primary) which is considered as the average attainment of the graduating cohort. The algorithm then uses the differential between that completion rate and the attainment rate of the adults (EDPRIPER) to back calculates a delta reduction for each of the older cohorts (EdPriPopPer) such that averaging attainments over cohorts one can obtain average attainment for all adults (EDPRIPER).&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span&amp;gt;&amp;lt;math&amp;gt;EDPriPopPer_{c,g,r,t=1}=f(EDPRIPER_{r,g,t=1},EDPRICR_{r,g,t=1})&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;where, subscript c stand for five year age cohorts going from 1 to 21. Cohort 4, represents the 15 to 19 years and NC, total number of age cohorts.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;For subsequent forecast years, cohort educational attainment for each level of education is calculated by adding graduates from that level of education to the appropriate age cohort, advancing graduates from the younger cohort, and passing graduates to the older cohort.&amp;amp;nbsp;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span&amp;gt;&amp;lt;math&amp;gt;EDPriPopPer_{c=pc,g,r,t}=0.8*EDPriPopPer_{c=pc,g,r,t-1}+0.2*EDPRICR_{g,r,t}&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;where, pc stands for the five year age cohort where the primary graduates belong. For all other cohorts:&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span&amp;gt;&amp;lt;math&amp;gt;EdPriPopPer_{c,g,r,t}=0.8*EdPriPopPer_{c,g,r,t-1}+0.2*EdPriPopPer_{c-1,g,r,t-1}&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;Cohort attainments for secondary and tertiary education (EDSECPOPPER, EDTERPOPPER) are initialized and forecast in a similar fashion. An average years of education reflecting completion of levels is then calculated by from the cohort attainment, population and cohort length as shown in the next equation where&amp;amp;nbsp; &amp;amp;nbsp;AGEDST&amp;lt;sub&amp;gt;c,g,r,t&amp;lt;/sub&amp;gt; contains the population of five year age cohorts and &#039;&#039;&#039;EDPRILEN&#039;&#039;&#039; &amp;lt;sub&amp;gt;r,t&amp;lt;/sub&amp;gt; &amp;lt;/span&amp;gt; &amp;amp;nbsp;&amp;lt;span&amp;gt;&amp;amp;nbsp;is the duration of primary cycle in years.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span&amp;gt;&amp;lt;math&amp;gt;AvgYearsPriEdPop_{g,r,t}=\frac{\sum^{NCohorts}_{c=pc}\frac{EDPriPopPer_{c,g,r,t}}{100}*EDPRILEN_r*AGEDST_{c,g,r,t}}{\sum^{NCohorts}_{c=pc}AGEDST_{c,g,r,t}}&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;For those who dropout before completing a certain level we need to calculate the partial attainment and add that to the average years of education. The average of the partial years of education at a particular year is calculated from dropouts by level and grade as shown below. Calculation of the average of partial years resulting from dropouts in primary education is illustrated in the equations below. Partial years from current year dropouts at other levels of education are calculated in the same manner and all the partial years are averaged to an overall average. This new partial attainment is then added to the partial attainment of five year cohorts which are initialized and advanced in a similar manner as that used for cohort averages on completed attainment.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span&amp;gt;&amp;lt;math&amp;gt;DropoutRate_{g,r,t}=f(EDPRISUR_{g,r,t},\mathbf{EDPRILEN}_r)&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span&amp;gt;&amp;lt;math&amp;gt;GrStudents_{GCount,g,r,t}=f(EDPRIINT_{g,r,t},DropoutRate_{g,r,t},\mathbf{EDPRILEN}_r)&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span&amp;gt;&amp;lt;math&amp;gt;PartialPriPersYearsNew_{g,r,t}=\frac{(\sum^{EDPRILEN_r}_{GCount=2}GrStudents_{GCount,g,r,t}*DropoutRate_{g,r,t}*(GCount-1))*\mathbf{\sum^{EDPRILEN}_{c=EDPRISTART}}FAGEDST_{g,r,t}}{\mathbf{\sum^{EDPRILEN}_{c=EDPRISTART}}FAGEDST_{g,r,t}}&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;Here, &amp;amp;nbsp;EDPRISUR is the survival rate in primary education, EDPRISTART is the official entrance age for primary schooling, Gr_Students is the enrollment at a certain grade, GCount is the grade counter and FAGEDST is the population of the single year age cohort corresponding to the grade level.&amp;amp;nbsp;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;Overall attainment, i.e., average years of education are calculated by averaging the attainments and partial attainments of five year age cohorts as shown in the equation below. The suffixes on the variables EDYRSAG15, EDYRSAG15TO24 and EDYRSAG25 indicate the age thresholds at which or the age bracket over which attainment is averaged.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span&amp;gt;&amp;lt;math&amp;gt;EDYRSAG15_{g,r,t}=AvgYearsPriEdPop_{g,r,t}+AvgYearsSecEdPop_{g,r,t}+AvgYearsTerEdPop_{g,r,t}+PartialYearsEdPop_{g,r,t}&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;Attainments by level, i.e., EDPRIPER, EDSECPER and EDTERPER are also obtained by summing across the corresponding five year cohorts, i.e., EdPriPopPer etc.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span&amp;gt;&amp;lt;math&amp;gt;EDPRIPER_{g,r,t}=\frac{\sum^{NCohorts}_{c=4}EdPriPopPer_{c,g,r,t}*AGEDST_{c,g,r,t}}{\sum^{NCohorts}_{c=4}AGEDST_{c,g,r,t}}&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;Cohort attainments by level of education are also used in to build a specialized educational attainment display, commonly referred to as education pyramid in congruence with demographic pyramids used to display population by age cohorts stacked one on top of the other with the men and women cohorts put opposite to each other around a vertical axis. Education pyramid superimposes educational attainment on top of the demographic pyramid.&amp;amp;nbsp;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Knowledge Systems&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== Overview ===&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;Knowledge and innovation are important drivers of &amp;amp;nbsp;economic growth and human well-being. These activities also &amp;amp;nbsp;help societies address major social and environmental challenges. Education and research and a linear relationship between these and product development are no longer considered a good model of knowledge and innovation systems. However, the linear model was the first successful attempt (Bush, V, 1945) in conceptualizing the science, technology and innovation (STI) activities. One of the major contributions of these first models was the distinction between basic and applied researches and the identification of stakeholders and funding for each type as shown in the next figure.&amp;lt;/span&amp;gt;[[File:Edknowledge1.png|frame|right|Linear model of STI activities]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;The failure of the linear model to capture the intricacies and interactions involved in the innovation process and the broader role of the public and private institutions and individuals in facilitating creation and diffusion of knowledge prompted some experts to resort to rich qualitative description of so called “national systems of innovation” starting from late 1980s, early 1990s. Increased educational attainment, fast expansion of information and communication technologies, more sophisticated production technologies and an expansion in the exchange of goods, ideas and people over the last few decades tell of something broader than just innovation constrained within national boundaries. Recent literature (citation) use concepts like knowledge economy or knowledge society to describe the systemic nature and impact of knowledge-intensive activities.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;This new literature takes an evolutionary perspective and talks about a gradual unfolding of knowledge and innovation system (citation: Nelson, Freeman etc) within a country marked by a certain types of actors, institutions and organizations and the linkages across and within such components. Studies in this area range from more focused concepts of knowledge economy (citation: WB; OECD) to a broader knowledge society (citation: UNESCO; Bell), from a more qualitative innovation systems approach (citation: Nelson; Freeman) to a measurement focused innovation capacity approach (citation: GII Dutta, Archibucchi..). The complementarity of the components of such a system demands that the components be studied together. Accordingly, experts have come up with composite indices for assessing the knowledge and innovation capacities of countries around the world. Such indices give a good idea of the overall status of the innovation capacities of the country and the stage of knowledge society it is in. The components of the composite indices are categorized across four to five major dimensions (or, pillars, as some studies call these), for example, education and skills, information infrastructure, institutional regime, innovation activities (WB Knowledge Index etc).&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;International Futures (IFs) Knowledge module builds on other knowledge systems measurement approaches (cite WB KEI here) by designing a composite knowledge index (KNTOTALINDEX) comprised of five sub-indices containing a total of (x) components. The indices and the sub-indices are then forecast over the entire IFs’ horizon by combining the components which are themselves forecast through different modules of the integrated IFs model. To our knowledge, IFs is the only model capable of making such an organic forecast of the knowledge capacity of a country.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;IFs Knowledge Indices&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;The capacity of a society to tap from and add to the pool of existing knowledge, local and global, depends on&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
*skills and qualifications of people to assimilate existing and new knowledge,&lt;br /&gt;
*an innovation system to facilitate development or adoption of of new knowledge, processes and products&lt;br /&gt;
*a technological infrastructure to share, disseminate and regenerate knowledge and information within and across societies&lt;br /&gt;
*political and institutional environment conducive to the generation, diffusion and utilization of knowledge&lt;br /&gt;
*regulations that offer appropriate incentives towards and remove barriers from international transfer of knowledge&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;The above list of the driving dimensions of a knowledge system is exhaustive, to the best of our knowledge. The list has five dimensions contrasted to the four pillars identified by the WB KAM. However, World Bank includes tariff &amp;amp; non-tariff barriers, an indicator of international transfer, in their fourth pillar on economic and institutional environment.&amp;amp;nbsp;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;IFs now has five indices representing the five dimensions described above. The details of each of these indices, and a sixth one averaged from these five, will be described later. Suffice here to say that, the indices are calculated each of the forecast years by averaging the forecasted value of relevant IFs variables, normalized over a continuous interval going from 0 to 1. That is, IFs integrated simulation, first, forecasts a specific variable, e.g., adult literacy rate, it then converts the forecast to a normalized value lying between zero to one and then averages one or more of these normalized values to obtain an index along each of the dimensions of knowledge assessment. The table below compares IFs knowledge indices with those from World Bank.&amp;amp;nbsp;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width: 100%; border: 1px solid #cccccc&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | &#039;&#039;&#039;No.&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | &#039;&#039;&#039;Dimension/Pillar&#039;&#039;&#039;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | &#039;&#039;&#039;World Bank Variables&#039;&#039;&#039;&amp;lt;br/&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | &#039;&#039;&#039;IFs Index&#039;&#039;&#039;&amp;lt;br/&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | &#039;&#039;&#039;IFs Variables&#039;&#039;&#039;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | 1&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Human Capital&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Adult literacy rate; Secondary enrollment rate; Tertiary enrollment rate&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | KNHCINDEX&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Adult literacy rate; Adult secondary graduation rate&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | 2&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Innovation&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | R&amp;amp;D researchers, Patent count; Journal articles (all per million people)&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | KNINNOVINDEX&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Total R&amp;amp;D expenditure (% of GDP); Tertiary graduation rate in science and engineering&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | 3&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | ICT&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Telephones (land + mobile) per 1000 persons; Computers per 1000 persons; Internet users per 10000 persons&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | KNICTINDEX&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Telephone (fixed); Mobile phone; Personal Computers; Broadband&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | 4&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Economic and Institutional Regime&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; rowspan=&amp;quot;2&amp;quot; | Tariff and non-tariff barriers; Regulatory quality; Rule of law&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | KNENVINDEX&amp;lt;br/&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Freedom; Economic freedom; Government regulation quality&amp;lt;br/&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | 5&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | International Transfer of Knowledge&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | KNEXTINDEX&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Economic integration index&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center; padding-left: 5px; padding-right: 5px&amp;quot; | 6&amp;lt;br/&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Composite Index&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | Knowledge Index, KI (from the first three) and Knowledge Economy Index, KEI (from all 4)&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | KNTOTALINDEX&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 5px; padding-right: 5px&amp;quot; | &lt;br /&gt;
From all of the above&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
[[File:Edknowledge2.png|frame|center|IFs Knowledge Model]]&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Knowledge Systems Equations: Total Knowledge Index&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span&amp;gt;The composite index (KNTOTALINDEX) consists of five sub-indices, of which the first four contains national actors and institutions only. The fifth one, international transfer index (KNEXTINDEX), attempts to capture the impact of global knowledge flows through a measure of the country’s openness to the international system. The first four sub-indices - human capital (KNHCINDEX), information infrastructure (KNICTINDEX), innovation systems (KNINNOVINDEX) and governance and business environment (KNENVINDEX) – will be described below. The external index (KNEXTINDEX) is given a somewhat lower weight in the total index than the other four sub-indices which are equally weighted to a total of 90% of the total index. KNEXTINDEX itself is constructed from two equally weighted components of international trade and foreign direct investment.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;KNTOTALINDEX_{r,t}=0.9*\frac{(KNHCINDEX_{r,t}+KNICTINDEX_{r,t}+KNINNOVINDEX_{r,t}+KNENVINDEX_{r,t})}{4}+0.1*(KNEXTINDEX_{r,t})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Knowledge Systems Equations: Knowledge Sub-Indices&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
In this section we describe the calculation method for various IFs knowledge indices.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
=== Human capital Index: KNHCINDEX ===&lt;br /&gt;
&lt;br /&gt;
The purpose of this index is to capture the cross-country differences in the productive capacity of an average worker. We use two educational stock variables for the purpose. Differences in the rate of literacy, the sheer ability to read or write, make a big difference in productivity in more traditional type and/or informal activities. As the countries move gradually a more traditional agricultural economy to comparatively higher value added activities, e.g., assembling machineries or running a call center, secondary education become more important. The index is built through a combination of two sub-indices: literacy index, LitIndex and secondary attainment index, AdultSecPerIndex, weighted equally.&lt;br /&gt;
&lt;br /&gt;
This index could be improved by adding a measure of the quality of education and an indicator of the skill-base of the worker. Unfortunately, IFs forecasts on those two areas are limited or non-existent at this point. [Note: The sub-indices – LitIndex and AdultSecPerIndex – used for this and other knowledge indices are calculated only in the model code. They are not available for display.]&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;KNHCINDEX_{r,t}=(LitIndex_{r,t}+AdultSecPerIndex_{r,t})/2&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Literacy index, with a theoretical range of values from 0 to 1, is calculated by dividing literacy rate, LIT, which can range from 0 to 100, by 100.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;LitIndex_{r,t}=LIT_{r,t}/100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
For the sub-index on secondary attainment (percentage of adults with completed secondary education), we use a similar normalization algorithm like the literacy sub-index.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;AdultSecPerIndex_{r,t}=EDSECPER_{r,total,t}/100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
LIT and EDSECPER are forecast in the IFs [[Population|population]] and [[Education#Education|education]] modules.&lt;br /&gt;
&lt;br /&gt;
Because it excludes any measure of higher education which is included in the innovation sub-index (KNINNOVINDEX) described below, KNHCINDEX turns out to be very useful in showing the differences across developing countries. Even for richer countries, most of which achieved near universal secondary enrollment and universal literacy, the index shows significant variance coming from the secondary attainment differences among the elderly.&lt;br /&gt;
&lt;br /&gt;
[[File:Edknowledge3.png|frame|center|KNINNOVINDEX]]&lt;br /&gt;
&lt;br /&gt;
=== Innovation Index: KNINNOVINDEX ===&lt;br /&gt;
&lt;br /&gt;
This IFs knowledge sub-index measures the innovation capacity of a nation through its R&amp;amp;D inputs – resources and personnel. It comprises of a total R&amp;amp;D expenditure index and a tertiary science and engineering graduation index as shown in the equations below.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;KNINNOVINDEX_{r,t}=(RandDExpIndex_{r,t}+EdTerGrateIndex_{r,t})/2&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
For R&amp;amp;D expenditure, the highest spenders like Israel and Finland, spend close to or little over 4% of GDP and we use that number as a maximum to normalize all other countries in a zero to one range.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;RandDExpIndex_{r,t}=RANDDEXP_{r,t}/4&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
For science and engineering graduation rate, 25% is used as a maximum. The equations below show the calculation which uses tertiary graduation percentage, EDTERGRATE &amp;lt;sub&amp;gt;Total&amp;lt;/sub&amp;gt; and the share of total graduates that obtain a science or engineering degree, EDTERGRSCIEN, both of which are forecast in the IFs education model.&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;EdTerGrateIndex_{r,t}=EDTERGRATE_{r,total,t}*\frac{EEDTERGRSCIEN_{r,t}}{100}/25&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
=== ICT Index: KNICTINDEX ===&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;Information and communication technologies (ICT) have a very significant role in facilitating the creation and diffusion of knowledge. IFs knowledge sub-index on ICT is built from the diffusion rates of core ICT technologies mobile, landline, broadband and a personal computer access rate sub-index. The telephone lines (fixed lines) sub-index, unlike the other three, use the logarithm of telephone line access rates as the differences in impacts of plain old telephone system decreases at higher access rates. In fact, the gradual shift from a wired to a wireless line as a personal communication device, demands that we reconsider the inclusion of this component in the ICT index.&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;KNICTINDEX_{r,t}=(ICTTelephoneIndex_{r,t}+ICTMobileIndex_{r,t}+ICTBroadIndex_{r,t}+ICTComputersIndex_{r,t})/4&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;ICTTelephoneIndex_{r,t}=log(INFRATELE_{r,t})/3&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;ICTMobileIndex_{r,t}=ICTMOBIL_{r,t}/100&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;ICTBroadIndex_{r,t}=ICTBROAD_{r,t}/100&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;ICTComputersIndex_{r,t}=ICTCOMPUTERS_{r,t}/100&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;Governance and Regulatory Environment: KNENVINDEX&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;The existence of economic and regulatory institutions and an effective governance of such institutions are important for generation, diffusion and utilization of knowledge. IFs knowledge sub-index representing these, KNENVINDEX, is calculated from three sub-indices which are themselves indices forecast by other IFs modules. These indices, one for economic freedom, a second one for overall freedom in the society and a third one on governance regulatory quality are each normalized to a 0 to 1 scale and averaged to get KNENVINDEX.&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;For the variables economic freedom, political freedom and governance regulation quality and average them to KNENVINDEX.&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;KNENVINDEX_{r,t}=(EconFreeIndex_{r,t}+FreeDomIndex_{r,t}+GovRegQualIndex_{r,t})/3&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;EconFreeIndex_{r,t}=ECONFREE_{r,t}/10&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;FreeDomIndex_{r,t}=FREEDOM_{r,t}/14&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;GovRegQualIndex_{r,t}=GOVREGQUAL_{r,t}/5&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;International Transfer Index: KNEXTINDEX&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;KNEXTINDEX attempts to represent cross-national knowledge flows, a major phenomenon in today’s globalized world. The more open a country is the more likely it is for her to learn from the global advancements in science, technology and other forms of knowledge. The sub-index that IFs calculates uses two indicators, trade and foreign direct investment (FDI). FDI indicator is given twice the weight given to trade volume.&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;KNEXTINDEX_{r,t}=(TradeIndex_{r,t}+2*InvIndex_{r,t})/2&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;span data-mce-mark=&amp;quot;1&amp;quot;&amp;gt;&amp;lt;math&amp;gt;TradeIndex_{r,t}=log\frac{XRPA_{r,t}+MRPA_{r,t}}{GDPPOT_{r,t}}/log1000&amp;lt;/math&amp;gt;&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;InvIndex_{r,t}=(log(\frac{XFDISTOCK_{r,t}+XFDISTOUT_{r,t}}{GDPPOT_{r,t}}))/log(500)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Education Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Archibugi, Daniele, and Alberto Coco. 2005. “Measuring Technological Capabilities at the Country Level: A Survey and a Menu for Choice.” Research Policy 34(2). Research Policy: 175–194.&lt;br /&gt;
&lt;br /&gt;
Bush, Vannevar. 1945. Science: The Endless Frontier. Washington: United States Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert and Jong-Wha Lee. 2010. &amp;quot;A New Data Set of Educational Attainment in the World, 1950-2010.&amp;quot; NBER Working Paper No. 15902. National Bureau of Economic Research, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert and Jong-Wha Lee. 2000. “International Data on Educational Attainment: Updates and Implications.” NBER Working Paper No. 7911. National Bureau of Economic Research, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
Bruns, Barbara, Alain Mingat, and Ramahatra Rakotomalala. 2003. Achieving Universal Primary Education by 2015: A Chance for Every Child. Washington, DC: World Bank.&lt;br /&gt;
&lt;br /&gt;
Chen, Derek H. C., and Carl J. Dahlman. 2005. The Knowledge Economy, the KAM Methodology and World Bank Operations. The World Bank, October 19.&lt;br /&gt;
&lt;br /&gt;
Clemens, Michael A. 2004. The Long Walk to School: International education goals in historical perspective. Econ WPA, March.&amp;amp;nbsp;[http://ideas.repec.org/p/wpa/wuwpdc/0403007.html http://ideas.repec.org/p/wpa/wuwpdc/0403007.html].&lt;br /&gt;
&lt;br /&gt;
Cohen, Daniel, and Marcelo Soto. 2001. “Growth and Human Capital: Good Data, Good Results.” Technical Paper 179.&amp;amp;nbsp; Paris: OECD.&lt;br /&gt;
&lt;br /&gt;
Cuaresma, Jesus Crespo, and Wolfgang Lutz. 2007 (April).&amp;amp;nbsp; “Human Capital, Age Structure and Economic Growth:&amp;amp;nbsp; Evidence from a New Dataset.” Interim Report IR-07-011. Laxenburg, Austria:&amp;amp;nbsp; International Institute for Applied Systems Analysis.&lt;br /&gt;
&lt;br /&gt;
Delamonica, Enrique, Santosh Mehrotra, and Jan Vandemoortele.&amp;amp;nbsp;2001 (August).&amp;amp;nbsp; “Is EFA Affordable? Estimating the Global Minimum Cost of ‘Education for All’”. Innocenti Working Paper No. 87.&amp;amp;nbsp; Florence: UNICEF Innocenti Research Centre.&amp;amp;nbsp;[http://www.unicef-irc.org/publications/pdf/iwp87.pdf http://www.unicef-irc.org/publications/pdf/iwp87.pdf].&lt;br /&gt;
&lt;br /&gt;
Dickson, Janet R., Barry B. Hughes, and Mohammod T. Irfan. 2010. Advancing Global Education. Vol 2, Patterns of Potential Human Progress series.&amp;amp;nbsp; Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.&amp;amp;nbsp;[http://www.ifs.du.edu/documents http://www.ifs.du.edu/documents].&lt;br /&gt;
&lt;br /&gt;
Dutta, Soumitra (Ed.). 2013. The Global Innovation Index 2013. The Local Dynamics of Innovation.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2004b (March).&amp;amp;nbsp; “International Futures (IFs): An Overview of Structural Design.” Pardee Center for International Futures Working Paper, Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx http://www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. and Evan E. Hillebrand. 2006.&amp;amp;nbsp;&amp;amp;nbsp;&#039;&#039;Exploring and Shaping International Futures&#039;&#039;.&amp;amp;nbsp; Boulder, Co:&amp;amp;nbsp; Paradigm Publishers.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. with Anwar Hossain and Mohammod T. Irfan. 2004 (May).&amp;amp;nbsp; “The Structure of IFs.” Pardee Center for International Futures Working Paper, Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx http://www.ifs.du.edu/documents/reports.aspx].&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Irfan, Mohammod T. 2008.&amp;amp;nbsp; “A Global Education Transition: Computer Simulation of Alternative Paths in Universal Basic Education,” Ph.D. dissertation presented to the Josef Korbel School of International Studies, University of Denver, Denver, Colorado.&amp;amp;nbsp;&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx http://www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
Juma, Calestous, and Lee Yee-Cheong. 2005. Innovation: Applying Knowledge in Development. London: Earthscan. (Available online at&amp;amp;nbsp;[http://www.unmillenniumproject.org/documents/Science-complete.pdf http://www.unmillenniumproject.org/documents/Science-complete.pdf&amp;amp;nbsp;])&lt;br /&gt;
&lt;br /&gt;
McMahon, Walter W. 1999 (first published in paperback in 2002).&amp;amp;nbsp; Education and Development: Measuring the Social Benefits. Oxford:&amp;amp;nbsp; Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Wils, Annababette and Raymond O&#039;Connor. 2003. “The causes and dynamics of the global education transition.” AED Working Paper. Washington, DC: Academy for Educational Development&lt;br /&gt;
&lt;br /&gt;
UNESCO. 2010. UNESCO Science Report 2010. The Current Status of Science around the World. UNESCO. Paris.&lt;br /&gt;
&lt;br /&gt;
World Bank. 2010. Innovation Policy: A Guide for Developing Countries. (Available online at&amp;amp;nbsp;[https://openknowledge.worldbank.org/bitstream/handle/10986/2460/548930PUB0EPI11C10Dislosed061312010.pdf?sequence=1 https://openknowledge.worldbank.org/bitstream/handle/10986/2460/548930PUB0EPI11C10Dislosed061312010.pdf?sequence=1])&lt;br /&gt;
&lt;br /&gt;
World Bank. 2007. Building Knowledge Economies: Advanced Strategies for Development. WBI Development Studies. Washington, D.C: World Bank. (Available online at&amp;amp;nbsp;[http://siteresources.worldbank.org/KFDLP/Resources/461197-1199907090464/BuildingKEbook.pdf http://siteresources.worldbank.org/KFDLP/Resources/461197-1199907090464/BuildingKEbook.pdf])&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8310</id>
		<title>Introduction to IFs</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8310"/>
		<updated>2017-09-07T21:39:33Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Purposes&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;International Futures (IFs) is a tool for thinking about long-term global trends and planning more strategically for the&amp;amp;nbsp;future.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
IFs can help you:&lt;br /&gt;
&lt;br /&gt;
*Understand&amp;amp;nbsp;the state of major&amp;amp;nbsp;global systems&lt;br /&gt;
*Explore&amp;amp;nbsp;long-term trends and consider where they might take&amp;amp;nbsp;us&lt;br /&gt;
*Learn about the dynamic&amp;amp;nbsp;interactions between global systems&lt;br /&gt;
*Clarify long-term organizational goals/priorities&lt;br /&gt;
*Develop alternative scenarios (if-then statements) about the future&lt;br /&gt;
*Investigate how different groups (households, firms or governments) can shape the future&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;IFs development and analysis depend&amp;amp;nbsp;on core, underlying assumptions, including the following.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*Global issues are becoming&amp;amp;nbsp;more significant as the scope of human interaction and human impact on the broader environment grow.&lt;br /&gt;
*Goals and priorities for human systems are becoming clearer and are more frequently and consistently communicated.&lt;br /&gt;
*Understanding of the dynamics of human systems is improving&amp;amp;nbsp;rapidly.&lt;br /&gt;
*The domain of human choice and action is broadening.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;What issues can you&amp;amp;nbsp;investigate with IFs?&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*[[Environment]]: Atmospheric carbon dioxide levels, world forest area, fossil fuel usage&lt;br /&gt;
*[[Socio-Political|Socio-Political Change]]: Life expectancy, literacy rate, democracy level, status of women, value change&lt;br /&gt;
*[[Population|Demographics]]: Population levels and growth, fertility, mortality, migration&lt;br /&gt;
*[[Agriculture|Food and Agriculture]]: Land use and production levels, calorie availability, malnutrition rates&lt;br /&gt;
*[[Energy]]: Resource and production levels, demand patterns, renewable energy share&lt;br /&gt;
*[[Economics]]: Sectoral production, consumption, and trade patterns and structural change&lt;br /&gt;
*[[Interstate_Politics_(IP)|Geopolitics]]: Country and regional power levels&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Visual Representation&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
[[File:IFsOverviewChart.jpg|frame|center|Visual representation of IFs structure]]&lt;br /&gt;
&lt;br /&gt;
Among the philosophical premises of the International Futures (IFs) project is that the model cannot be a &amp;quot;black box&amp;quot; to users and be truly useful. Model users must be able to examine the structures of IFs in order (1) to have confidence in them, and (2) learn from them.&lt;br /&gt;
&lt;br /&gt;
There is available (see topics under Understanding the Mode in the contents of this help system):&lt;br /&gt;
&lt;br /&gt;
*[[Understand_IFs#Dominant_Relations|Dominant Relations]] of the model structure&lt;br /&gt;
*[[Understand_IFs#2.2|Structure-Based and Agent-Class Driven Modeling]]&lt;br /&gt;
*[[Understand_IFs#Equation_Notation|Equation Notation]]&lt;br /&gt;
*[[Introduction_to_IFs#IFs_Bibliography|IFs Bibliography]] of data and data sources&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Quick Survey&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;population&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents 22 age-sex cohorts to age 100+&lt;br /&gt;
*calculates change in fertility and mortality rates in response to income, income distribution, and analysis multipliers&lt;br /&gt;
*computes average life expectancy at birth, literacy rate, and overall measures of human development (HDI) and physical quality of life&lt;br /&gt;
*represents migration and HIV/AIDS&lt;br /&gt;
*includes a newly developing submodel of formal education across primary, secondary, and tertiary levels&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;economic&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents the economy in six sectors: agriculture, materials, energy, industry, services, and ICT (other sectors could be configured, using raw data from the GTAP project)&lt;br /&gt;
*computes and uses input-output matrices that change dynamically with development level&lt;br /&gt;
*is a general equilibrium-seeking model that does not assume exact equilibrium will exist in any given year; rather it uses inventories as buffer stocks and to provide price signals so that the model chases equilibrium over time&lt;br /&gt;
*contains an endogenous production function that represents contributions to growth in multifactor productivity from R&amp;amp;D, education, worker health, economic policies (&amp;quot;freedom&amp;quot;), and energy prices (the &amp;quot;quality&amp;quot; of capital)&lt;br /&gt;
*uses a Linear Expenditure System to represent changing consumption patterns&lt;br /&gt;
*utilizes a &amp;quot;pooled&amp;quot; rather than the bilateral trade approach for international trade&lt;br /&gt;
*is being imbedded during 2002 in a social accounting matrix (SAM) envelope that will tie economic production and consumption to intra-actor financial flows&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;agricultural&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents production, consumption and trade of crops and meat; it also carries ocean fish catch and aquaculture in less detail&lt;br /&gt;
*maintains land use in crop, grazing, forest, urban, and &amp;quot;other&amp;quot; categories&lt;br /&gt;
*represents demand for food, for livestock feed, and for industrial use of agricultural products&lt;br /&gt;
*is a partial equilibrium model in which food stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the agricultural sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;energy&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*portrays production of six energy types: oil, gas, coal, nuclear, hydroelectric, and other renewable&lt;br /&gt;
*represents consumption and trade of energy in the aggregate&lt;br /&gt;
*represents known reserves and ultimate resources of the fossil fuels&lt;br /&gt;
*portrays changing capital costs of each energy type with technological change as well as with draw-downs of resources&lt;br /&gt;
*is a partial equilibrium model in which energy stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the energy sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The two &#039;&#039;&#039;socio-political&#039;&#039;&#039; sub-modules:&lt;br /&gt;
&lt;br /&gt;
Within countries or geographic groupings&lt;br /&gt;
&lt;br /&gt;
*represents fiscal policy through taxing and spending decisions&lt;br /&gt;
*shows six categories of government spending: military, health, education, R&amp;amp;D, foreign aid, and a residual category&lt;br /&gt;
*represents changes in social conditions of individuals (like fertility rates or literacy levels), attitudes of individuals (such as the level of materialism/postmaterialism of a society from the World Value Survey), and the social organization of people (such as the status of women)&lt;br /&gt;
*represents the evolution of democracy&lt;br /&gt;
*represents the prospects for state instability or failure&lt;br /&gt;
&lt;br /&gt;
Between countries or groupings of countries&lt;br /&gt;
&lt;br /&gt;
*traces changes in power balances across states and regions&lt;br /&gt;
*allows exploration of changes in the level of interstate threat&lt;br /&gt;
*represents possible action-reaction processes and arms races with associated potential for conflict among countries&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;environmental&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows tracking of remaining resources of fossil fuels, of the area of forested land, of water usage, and of atmospheric carbon dioxide emissions&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;technology&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows changes in assumptions about rates of technological advance in agriculture, energy, and the broader economy&lt;br /&gt;
*explicitly represents the extent of electronic networking of individuals in societies&lt;br /&gt;
*is tied to the governmental spending model with respect to R&amp;amp;D spending&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Background&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
International Futures (IFs) has evolved since 1980 through three &amp;quot;generations,&amp;quot; with a fourth generation now taking form.&lt;br /&gt;
&lt;br /&gt;
The first generation had deep roots in the world models of the 1970s, including those of the Club of Rome. In particular, IFs drew on the Mesarovic-Pestel or World Integrated Model (Mesarovic and Pestel 1974). The author of IFs had contributed to that project, including the construction of the energy submodel. IFs consciously also drew on the Leontief World Model (Leontief et al. 1977), the Bariloche Foundation’s world model (Herrera et al. 1976), and Systems Analysis Research Unit Model (SARU 1977), following comparative analysis of those models by Hughes (1980). That generation was written in FORTRAN and available for use on main-frame computers through CONDUIT, an educational software distribution center at the University of Iowa. Although the primary use of that and subsequent generations was by students, IFs has always had some policy analysis capability that has appealed to specialists. For example, the U.S. Foreign Service Institute used the first generation of IFs in a mid-career training program.&lt;br /&gt;
&lt;br /&gt;
The second generation of International Futures moved to early microcomputers in 1985, using the DOS platform. It was a very simplified version of the original IFs without regional or country differentiation.&lt;br /&gt;
&lt;br /&gt;
The third generation, first available in 1993, became a full-scale microcomputer model. The third generation improved earlier representations of demographic, energy, and food systems, but added new environmental and socio-political content. It built upon the collaboration of the author with the GLOBUS project, and it adopted the economic submodel of GLOBUS (developed by the author). GLOBUS had been created with the inspiration of Karl Deutsch and under the leadership of Stuart Bremer (1987) at the Wissenschaftszentrum in Berlin.&lt;br /&gt;
&lt;br /&gt;
The third generation has produced three editions/major releases of IFs, each accompanied by a book also called International Futures (Hughes 1993, 1996, 1999). The second edition moved to a Visual Basic platform that allowed a much improved menu-driven interface, running under Windows. The third edition incorporated an early global mapping capability and an initial ability to do cross-sectional and longitudinal data analysis.&lt;br /&gt;
&lt;br /&gt;
The fourth generation has been taking shape since early 2000. It has been heavily influenced by the usage of the model in an increasingly policy-analysis mode by several important organizations. First, General Motors commissioned a specialized version of IFs named CoVaTrA (Consumer Values Trends Analysis) with a need for updated and extended demographic modeling and representation of value change. An alliance was established with the World Values Survey, directed by Ronald Inglehart, to create that version. Second, the Strategic Assessments Group of the Central Intelligence Agency commissioned a specialized version named IFs for SAG. The work involved in preparing that greatly extended and enhanced the socio-political representations of the model, both domestic and international. Third, the European Commission sponsored a project named TERRA which has led to a specialized version named IFs for TERRA. IFs for TERRA work led to enhancements across the model, including improved representation of economic sectors, updated IO matrices and a basic Social Accounting Matrix, GINI and Lorenz curves, and preparing for extended environmental impact representation (drawing upon the Advanced Sustainability Analysis framework of the Finland Futures Research Center).&lt;br /&gt;
&lt;br /&gt;
Throughout this emergence of a fourth generation IFs (incorporating all of the above elements for additional users) there has been also a heavy emphasis on enhanced usability. Ideas from Robert Pestel in the TERRA project led to the creation of a new tree-structure for scenario creation and management. Ideas from Ronald Inglehart led to the development of the Guided Use structure and a somewhat more game-like character within that structure. Inglehart also help arrange funding to support the programming of Guided Use through the European Union Center of the University of Michigan.&lt;br /&gt;
&lt;br /&gt;
The fifth version of IFs is currently in use and represents broad strides to improving the model and its usability. It is the first version of this software to be placed online due to the help of the National Intelligence Council ([http://www.ifs.du.edu http://www.ifs.du.edu]). Also, usability has been increased as Packaged Displays and Flex Packaged Displays were introduced that allowed for the creation of very specific lists of countries/regions, groups or Glists. A new education model has also been incorporated into the broader IFs model. New scenarios were created for UNEP (focusing on environmental change) and Pardee (focusing on poverty). Finally, one of the largest changes made was incorporating 182 countries into the Base-Case scenario used by IFs. Previous versions of IFs used broader regions to forecast global trends. This change also did away with the Student and Professional versions.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Geographic Representation of the World&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
186 countries underpin the functioning of IFs and these countries can be displayed separately or as parts of larger groups that users can determine.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;Below is a visual representation of how different entities are organized into Countries/Regions, Groups or Glists:&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
[[File:Geo 186.png|frame|right|Visual representation of IF&#039;s definition of regions/countries/groups/glists]]&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;*Note: In older versions of IFs, Regions were used as intermediaries between Countries and Groups. In the future, they, or some similarly named unit, will be a sub-unit of Countries. Regions, acting as a sub-unit of Countries, are currently not a feature of IFs. See the image located at the bottom of this Help topic.&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
When using IFs, there are many occasions where the user is asked whether or not they would like to display their results as a product of single countries, or larger groups. This is typically a toggle switch that moves between Country/Region and Groups, however, it might be a three-way-toggle that includes Country/Region, Group and Glist.&lt;br /&gt;
&lt;br /&gt;
Countries/Regions are currently the smallest geographical unit that users can represent. The ability to split countries down into smaller regions, or states, is under development. There are 186 different countries/regions that users can display.&lt;br /&gt;
&lt;br /&gt;
Groups are variably organized geographically or by memberships in international institutions/regimes. You can find out who is represented in each group and add or delete members by exploring the [[Extended_Features#Manage_Groups/Regions|Managing Regionalization]] function.&lt;br /&gt;
&lt;br /&gt;
Glists merge both Groups and Countries/Regions. These lists are mostly geographically bound. In the future, the Glist distinction will become more important as some users may want to place, for example, both the Indian state of Kerala in a Glist with Sri Lanka and Nepal.&lt;br /&gt;
&lt;br /&gt;
Users may also want to [[Extended_Features#Change_Grouping/Regionalization|create]] their own groups or [[Extended_Features#Identify_Groups_or_Country/Region_Members|explore]] what countries are members of what groups.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Time Horizon&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Future Forecasts.&#039;&#039;&#039; IFs begins computation with data from 2000 and can dynamically calculate values for all variables annually through 2100.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Historical Analysis and &amp;quot;Forecasts.&amp;quot;&#039;&#039;&#039; IFs also includes an extensive and growing historical data base starting in 1960. The data basis allows analysis of relationships among variables across countries and across time.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Instructional Use&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The standard modes for using IFs in a classroom are:&lt;br /&gt;
&lt;br /&gt;
1. Assigning class members to an issue area or topic. Consider identifying specific questions for them to address.&lt;br /&gt;
&lt;br /&gt;
2. Assigning class members to a country/geographic region. Again, specificity helps.&lt;br /&gt;
&lt;br /&gt;
Most often, students will work independently or in groups on projects and share information after completing them. It is possible, however, to have students work interactively, by assigning them topics or regions, letting them begin work, and then have the interacting groups (or individuals) create a collective model run with the changes that each group proposes by topic or region. That process, although more difficult to organize, allows the class as whole to investigate the interaction of their topics or regions (and to share learning about model use).&lt;br /&gt;
&lt;br /&gt;
There is a&amp;amp;nbsp;[http://portfolio.du.edu/bhughes web site]&amp;amp;nbsp;available in support of the educational use of IFs. You will find syllabi at that site. There are several [[Introduction_to_IFs#Publications_on_IFs|publications]] on IFs, including a book structured specifically for educational use.&lt;br /&gt;
&lt;br /&gt;
Donald Borock has described his classroom use of IFs in print. Borock, Donald. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot; Advances in Social Science and Computers 4, 103-127.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Acknowledgements&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The author gratefully recognizes critical contributions in the forms of:&lt;br /&gt;
&lt;br /&gt;
:1. Testing and suggestions for development of IFs in one or more of multiple generations. By Donald Borock, Richard Chadwick, William Dixon, Dale Rothman, Phil Schrodt, Douglas Stuart, Donald Sylvan, Jonathan Wilkenfeld, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
:2. Computer assistance across many releases. By Michael Niemann, Terrance Peet-Lukes, Douglas McClure, Mohammod Irfan, and Jose Solorzano.&lt;br /&gt;
&lt;br /&gt;
:3. Data gathering and general assistance. By James Chung, Padma Padula, Shannon Brady, David Horan, Michael Ferrier, Kay Drucker, Warren Christopher, and Anwar Hossain.&lt;br /&gt;
&lt;br /&gt;
:4. Long-term encouragement and support. By Harold Guetzkow, Karl Deutsch, Richard Chadwick, Gerald Barney, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
:5. Association in related world modeling projects and projects building upon IFs. By Mihajlo Mesarovic, Aldo Barsotti, Juan Huerta, John Richardson, Thomas Shook, Patricia Strauch, and other members of the World Integrated Model (WIM) team. By Stuart Bremer, Peter Brecke, Thomas Cusack, Wolf Dieter-Eberwein, Brian Pollins, and Dale Smith of the GLOBUS modeling project. By Evan Hillebrand, Paul Herman, and others of the IFs for SAG project. By Rob Lempert and Steve Bankes at RAND, Santa Monica. By Robert Pestel, Jonathan Cave, Ronald Inglehart, Sergei Parinov, Pentti Malaska, and many others in the IFs for TERRA project.&lt;br /&gt;
&lt;br /&gt;
:6. Financial assistance (without responsibility for the form of the evolving product). By the National Science Foundation, the Cleveland Foundation, the Exxon Education Foundation, the Kettering Family Foundation, the Pacific Cultural Foundation, the United States Institute of Peace, General Motors, the Strategic Assessments Group of the Central Intelligence Agency, the European Commission (Information Society Technology) Programme, the European Union Center of the University of Michigan, the National Intelligence Council (for web conversion), and Frederick S. Pardee. &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Feedback&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
Feedback on how to improve IFs is always appreciated, especially if you find something that is not working. Compliments are also accepted. Please contact. To send the IFs team an e-mail, click on&amp;amp;nbsp;[mailto:pardee.center@du.edu Pardee Center]&amp;amp;nbsp;in stand-alone versions or on the web.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Support for IFs Use&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Publications on IFs&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
To obtain additional information about IFs and its use, consult:&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes and Evan E. Hillebrand, &#039;&#039;&#039;Exploring and Shaping International Futures.&#039;&#039;&#039; Boulder, CO: Paradigm Publishers, 2006. Specifically, see chapter 4.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &#039;&#039;&#039;International Futures: Choices in the Face of Uncertainty,&#039;&#039;&#039; 3rd ed. Boulder, CO: Westview Press, 1999. This volume is built around IFs and contains detailed suggestions for its use. Version 3.17 of IFs, which runs under Windows 95, is distributed with the third edition of the book. The second edition contained a version for Windows 3.1, and the first edition ran under DOS. Chapter 4 of the 2nd edition of IFs included Flow Charts of Worldviews , reproduced now in this Help system.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &#039;&#039;&#039;Continuity and Change in World Politics,&#039;&#039;&#039; 4th ed. Englewood Cliffs, N.J.: Prentice Hall, 2000. IFs can also usefully supplement this textbook on global politics.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &amp;quot;The International Futures (IFs) Modeling Project. 1999. &#039;&#039;&#039;Simulation and Gaming&#039;&#039;&#039; 30, No. 3 (September): 304-326.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;IFs Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Alcamo, Joseph, Rik Leemans and Eric Kreileman, eds. 1998.&amp;amp;nbsp;&#039;&#039;Global Change Scenarios of the 21st Century: Results from the IMAGE 2.1 Model&#039;&#039;. The Netherlands: Pergamon.&lt;br /&gt;
&lt;br /&gt;
Alcamo, Joseph. 1994.&amp;amp;nbsp;&#039;&#039;IMAGE 2.0: Integrated Modeling of Global Climate Change&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
&lt;br /&gt;
Alexandratos, Nikos, ed. 1995.&amp;amp;nbsp;&#039;&#039;World Agriculture: Towards 2010&#039;&#039;&amp;amp;nbsp;(An FAO Study). New York: FAO and John Wiley and Sons.&lt;br /&gt;
&lt;br /&gt;
Allen, R. G. D. 1968.&amp;amp;nbsp;&#039;&#039;Macro-Economic Theory: A Mathematical Treatment&#039;&#039;. New York: St. Martin&#039;s Press.&lt;br /&gt;
&lt;br /&gt;
Avery, Dennis. 1995. &amp;quot;Saving the Planet with Pesticides,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 50-82.&lt;br /&gt;
&lt;br /&gt;
Bailey, Ronald, ed. 1995.&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;. New York: The Free Press.&lt;br /&gt;
&lt;br /&gt;
Barbieri, Kathleen. 1996. &amp;quot;Economic Interdependence: A Path to Peace or a Source of Interstate Conflict?&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Peace Research&#039;&#039;&amp;amp;nbsp;33: 29-50.&lt;br /&gt;
&lt;br /&gt;
Barker, T.S. and A.W.A. Peterson, eds. 1987.&amp;amp;nbsp;&#039;&#039;The Cambridge Multisectoral Dynamic Model of the British Economy&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Barney, Gerald O., W. Brian Kreutzer, and Martha J. Garrett, eds. 1991.&amp;amp;nbsp;&#039;&#039;Managing a Nation&#039;&#039;, 2nd ed. Boulder: Westview Press.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. 1997.&amp;amp;nbsp;&#039;&#039;Determinants of Economic Growth: A Cross-Country Empirical Study&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. and Xavier Sala-i-Martin. 1999.&amp;amp;nbsp;&#039;&#039;Economic Growth&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
&lt;br /&gt;
Bennett, D. Scott, and Allan Stam. 2003.&amp;amp;nbsp;&#039;&#039;The Behavioral Origins of War: Cumulation and Limits to Knowledge in Understanding International Conflict&#039;&#039;. Ann Arbor: University of Michigan Press&lt;br /&gt;
&lt;br /&gt;
Birg, Herwig. 1995.&amp;amp;nbsp;&#039;&#039;World Population Projections for the 21st Century&#039;&#039;. Frankfurt: Campus Verlag.&lt;br /&gt;
&lt;br /&gt;
Borock, Donald M. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot;&amp;amp;nbsp;&#039;&#039;Advances in Social Science and Computers&#039;&#039;&amp;amp;nbsp;4, 103-127.&lt;br /&gt;
&lt;br /&gt;
Bos, Eduard, My T. Vu, Ernest Massiah, and Rodolfo A. Bulatao. 1994.&amp;amp;nbsp;&#039;&#039;World Population Projections 1994-95 Edition&#039;&#039;&amp;amp;nbsp;[editions are biannual] Baltimore: Johns Hopkins Press.&lt;br /&gt;
&lt;br /&gt;
Boulding, Elise and Kenneth E. Boulding. 1995.&amp;amp;nbsp;&#039;&#039;The Future: Images and Processes&#039;&#039;. Thousand Oaks, CA: Sage Publications.&lt;br /&gt;
&lt;br /&gt;
Brecke, Peter. 1993. &amp;quot;Integrated Global Models that Run on Personal Computers,&amp;quot;&amp;amp;nbsp;&#039;&#039;Simulation&#039;&#039;60 (2).&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. 1977.&amp;amp;nbsp;&#039;&#039;Simulated Worlds: A Computer Model of National Decision-Making&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A., ed. 1987.&amp;amp;nbsp;&#039;&#039;The GLOBUS Model: Computer Simulation of World-wide Political and Economic Developments&#039;&#039;. Boulder, CO: Westview.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. and Walter Gruhn. 1988.&amp;amp;nbsp;&#039;&#039;Micro GLOBUS: A Computer Model of Long-Term Global Political and Economic Processes&#039;&#039;. Berlin: edition sigma.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. and Barry B. Hughes. 1990.&amp;amp;nbsp;&#039;&#039;Disarmament and Development: A Design for the Future?&#039;&#039;&amp;amp;nbsp;Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Brockmeier, Martina and Channing Arndt (presentor). 2002. Social Accounting Matrices. Powerpoint presentation on GTAP and SAMs (June 21). Found on the web.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1981.&amp;amp;nbsp;&#039;&#039;Building a Sustainable Society&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1988. &amp;quot;Analyzing the Demographic Trap,&amp;quot; in&amp;amp;nbsp;&#039;&#039;State of the World 1987&#039;&#039;, eds. Lester R. Brown and others. New York: W.W. Norton, pp. 20-37.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1995.&amp;amp;nbsp;&#039;&#039;Who Will Feed China?&#039;&#039;&amp;amp;nbsp;New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1996.&amp;amp;nbsp;&#039;&#039;Tough Choices: Facing the Challenge of Food Scarcity&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., et al. 1996&amp;amp;nbsp;&#039;&#039;State of the World 1996&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., Nicholas Lenssen, and Hal Kane. 1995.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1995. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., Christopher Flavin, and Hal Kane. 1996.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1996. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Burkhardt, Helmut. 1995. &amp;quot;Priorities for a Sustainable Civilization,&amp;quot; unpublished conference paper. Department of Physics, Ryerson Polytechnic University, Toronto, Canada.&lt;br /&gt;
&lt;br /&gt;
Bussolo, Maurizio, Mohamed Chemingui and David O’Connor. 2002. A Multi-Region Social Accounting Matrix (1995) and Regional Environmental General Equilibrium Model for India (REGEMI). Paris: OECD Development Centre (February). Available at&amp;amp;nbsp;[http://www.oecd.org/dev/technics www.oecd.org/dev/technics].&lt;br /&gt;
&lt;br /&gt;
British Petroleum Company. 1995.&amp;amp;nbsp;&#039;&#039;BP Statistical Review of World Energy&#039;&#039;. London: British Petroleum Company.&lt;br /&gt;
&lt;br /&gt;
Central Intelligence Agency (CIA). 1991.&amp;amp;nbsp;&#039;&#039;Handbook of Economic Statistics, 1991&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
&lt;br /&gt;
Central Intelligence Agency (CIA). 1994.&#039;&#039;&amp;amp;nbsp;The World Factbook 1994&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
&lt;br /&gt;
Chang, Sheldon S. L. 1961.&amp;amp;nbsp;&#039;&#039;Synthesis of Optimum Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Chenery, Hollis and Moises Syrquin. 1975.&amp;amp;nbsp;&#039;&#039;Patterns of Development 1950-1970&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Cipolla, Carlo M. 1962.&amp;amp;nbsp;&#039;&#039;The Economic History of World Population&#039;&#039;. Baltimore: Penguin.&lt;br /&gt;
&lt;br /&gt;
Cook, Earl. 1976.&amp;amp;nbsp;&#039;&#039;Man, Energy, Society&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
&lt;br /&gt;
Committee on the Strategic Assessment of the U.S. Department of Energy’s Coal Program. 1995.&amp;amp;nbsp;&#039;&#039;Coal: Energy for the Future&#039;&#039;. Washington, D.C.: National Academy Press.&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1981.&amp;amp;nbsp;&#039;&#039;The Global 2000 Report to the President&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1981b.&amp;amp;nbsp;&#039;&#039;Environmental Trends&#039;&#039;. Washington, D.C. (July).&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1991.&amp;amp;nbsp;&#039;&#039;21st Annual Report&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Crescenzi, Mark J.C. and Andrew J. Enterline. 2001. &amp;quot;Time Remembered: A Dynamic Model of Interstate Interaction,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;45, no. 3 (September): 409-431.&lt;br /&gt;
&lt;br /&gt;
Crosson, Pierre, and Jock R. Anderson. 1992.&amp;amp;nbsp;&#039;&#039;Resources and Global Food Prospects&#039;&#039;. Washington, D.C.: The World Bank. World Bank Technical Paper Number 184.&lt;br /&gt;
&lt;br /&gt;
Cusack, Thomas R. and Richard J. Stoll. 1990.&amp;amp;nbsp;&#039;&#039;Exploring Realpolitik: Probing International Relations with Computer Simulatio&#039;&#039;n. Boulder: Lynne Rienner Publishers.&lt;br /&gt;
&lt;br /&gt;
Dargay, Joyce and Dermot Gately. 1999. &amp;quot;Income’s Effect on Car and Vehicle Ownership, Worldwide: 1960-2015,&amp;quot;&amp;amp;nbsp;&#039;&#039;Transportation Research Part A&#039;&#039;&amp;amp;nbsp;33: 101-138.&lt;br /&gt;
&lt;br /&gt;
Dall, P., Kaspar, F. and Alcamo, J. 1998. &amp;quot;Modeling World-wide Water Availability and Water Use Under the Influence of Climate Change,&amp;quot;&amp;amp;nbsp;&#039;&#039;Proceedings of the Second International Conference on Climate and Water&#039;&#039;, July 17-20, Espoo, Finland.&lt;br /&gt;
&lt;br /&gt;
Dimaranan, Betina V. and Robert A. McDougall, eds. 2002.&amp;amp;nbsp;&#039;&#039;Global Trade, Assistance, and Production: The GTAP 5 Data Base&#039;&#039;. Center for Global Trade Analysis, Purdue University. Available at [http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp].&lt;br /&gt;
&lt;br /&gt;
Dowlatabadi, H., and Morgan, M.G. 1993. &amp;quot;A Model Framework for Integrated Studies of the Climate Problem,&amp;quot;&amp;amp;nbsp;&#039;&#039;Energy Policy&#039;&#039;&amp;amp;nbsp;(March): 209-221.&lt;br /&gt;
&lt;br /&gt;
Duchin, Faye. 1998.&amp;amp;nbsp;&#039;&#039;Structural Economics: Measuring Change in Technology, Lifestyles, and the Environment&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
&lt;br /&gt;
Edwards, Stephen R. 1995. &amp;quot;Conserving Biodiversity,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 212-265.&lt;br /&gt;
&lt;br /&gt;
Edmonds, J., and Reilly, J.M. 1985.&amp;amp;nbsp;&#039;&#039;Global Energy: Assessing the Future&#039;&#039;. Oxford, UK: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Edmonds, J., Pitcher, H. Rosenberg, N., and Wigley, T. &amp;quot;Design for the Global Change Assessment Model.&amp;quot;&amp;amp;nbsp;&#039;&#039;Integrative Assessment of Mitigation, Impacts and Adaptation to Climate Change&#039;&#039;. Laxenburg, Austria.&lt;br /&gt;
&lt;br /&gt;
Ehrlich, Paul R. and Anne H. Ehrlich. 1972.&amp;amp;nbsp;&#039;&#039;Population, Resources, Environment&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
&lt;br /&gt;
Eicher, Carl. 1982. &amp;quot;Facing up to Africa&#039;s Food Crisis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Foreign Affairs&#039;&#039;&amp;amp;nbsp;61, no. 1 (Fall): 151-74.&lt;br /&gt;
&lt;br /&gt;
Eberstadt, Nicholas. 1995. &amp;quot;Population, Food, and Income,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 8-47.&lt;br /&gt;
&lt;br /&gt;
Esty, Daniel C., Jack A. Goldstone, Ted Robert Gurr, Barbara Harff, Marc Levy, Geoffrey D. Dabelko, Pamela T. Surko, and Alan N. Unger. 1998. State Failure Task Force Report: Phase II Findings. Volume provided courtesy of Ted Robert Gurr.&lt;br /&gt;
&lt;br /&gt;
Flavin, Christopher. 1996. &amp;quot;Facing Up to the Risks of Climate Change,&amp;quot; in Lester R. Brown and others, eds., State of the World 1996 (New York: W.W. Norton), pp. 21-39.&lt;br /&gt;
&lt;br /&gt;
Forrester, Jay W. 1968.&amp;amp;nbsp;&#039;&#039;Principles of Systems&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Gilpin, Robert. 1981.&amp;amp;nbsp;&#039;&#039;War and Change in World Politics&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Globerman, Steven. 2000 (May). Linkages Between Technological Change and Productivity Growth. Industry Canada Research Publications Program: Occasional Paper 23.&lt;br /&gt;
&lt;br /&gt;
Grant, Lindsey. 1982.&amp;amp;nbsp;&#039;&#039;The Cornucopian Fallacies&#039;&#039;. Washington, D.C.: Environmental Fund.&lt;br /&gt;
&lt;br /&gt;
Griffith, Rachel, Stephen Redding, and John Van Reenen. 2000.&amp;amp;nbsp;&#039;&#039;Mapping the Two Faces of R&amp;amp;D: Productivity Growth in a Panel of OECD Industries&#039;&#039;. Institute for Fiscal Studies (January)&lt;br /&gt;
&lt;br /&gt;
Gwartney, James and Robert Lawson with Dexter Samida. 2000.&amp;amp;nbsp;&#039;&#039;Economic Freedom of the World: 2000 Annual Report&#039;&#039;. Vancouver, B.C.: the Fraser Institute.&lt;br /&gt;
&lt;br /&gt;
Hammond, Allen. 1998.&amp;amp;nbsp;&#039;&#039;Which World? Scenarios for the 21st Century&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
&lt;br /&gt;
Harff, Barbara, with Ted Robert Gurr and Alan Unger. 1999. Preconditions of Genocide and Politicide: 1955-1998. Paper prepared for the State Failure Task Force and provided courtesy of Barbara Harff and Ted Gurr.&lt;br /&gt;
&lt;br /&gt;
Henderson, Hazel. 1996. &amp;quot;Changing Paradigms and Indicators: Implementing Equitable, Sustainable and Participatory Development,&amp;quot; in Jo Marie Griesgraber and Bernhard G. Gunter,&amp;amp;nbsp;&#039;&#039;Development: New Paradigms and Principles for the 21st Century&#039;&#039;. East Haven, CT: Pluto Press, pp. 103-136.&lt;br /&gt;
&lt;br /&gt;
Herrera, Amilcar O., et al. 1976.&#039;&#039;&amp;amp;nbsp;Catastrophe or New Society? A Latin American World Model&#039;&#039;. Ottawa: International Development Research Centre.&lt;br /&gt;
&lt;br /&gt;
Hoekstra, A.Y. 1998.&amp;amp;nbsp;&#039;&#039;Perspectives on Water: An Integrated Model-Based Exploration of the Future&#039;&#039;. Utrecht, the Netherlands: International Books.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1980.&amp;amp;nbsp;&#039;&#039;World Modeling&#039;&#039;. Lexington, Mass: Lexington Books.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1982.&amp;amp;nbsp;&#039;&#039;International Futures Simulation: User&#039;s Manual&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985a.&amp;amp;nbsp;&#039;&#039;International Futures Simulation&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985b. &amp;quot;World Models: The Bases of Difference,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;29, 77-101.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985c.&amp;amp;nbsp;&#039;&#039;World Futures: A Critical Analysis of Alternatives&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1987. &amp;quot;Domestic Economic Processes,&amp;quot; in Stuart A. Bremer, ed.,&amp;amp;nbsp;&#039;&#039;The Globus Model: Computer Simulation of Worldwide Political Economic Development&#039;&#039;&amp;amp;nbsp;(Frankfurt and Boulder: Campus and Westview), pp. 39-158.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1988. &amp;quot;International Futures: History and Status,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Science Microcomputer Review&#039;&#039;&amp;amp;nbsp;6, 43-48.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1999. &amp;quot;The International Futures (IFs) Modeling Project.&#039;&#039;&amp;amp;nbsp;Simulation and Gaming&#039;&#039;&amp;amp;nbsp;Vol 30, No. 3 (September): 304-326.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1999.&amp;amp;nbsp;&#039;&#039;International Futures&#039;&#039;, 3rd edition Boulder: Westview Press, 1999.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2000.&amp;amp;nbsp;&#039;&#039;Continuity and Change in World Politics&#039;&#039;. Englewood Cliffs, N.J.: Prentice-Hall, fourth edition.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2001. &amp;quot;Global Social Transformation: The Sweet Spot, the Steady Slog, and the Systemic Shift,&amp;quot;&amp;amp;nbsp;&#039;&#039;Economic Development and Cultural Change&#039;&#039;&amp;amp;nbsp;49, No. 2 (January): 423-458.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2002.&amp;amp;nbsp;&#039;&#039;Theats and Opportunities Analysis&#039;&#039;. Living document prepared for the Strategic Assessments Group, Office of Transnational Issues, Central Intelligence Agency, August 2002.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. and Anwar Hossain. 2003. Long-Term Socio-Economic Modeling: With Universal, Globally-Integrated Social Accounting Matrices (SAMs) in a General Equilibrium Model Structure. IFs Project Living Document, University of Denver.&lt;br /&gt;
&lt;br /&gt;
Huth, Paul. 1996.&amp;amp;nbsp;&#039;&#039;Standing Your Ground: Territorial Disputes and International Conflict&#039;&#039;. Ann Arbor, MI: University of Michigan Press.&lt;br /&gt;
&lt;br /&gt;
Inglehart, Ronald. 1997.&amp;amp;nbsp;&#039;&#039;Modernization and Postmodernization: Cultural, Economic, and Political Change in 43 Societies&#039;&#039;. Ewing, NJ: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1995.&amp;amp;nbsp;&#039;&#039;Oil, Gas, and Coal Supply Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996.&amp;amp;nbsp;&#039;&#039;World Energy Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996b.&amp;amp;nbsp;&#039;&#039;The Strategic Value of Fossil Fuels: Challenges and Responses&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;International Financial Statistics&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;World Economic Outlook&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
Intergovernmental Panel on Climate Change (IPCC). 1995. Several volumes by various working groups. Published by Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Jansen, Karel and Rob Vos, eds. 1997.&amp;amp;nbsp;&#039;&#039;External Finance and Adjustment: Failure and Success in the Developing World&#039;&#039;. London: Macmillan Press Ltd.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1998.&amp;amp;nbsp;&#039;&#039;Modeling Global Change: The Art of Integrated Assessment Modelling&#039;&#039;. Cheltenham, UK: Edward Elgar.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1996.&amp;amp;nbsp;&#039;&#039;Meeting Targets: Tools to Support Integrated Modelling of Global Change&#039;&#039;. Den Haag: CIP-Gegevens Koninklijke Bibliotheek.&lt;br /&gt;
&lt;br /&gt;
Jansson, Kurt, Michael Harris, Angela Penrose. 1987.&amp;amp;nbsp;&#039;&#039;The Ethiopian Famine&#039;&#039;. London: Zed Books Ltd.&lt;br /&gt;
&lt;br /&gt;
Jeffreys, Kent. 1995. &amp;quot;Rescuing the Oceans,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 296-338.&lt;br /&gt;
&lt;br /&gt;
Jones, Daniel M., Stuart A. Bremer, and J. David Singer. 1996. &amp;quot;Militarized Interstate Disputes, 1816-1992: Rationale, Coding Rules, and Empirical Patterns,&amp;quot;&amp;amp;nbsp;&#039;&#039;Conflict Management and Peace Science&#039;&#039;&amp;amp;nbsp;XV, No. 2: 163-215.&lt;br /&gt;
&lt;br /&gt;
Khan, Haider A. 1998.&amp;amp;nbsp;&#039;&#039;Technology, Development and Democracy&#039;&#039;. Northhampton, Mass: Edward Elgar Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Kahn, Herman, William Brown, and Leon Martel. 1976.&amp;amp;nbsp;&#039;&#039;The Next 200 Years&#039;&#039;. New York: William Morrow.&lt;br /&gt;
&lt;br /&gt;
Kalymon, Basil A. 1975. &amp;quot;Economic Incentives in OPEC Oil Pricing Policy.&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Development Economics&#039;&#039;&amp;amp;nbsp;2: 337-362.&lt;br /&gt;
&lt;br /&gt;
Kaplan, Robert. 1994. &amp;quot;The Coming Anarchy,&amp;quot;&amp;amp;nbsp;&#039;&#039;The Atlantic Monthly&#039;&#039;&amp;amp;nbsp;273 (February): .&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999a. &amp;quot;Aggregating Governance Indicators&amp;quot;. World Bank Policy Research Department Working Paper No. 2195.&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999b. &amp;quot;Governance Matters&amp;quot;. World Bank Policy Research Department Working Paper No. 2196.&lt;br /&gt;
&lt;br /&gt;
Keepin, B. and B. Wynne. 1984. &amp;quot;Technical Analysis of the IIASA Energy Scenarios,&amp;quot;&amp;amp;nbsp;&#039;&#039;Nature&#039;&#039;312: 691-695.&lt;br /&gt;
&lt;br /&gt;
Kehoe, Timothy J. 1996. Social Accounting Matrices and Applied General Equilibrium Models. Federal Reserve Bank of Minneapolis, Working Paper 563.&lt;br /&gt;
&lt;br /&gt;
Kennedy, Paul. 1993.&amp;amp;nbsp;&#039;&#039;Preparing for the Twenty-First Century&#039;&#039;. New York: Random House.&lt;br /&gt;
&lt;br /&gt;
Klein, Lawrence R. and Fu-chen Lo, eds. 1995.&amp;amp;nbsp;&#039;&#039;Modeling Global Change&#039;&#039;. Tokyo: United Nations University Press.&lt;br /&gt;
&lt;br /&gt;
Kornai, J. 1971.&amp;amp;nbsp;&#039;&#039;Anti-Equilibrium&#039;&#039;. Amsterdam: North Holland.&lt;br /&gt;
&lt;br /&gt;
Kwasnicki, Witold and Halina Kwasnicka. 1996. &amp;quot;Long-Term Diffusion Factors of Technological Development: An Evolutionary Model and Case Study,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 31-57.&lt;br /&gt;
&lt;br /&gt;
Leontief, Wassily, Anne Carter and Peter Petri. 1977.&amp;amp;nbsp;&#039;&#039;The Future of the World Economy&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander H., and Elizabeth R. Ducot. 1976. &amp;quot;AGRIMOD: A Simulation Model for the Analysis of U.S. Food Policies.&amp;quot; Paper delivered at Conference on Systems Analysis of Grain Reserves, Joint Annual Meeting of GRSA and TIMS, Philadelphia, Pa., March 31-April 2.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander, H., et al. 1977. Energy in Agriculture: On Modeling Inputs in AGRIMOD. Final Report to U.S. Department of Energy. Palo Alto: Systems Control, Inc., August, available through NTIS.&lt;br /&gt;
&lt;br /&gt;
Lichbach, Mark Irving. 1989. &amp;quot;An Evaluation of ‘Does Economic Inequality Breed Political Conflict?,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;, Vol 41 , No. 4 (July 1989): 431-470.&lt;br /&gt;
&lt;br /&gt;
Liverman, Dianne. 1983.&amp;amp;nbsp;&#039;&#039;The Use of Global Simulation Models in Assessing Climate Impacts on the World Food System&#039;&#039;. Dissertation, University of California, Los Angeles.&lt;br /&gt;
&lt;br /&gt;
Londregan, John B. and Keith T. Poole. 1996. &amp;quot;Does High Income Promote Democrary?&amp;quot;,&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 1 (October): 1-30.&lt;br /&gt;
&lt;br /&gt;
MacKenzie, James J. 1996. &amp;quot;Oil as a Finite Resource: When is Global Production Likely to Peak?&amp;quot; Paper of the World Resources Institute. Washington, D.C.: WRI.&lt;br /&gt;
&lt;br /&gt;
Maddison, Angus. 1995.&amp;amp;nbsp;&#039;&#039;Monitoring the World Economy 1820-1992&#039;&#039;. Paris: OECD.&lt;br /&gt;
&lt;br /&gt;
Malthus, Thomas. 1798.&amp;amp;nbsp;&#039;&#039;An Essay on the Principle of Population as It Affects the Future Improvement of Society&#039;&#039;. London (reprinted many times).&lt;br /&gt;
&lt;br /&gt;
Mansfield, Edward D. 1994.&amp;amp;nbsp;&#039;&#039;Power, Trade, and War&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Marchetti, Cesare, Perrin S. Meyer, and Jesse H. Ausubel. 1996. &amp;quot;Human Population Dynamics Revisited with the Logistic Model: How Much Can be Modeled and Predicted?,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 1-30.&lt;br /&gt;
&lt;br /&gt;
Martens, Pim and Jan Rotmans, eds. 1999.&amp;amp;nbsp;&#039;&#039;Climate Change: An Integrated Perspective&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
&lt;br /&gt;
Martens, W.J.M. 1997. &amp;quot;Health Impacts of Climate Change and Ozone Depletion: An Eco-Epidemiological Approach,&amp;quot; Maastricht, the Netherlands: Maastricht University.&lt;br /&gt;
&lt;br /&gt;
Mason, Andrew. 1997. &amp;quot;The Role of Population Change in the Asian Economic Miracle,&amp;quot; Honolulu, Hawaii: East-West Center, AsiaPacific Issues, No. 33 (October), 8 pages.&lt;br /&gt;
&lt;br /&gt;
McMahon, Walter W. 1997.&amp;amp;nbsp;&#039;&#039;Education and Development: Measuring the Social Benefits&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, Jorgen Randers, and William K. Behrens, III. 1972.&amp;amp;nbsp;&#039;&#039;Limits to Growth&#039;&#039;. New York: Universe Books.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, and Jorgen Randers. 1992.&amp;amp;nbsp;&#039;&#039;Beyond the Limits&#039;&#039;. Post Mills, Vermont: Chelsea Green Publishing Company.&lt;br /&gt;
&lt;br /&gt;
Meadows, Dennis L. et al. 1974.&amp;amp;nbsp;&#039;&#039;Dynamics of Growth in a Finite World&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Mesarovic, Mihajlo D. and Eduard Pestel. 1974.&amp;amp;nbsp;&#039;&#039;Mankind at the Turning Point&#039;&#039;. New York: E.P. Dutton &amp;amp; Co.&lt;br /&gt;
&lt;br /&gt;
Mishkin, Eli. And Ludwig Braun, ed. 1961.&amp;amp;nbsp;&#039;&#039;Adaptive Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Moore, Will H., Ronny Lindstrom, and Valerie O’Regan. 1996. &amp;quot;Land Reform, Political Violence and the Economic Inequality-Political Conflict Nexus: A Longitudinal Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Interactions&#039;&#039;&amp;amp;nbsp;21, No. 4: 335-363.&lt;br /&gt;
&lt;br /&gt;
Mori, Shunsuke and Masato Takahaashi, 1997. An Integrated Assessment Model for the Evaluation of New Energy Technologies and Food Production, accepted by&amp;amp;nbsp;&#039;&#039;International Journal of Global Energy Issues&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
Naill, Roger F. 1977.&amp;amp;nbsp;&#039;&#039;Managing the Energy Transition&#039;&#039;. Vols. 1 and 2. Cambridge, Mass: Ballinger Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1992. &amp;quot;The DICE Model: Background and Structure of a Dynamic Integrated Climate Economy,&amp;quot; New Haven: Yale University.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1979.&amp;amp;nbsp;&#039;&#039;The Efficient Use of Energy Resources&#039;&#039;. New Haven, CT: Yale University Press.&lt;br /&gt;
&lt;br /&gt;
Oneal, John R. and Bruce M. Russett. 1997. The Classical Liberals were Right: Democracy, Interdependence, and Conflict, 1950-1985.&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;41, no. 2 (June): 267-294.&lt;br /&gt;
&lt;br /&gt;
Pan, Xiaoming. 2000 (January). &amp;quot;Social and Ecological Accounting Matrix: an Empirical Study for China,&amp;quot; paper submitted for the Thirteenth International Conference on Input-Output Techniques, Macerata, Italy, August 21-25, 2000.&lt;br /&gt;
&lt;br /&gt;
Pesaran, M. Hashem and G. C. Harcourt. 1999. Life and Work of John Richard Nicholas Stone.&lt;br /&gt;
&lt;br /&gt;
Pirages, Dennis. 1989.&amp;amp;nbsp;&#039;&#039;Global Technopolitics&#039;&#039;. Pacific Grove, Calif: Brooks/Cole Publishing.&lt;br /&gt;
&lt;br /&gt;
Prinn, R. H.J., A. Sokolov, C. Wand, X. Xiao, Z. Yang, R. Eckhaus, P. Stone, D. Ellerman, J Melilo, J. Fitzmaurice, D. Kicklighter, and Y. Liu. 1996. &amp;quot;Integrated Global System Model for Climate Policy Analysis: Model Framework and Sensitivity Analysis.&amp;quot; Cambridge, Mass: Global Change Center, Massachusetts Institute of Technology.&lt;br /&gt;
&lt;br /&gt;
Przeworski, Adam and Fernando Limongi. 1997. &amp;quot;Modernization: Theories and Facts,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 2 (January): 155-183.&lt;br /&gt;
&lt;br /&gt;
Population Reference Bureau. 1996. World Population Data Sheet 1996. Washington, D.C.: Population Reference Bureau.&lt;br /&gt;
&lt;br /&gt;
Postel, Sandra. 1996.&amp;amp;nbsp;&#039;&#039;Dividing the Waters: Food Security, Ecosystem Health, and the New Politics of Scarcity&#039;&#039;. Worldwatch Paper 132. Washington, D.C.: Worldwatch Institute, September.&lt;br /&gt;
&lt;br /&gt;
Pyatt, G. and J.I. Round, eds. 1985.&amp;amp;nbsp;&#039;&#039;Social Accounting Matrices: A Basis for Planning&#039;&#039;. Washington, D.C.: The World Bank.&lt;br /&gt;
&lt;br /&gt;
Raskin, P., T. Banuri, G. Gallopín, P. Gutman, A. Hammond, R. Kates, and R. Swart. 2001. Great Transition:&amp;amp;nbsp;&#039;&#039;The Promise and Lure of the Times Ahead&#039;&#039;. Forthcoming.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1990.&amp;amp;nbsp;&#039;&#039;Global Politics&#039;&#039;, 4th edition. Boston: Houghton Mifflin.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1995.&amp;amp;nbsp;&#039;&#039;Democracy and International Conflict&#039;&#039;. Columbia: University of South Carolina Press.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee and J. David Singer. 1973. &amp;quot; Measuring the Concentration of Power in the International System,&amp;quot;&#039;&#039;&amp;amp;nbsp;Sociological Methods and Research&#039;&#039;&amp;amp;nbsp;1, no. 4: 403-436. Reprinted in&amp;amp;nbsp;&#039;&#039;Measuring the Correlates of War&#039;&#039;, edited by J. David Singer and Paul Diehl. Ann Arbor: University of Michigan Press, 1990.&lt;br /&gt;
&lt;br /&gt;
Rayner. S. 1992. &amp;quot;Cultural Theory and Risk Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Theory of Risk&#039;&#039;, ed. G. D. Preagor. Westport, USA.&lt;br /&gt;
&lt;br /&gt;
Repetto, Robert and Duncan Austin. 1997.&amp;amp;nbsp;&#039;&#039;The Costs of Climate Protection&#039;&#039;. Washington, D.C.: World Resources Institute.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis Fry. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Chicago: Quadrangle Books.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis F. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Pittsburgh: Boxwood Press.&lt;br /&gt;
&lt;br /&gt;
Romer, Paul M. 1994. &amp;quot;The Origins of Endogenous Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Economic Perspectives&#039;&#039;Vol 8, No. 1 (Winter): 3-22.&lt;br /&gt;
&lt;br /&gt;
Root T. and Stephen Schneider. 1995. &amp;quot;Ecology and Climate: Research Strategies and Implications,&amp;quot; Science 269 (52): 334-341.&lt;br /&gt;
&lt;br /&gt;
Rosegrant, Mark W., Mercedita Agcaoili-Sombilla, and Nicostrato D. Perez. 1995. &amp;quot;Global Food Projections to 2020: Implications for Investment.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 5.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1999. Integrated Assessment Models: Uncertainty, Quality and Use. Maastricht, the Netherlands: Maastricht University, International Centre for Integrative Studies (ICIS), Working Paper 199-E005.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and Burt de Vries, eds. 1997.&amp;amp;nbsp;&#039;&#039;Perspectives on Global Change: The Targets Approach&#039;&#039;. Cambridge, UK: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and M.B.A. van Asselt. 1996. &amp;quot;Integrated Assessment: A Growing Child on its Way to Maturity,&amp;quot;&amp;amp;nbsp;&#039;&#039;Climatic Change&#039;&#039;&amp;amp;nbsp;34 (3-4): 327-336.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1990.&amp;amp;nbsp;&#039;&#039;IMAGE: An Integrated Model to Assess the Greenhouse Effect&#039;&#039;. Dordrecht, the Netherlands: Kluwer Academics.&lt;br /&gt;
&lt;br /&gt;
Saaty, Thomas L. 1996. The Analytic Network Process: Decision Making with Dependence and Feedback. Pittsburgh: RWS Publications.&lt;br /&gt;
&lt;br /&gt;
Schafer, Andreas and David G. Victor. 1997. The Future Mobility of the World Population. Massachusetts Institute of Technology and International Institute for Applied Systems Analysis, Discussion Paper 97-6-4 (revision 2, September).&lt;br /&gt;
&lt;br /&gt;
Scheer, Sara J. and Satya Yadav. 1996. &amp;quot;Land Degradation in the Developing World: Implications for Food, Agriculture, and the Environment to 2020.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 14.&lt;br /&gt;
&lt;br /&gt;
Schneider, Stephen. 1997. &amp;quot;Integrated Assessment Modeling of Climate Change: Transparent Rational Tool for Policy Making or Opaque Screen Hiding Value-Laden Assumptions?&amp;quot;&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(4): 229-250.&lt;br /&gt;
&lt;br /&gt;
Schwartz, Peter. 1996.&#039;&#039;&amp;amp;nbsp;The Art of the Long View.&#039;&#039;&amp;amp;nbsp;New York: Doubleday.&lt;br /&gt;
&lt;br /&gt;
Sedjo, Roger A. 1995. &amp;quot;Forests: Conflicting Signals,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 178-209.&lt;br /&gt;
&lt;br /&gt;
Shane, Harold G. and Gary A. Sojka. 1990. &amp;quot;John Elfreth Watkins, Jr.: Forgotten Genius of Forecasting,&amp;quot; in Edward Cornish, ed.,&#039;&#039;&amp;amp;nbsp;The 1990s and Beyond&#039;&#039;. Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Shaw, Timothy W. and Clement E. Adibe. 1995-96. &amp;quot;Africa and Global Developments in the Twenty-First Century,&amp;quot; International Journal 51 (Winter): 1-26.&lt;br /&gt;
&lt;br /&gt;
Siegmann, Heinrich. 1985.&amp;amp;nbsp;&#039;&#039;Recent Developments in World Modeling&#039;&#039;. Berlin: Science Center.&lt;br /&gt;
&lt;br /&gt;
Simon, Julian. 1981.&amp;amp;nbsp;&#039;&#039;The Ultimate Resource&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Singer, J. David, Stuart Bremer, and John Stuckey. 1972. &amp;quot;Capability Distribution, Uncertainty, and Major Power Wars, 1820-1965.&amp;quot; In Bruce Russett, ed.,&amp;amp;nbsp;&#039;&#039;Peace, War, and Numbers.&#039;&#039;&amp;amp;nbsp;Beverly Hills: Sage.&lt;br /&gt;
&lt;br /&gt;
Sivard, Ruth Leger. 1993.&amp;amp;nbsp;&#039;&#039;World Military and Social Expenditures 1993.&#039;&#039;&amp;amp;nbsp;Washington, D.C. 20007: World Priorities, Box 25140.&lt;br /&gt;
&lt;br /&gt;
Solow, Robert M. 1956. &amp;quot;A Contribution to the Theory of Economic Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Quarterly Journal of Economics&#039;&#039;&amp;amp;nbsp;70, 1 (February): 65-94.&lt;br /&gt;
&lt;br /&gt;
Stanford University. 1978.&amp;amp;nbsp;&#039;&#039;Stanford Pilot Energy/Economic Model&#039;&#039;. Stanford: Department of Research, Interim Report, Vol. 1.&lt;br /&gt;
&lt;br /&gt;
Stockholm International Peace Research Institute (SIPRI). 1994.&amp;amp;nbsp;&#039;&#039;SIPRI Yearbook&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Stone, Richard. 1986. &amp;quot;The Accounts of Society,&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Applied Econometrics&#039;&#039;&amp;amp;nbsp;1, no. 1 (January): 5-28.&lt;br /&gt;
&lt;br /&gt;
Strategic Assessments Group (SAG), Office of Transnational Issues, Directorate of Intelligence. 2001 (February). The Global Economy in the Long Term. OTI IR 2001-013.&lt;br /&gt;
&lt;br /&gt;
Systems Analysis Research Unit (SARU). 1977.&amp;amp;nbsp;&#039;&#039;SARUM 76 Global Modeling Project&#039;&#039;. Departments of the Environment and Transport, 2 Marsham Street, London, 3WIP 3EB.&lt;br /&gt;
&lt;br /&gt;
Tammen, Ronald L, Jacek Kugler, Douglas Lemke, Allan C. Stam III, Carole Alsharabati, Mark Andrew Abdollahian, Brian Efird, and A.F.K. Organski. 2000. Power Transitions: Strategies for the 21st Century. New York: Chatham House Publishers.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1975. &amp;quot;Theoretical Foundations and Technical Implications.&amp;quot; in Charles Blitzer, Peter Clark and Lance Taylor, eds.,&amp;amp;nbsp;&#039;&#039;Economy-Wide Models and Development Planning.&#039;&#039;&amp;amp;nbsp;Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1979.&amp;amp;nbsp;&#039;&#039;Macro Models for Developing Countries&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Thirlwall, A. P. 1977.&amp;amp;nbsp;&#039;&#039;Growth and Development&#039;&#039;. New York: John Wiley and Sons.&lt;br /&gt;
&lt;br /&gt;
Thompson, M. 1997. Cultural Theory and Integrated Assessment.&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(3): 139-150.&lt;br /&gt;
&lt;br /&gt;
Thompson, M., R. Ellis and A. Wildavsky. 1990.&amp;amp;nbsp;&#039;&#039;Cultural Theory&#039;&#039;. Boulder, Co: Westview Press.&lt;br /&gt;
&lt;br /&gt;
Thorbecke, Erik. 2001. &amp;quot;The Social Accounting Matrix: Deterministic or Stochastic Concept?&amp;quot;, paper prepared for a conference in honor of Graham Pyatt&#039;s retirement, at the Institute of Social Studies, The Hague, Netherlands (November 29 and 30). Available at [http://people.cornell.edu/pages/et17/etpapers.html http://people.cornell.edu/pages/et17/etpapers.html].&lt;br /&gt;
&lt;br /&gt;
United Nations, Department of Economic and Social Affairs. 1956.&amp;amp;nbsp;&#039;&#039;Methods of Population Projections by Sex and Age&#039;&#039;. New York: United Nations, ST/SOA Series A.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1992.&amp;amp;nbsp;&#039;&#039;Long-Range World Population Projections. Two Centuries of Population Growth: 1950-2150&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1993.&amp;amp;nbsp;&#039;&#039;World Population Prospects - the 1992 Revision&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations Development Program (UNDP). 1995.&amp;amp;nbsp;&#039;&#039;Human Development Report&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1992.&amp;amp;nbsp;&#039;&#039;Production Yearbook.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1995.&#039;&#039;&amp;amp;nbsp;World Agriculture: Towards 2010.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 1999. The World at Six Billion New York: UN.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2000. Replacement Migration: Is it a Solution to Declining and Ageing Populations? New York: UN.&lt;br /&gt;
&lt;br /&gt;
United States Arms Control and Disarmament Agency (ACDA). 1995.&amp;amp;nbsp;&#039;&#039;World Military Expenditures and Arms Transfers 1995&#039;&#039;. Washington, D.C.: Arms Control and Disarmament Agency.&lt;br /&gt;
&lt;br /&gt;
United States Bureau of the Census. 1991.&amp;amp;nbsp;&#039;&#039;World Population Profile: 1991&#039;&#039;. Report WP/91 Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Walters, Robert S. and David H. Blake. 1992.&amp;amp;nbsp;&#039;&#039;The Politics of Global Economic Relations&#039;&#039;, 4th edition. Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Waltz, Kenneth N. 1959. Man, the State, and War: A Theoretical Analysis. New York: Columbia University Press.&lt;br /&gt;
&lt;br /&gt;
Watkins, John Elfreth, Jr. 1990. &amp;quot;What May Happen in the Next Hundred Years,&amp;quot; in Edward Cornish, ed.,&amp;amp;nbsp;&#039;&#039;The 1990s and Beyond.&#039;&#039;&amp;amp;nbsp;Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Wildavsky, Aaron, and Ellen Tenenbaum. 1981.&amp;amp;nbsp;&#039;&#039;The Politics of Mistrust&#039;&#039;. Beverly Hills: Sage Publications.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1991b.&amp;amp;nbsp;&#039;&#039;World Tables 1991&#039;&#039;. New York: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1995&amp;amp;nbsp;&#039;&#039;World Development Report 1995&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
World Energy Council (WEC) Commission. 1993.&amp;amp;nbsp;&#039;&#039;Energy for Tomorrow’s World.&#039;&#039;&amp;amp;nbsp;New York: St. Martin’s Press.&lt;br /&gt;
&lt;br /&gt;
World Resources Institute (WRI). 1994.&amp;amp;nbsp;&#039;&#039;World Resources 1994-95.&#039;&#039;&amp;amp;nbsp;New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Wortman, Sterling and Ralph W. Cummings, Jr. 1978.&#039;&#039;&amp;amp;nbsp;To Feed This World&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Zinnes, Dina A. and John W. Gillespie, eds. 1976.&amp;amp;nbsp;&#039;&#039;Mathematical Models in International Relations&#039;&#039;&amp;amp;nbsp;(New York: Preaeger).&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Education Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Archibugi, Daniele, and Alberto Coco. 2005. “Measuring Technological Capabilities at the Country Level: A Survey and a Menu for Choice.” Research Policy 34(2). Research Policy: 175–194.&lt;br /&gt;
&lt;br /&gt;
Bush, Vannevar. 1945. Science: The Endless Frontier. Washington: United States Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert and Jong-Wha Lee. 2010. &amp;quot;A New Data Set of Educational Attainment in the World, 1950-2010.&amp;quot; NBER Working Paper No. 15902. National Bureau of Economic Research, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert and Jong-Wha Lee. 2000. “International Data on Educational Attainment: Updates and Implications.” NBER Working Paper No. 7911. National Bureau of Economic Research, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
Bruns, Barbara, Alain Mingat, and Ramahatra Rakotomalala. 2003. Achieving Universal Primary Education by 2015: A Chance for Every Child. Washington, DC: World Bank.&lt;br /&gt;
&lt;br /&gt;
Chen, Derek H. C., and Carl J. Dahlman. 2005. The Knowledge Economy, the KAM Methodology and World Bank Operations. The World Bank, October 19.&lt;br /&gt;
&lt;br /&gt;
Clemens, Michael A. 2004. The Long Walk to School: International education goals in historical perspective. Econ WPA, March.&amp;amp;nbsp;[http://ideas.repec.org/p/wpa/wuwpdc/0403007.html http://ideas.repec.org/p/wpa/wuwpdc/0403007.html].&lt;br /&gt;
&lt;br /&gt;
Cohen, Daniel, and Marcelo Soto. 2001. “Growth and Human Capital: Good Data, Good Results.” Technical Paper 179.&amp;amp;nbsp; Paris: OECD.&lt;br /&gt;
&lt;br /&gt;
Cuaresma, Jesus Crespo, and Wolfgang Lutz. 2007 (April).&amp;amp;nbsp; “Human Capital, Age Structure and Economic Growth:&amp;amp;nbsp; Evidence from a New Dataset.” Interim Report IR-07-011. Laxenburg, Austria:&amp;amp;nbsp; International Institute for Applied Systems Analysis.&lt;br /&gt;
&lt;br /&gt;
Delamonica, Enrique, Santosh Mehrotra, and Jan Vandemoortele.&amp;amp;nbsp;2001 (August).&amp;amp;nbsp; “Is EFA Affordable? Estimating the Global Minimum Cost of ‘Education for All’”. Innocenti Working Paper No. 87.&amp;amp;nbsp; Florence: UNICEF Innocenti Research Centre.&amp;amp;nbsp;[http://www.unicef-irc.org/publications/pdf/iwp87.pdf http://www.unicef-irc.org/publications/pdf/iwp87.pdf].&lt;br /&gt;
&lt;br /&gt;
Dickson, Janet R., Barry B. Hughes, and Mohammod T. Irfan. 2010. Advancing Global Education. Vol 2, Patterns of Potential Human Progress series.&amp;amp;nbsp; Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.&amp;amp;nbsp;[http://www.ifs.du.edu/documents http://www.ifs.du.edu/documents].&lt;br /&gt;
&lt;br /&gt;
Dutta, Soumitra (Ed.). 2013. The Global Innovation Index 2013. The Local Dynamics of Innovation.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2004b (March).&amp;amp;nbsp; “International Futures (IFs): An Overview of Structural Design.” Pardee Center for International Futures Working Paper, Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx http://www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. and Evan E. Hillebrand. 2006.&amp;amp;nbsp;&amp;amp;nbsp;&#039;&#039;Exploring and Shaping International Futures&#039;&#039;.&amp;amp;nbsp; Boulder, Co:&amp;amp;nbsp; Paradigm Publishers.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. with Anwar Hossain and Mohammod T. Irfan. 2004 (May).&amp;amp;nbsp; “The Structure of IFs.” Pardee Center for International Futures Working Paper, Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx http://www.ifs.du.edu/documents/reports.aspx].&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Irfan, Mohammod T. 2008.&amp;amp;nbsp; “A Global Education Transition: Computer Simulation of Alternative Paths in Universal Basic Education,” Ph.D. dissertation presented to the Josef Korbel School of International Studies, University of Denver, Denver, Colorado.&amp;amp;nbsp;&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx http://www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
Juma, Calestous, and Lee Yee-Cheong. 2005. Innovation: Applying Knowledge in Development. London: Earthscan. (Available online at&amp;amp;nbsp;[http://www.unmillenniumproject.org/documents/Science-complete.pdf http://www.unmillenniumproject.org/documents/Science-complete.pdf&amp;amp;nbsp;])&lt;br /&gt;
&lt;br /&gt;
McMahon, Walter W. 1999 (first published in paperback in 2002).&amp;amp;nbsp; Education and Development: Measuring the Social Benefits. Oxford:&amp;amp;nbsp; Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Wils, Annababette and Raymond O&#039;Connor. 2003. “The causes and dynamics of the global education transition.” AED Working Paper. Washington, DC: Academy for Educational Development&lt;br /&gt;
&lt;br /&gt;
UNESCO. 2010. UNESCO Science Report 2010. The Current Status of Science around the World. UNESCO. Paris.&lt;br /&gt;
&lt;br /&gt;
World Bank. 2010. Innovation Policy: A Guide for Developing Countries. (Available online at&amp;amp;nbsp;[https://openknowledge.worldbank.org/bitstream/handle/10986/2460/548930PUB0EPI11C10Dislosed061312010.pdf?sequence=1 https://openknowledge.worldbank.org/bitstream/handle/10986/2460/548930PUB0EPI11C10Dislosed061312010.pdf?sequence=1])&lt;br /&gt;
&lt;br /&gt;
World Bank. 2007. Building Knowledge Economies: Advanced Strategies for Development. WBI Development Studies. Washington, D.C: World Bank. (Available online at&amp;amp;nbsp;[http://siteresources.worldbank.org/KFDLP/Resources/461197-1199907090464/BuildingKEbook.pdf http://siteresources.worldbank.org/KFDLP/Resources/461197-1199907090464/BuildingKEbook.pdf])&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Energy Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Kalymon, Basil A. 1975. &amp;quot;Economic Incentives in OPEC Oil Pricing Policy.&amp;quot; &#039;&#039;Journal of Development Economics&#039;&#039; 2: 337-362.&lt;br /&gt;
&lt;br /&gt;
Naill, Roger F. 1977.&#039;&#039;Managing the Energy Transition.&#039;&#039; Vols. 1 and 2. Cambridge, Mass: Ballinger Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Stanford University. 1978. &#039;&#039;Stanford Pilot Energy/Economic Model.&#039;&#039; Stanford: Department of Research, Interim Report, Vol. 1.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Governance Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. and Jong-Wha Lee. 2001. &amp;quot;International Data on Educational Attainment: Updates and Implications,&amp;quot;&amp;amp;nbsp;&#039;&#039;Oxford Economic Papers&#039;&#039;&amp;amp;nbsp;53(3): 541-563.&lt;br /&gt;
&lt;br /&gt;
Cilliers, Jakkie, Barry Hughes, and Jonathan Moyer. 2011.&amp;amp;nbsp;&#039;&#039;African Futures 2050: The Next 40 Years&#039;&#039;. Pretoria, South Africa and Denver, Colorado: Institute for Security Studies and Frederick S. Pardee Center for International Futures.&lt;br /&gt;
&lt;br /&gt;
Correlates of War Project. 2011. “State System Membership List, v2011.” Online,&amp;amp;nbsp;[http://correlatesofwar.org/ http://correlatesofwar.org&amp;amp;nbsp;].&lt;br /&gt;
&lt;br /&gt;
Diamond, Larry. 1992. “Economic Development and Democracy Reconsidered.”&amp;amp;nbsp;&#039;&#039;American Behavioral Scientist&#039;&#039;&amp;amp;nbsp;35(4/5): 450-499.&lt;br /&gt;
&lt;br /&gt;
Diehl, Paul F., ed. 1999.&amp;amp;nbsp;&#039;&#039;A Roadmap to War: Territorial Dimensions of International Conflict&#039;&#039;, 1&amp;lt;sup&amp;gt;st&amp;lt;/sup&amp;gt;&amp;amp;nbsp;ed. Nashville: Vanderbilt University Press.&lt;br /&gt;
&lt;br /&gt;
Easton, David. 1965.&amp;amp;nbsp;&#039;&#039;A Framework for Political Analysis&#039;&#039;. Englewood Cliffs, New Jersey: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Esty, Daniel C., Jack A. Goldstone, Ted Robert Gurr, Barbara Harff, Marc Levy, Geoffrey D. Dabelko, Pamela Surko, and Alan N. Unger. 1998. “State Failure Task Force Report: Phase II Findings.” Study Commissioned by the Central Intelligence Agency and George Mason University School of Public Policy. Political Instability Task Force, Arlington VA.&lt;br /&gt;
&lt;br /&gt;
Freedom House, Inc. 2009.&amp;amp;nbsp;&#039;&#039;Freedom in the World 2009: The Annual Survey of Political Rights and Civil Liberties&#039;&#039;. Washington, DC: Freedom House, Inc.\&lt;br /&gt;
&lt;br /&gt;
Goldstone, Jack A. 2010. “The New Population Bomb”&amp;amp;nbsp;&#039;&#039;Foreign Affairs&#039;&#039;&amp;amp;nbsp;(January/February): 31-43.&lt;br /&gt;
&lt;br /&gt;
Goldstone, Jack A., Robert H. Bates, David L. Epstein, Ted Robert Gurr, Michael B. Lustik, Monty G. Marshall, Jay Ulfelder, and Mark Woodward. 2010. “A Global Model for Forecasting Political Instability.”&amp;amp;nbsp;&#039;&#039;American Journal of Political Science&#039;&#039;&amp;amp;nbsp;54(1): 190-208. doi: 10.1111/j.1540-5907.2009.00426.x.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2001. “Global Social Transformation: The Sweet Spot, the Steady Slog, and the Systemic Shift.”&amp;amp;nbsp;&#039;&#039;Economic Development and Cultural Change&#039;&#039;&amp;amp;nbsp;49(2): 423-458. doi: 10.1086/452510.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2002. &amp;quot;Threats and Opportunities Analysis,&amp;quot; working document prepared for the Strategic Assessments Group, Office of Transnational Issues, Central Intelligence Agency.&amp;amp;nbsp; Available on the IFs project web site at&amp;amp;nbsp;[http://www.ifs.du.edu/ www.ifs.du.edu].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., and Anwar Hossain. 2003. “Long-Term Socio-Economic Modeling: With Universal, Globally-Integrated Social Accounting Matrices (SAMs) in a General Equilibrium Model Structure.” Working Paper, University of Denver, Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/assets/documents/economyandsamdocument46.pdf http://www.ifs.du.edu/assets/documents/economyandsamdocument46.pdf]&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., Devin Joshi, Jonathan Moyer, Timothy Sisk and José Roberto Solórzano. 2014.&amp;amp;nbsp;&#039;&#039;Strengthening Governance Globally.&amp;amp;nbsp;&#039;&#039;vol. 5, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Huntington, Samuel P. 1991.&amp;amp;nbsp;&#039;&#039;The Third Wave: Democratization in the Late Twentieth Century&#039;&#039;. Norman, OK: University of Oklahoma.&lt;br /&gt;
&lt;br /&gt;
Inglehart, Ronald. 1997.&amp;amp;nbsp;&amp;amp;nbsp;&#039;&#039;Modernization and Postmodernization&#039;&#039;.&amp;amp;nbsp; Princeton: PrincetonUniversity Press.&lt;br /&gt;
&lt;br /&gt;
Joshi, Devin. 2011a. “Good Governance, State Capacity, and the Millennium Development Goals.”&amp;amp;nbsp;&#039;&#039;Perspectives on Global Development and Technology&amp;amp;nbsp;&#039;&#039;10(2): 339-360. doi: 10.1163/156914911X5824.68.&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi. 2010. “The Worldwide Governance Indicators: Methodology and Analytical Issues.” World Bank Policy Research Working Paper no. 5430. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Marshall, Monty G. and Benjamin R. Cole. 2008. “Global Report on Conflict, Governance and State Fragility 2008.”&amp;amp;nbsp;&#039;&#039;Foreign Policy Bulletin&#039;&#039;&amp;amp;nbsp;18: 3-21. doi: 10.1017/S1052703608000014.&lt;br /&gt;
&lt;br /&gt;
Marshall, Monty G., and Benjamin R. Cole. 2009. “Global Report 2009: Conflict, Governance, and State Fragility.” Vienna, VA.: Center for Systemic Peace and Center for Global Policy.&lt;br /&gt;
&lt;br /&gt;
Marshall, Monty G., and Benjamin R. Cole. 2011. &amp;quot;Global Report 2011: Conflict, Governance, and State Fragility.&amp;quot; Vienna, VA. Center for Systemic Peace.&lt;br /&gt;
&lt;br /&gt;
Marshall, Monty G., and Keith Jaggers. 2011. “Polity IV Project: Political Regime Characteristics and Transitions 1800-2010.”&amp;amp;nbsp;[http://www.systemicpeace.org/polity/polity4.htm http://www.systemicpeace.org/polity/polity4.htm]&amp;amp;nbsp;[accessed December 22 2012]&lt;br /&gt;
&lt;br /&gt;
Mauro, Paolo. 1995. “Corruption and Growth.”&amp;amp;nbsp;&#039;&#039;The Quarterly Journal of Economics&#039;&#039;&amp;amp;nbsp;110(3) (August): 681-712.&lt;br /&gt;
&lt;br /&gt;
Migdal, Joel. 1988.&amp;amp;nbsp;&#039;&#039;Strong Societies and Weak Sates: State-Society Relations and State Capabilities in the&amp;amp;nbsp;Third World&#039;&#039;. Princeton: Princeton University Press&lt;br /&gt;
&lt;br /&gt;
Mo, Pak Hung. 2001. “Corruption and Economic Growth.”&amp;amp;nbsp;&#039;&#039;Journal of Comparative Economics&amp;amp;nbsp;&#039;&#039;29(1) (March): 66-79. doi:10.1006/jcec.2000.1703.&lt;br /&gt;
&lt;br /&gt;
North, Douglass C., John Joseph Wallis, and Barry R. Weingast. 2009.&amp;amp;nbsp;&#039;&#039;Violence and Social Orders: A Conceptual Framework for Interpreting Recorded Human History&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Pierson, Paul. 2004.&amp;amp;nbsp;&#039;&#039;Politics in Time: History, Institutions, and Social Analysis&#039;&#039;. Princeton, NJ: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Rice, Susan E., and Stewart Patrick. 2008.&amp;amp;nbsp;&#039;&#039;Index of State Weakness in the Developing World.&#039;&#039;&amp;amp;nbsp;Washington, DC: The Brookings Institution.&lt;br /&gt;
&lt;br /&gt;
Shihata, Ibrahim F. I. 1996. “Corruption - A General Review with an Emphasis on the Role of the World Bank.”&amp;amp;nbsp;&#039;&#039;Dickinson Journal of International Law&#039;&#039;&amp;amp;nbsp;15: 451.&lt;br /&gt;
&lt;br /&gt;
Tanzi, Vito. 1998. “Corruption Around the World: Causes, Consequences, Scope, and Cures.” Staff Papers - International Monetary Fund 45(4) (December): 559-594.&lt;br /&gt;
&lt;br /&gt;
Urdal, H. 2004. “The devil in the demographics: the effect of youth bulges on domestic armed conflict, 1950-2000.” Social Development Papers: Conflict and Reconstruction Paper 14.&lt;br /&gt;
&lt;br /&gt;
Ware, H. 2004. “Pacific instability and youth bulges: the devil in the demography and the economy.” Paper delivered at the 12th Biennial Conference of the Australian Population Association, 15-17.&lt;br /&gt;
&lt;br /&gt;
Wagner, Adolph. 1892.&amp;amp;nbsp;&#039;&#039;Grundlegung der Politischen Ökonomie&#039;&#039;. Leipzig: C.F. Winter Publishing Firm.&lt;br /&gt;
&lt;br /&gt;
World Bank. 2011.&amp;amp;nbsp;&#039;&#039;World Development Indicators 2011.&#039;&#039;&amp;amp;nbsp;Washington, DC: World Bank. Available at&amp;amp;nbsp;[http://data.worldbank.org/data-catalog/world-development-indicators http://data.worldbank.org/data-catalog/world-development-indicators].&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Health Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Adams 1987.&amp;amp;nbsp;[http://www.geog.ucl.ac.uk/~jadams/PDFs/smeed&#039;s%20law.pdf &amp;quot;Smeed&#039;s Law: some further thoughts.&amp;quot;]&amp;amp;nbsp;&#039;&#039;Traffic Engineering and Control&#039;&#039;&amp;amp;nbsp;(Feb) 70-73.&lt;br /&gt;
&lt;br /&gt;
Alsan, Marcella, David E. Bloom, and David Canning. 2006. “The Effects of Population Health on Foreign Direct Investment Inflows to Low- and Middle-Income Countries,”&amp;amp;nbsp;&#039;&#039;World Development&#039;&#039;&amp;amp;nbsp;34(4): 613-630.&lt;br /&gt;
&lt;br /&gt;
Anand, Sudhir and Martin Ravallion. 1993. “Human development in poor countries: on the role of private incomes and public services,”&amp;amp;nbsp;&#039;&#039;Journal of Economic Perspectives&#039;&#039;&amp;amp;nbsp;7(1): 133–150.&lt;br /&gt;
&lt;br /&gt;
Ashraf, Quamrul H., Ashley Lester, and David N. Weil. 2008. “When Does Improving Health Raise GDP?”&amp;amp;nbsp; NBER Working Paper No. 14449. National Bureau of Economic Research, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
Bidani, Benu and Martin Ravallion. 1997. “Decomposing social indicators using distributional data.”&amp;amp;nbsp;&#039;&#039;Journal of Econometrics&#039;&#039;&amp;amp;nbsp;77: 125–139.&lt;br /&gt;
&lt;br /&gt;
Bloom, David E., and David Canning. 2004. “Global Demographic Change: Dimensions and Economic Significance.” NBER Working Paper No. 10817.&amp;amp;nbsp; National Bureau of Economic Research, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
Blössner, Monika, and Mercedes de Onis. 2005.&amp;amp;nbsp;&#039;&#039;Malnutrition: quantifying the health impact at national and local levels.&#039;&#039;&amp;amp;nbsp;Geneva, World Health Organization. (WHO Environmental Burden of Disease Series, No. 12).&lt;br /&gt;
&lt;br /&gt;
Dargay, Gately, and Sommer 2007. “Vehicle Ownership and Income Growth, Worldwide: 1960-2030”. Joyce Dargay, Dermot Gately and Martin Sommer, January 2007.&lt;br /&gt;
&lt;br /&gt;
Deaton, Angus, and Christina Paxson. 2000 (May). “Growth and Savings Among Individuals and Households.”&amp;amp;nbsp;&#039;&#039;The Review of Economics and Statistics&#039;&#039;&amp;amp;nbsp;82(2): 212-225.&lt;br /&gt;
&lt;br /&gt;
Desai, Manish A., Sumi Mehta, and Kirk R. Smith. 2004. “Indoor smoke from solid fuels: Assessing the environmental burden of disease.”WHOEnvironmental Burden of Disease Series No. 4&#039;&#039;.&amp;amp;nbsp;&#039;&#039;Annette Pruss-Üstun, Diamid Campbell-Lendrum, Carlos Corvalán, and Alistair Woodward, series eds. World Health Organization, Geneva.&lt;br /&gt;
&lt;br /&gt;
Ezzati, Majid and Alan D. Lopez. 2004. “Smoking and oral tobacco use.” In Majid Ezzati, Alan D. Lopez, Anthony Rodgers, and Cristopher J.L. Murray, eds.,&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors&#039;&#039;. Geneva: World Health Organization, 883-957.&amp;amp;nbsp; Retrieved 4 Feb 2009, from&amp;amp;nbsp;[http://www.who.int/publications/cra/chapters/volume1/part4/en/index.html http://www.who.int/publications/cra/chapters/volume1/part4/en/index.html].&lt;br /&gt;
&lt;br /&gt;
Ezzati, Majid, Alan D. Lopez, Anthony Rodgers, Christopher J.L. Murray, eds. 2004.&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors&#039;&#039;. Geneva: World Health Organization.&lt;br /&gt;
&lt;br /&gt;
Fernández-Villaverde, Jesús, and Dirk Kruegger. 2004 (September 14). “Consumption over the Life Cycle: Facts from Consumer Expenditure Survey Data,” unpublished manuscript, University of Pennsylvania and University of Frankfort.&amp;amp;nbsp;&amp;amp;nbsp;[http://www.dklevine.com/archive/refs4506439000000000304.pdf http://www.dklevine.com/archive/refs4506439000000000304.pdf]&lt;br /&gt;
&lt;br /&gt;
Fernández-Villaverde, Jesús, and Dirk Kruegger. 2005 (December 19). “Consumption over the Life Cycle: How Important are Consumer Durables?,” unpublished manuscript, University of Pennsylvania and Goethe University.&amp;amp;nbsp;&amp;amp;nbsp;[http://journals.cambridge.org/action/displayAbstract?fromPage=online&amp;amp;aid=8466457 http://journals.cambridge.org/action/displayAbstract?fromPage=online&amp;amp;aid=8466457]&lt;br /&gt;
&lt;br /&gt;
Gakidou, Emmanuela, Shefali Oza, Cecilia Vidal Fuertes, Amy Y. Li, Diana K. Lee, Angelica Sousa, Margaret C. Hogan, Stephen Vander Hoorn, and Majid Ezzati. 2007.” Improving Child Survival Through Environmental and Nutritional Interventions: The Importance of Targeting Interventions Toward the Poor.”&amp;amp;nbsp;&#039;&#039;Journal of the American Medical Association&#039;&#039;&amp;amp;nbsp;298(16): 1876-1887.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. and Hillebrand, Evan E. 2006. “Exploring and shaping International Futures”. Boulder, CO: Paradigm Publishers.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., Randall Kuhn, Cecilia Peterson, Dale Rothman, and Jose Solorzano. 2011.&amp;amp;nbsp;&amp;amp;nbsp;&#039;&#039;Improving Global Health: Patterns of Potential Human Progress, Volume 3&#039;&#039;.&amp;amp;nbsp; Paradigm Publishing and Oxford India.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2005.&amp;amp;nbsp; “Productivity in IFs.” Pardee Center for International Futures Working Paper, University of Denver, Denver, CO.&amp;amp;nbsp;&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx http://www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
James, W. Philip T., Rachel Jackson-Leach , Cliona Ni Mhurchu, Eleni Kalamara, Maryam Shayeghi, Neville J. Rigby, Chizuru Nishida, and Anthony Rodgers. 2004.&amp;amp;nbsp; “Overweight and obesity (high body mass index).” In Majid Ezzati, Alan D. Lopez, Anthony Rodgers and Christopher J.L. Murray, eds.,&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors.&#039;&#039;&amp;amp;nbsp;Geneva: World Health Organization, 959-1108.&lt;br /&gt;
&lt;br /&gt;
Jamison, Dean T., Jia Wang, Kenneth Hill, and Juan-Luis Londono. 1996. “Income, Mortality and Fertility in Latin America: Country-Level Performance, 1960 - 90.”&amp;amp;nbsp;&#039;&#039;Analisis Economico&#039;&#039;11(2): 219-261.&lt;br /&gt;
&lt;br /&gt;
Kelly, Christopher, Nora Pashayan, Sreetharan Munisamy, and Joshn W. Powles. 2009.&amp;amp;nbsp; “Mortality attributable to excess adiposity in England and Wales in 2003 and 2015: explorations with a spreadsheet implementation of the Comparative Risk Assessment mentodology.”&amp;amp;nbsp;&#039;&#039;Population Health Metrics&#039;&#039;&amp;amp;nbsp;7(11): 1-7.&lt;br /&gt;
&lt;br /&gt;
Lopez, Alan D., Neil E. Collishaw, and Tapani Piha. 1994. “A descriptive model of the cigarette epidemic in developed countries.”&amp;amp;nbsp;&#039;&#039;Tobacco Control&#039;&#039;&amp;amp;nbsp;3(3): 242-247. &amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Mathers, Colin D., and Dejan Loncar. 2005. &amp;quot;Updated Projections of Global Mortality and Burden of Disease, 2002-2030: Data Sources, Methods and Results.&amp;quot; Evidence and Information for Policy Working Paper. World Health Organization, Geneva.&lt;br /&gt;
&lt;br /&gt;
Mathers, Colin D., and Dejan Loncar. 2006. &amp;quot;Projections of Global Mortality and Burden of Disease from 2002 to 2030.&amp;quot;&amp;amp;nbsp;&#039;&#039;PLoS Medicine&#039;&#039;&amp;amp;nbsp;3(11): e442, 2011-2030.&amp;amp;nbsp; Retrieved 13 March 2009. doi:10.1371/journal.pmed.0030442.&lt;br /&gt;
&lt;br /&gt;
Mathers, Colin D., and Dejan Loncar. 2006b. “New projections of global mortality and burden of disease from 2002 to 2030.” Protocol S1. Technical Appendix to Mathers and Loncar 2006.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Mathers, Colin D., and Dejan Loncar. 2006c. “Results of Regressions of Age–Sex-Specific Mortality for Detailed Causes on the Respective Cause Cluster Based on the Full Country Panel Dataset, 1950–2002.” Technical Appendix to Mathers and Loncar 2006.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Nixon, John, and Philippe Ulmann. 2006. “The Relationship Between Health Care Expenditure and Health Outcomes: Evidence and caveats for a Causal Link.”&amp;amp;nbsp;&#039;&#039;European Journal of Health Economics&#039;&#039;&amp;amp;nbsp;7: 7-18.&lt;br /&gt;
&lt;br /&gt;
Peto, Richard, Jillian Boreham, Alan D. Lopez, Michael Thun, and Clark Heath, Jr. 1992. “Mortality from Tobacco in Developed Countries: Indirect Estimation from National Vital Statistics.”&amp;amp;nbsp;&#039;&#039;Lancet&amp;amp;nbsp;&#039;&#039;339(8804): 1268–1278. doi:10.1016/0140- 6736(92)91600-D.&lt;br /&gt;
&lt;br /&gt;
Ploeg, Martine, Katja K. H. Aben, and Lambertus A. Kiemeney. 2009. “The Present and Future Burden of Urinary Bladder Cancer in the World.”&amp;amp;nbsp;&#039;&#039;World Journal of Urology&#039;&#039;&amp;amp;nbsp;27(3): 289-293. doi:[http://dx.doi.org/10.1007/s00345-009-0383-3 &amp;amp;nbsp;10.1007/s00345-009-0383-3&amp;amp;nbsp;]. &amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Shibuya, Kenji, Mie Inoue, and Alan D. Lopez. 2005. “Statistical Modeling and Projections of Lung Cancer Mortality in 4 Industrialized Countries.”&amp;amp;nbsp;&#039;&#039;International Journal of Cancer&#039;&#039;&amp;amp;nbsp;117(3): 476-485. doi:[http://dx.doi.org/10.1002/ijc.21078 &amp;amp;nbsp;10.1002/ijc.21078&amp;amp;nbsp;]. &amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Smeed, RJ 1949. &amp;quot;Some statistical aspects of road safety research&amp;quot;.&amp;amp;nbsp;[http://en.wikipedia.org/wiki/Royal_Statistical_Society &#039;&#039;Royal Statistical Society&#039;&#039;], Journal (A) CXII (Part I, series 4). 1-24.&lt;br /&gt;
&lt;br /&gt;
Smith, Lisa C. and Lawrence Haddad. 2000. “Explaining Child Malnutrition in Developing Countries: A Cross-Sectional Analysis.” Washington, D.C.: International Food Policy Research Institute.&lt;br /&gt;
&lt;br /&gt;
Soares, Rodrigo R. 2007. “On the Determinants of Mortality Reductions in the Developing World.”&amp;amp;nbsp;&#039;&#039;Population and Development Review&amp;amp;nbsp;&#039;&#039;33(2): 247-287.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2003.&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;World Population Prospects: The 2002 Revision, Highlight.&#039;&#039;&amp;amp;nbsp; New York:&amp;amp;nbsp; United Nations. Department of Economics and Social Affairs.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2009.&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;World Population Prospects: The 2008 Revision, Highlights.&#039;&#039;&amp;amp;nbsp; New York:&amp;amp;nbsp; United Nations. Department of Economics and Social Affairs.&lt;br /&gt;
&lt;br /&gt;
Wagstaff, Adam. 2002. “Inequalities in Health in Developing Countries: Swimming Against the Tide?” Unpublished Manuscript&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Infrastructure Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Agénor, Pierre-Richard, Mustapha Kamel Nabli, and Tarik M. Yousef. 2007. “Public Infrastructure and Private Investment in the Middle East and North Africa.” In Mustapha Kamel Nabli, ed.,. Breaking the Barriers to Higher Economic Growth: Better Governance and Deeper Reforms in the Middle East and North Africa. Washington, DC: World Bank Publications, 399–422.&lt;br /&gt;
&lt;br /&gt;
Asian Development Bank, Japan Bank for International Cooperation, and World Bank. 2005.&amp;amp;nbsp;&#039;&#039;Connecting East Asia: A New Framework for Infrastructure&#039;&#039;. Tokyo: Asian Development Bank, Japan Bank for International Cooperation, and World Bank.&amp;amp;nbsp;[http://siteresources.worldbank.org/INTEASTASIAPACIFIC/Resources/Connecting-East-Asia.pdf http://siteresources.worldbank.org/INTEASTASIAPACIFIC/Resources/Connecting-East-Asia.pdf].&lt;br /&gt;
&lt;br /&gt;
Bhattacharyay, Biswa Nath. 2010. “Estimating Demand for Infrastructure in Energy, Transport, Telecommunications, Water and Sanitation in Asia and the Pacific: 2010-2020”. Working Paper no. 248. Asian Development Bank Institute, Tokyo.&amp;amp;nbsp;[http://www.adbi.org/working-paper/2010/09/09/4062.infrastructure.demand.asia.pacific/ http://www.adbi.org/working-paper/2010/09/09/4062.infrastructure.demand.asia.pacific/].&lt;br /&gt;
&lt;br /&gt;
Bruinsma, Jelle. 2011. “The Resources Outlook: By How Much Do Land, Water and Crop Yields Need to Increase by 2050?” In Piero Conforti, ed.,.&amp;amp;nbsp;&#039;&#039;Looking Ahead in World Food and Agriculture: Perspectives to 2050&#039;&#039;. Rome: Food and Agriculture Organization of the United Nations (FAO), 233–275.&amp;amp;nbsp;[http://www.fao.org/docrep/014/i2280e/i2280e.pdf http://www.fao.org/docrep/014/i2280e/i2280e.pdf].&lt;br /&gt;
&lt;br /&gt;
Calderón, César, and Luis Servén. 2010a. “Infrastructure and Economic Development in Sub-Saharan Africa.”&amp;amp;nbsp;&#039;&#039;Journal of African Economies&#039;&#039;&amp;amp;nbsp;19(Supplement 1): i13–i87. doi:10.1093/jae/ejp022.&amp;amp;nbsp;[http://jae.oxfordjournals.org/cgi/content/abstract/19/suppl_1/i13 http://jae.oxfordjournals.org/cgi/content/abstract/19/suppl_1/i13].&lt;br /&gt;
&lt;br /&gt;
Calderón, César, and Luis Servén. 2010b. “Infrastructure in Latin America”. World Bank Policy Research Working Paper. Report Number 5317. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Canning, David. 1998. “A Database of World Stocks of Infrastructure, 1950-1995.”&amp;amp;nbsp;&#039;&#039;The World Bank Economic Review&#039;&#039;&amp;amp;nbsp;12(3): 529–548.&lt;br /&gt;
&lt;br /&gt;
Canning, David, and Mansour Farahani. 2007. “A Database of World Stocks of Infrastructure: Update 1950-2005”. Harvard School of Public Health, Boston, MA.&amp;amp;nbsp;[http://www.hsph.harvard.edu/faculty/david-canning/data-sets/ http://www.hsph.harvard.edu/faculty/david-canning/data-sets/].&lt;br /&gt;
&lt;br /&gt;
Cavallo, Eduardo Alfredo, and Christian Daude. 2008. “Public Investment in Developing Countries: A Blessing or a Curse?” RES Working Paper #4597. Inter-American Development Bank (IADB) - Research Department, OECD, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Chatterton, Isabe, and Olga S. Puerto. 2006.&amp;amp;nbsp;&#039;&#039;Estimation of Infrastructure Investment Needs in the South Asia Region: Executive Summary&#039;&#039;. Washington, DC: World Bank.&amp;amp;nbsp;[http://siteresources.worldbank.org/INTSARREGTOPTRANSPORT/Resources/Inf_Investment_Needs_IC_version4.pdf http://siteresources.worldbank.org/INTSARREGTOPTRANSPORT/Resources/Inf_Investment_Needs_IC_version4.pdf].&lt;br /&gt;
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Congressional Budget Office. 2010.&amp;amp;nbsp;&#039;&#039;Public Spending on Transportation and Water Infrastructure&#039;&#039;. Washington, DC: Congressional Budget Office.&amp;amp;nbsp;[http://www.cbo.gov/publication/21902 http://www.cbo.gov/publication/21902].&lt;br /&gt;
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Estache, Antonio, and Ana Goicoechea. 2005. “A Research Database on Infrastructure Economic Performance”. Policy Research Working Paper no. 3643. World Bank, Washington, DC.&amp;amp;nbsp;[http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2005/06/16/000016406_20050616100502/Rendered/PDF/wps3643.pdf http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2005/06/16/000016406_20050616100502/Rendered/PDF/wps3643.pdf].&lt;br /&gt;
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Ezzati, Majid, Alan D. Lopez, Anthony Rodgers, and Christopher J. L. Murray, eds. 2004.&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors&#039;&#039;. Geneva, Switzerland: World Health Organization (WHO).&lt;br /&gt;
&lt;br /&gt;
Fay, Marianne. 2001. “Financing the Future: Infrastructure Needs in Latin America, 2000-05”. Policy Research Working Paper no. 2545. World Bank, Washington, DC.&amp;amp;nbsp;[http://elibrary.worldbank.org/docserver/download/2545.pdf?expires=1375200693&amp;amp;id=id&amp;amp;accname=guest&amp;amp;checksum=DB7E5FB146EE28C93511B5ADAB2FD3CB http://elibrary.worldbank.org/docserver/download/2545.pdf?expires=1375200693&amp;amp;id=id&amp;amp;accname=guest&amp;amp;checksum=DB7E5FB146EE28C93511B5ADAB2FD3CB].&lt;br /&gt;
&lt;br /&gt;
Fay, Marianne, and Tito Yepes. 2003. “Investing in Infrastructure: What Is Needed from 2000 to 2010?” Policy Research Working Paper no. 3102. World Bank, Washington, DC. RePEc.&amp;amp;nbsp;[http://ideas.repec.org/p/wbk/wbrwps/3102.html http://ideas.repec.org/p/wbk/wbrwps/3102.html].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2007. “Forecasting Global Economic Growth with Endogenous Multifactor Productivity: The International Futures (IFs) Approach”. Pardee Center for International Futures Working Paper, University of Denver. Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., Devin Joshi, Jonathan Moyer, Timothy Sisk and José Roberto Solórzano. 2014. Strengthening Governance Globally. vol. 5, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Hughes, Gordon, Paul Chinowsky, and Ken Strzepek. 2009. “The Costs of Adapting to Climate Change for Infrastructure”. Economics of Adaptation to Climate Change Discussion Paper no. 2. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
International Transport Forum, and Organisation for Economic Cooperation and Development (OECD). 2011. “Trends in Transport Infrastructure Investment 1995-2009”. Paris.&lt;br /&gt;
&lt;br /&gt;
Kohli, Harpaul Alberto, and Phillip Basil. 2011. “Requirements for Infrastructure Investment in Latin America Under Alternate Growth Scenarios.”&amp;amp;nbsp;&#039;&#039;Global Journal of Emerging Market Economies&#039;&#039;&amp;amp;nbsp;3(1): 59 –110. doi:10.1177/097491011000300103.&amp;amp;nbsp;[http://eme.sagepub.com/content/3/1/59.abstract http://eme.sagepub.com/content/3/1/59.abstract].&lt;br /&gt;
&lt;br /&gt;
Kim, M. Julie, and Rita Nangia. 2010. “Infrastructure Development in India and China—A Comparative Analysis.” In William Ascher and Corinne Krupp, eds.,.&amp;amp;nbsp;&#039;&#039;Physical Infrastructure Development: Balancing The Growth, Equity, and Environmental Imperatives&#039;&#039;. New York, NY: Palgrave Macmillan, 97–140.&lt;br /&gt;
&lt;br /&gt;
Lora, Eduardo A. 2007.&amp;amp;nbsp;&#039;&#039;Public Investment in Infrastructure in Latin America: Is Debt the Culprit?&#039;&#039;&amp;amp;nbsp;Inter-American Development Bank Working Paper. Washington, DC: Inter-American Development Bank (IADB) - Research Department.&lt;br /&gt;
&lt;br /&gt;
Nelson, Gerald C., Mark W. Rosegrant, Amanda Palazzo, Ian Gray, Christina Ingersoll, Richard Robertson, Simla Tokgoz, Tingju Zhu, Timothy B. Sulser, Claudia Ringler, Siwa Msangi, and Liangzhi You. 2010.&amp;amp;nbsp;&#039;&#039;Food Security, Farming, and Climate Change to 2050: Scenarios, Results, Policy Options&#039;&#039;. Washington, DC: International Food Policy Research Institute.&amp;amp;nbsp;[http://www.ifpri.org/publication/food-security-farming-and-climate-change-2050 http://www.ifpri.org/publication/food-security-farming-and-climate-change-2050].&lt;br /&gt;
&lt;br /&gt;
Organisation for Economic Co-operation and Development. 2006.&amp;amp;nbsp;&#039;&#039;Infrastructure to 2030 Volume 1: Telecom, Land Transport, Water and Electricity&#039;&#039;. Infrastructure to 2030. Paris: Organisation for Economic Cooperation and Development.&lt;br /&gt;
&lt;br /&gt;
Organisation for Economic Co-operation and Development. 2009.&amp;amp;nbsp;&#039;&#039;Going for Growth: Economic Policy Reforms&#039;&#039;. Paris: Organisation for Economic Cooperation and Development (OECD).&lt;br /&gt;
&lt;br /&gt;
Qiang, Christine Zhen-Wei, Carlo M. Rossotto, and Kaoru Kimura. 2009. “Economic Impacts of Broadband.” In World Bank, ed.,.&amp;amp;nbsp;&#039;&#039;2009 Information and Communications for Development: Extending Reach and Increasing Impact&#039;&#039;. Washington, DC: World Bank, 35–50.&lt;br /&gt;
&lt;br /&gt;
Rothman, Dale S. Mohammod T. Irfan, Eli Margolese-Malin, Barry B. Hughes, Jonathan Moyer, and Janet Dickson. 2013.&amp;amp;nbsp;&#039;&#039;Building Global Infrastructure.&amp;amp;nbsp;&#039;&#039;vol. 4, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press. Stambrook, David. 2006. “Key Factors Driving the Future Demand for Surface Transport Infrastructure and Services.” In Organisation for Economic Cooperation and Development (OECD), ed.,.&amp;amp;nbsp;&#039;&#039;Infrastructure to 2030 Volume 1: Telecom, Land Transport, Water and Electricity&#039;&#039;. Infrastructure to 2030. Paris: Organisation for Economic Cooperation and Development (OECD), 185–239.&lt;br /&gt;
&lt;br /&gt;
World Health Organization, and UNICEF. 2013.&amp;amp;nbsp;&#039;&#039;Progress on Sanitation and Drinking-Water - 2013 Update&#039;&#039;. Geneva: World Health Organization.&lt;br /&gt;
&lt;br /&gt;
Yepes, Tito. 2008. “Investment Needs for Infrastructure in Developing Countries 2008-15”. Draft. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Yepes, Tito. 2005.&amp;amp;nbsp;&#039;&#039;Expenditure on Infrastructure in East Asia Region, 2006–2010&#039;&#039;. East Asia Pacific Infrastructure Flagship Study. Manila: Asian Development Bank (ADB), Japan Bank for International Cooperation (JBIC), World Bank.&lt;br /&gt;
&lt;br /&gt;
You, Liangzhi, Claudia Ringler, Ulrike Wood-Sichra, Richard Robertson, Stanley Wood, Tingju Zhu, Gerald Nelson, Zhe Guo, and Yan Sun. 2011. “What Is the Irrigation Potential for Africa? A Combined Biophysical and Socioeconomic Approach.”&amp;amp;nbsp;&#039;&#039;Food Policy&#039;&#039;&amp;amp;nbsp;36(6): 770–782. doi:10.1016/j.foodpol.2011.09.001.&amp;amp;nbsp;[http://www.sciencedirect.com/science/article/pii/S030691921100114X http://www.sciencedirect.com/science/article/pii/S030691921100114X].&lt;br /&gt;
&lt;br /&gt;
== [[Development_Mode_Features|Development Mode Features]] ==&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Agriculture&amp;diff=8309</id>
		<title>Agriculture</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Agriculture&amp;diff=8309"/>
		<updated>2017-09-07T21:38:51Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;The most recent and complete agriculture model documentation is available on Pardee&#039;s [http://pardee.du.edu/ifs-agriculture-model-documentation website]. Although the text in this interactive system is, for some IFs models, often significantly out of date, you may still find the basic description useful to you.&lt;br /&gt;
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The IFs agricultural model tracks the supply and demand, including imports, exports, and prices, of three agricultural commodities: crops, meat, and fish. Crops, meat and fish have direct food, animal feed, industrial and food manufacturing uses. The agricultural model is also where land use dynamics and water use are tracked in IFs, as these are key resources for the agricultural sector.&lt;br /&gt;
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The structure of the agriculture model is very much like that of the economic model. It combines a growth process with a partial economic equilibrium process using stocks and prices to seek a balance between the demand and supply sides. As in the economic model, no effort is made in the standard adjustment mechanism to obtain a precise equilibrium in any time step. Instead stocks serve as a temporary buffer and the model chases equilibrium over time.&lt;br /&gt;
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The most important linkages between the agriculture model and other models within IFs are with the economic model. The economic model provides forecasts of average income levels, labor supply, total consumer spending, and agricultural investment, all of which are used in the agriculture model. In turn, the agriculture model provides forecasts on agricultural production, imports, exports, and demand for investment, which override the sectoral computations in the economic model. The agricultural model also has important links to the population and health models, using population forecasts and providing forecasts of calorie availability.&lt;br /&gt;
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= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Dominant Relations&amp;lt;/span&amp;gt; =&lt;br /&gt;
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Agricultural production is a function of the availability of resources, e.g. land, livestock, capital, and labor, as well as climate factors and technology. Technology is most directly seen in the changing productivity of land in terms of crop yields, and in the production of meat relative to the input level of feed grain. The model also accounts for lost production (such as spoilage in the fields or in the first stages of the food supply chain), distribution and transformation losses and consumption losses (which account for food lost at the household levels) which are all determined by average income.&lt;br /&gt;
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Agricultural demand depends on average incomes, prices, and a number of other factors. For example, changing diets can affect the demand for meat, which in turn affects the demand for feed crops. The industrial demand for crops, some of which is directed to the production of biofuels, is also affected by energy prices.&lt;br /&gt;
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Production and demand, along with existing and desired stocks and historical trade patterns determine the trade in agricultural products. The differences in the supply of crops, meat, and fish (production after accounting for losses and trade) and the demand for these commodities are reflected in shifts in agricultural stocks. Stock shortages feed forward to actual consumption, which is addressed in the population model of IFs. Stocks, particularly changes in stocks, are a key driver of changes in crop prices. Crop prices are also influenced by the returns to agricultural investment and therefore to the basic underlying cost structure. Meat prices are tied to, and track world crop prices, while changes in fish prices are driven by changes in fish stocks.&lt;br /&gt;
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Stocks and stock changes also play a role, along with general economic and agricultural demand growth, in driving the demand for agricultural investment. The actual levels of investment are finalized in the economic model of IFs and subject to constraints there. The investment can be of two types – investment for expanding and maintaining cropland (extensification) and investment for increasing crop yields per unit area (intensification). The expected relative rates of return determine the split.&lt;br /&gt;
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The final key dynamics addressed in the agriculture model relate to land, livestock, and water. The latter of these is very straightforward, driven only by crop production. Changes in livestock are determined by changes in the amount of available grazing land, changes in the demand for meat, and the ability of countries to meet this demand as reflected in changing stocks.&lt;br /&gt;
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In the IFs model, land is divided into 5 categories: crop land, grazing land, forest land, ’other’ land, and urban or built-up land. First, changes in urban land are driven by changes in average income and population, and draws from all other land types. Second, the investment in cropland development is the primary driver of changes in cropland, with shifts being compensated by changes in forest and &amp;quot;other&amp;quot; land. Third, changes in grazing land are a function of average income, with shifts again being compensated by changes in forest and &amp;quot;other&amp;quot; land. Finally, conservation policies can influence the amount of forest land, with any necessary adjustments coming from crop and grazing land.&amp;amp;nbsp;&lt;br /&gt;
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= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Structure and Agent System&amp;lt;/span&amp;gt; =&lt;br /&gt;
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{| class=&amp;quot;tableGrid&amp;quot; style=&amp;quot;width:100%;&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;5&amp;quot; border=&amp;quot;1&amp;quot;&lt;br /&gt;
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| style=&amp;quot;width: 50%&amp;quot; | &amp;lt;div&amp;gt;&amp;lt;span style=&amp;quot;font-size:small&amp;quot;&amp;gt;&#039;&#039;&#039;System/Subsystem&#039;&#039;&#039;&amp;lt;/span&amp;gt;&amp;lt;br/&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Agriculture&amp;lt;/div&amp;gt;&lt;br /&gt;
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| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;&amp;lt;span style=&amp;quot;font-size:small&amp;quot;&amp;gt;&#039;&#039;&#039;Organizing Structure&#039;&#039;&#039;&amp;lt;/span&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Partial market&amp;amp;nbsp;equilibrium&amp;lt;/div&amp;gt;&lt;br /&gt;
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| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;&amp;lt;span style=&amp;quot;font-size:small&amp;quot;&amp;gt;&#039;&#039;&#039;Stocks&#039;&#039;&#039;&amp;lt;/span&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Capital, labor, accumulated technology, agricultural commodities, land&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;&amp;lt;span style=&amp;quot;font-size:small&amp;quot;&amp;gt;&#039;&#039;&#039;Flows&#039;&#039;&#039;&amp;lt;/span&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Production,&amp;amp;nbsp;loss, consumption, trade, investment&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div&amp;gt;&amp;lt;span style=&amp;quot;font-size:small&amp;quot;&amp;gt;&#039;&#039;&#039;Key Aggregate&amp;amp;nbsp;&#039;&#039;&#039; &#039;&#039;&#039;Relationships&amp;amp;nbsp;&#039;&#039;&#039;&amp;lt;/span&amp;gt;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;lt;span style=&amp;quot;font-size:small&amp;quot;&amp;gt;(illustrative, not comprehensive)&amp;lt;/span&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &amp;lt;div&amp;gt;Production function with endogenous technological change&amp;amp;nbsp;&amp;lt;br/&amp;gt;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;lt;br/&amp;gt;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;Price determination&amp;lt;/div&amp;gt;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: left&amp;quot; | &amp;lt;div style=&amp;quot;text-align: left&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;font-size:small&amp;quot;&amp;gt;&#039;&#039;&#039;Key Agent-Class Behavioral&amp;amp;nbsp;&#039;&#039;&#039; &#039;&#039;&#039;Relationships&#039;&#039;&#039;&amp;lt;/span&amp;gt;&amp;lt;/div&amp;gt;&amp;lt;div style=&amp;quot;text-align: left&amp;quot;&amp;gt;&amp;lt;span style=&amp;quot;font-size:small&amp;quot;&amp;gt;(illustrative, not comprehensive)&amp;lt;/span&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
| style=&amp;quot;text-align: left; padding-left: 10px&amp;quot; align=&amp;quot;center&amp;quot; | &lt;br /&gt;
Household crop, meat, and fish consumption&lt;br /&gt;
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Industry crop use&lt;br /&gt;
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Livestock producers crop use&lt;br /&gt;
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|}&lt;br /&gt;
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= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Flow Charts&amp;lt;/span&amp;gt; =&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:medium;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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The agriculture model combines a growth process in production with a partial equilibrium process that replaces the agricultural sector in the full-equilibrium economic model unless the user disconnects it. The model represents three agricultural commodities: crop, meat, and fish.&lt;br /&gt;
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The key equilibrating variables are the stocks of the three commodities. Equilibration works via investment to control capital stock and via prices to control domestic demand.&lt;br /&gt;
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Specifically, as food stocks rise, investment falls, restraining capital stock and agricultural production, and thus holding down stocks. Also, as stocks rise, prices fall, thereby increasing domestic demand, further holding down stocks. Domestic production and demand also influence imports and exports directly, which further affect stocks.&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Agricultural Production&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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=== &amp;lt;span style=&amp;quot;font-size:large;&amp;quot;&amp;gt;Crop Production&amp;lt;/span&amp;gt; ===&lt;br /&gt;
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Crop production is most simply a product of the land under cultivation (cropland) and the crop yield per hectare of land. Yield is determined in a Cobb-Douglas type production function, the inputs to which are agricultural capital, labor, and technical change. Technical change is conceptualized as being responsive to price signals, but the model uses food stocks in the computation to enhance control over the temporal dynamics of responsiveness.&amp;amp;nbsp; Specifically, technology responds to the imbalance between desired and actual food stocks globally.&amp;amp;nbsp; In addition there is a direct response of yield change to domestic food stocks that represents not so much technical change as farmer behavior in the fact of market conditions (e.g. planting more intensively). Overall, basic annual yield growth is bound by the maximum of the initial model year&#039;s yield growth and an exogenous parameter of maximum growth.&lt;br /&gt;
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This basic yield function is further subject to a saturation factor that is computed internally to the model̶–investments in increasing yield are subject to diminishing rather than constant returns to scale. Moreover, changes in atmospheric carbon dioxide (CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;) will affect agricultural yields both directly through CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and indirectly through changes in temperature and precipitation. Finally, the user can rely on parameters to increase or decrease yield patterns indirectly with a multiplier or to use parameters to control the saturation effect and the direct and indirect effects of CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; on crop yield.&lt;br /&gt;
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[[File:CropproductionFlowchartKN.png|frame|center|text-bottom|upright|Agricultural Production Flowchart]]&lt;br /&gt;
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=== &amp;lt;span style=&amp;quot;font-size:large;&amp;quot;&amp;gt;Meat and Fish Production&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Meat and fish production are represented far more simply than crop production. Meat production is simply the product of livestock herd size and the slaughter rate. Meat production includes production of non-meat animal products (eg. Milk and eggs). The herd size changes over time in response to global and domestic meat stocks, as well as changes in the demand for meat and the amount of grazing land.&lt;br /&gt;
&lt;br /&gt;
Fish production has two components: wild catch and aquaculture. The former is based on actual data and an exogenous parameter that allows the user to influence rate of catch. Aquaculture is assumed to continue to grow at a country-specific growth rate; a multiplier can also be used to increase or decrease aquaculture production. &amp;amp;nbsp; &amp;lt;!--[if gte mso 9]&amp;gt;&amp;lt;xml&amp;gt;&lt;br /&gt;
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 &amp;lt;/o:OLEObject&amp;gt;&lt;br /&gt;
&amp;lt;/xml&amp;gt;&amp;lt;![endif]--&amp;gt;&lt;br /&gt;
&lt;br /&gt;
[[File:Meat and fish production FlowchartKN.png|frame|center|text-top|upright|Meat and Fish Production Flowchart]]&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Agricultural Demand&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:large;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Agricultural demand is divided into crops, meat, and fish. Crop demand is further divided into industrial, animal feed, and human food demand.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Food demand from crops, meat and fish are responsive to calorie demand, which in turn responds to GDP per capita (as a proxy for income).&amp;amp;nbsp; The division of calorie demand between demand for calories from crops and from meat and fish changes in response also to GDP per capita (increasing with income). Caloric demand is used as the basis to compute food demand through conversion to food demand in terms of grams per capita. The caloric value of demand is also used to compute food demand in terms of proteins per capita.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
In addition to food demand, demand for feed, industrial demand for meat, crops and fish and food manufacturing demand are also computed. When all components of agricultural demand are computed, the price of the food elements of it are checked to assure that the total household demand for food does not exceed a high percentage of total country-level household consumption expenditures.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font size=&amp;quot;4&amp;quot;&amp;gt;Calorie Demand&amp;lt;/font&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Crop use for food and meat demand are both influenced by calorie demand. Total per capita calorie demand is driven by GDP per capita, but can be limited by calorie availability as well as by an exogenous parameter specifying maximum calorie need.[[File:Calorie Demand FlowchartKN.png|frame|center|middle|upright|Calorie demand flowchart]]&lt;br /&gt;
&lt;br /&gt;
The calculations of demand for meat, fish and food crop determine the ultimate division of calorie sources.&amp;amp;nbsp; There is also a limit to the share of calories that can come from meat. The demand for calories from crops is simply the residual obtained by subtracting the demand for calories from meat and fish from the demand for total calories. Caloric value of demand is used to compute food demand in terms of grams per capita and in terms of proteins per capita.&amp;amp;nbsp; Caloric value of demand is adjusted for elasticities to prices for all three categories namely crops, meat and fish.&lt;br /&gt;
&lt;br /&gt;
The user can manipulate calorie demand through the use of an exogenous calorie multiplier and can reduce undernourishment to 5 percent of the population over time through the usage of two other hunger elimination parameters.&lt;br /&gt;
&lt;br /&gt;
=== Food Demand for Crops, Meat and Fish ===&lt;br /&gt;
&lt;br /&gt;
Food demand is driven by the demand for calories. A conversion factor translates calorie demand into food demand in terms of grams per capita.&amp;amp;nbsp; Crop prices and an elasticity affect the resultant food demand.&amp;amp;nbsp; So too does a constraint on the maximum calories per capita and the size of the population.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
[[File:Food demand flowchart KN.png|frame|center|text-top|564x476px|Food demand flowchart]]&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:large;&amp;quot;&amp;gt;Industrial Demand&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Industrial demand (examples would be textile use of cotton or beverage inputs use of barley) is driven primarily by GDP per capita and population.&amp;amp;nbsp;&amp;amp;nbsp; Another important use in recent years has been for biofuels, and that demand component is responsive to world energy price and an elasticity.&lt;br /&gt;
&lt;br /&gt;
Crop prices also influence total industrial demand for crops.&amp;amp;nbsp; A maximum per capita demand parameter constrains the total and an exogenous multiplier allows users to alter the total.&lt;br /&gt;
&lt;br /&gt;
[[File:IndustrialdemflowchartKN.png|frame|center|text-bottom|564x300px|Industrial demand flowchart]]&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:large;&amp;quot;&amp;gt;Feed Demand&amp;amp;nbsp;&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
The total feed demand for the livestock herd is dependent on the weight of the livestock herd and per unit weight feed requirements.&amp;amp;nbsp; The per unit feed requirements increase with GDP per capita as populations move from meat sources such as chickens to more feed intensive ones such as pork and especially beef.&amp;amp;nbsp; But they also are reduced by change in the efficiency of converting feed to animal weight.&lt;br /&gt;
&lt;br /&gt;
Some of the food requirements of livestock are met by grazing, thereby reducing the feed requirements.&amp;amp;nbsp; The feed equivalent of grazing depends on the amount of grazing land, the productivity of that land (computed in the initial year and highly variable across countries), and grazing intensity (which increases with crop prices).&lt;br /&gt;
&lt;br /&gt;
Finally, the feed demand can be modified directly by an exogenous demand parameter that modifies industrial crop demand. The feed demand for meat and fish are calculated using ratios of the food demand to feed demand which are calculated in the initial years of the model. In addition to industrial demand and feed demand, food manufacturing demand is also calculated in the model on the basis of the food demand for all three categories (meat, crops and fish)&lt;br /&gt;
&lt;br /&gt;
[[File:FeeddemandKN.png|frame|center|564x476px|Feed demand flowchart]]&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;font size=&amp;quot;4&amp;quot;&amp;gt;Total Agricultural Demand&amp;lt;/font&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Total Agricultural demand is the sum of demand for crops to serve industrial, animal feed, food manufacturing and human food purposes.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
[[File:Total Ag demand KN.png|frame|center|564x476px|Total Agricultural Demand Flowchart]]&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:large;&amp;quot;&amp;gt;Financial Constraint on Food Demand&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Total food demand in million metric tons consists of the sum of crop demand, meat demand and food demand and fish demand.&amp;amp;nbsp; It can be, however, that the monetary value of those calculated demands is greater than the financial ability of households to pay for them.&amp;amp;nbsp; When that is the case, the food ,meat and fish demand are proportionately reduced.&amp;lt;br/&amp;gt;[[File:Financial constraint on food demand KN.png|frame|center|575x211px|Visual representation of financial constraint on food demand]]&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Agricultural Investment and Capital&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
The level of total desired agricultural investment are driven by the rate of past investment as a portion of GDP, changes in global crop demand as a portion of GDP, and global crop stocks relative to desired levels. We have experimented also with tying investment to profit rates in agriculture, thereby linking it also to prices relative to costs. The user can use a multiplier to increase or decrease the desired level of investment.&amp;amp;nbsp; This desired amount of investment is passed to the economic model, where it must ‘compete’ with demands for investments in other sectors.&amp;amp;nbsp; The economic model returns a final investment level for use in agriculture.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Investment in agriculture has two possible targets. The first is capital stock. The second is land. The split between the two destinations is a function of the relative returns to cropland development and agricultural capital, the latter of which is determined by the increased yield that could be expected from an additional unit of agricultural capital.&lt;br /&gt;
&lt;br /&gt;
[[File:AginvandcapitalFlowchartKN.png|frame|center|564x476px|Visual representation of agricultural investment and capital]]&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Land Dynamics&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
In IFs, land use is divided into 5 categories: cropland, grazing land, forest land, &amp;quot;other&amp;quot; land, and urban or built-up land. Four key dynamics are involved in land use change. First, changes in urban land are driven by changes in average income and population, and draws from all other land types. Second, the investment in cropland development is the primary driver of changes in cropland, but this is also influenced by the cost of developing cropland, the depreciation rate, or maintenance cost, of cropland investment, and a user-controllable multiplier. The costs of developing cropland increase as the amount of cropland increases and, therefore, there is less other land available for conversion. Shifts in cropland are compensated by changes in forest and &amp;quot;other&amp;quot; land. Third, changes in grazing land are a function of average income, with shifts again being compensated by changes in forest and &amp;quot;other&amp;quot; land. Finally, conservation policies can influence the amount of forest land, with any necessary adjustments coming from crop and grazing land.&amp;lt;br/&amp;gt;[[File:Land dynamics.png|frame|center|Visual representation of land dynamics]]&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Agricultural Equations&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:large;&amp;quot;&amp;gt;Overview&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Briefly, each year the agriculture model begins by estimating the production (pre- and post-production loss) of crops, meat, and fish. It then turns to the demand for these commodities. This begins with a computation of caloric demand from crops, meat, and fish, which is translated into demand for food going directly to consumers. Other demands for crops, meat, and fish are for feed, industrial uses (e.g. biofuels), and food manufacturing. Losses in the production, distribution and consumption of agricultural commodities are also accounted for. This is followed by computations for trade. The model then considers the balance between the demands and the available supply based on production, imports, and exports. Any excess supply increases stocks. In the case of excess demand, stocks are drawn down; this can result in shortages if there are not enough stocks, which leads to an inability to meet all of the demands. Levels of, and changes in, stocks influence prices for the coming year, as well as desired investment, which are passed to the economic model, which determines the actual amount of investment that will be available. With this knowledge, the model can then estimate values for changes in land development, agricultural capital, and livestock for the coming year.&lt;br /&gt;
&lt;br /&gt;
=== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Agricultural Supply&amp;lt;/span&amp;gt; ===&lt;br /&gt;
&lt;br /&gt;
Crop, meat, and fish supply have very different bases and IFs determines them in separate procedures.&lt;br /&gt;
&lt;br /&gt;
==== &amp;lt;span style=&amp;quot;font-size:large;&amp;quot;&amp;gt;Crop Production&amp;lt;/span&amp;gt; ====&lt;br /&gt;
&lt;br /&gt;
Crop production, pre-loss, (AGPppl&amp;lt;sub&amp;gt;f=1&amp;lt;/sub&amp;gt;) i is the product of total yield and land devoted to crops (LD&amp;lt;sub&amp;gt;l=1&amp;lt;/sub&amp;gt;).&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;AGPppl_{r,f=1}= YL_r*LD_{r,l=1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
We focus here on the determination of yield; the amount of land devoted to crops is addressed in the sections below.Yield functions are almost invariably some kind of saturating exponential that represents decreasing marginal returns on inputs such as fertilizer or farm machinery. Such functions have been used, for instance in World 3&amp;lt;ref&amp;gt;Meadows, Dennis L. et al. 1974. Dynamics of Growth in a Finite World. Cambridge, Mass: Wright-Allen Press.&amp;lt;/ref&amp;gt; , SARUM&amp;lt;ref&amp;gt;Systems Analysis Research Unit (SARU). 1977. SARUM 76 Global Modeling Project. Departments of the Environment and Transport, 2 Marsham Street, London, 3WIP 3EB&amp;lt;/ref&amp;gt;, the Bariloche Model &amp;lt;ref&amp;gt;Herrera, Amilcar O., et al. 1976. Catastrophe or New Society? A Latin American World Model. Ottawa: International Development Research Centre.&amp;lt;/ref&amp;gt;, and AGRIMOD &amp;lt;ref&amp;gt;Levis, Alexander H., and Elizabeth R. Ducot. 1976. &amp;quot;AGRIMOD: A Simulation Model for the Analysis of U.S. Food Policies.&amp;quot; Paper delivered at Conference on Systems Analysis of Grain Reserves, Joint Annual Meeting of GRSA and TIMS, Philadelphia, Pa., March 31-April 2.&amp;lt;/ref&amp;gt;. IFs also uses a saturating exponential, but relies on a Cobb-Douglas form. The Cobb-Douglas function is used in part to maintain symmetry with the economic model but more fundamentally to introduce labor as a factor of production. Especially in less developed countries (LDCs) where a rural labor surplus exists, there is little question that labor, and especially labor efficiency improvement, can be an important production factor.&lt;br /&gt;
&lt;br /&gt;
===== Pre-processor and first year =====&lt;br /&gt;
&lt;br /&gt;
In the pre-processor, agricultural production is initialized using data from the FAO food balance sheets. For details of the series that are used in this initialization, refer Annex 1 of this document. In the first year of the model, total crop production is calculated by adjusting the initialized value of crop production for production losses, as the FAO data are for post-loss production. Yield (YL) is computed simply as the ratio of total crop production (AGPppl&amp;lt;sub&amp;gt;f=1&amp;lt;/sub&amp;gt;) to cropland (LD&amp;lt;sub&amp;gt;l=1&amp;lt;/sub&amp;gt;). It is bound, however, to be no greater than 100 tons per hectare in any country.&lt;br /&gt;
&lt;br /&gt;
In addition to yield, a number of other values related to production are calculated in the first year of the model that are used in forecast years.&lt;br /&gt;
&lt;br /&gt;
First, a scaling factor cD is calculated in the first year of the model. This is basically the constant in the Cobb-Douglas formulation for estimating yields. It is based upon the base year yield (YL), capital (KAG), and labor supply (LABS). The labor supply is adjusted using a Cobb-Douglass alpha exponent (CDALF) which is explained in detail below. &amp;amp;nbsp;cD is similar to the shift factors elsewhere in the model, which are used to match predicted values in the base year to actual values.&amp;amp;nbsp; It does not change over time. It is computed using the following equation,&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;cD_r= YL_{r,t=1}/ KAG_{r,t=1} ^ {CDALF_{r,s=1}} * LABS_{r,S=1,t=1} ^ {(1-CDALF_{r,s=1})}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Second, a target growth rate in yield is computed (TgrYli) which is used in forecast years to restrict the growth rate of the yield. This target growth is a function of current crop demand (AGDEM), expected crop demand (Etdem), and a target growth rate in cropland.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;Tgryli_{r}= (Etdem/AGDEM_{r,s=1}) -1-tgrld_{r} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
where,&lt;br /&gt;
&lt;br /&gt;
tgrld is a country-specific parameter indicating target growth in crop land&lt;br /&gt;
&lt;br /&gt;
Etdem is an initial year estimate of the sum of industrial, feed and food demand for crops in the following year&lt;br /&gt;
&lt;br /&gt;
===== Forecast years =====&lt;br /&gt;
&lt;br /&gt;
In forecast years, IFs computes yield in stages. The first provides a basic yield (byl) representing change in long-term factors such as capital, labor and technology. The second stage uses this basic yield as an input and modifies it based on prices, so as to represent changes in shorter-term factors (e.g. amounts of fertilizer used, even the percentage of land actually under cultivation). Finally, in a third stage, yields are adjusted in response to changing climate conditions.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;&#039;&amp;lt;u&amp;gt;First stage (Adjustment for long-term factors)&amp;lt;/u&amp;gt;&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
The basic yield (Byl) relates yield to agriculture capital (KAG), agricultural labor (LABS), technological advance (Agtec), a scaling parameter (cD), an exponent (CDALF), and a saturation coefficient (Satk).&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;Byl_{r}= cD_{r}*(1+Agtec_{r} )_{t-1}* KAG_{r}^ {CDALF_{r,s=1}} * LABS_{r,s=1} ^ {(1-CDALF)_{r,s=1}} * Satk_{r} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The equations for KAG and LABS are described elsewhere (see sections 3.9 and the economic model, respectively).&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
*cD is the scaling factor calculated in the first year of the model. Its calculation is described in the section above&lt;br /&gt;
&lt;br /&gt;
*CDALF is the standard Cobb-Douglas alpha reflecting the relative elasticities of yield to capital and labor.&amp;amp;nbsp; It is computed each year in a function, rooted in data on factor shares from the Global Trade and Analysis Project, driven by GDP per capita at PPP.[[#_ftn1|[1]]]&lt;br /&gt;
&lt;br /&gt;
Agtec is a factor-neutral technological progress coefficient similar to a multifactor productivity coefficient. It is initially set to 1 and changes each year based upon a technological growth rate (YlGroTech). Its computation is described below.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;Agtec_{r}= Agtec_{r,t-1}*(1+ YlGroTech_{r})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
*The saturation coefficient Satk is a multiplier of the Cobb-Douglas function and of the technological change element. It is the ratio of the gap between a maximum possible yield (YLLim) and a moving average of yields to the gap between a maximum possible yield and the initial yield, raised to an exogenous yield exponent (&#039;&#039;&#039;&#039;&#039;ylexp&#039;&#039;&#039;&#039;&#039;). With positive parameters the form produces decreasing marginal returns.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;Satk_{r+1}=(YLLim_{r}-Syl_{r}/YLLim_{r}-YL_{r,t=1})^ {ylexp}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
Syl&amp;lt;sub&amp;gt;r&amp;lt;/sub&amp;gt; is a moving average of byl, the historical component of which is weighted by 1 minus the user-controlled global parameter &#039;&#039;&#039;&#039;&#039;ylhw&#039;&#039;&#039;&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;&#039;ylexp&#039;&#039;&#039;&#039;&#039;is a global parameter&lt;br /&gt;
&lt;br /&gt;
The maximum possible yield (YLLim) is estimated for each country and can change over time.&amp;amp;nbsp; It is calculated as the maximum of 1.5 times the initial yield (YL&amp;lt;sub&amp;gt;r,t=1&amp;lt;/sub&amp;gt;) and the multiple of an external user-controlled parameter (&#039;&#039;&#039;&#039;&#039;ylmax&#039;&#039;&#039;&#039;&#039;) and an adjustment factor (YLMaxM).&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;YLLim_{r} = max(ylmax_{r}* YLMaxM_{r}, 1.5* YL_{r,t=1})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;&#039;ylmax&#039;&#039;&#039;&#039;&#039; is a country-specific parameter&lt;br /&gt;
&lt;br /&gt;
The adjustment factor YLMaxM allows for some additional growth in the yields for poorer countries&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;YLMaxM_{r} = 1*((1-DevWeight_{r})+(YL_{r}/YlMaxFound)^ {0.35* DevWeight_{r}})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
DevWeight&amp;lt;sub&amp;gt;r&amp;lt;/sub&amp;gt; is GDPPCP&amp;lt;sub&amp;gt;r&amp;lt;/sub&amp;gt;/30, with a maximum value of 1&lt;br /&gt;
&lt;br /&gt;
YlMaxFound is the maximum value of YL found in the first year&lt;br /&gt;
&lt;br /&gt;
{| border=&amp;quot;1&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;0&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
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&#039;&#039;&#039;&amp;lt;u&amp;gt;Box1: Computation of technological growth rate for yield&amp;lt;/u&amp;gt;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
The algorithmic structure for computing the annual values of YlGroTech involves four elements:&amp;amp;nbsp;&lt;br /&gt;
&amp;lt;ol style=&amp;quot;list-style-type:lower-alpha;&amp;quot;&amp;gt;&lt;br /&gt;
&amp;lt;li&amp;gt;The difference between a targeted yield growth calculated the first year and the portion of that growth not initially related to growth of capital and labor (hence the underlying initial technology element of agricultural production growth); call it AgTechInit.&amp;lt;/li&amp;gt;&lt;br /&gt;
&amp;lt;li&amp;gt;The gap between desired global crop stock levels and actual stocks (hence the global pressure for technological advance in agriculture); call it AgTechPress. This contribution is introduced by way of the ADJUSTR function of IFs. [[#_ftn2|[2]]]&amp;lt;/li&amp;gt;&lt;br /&gt;
&amp;lt;li&amp;gt;The difference between the productivity of the agricultural sector calculated in the economic model and the initial year&#039;s value of that (hence reflecting changes in the contributions of human, social, physical, and knowledge capital to technological advance of the society generally); call if AgMfpLt.&amp;lt;/li&amp;gt;&lt;br /&gt;
&amp;lt;li&amp;gt;The degree to which crop production is approaching upper limits of potential; this again involves the saturation coefficient (Satk).&amp;lt;/li&amp;gt;&lt;br /&gt;
&amp;lt;/ol&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The algorithmic structure this is:&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;YlgroTech_{r} = F(AgTechInit_{r},AgTechPress_{r},AgMfpLt_{r},Satk_{r})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;&#039;&amp;lt;u&amp;gt;Second stage of yield calculation (short term factors)&amp;lt;/u&amp;gt;&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
Before moving to the next stage, a check is made to see if the growth in byl is within reason.&amp;amp;nbsp; Specifically, Byl is not allowed to exceed the moving average of Byl (Syl) times a given growth rate (YlGrbound).&amp;amp;nbsp; This bound is the maximum of a user-controlled global parameter - &#039;&#039;&#039;&#039;&#039;ylmaxgr&#039;&#039;&#039;&#039;&#039; and an initial country specific target growth rate (Tgryli&amp;lt;sub&amp;gt;r&amp;lt;/sub&amp;gt;).[[#_ftn3|[3]]]&lt;br /&gt;
&lt;br /&gt;
At this point, the basic yield (byl) is further adjusted by a number of factors.&amp;amp;nbsp; The first of these is a simple country-specific user-controlled multiplier – &#039;&#039;&#039;&#039;&#039;ylm&#039;&#039;&#039;&#039;&#039;. This can be used to represent the effects of any number of exogenous factors, such as political/social management (e.g., collectivization of agriculture).&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;YL_r= YL_r*ylm &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The basic yield represents the long-term tendency in yield but agricultural production levels are quite responsive to short-term factors such as fertilizer use levels and intensity of cultivation. Those short-term factors under farmer control (therefore excluding weather) depend in turn on prices, or more specifically on the profit (FPROFITR) that the farmer expects. Because of computational sequence, we use domestic food stocks as a proxy for profit level. Note that this adjustment is distinct from the adjustment above where global stocks affect the technological growth rate.&lt;br /&gt;
&lt;br /&gt;
The stock adjustment factor uses the ADJSTR function to calculate an adjustment factor related to the current stocks, the recent change in stocks, and a desired stock level.&amp;amp;nbsp; The desired stock level is given as a fraction (Agdstl) of the sum of crop demand (AGDEM&amp;lt;sub&amp;gt;f=1&amp;lt;/sub&amp;gt;) and crop production (AGP&amp;lt;sub&amp;gt;f=1&amp;lt;/sub&amp;gt;). Agdstl is set to be 1.5 times &#039;&#039;&#039;&#039;&#039;dstl&#039;&#039;&#039;&#039;&#039;, which is a global parameter that can be adjusted by the user.&lt;br /&gt;
&lt;br /&gt;
The focus in IFs on yield response to prices differs somewhat from the normal use of price elasticities of supply. For reference, Rosegrant, Agcaoili-Sombila, and Perez (1995: 5) report that price elasticities for crops are quite small, in the range of .05 to .4.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;&#039;&amp;lt;u&amp;gt;Third stage of yield calculation (Adjustment for a changing climate)&amp;lt;/u&amp;gt;&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
In the third stage, IFs considers the potential effects of a changing climate on crop yields. This is introduced through the variable ENVYLCHG[[#_ftn4|[4]]] which is calculated in the environmental model. This variable consists of two parts: the direct effect of atmospheric carbon dioxide concentrations and the effects of changes in temperature and precipitation.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;ENVYLCHG_{r,f} =(((CO2Fert_{t}/100)+1)*((DeltaYClimate_{R,t}/100)+1)-1)*100&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The direct effect of atmospheric carbon dioxide assumes a linear relationship between changes in the atmospheric concentration from a base year of 1990 and the percentage change in crop yields.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;CO2Fert_{t+1} = envco2fert *((CO2PPM- CO2PPM_{t=1990})/CO2PPM_{t=1990} )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;&#039;envco2fert&#039;&#039;&#039;&#039;&#039; is a global, user-controllable parameter&lt;br /&gt;
&lt;br /&gt;
CO2PPM&amp;lt;sub&amp;gt;t=1990&amp;lt;/sub&amp;gt; is hard coded as 354.19 parts per million&lt;br /&gt;
&lt;br /&gt;
The effect of changes in annual average temperature and precipitation are based upon two assumptions: 1) there is an optimal temperature (Topt) for crop growth, with yields falling both below and above this temperature and 2) there is a logarithmic relationship between precipitation and crop yields.&amp;amp;nbsp; The choice of this functional form was informed by work reviewed in Cline (2007).&amp;amp;nbsp; Together, these result in the following equation:&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;ClimateEffect_{t+1} = 100*{({e^{(-0.5*(T0_{r}+ DeltaT{r} - Topt)^2)/SigmaTsqd}*ln(P0_{r}*(DeltaP_{r}/100+1))/e^{(-0.5*(T0_{r}-Topt)^2/SigmaTsqd}*ln(P0_r))-1}} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
T0 and P0 are country-specific annual average temperature (degrees C) and precipitation (mm/year) for the period 1980-99.&lt;br /&gt;
&lt;br /&gt;
DeltaT and DeltaP are country specific changes in annual average temperature (degrees C) and precipitation (percent) compared to the period 1980-99.&amp;amp;nbsp; These are tied to global average temperature changes and described in the documentation of the IFs environment model.&lt;br /&gt;
&lt;br /&gt;
Topt is the average annual temperature at which yield is maximized.&amp;amp;nbsp; It is hard coded with a value of 0.602 degrees C.&lt;br /&gt;
&lt;br /&gt;
SigmaTsqd is a shape parameter determining how quickly yields decline when the temperature moves away from the optimum. It is hard coded with a value of 309.809.&lt;br /&gt;
&lt;br /&gt;
CO2Fert and ClimateEffect are multiplied by each other to determine the effect on crop yields.&lt;br /&gt;
&lt;br /&gt;
There are two final checks on crop yields.&amp;amp;nbsp; They are not allowed to be less than one-fifth of the estimate of basic yield (Byl) and they cannot exceed the country-specific maximum (&#039;&#039;&#039;&#039;&#039;ylmax&#039;&#039;&#039;&#039;&#039;) or 100 tons per hectare. Finally crop production is adjusted for production losses to arrive at post loss production (AGP). Losses are discussed in detail in section 3.1.4 below&lt;br /&gt;
&lt;br /&gt;
==== Meat Production ====&lt;br /&gt;
&lt;br /&gt;
Meat production in IFs is the sum of animal meat production and non-meat animal products (AGPMILKEGGS). Animal meat production in a particular country is a function of the herd size and the slaughter rate and non-animal meat products are calculated by applying a ratio MilkEggstoMeatI which is calculated in the first year of the model as the ratio of non-meat animal production to the meat production. Meat production is then adjusted for production losses which are described in detail in the sections below.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;AGP_{r,f=2} =((LVHERD_{r}* slr)+ AGPMILKEGGS_r )- AGLOSSPROD_{r,f=2}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Where,&lt;br /&gt;
&lt;br /&gt;
LVHERD is the size of livestock in a particular country in a particular year&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;&#039;slr&#039;&#039;&#039;&#039;&#039;is the slaughter rate which is a global parameter&lt;br /&gt;
&lt;br /&gt;
AGLOSSPROD is the meat production loss.&lt;br /&gt;
&lt;br /&gt;
===== Pre-processor and first year =====&lt;br /&gt;
&lt;br /&gt;
In the pre-processor, meat production is initialized in the model using data from the FAO food balance sheets. Total meat production and animal meat production (which is the sum of bovine meat production, mutton and goat meat production, pig meat production, poultry meat prod, and other meat production) are initialized separately. If data on all of the animal meat sub-categories is unavailable, then Animal meat production is calculated as 30 percent of total meat production. Animal production is also not allowed to exceed 99% of the value of total meat production.&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
AGPMILKEGGS, which is the non-meat animal production is then calculated as total meat production minus total animal meat production. The non-meat production ratio MilkEggstoMeatI is calculated as the ratio of the initialized value of AGPMILKANDEGGS and meat production in the first year. This is used in forecast years to calculate the value of non-meat animal production, and is held constant over time.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;MilkEggstoMeatI_{r} = AGPMILKEGGS_{r}/(AGP_{r,f=2}- AGPMILKEGGS_{r})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The size of the livestock (LVHERD) is also computed in the first year using the initialized value of pre-loss meat production. This value of LVHERD is used in forecast years to compute meat production.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;LVHERD_{r} = (AGPppl_{r,f=2} - AGPMILKEGGS_{r} )/slr &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
For a detailed discussion on the dynamics of livestock herd, refer to section 3.11 of this document.&lt;br /&gt;
&lt;br /&gt;
===== Forecast years =====&lt;br /&gt;
&lt;br /&gt;
Pre-production loss values for meat production are calculated in IFs as meat production (AGPppl) and production of non-meat animal products (AGPMILKANDEGGS). Meat production, in metric tons, is given as the multiple of the herd size (LVHERD) and the slaughter rate (&#039;&#039;&#039;&#039;&#039;slr&#039;&#039;&#039;&#039;&#039;). The latter is a global parameter. These values are then adjusted for production losses for meat (AGPRODLOSS) to arrive at post production loss values (AGP). The same meat production loss percentage is also applied to the non-meat production to arrive at post loss production values for the variable. The dynamics of production losses are discussed here.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;AGP_{r,f=2} = AGPppl_{r,f=2} - AGLOSSPROD_ {r,f=2}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Where,&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;AGPppl_{r,f=2} = (AGPMILKANDEGGSppl_{r} +( LVHERD_{r}*slr)) &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Production of non-animal meat products is computed using the non-meat production ratio&amp;amp;nbsp;which is applied to the animal meat production.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;AGPMILKANDEGGSppl_{r} = MilkEggstoMeatI_{r} *( LVHERD_{r} * slr)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The dynamics of the livestock herd are described in section 3.11.&lt;br /&gt;
&amp;lt;div&amp;gt;&amp;lt;/div&amp;gt;&lt;br /&gt;
==== Fish Production ====&lt;br /&gt;
&lt;br /&gt;
The production of fish has two components, wild catch and aquaculture. Fish caught through aquaculture is treated as a stock in the model and is a function of a growth component.&amp;amp;nbsp; Wild catch on the other hand is treated as a flow in the model.&lt;br /&gt;
&lt;br /&gt;
===== Pre-processor and first year =====&lt;br /&gt;
&lt;br /&gt;
Data for fish catch and aquaculture is derived through two main sources, namely the FAO food balance sheets and the FAO Fishstatj software. Data for fish production, imports and exports is initially extracted from the FAO Food Balance Sheets. However, no breakout is available for fish caught as wild catch and fish caught through aquaculture. This bifurcation is available in the dataset from the FAO Fishstatj database. The data from the FAO food balance sheets is broken down into fish catch (AGFISHCATCH) and aquaculture (AQUACUL) using data from the FAO fishstatj dataset.&lt;br /&gt;
&lt;br /&gt;
In the first year, the values for pre-loss production of wild fish, AGFISHCATCHppl and aquaculture, AQUACULppl, are calculated by adding in a level of catch loss, which is not reflected in the FAO and Fishstatj data. Separate parameters, &#039;&#039;&#039;&#039;&#039;aglossprodperc&#039;&#039;&#039;&#039;&#039;&amp;lt;i&amp;gt;&amp;lt;sub&amp;gt;f=3&amp;lt;/sub&amp;gt; &amp;lt;/i&amp;gt;&#039;&#039;and &#039;&#039;&#039;aglossprodperc&#039;&#039;&#039;&amp;lt;sub&amp;gt;f=4&amp;lt;/sub&amp;gt;, &#039;&#039;are used for wild catch and aquaculture.&lt;br /&gt;
&lt;br /&gt;
===== Forecast years =====&lt;br /&gt;
&lt;br /&gt;
The amount of aquaculture (AQUACUL) in forecast years can be modified by the user. Production is assumed to grow over time. The default growth rate in the first year for all countries is 3.5 percent, but this value can be modified by the user, by country, with the parameter &#039;&#039;&#039;&#039;&#039;aquaculgr&#039;&#039;&#039;&#039;&#039;. This growth rate declines to 0 over a number of years given by the global parameter &#039;&#039;&#039;&#039;&#039;aquaculconv&#039;&#039;&#039;&#039;&#039;. Users can change the amount of aquaculture production, by country, with the multiplier &#039;&#039;&#039;&#039;&#039;aquaculm[[#_ftn1|&#039;&#039;&#039;[1]&#039;&#039;&#039;]]&#039;&#039;&#039;&#039;&#039;. Finally, this is adjusted for production losses from aquaculture with Aquaculloss&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;AQUACUL_{r} = (AQUACULppl_{r,t-1} * (1+ aquaculgr_{r,t} )* aquaculm_{r})- Aquaculloss_{r} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;aquaculgr&amp;lt;sub&amp;gt;r,t&amp;lt;/sub&amp;gt; declines from &#039;&#039;&#039;aquaculgr&#039;&#039;&#039;&amp;lt;sub&amp;gt;r,t=1&amp;lt;/sub&amp;gt;&#039;&#039; to 0 over &#039;&#039;&#039;&#039;&#039;aquaculconv&#039;&#039;&#039;&#039;&#039; years&lt;br /&gt;
&lt;br /&gt;
Wild catch is initialized in the pre-processor as the variable AGFISHCATCH. The pre- production loss of wild catch is computed after applying a multiplier &#039;&#039;&#039;&#039;&#039;fishcatchm&#039;&#039;&#039;&#039;&#039; and this is adjusted for losses[[#_ftn2|[2]]] (Catchloss) to arrive at post production loss wild fish catch.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;AGFISHCATCH_{r} = (AGFISHCATCHppl_{r,t-1} * fishcatchm_{r} )- Catchloss_{r} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Total, post-production loss fish production (AGP) is then given as:&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;AGP_{r,t=3} = AQUACUL_{r} + AGFISHCATCH_{r} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Losses and waste&lt;br /&gt;
&lt;br /&gt;
Losses can occur at several places along the chain from production. In earlier sections, we mentioned losses at the production stage. Losses can also occur in the process of transmission and distribution from the producer to the final consumer and at the consumer stage. The latter is sometimes referred to as food waste, but for our purposes, we will use the term loss for all three stages: production, transmission and distribution, and consumption.&lt;br /&gt;
&lt;br /&gt;
The FAO Food Balance Sheets provide data on losses during transmission and distribution, but not at the production or consumption stages. Until we are able to find data showing a clear relationship between these losses and GDP per capita, or some other explanatory factor, we make an assumption of production losses and consumption losses of 10% for all countries. The user can make changes in these values with the parameters &#039;&#039;&#039;aglossprodperc&#039;&#039;&#039;and&#039;&#039;&#039;aglossconsperc&#039;&#039;&#039;respectively. The former can be set for crops, meat, wild catch, and aquaculture separately. The latter combines wild catch and aquaculture as fish, as we do not have separate data on the consumption of wild caught versus farmed fish. More details on the use of these parameters and the actual calculation of production and consumption losses are provided in sections 3.1.1-3.1.3 and 3.2.1, respectively.&lt;br /&gt;
&lt;br /&gt;
Turning to transmission and distribution losses, some agricultural commodities will never make it from the producer to the final consumer because of pests, spoilage, etc. &amp;amp;nbsp;The FAO food balance sheets provide data on food lost to waste for crops and meat , but not for fish. Thus, for now we assume that there are no losses in this stage for fish. For crops and meat, though we were able to establish relationships between transmission and distribution losses and GDP per capita. These are shown in the figures below:&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
===== Pre-processor and first year =====&lt;br /&gt;
&lt;br /&gt;
The initial values for transmission and distribution losses are taken directly from the FAO Food balance sheets. For those countries without data, an assumed loss of 1 ton (0.000001 MMT) is used. These are given by the variable AGLOSSTRANS[[|&amp;lt;sub&amp;gt;r, f=1-3&amp;lt;/sub&amp;gt;]]. As with consumption, wild catch and aquaculture are combined into a single category, fish, as we do not have separate data; also, for the moment the value of AGLOSSTRANS&amp;lt;sub&amp;gt;r, f=3&amp;lt;/sub&amp;gt; is set to 0 for all countries.&lt;br /&gt;
&lt;br /&gt;
In the first year, a ratio of [[Transmission/distribution_loss_to_food_demand|transmission/distribution loss to food demand]], FDEM, &amp;amp;nbsp;is computed as:&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;AgLossTransToFoodRatI_{r,f=1to3} = AGLOSSTRANS_{r,f=1to3} / FDEM_{r,f=1to3} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===== Forecast years =====&lt;br /&gt;
&lt;br /&gt;
In future years, for crops and meat, the initial estimate for transmission and distribution losses are calculated as follows:&lt;br /&gt;
&lt;br /&gt;
·&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp; Predictions are made for the ratio of transmission/distribution loss to food demand as a function of GDP per capita (predaglosstrans) for the first year and the current year.&lt;br /&gt;
&lt;br /&gt;
·&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp; The ratio of the predicted values for the current year to the predicted value for the first year is multiplied by AgLossTransToFoodRatI.&lt;br /&gt;
&lt;br /&gt;
·&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp; That result is multiplied by FDEM for the current year to get losses in MMT.&lt;br /&gt;
&lt;br /&gt;
·&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp; That result is multipled by the parameter &#039;&#039;&#039;aglosstransm&#039;&#039;&#039;, to get a final value.&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
This can be expressed as:&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;AGLOSSTRANS_{r,f=1,2,3} = FDEM_{r,f=1,2,3,t=1} * predaglosstrans_{r,f=1,2,3,t}/predaglosstrans_{r,f=1,2,3,t=1}*AgLossTransToFoodRatioI_{r,f=1,2,3}*aglosstransm_{r,f=1,2,3}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Some further adjustments may be made to AGLOSSTRANS in the process of balancing global trade and balancing domestic supply and demand. These are discussed later in this documentation.&lt;br /&gt;
&lt;br /&gt;
== Agricultural Demand ==&lt;br /&gt;
&lt;br /&gt;
IFs computes demand, or uses, for three agricultural categories—crops, meat, and fish. &amp;amp;nbsp;These commodities are used for direct human consumption (FDEM), animal feed (FEDEM), industrial uses, e.g. biofuels (INDEM), and food processing and manufacturing (FMDEM). IFs also tracks the losses in transmission and distribution (AGLOSSTRANS). Total demand (AGDEM) is the sum of these five use categories and is given in MMT per year.&lt;br /&gt;
&lt;br /&gt;
The sections above&amp;amp;nbsp;describe&amp;amp;nbsp;the calculation of AGLOSSTRANS, so that is not repeated here. The calculation of the demand for direct human consumption, FDEM begins with estimates of daily per capita calorie demand for crops, meat, and fish. Briefly, IFs first estimates total per capita calorie demand, which responds to GDP per capita (as a proxy for income).&amp;amp;nbsp; The division of total demand between demand for calories from crops and from meat and fish also changes in response to GDP per capita (more meat and fish demand with increasing income).&amp;amp;nbsp; Finally, the division of calories from meat and fish is calculated based on historic patterns. Using country and commodity specific factors, the daily per capita calorie demands are converted to grams per capita per day and protein per capita per day. The grams per capita per day are then multiplied by the size of the population, POP, and the number of days in a year, 365, to arrive at FDEM.&lt;br /&gt;
&lt;br /&gt;
The other demands, FEDEM, INDEM, and FMDEM are driven by factors such as the size of the livestock herd, LVHERD, and the use of crops for fuel production. In cases where information is lacking, these demands are determined in relation to FDEM. Finally, there may be some modifications to all of the demand categories due to shortages or other factors, as described in the rest of this section.&lt;br /&gt;
&lt;br /&gt;
==== Daily per capita demands – calories, grams, and protein ====&lt;br /&gt;
&lt;br /&gt;
IFs tracks one set of variables for agricultural demands, or uses, on a daily per capita basis. These are. specifically, calories (CLPC), protein (PROTEINPC), and grams (GRAMSPC), for each category – crops, meat, and fish.&lt;br /&gt;
&lt;br /&gt;
===== Pre-processor and first year =====&lt;br /&gt;
&lt;br /&gt;
Daily calories per capita (CLPC), by category, are initialized in the IFs pre-processor using data from the FAO food balance sheets. Data on daily protein per capita and grams per capita are also read into the pre-processor.[[#_ftn1|[1]]] If data are available for crops, meat, and fish, total values for calories, protein, and grams are calculated as sums of the three categories. For countries where no data are available for one or more of the categories, the model follows a set of procedures to fill in the missing data. These procedures uses, among other things, 1) equations that relate total calories per capita per day and the share of these calories from crops versus meat and fish to GDP per capita and 2) other ratios derived from global averages of those countries with data. Later in the pre-processor, CLAVAL, which represent the total calories (across all categories) per day for the population as a whole is also calculated.&lt;br /&gt;
&lt;br /&gt;
The equation for total calories as a function of GDP per capita is stored as &amp;quot;GDP/Capita (PPP 2011) Versus Calorie Demand (fixed-effect)&amp;quot; and is illustrated below.&#039;&#039;[[#_ftn2|&amp;lt;sup&amp;gt;&amp;lt;sup&amp;gt;[2]&amp;lt;/sup&amp;gt;&amp;lt;/sup&amp;gt;]]&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
[[File:Calorie Demand vs GDP per capita.png|frame|center|300x476px|Calorie demand vs GDP per capita at PPP (fixed effect)]]&lt;br /&gt;
&lt;br /&gt;
The equation for the share of calories from meat and fish as a function of GDP per capita is stored as &amp;quot; GDP/Capita (PPP 2011) Versus CLPC from MeatandFish (2010) Log&amp;quot;&lt;br /&gt;
&lt;br /&gt;
Both of these are in a logarithmic form, indicating that both total calories and the share of calories from meat and fish increase with GDP per capita, but at a decreasing rate. As the data do not show a clear pattern for the breakdown between meat and fish, which is largely due to cultural patterns and geography, the model uses historical values rather than an estimated equation, as discussed below. In the pre-processor, an average global value is used for countries without data.&lt;br /&gt;
&lt;br /&gt;
In the first year of the model, one of the first things that occurs is a recalculation of GRAMSPC as GRAMSPC = FDEM/(POP * 365) * 100000. This is to ensure the consistency between the daily per capita variable, GRAMSPC, and the annual national value, FDEM. This is necessary because FDEM may have been modified in the pre-processor as part of ensuring a balance between the initial year supply of agricultural produces and their use. This is described in more detail in Box 1.&lt;br /&gt;
&lt;br /&gt;
In addition, a number of additional values related to calories to be used in the forecast period are calculated.&lt;br /&gt;
&lt;br /&gt;
#CalActPredRat: the ratio between actual calories available and the predicted value.[[#_ftn3|[3]]] It is used as a multiplicative shift factor. The predicted level of is estimated using the equation for total calories per capita as a function of GDP per capita described above. This is bound from above by an assumed maximum value, given by the global parameter &#039;&#039;&#039;calmax&#039;&#039;&#039;. The value of calactpredrat gradually converges to 1 over a period given by the global parameter &#039;&#039;&#039;agconv&#039;&#039;&#039;and appears in future equations with the name AdjustForInitialDevc.&lt;br /&gt;
#MeatAndFishActPredRat: the ratio between actual share of calories from meat and fish to the predicted value. It is used as a multiplicative shift factor. The predicted level of is estimated using the equation for share of calories from meat and fish per capita as a function of GDP per capita described above.&lt;br /&gt;
#MeatToMeatFishRatI: the ratio between calories from meat and calories from meat and fish. It is used to separate the future estimates of calories from meat and fish into separate values for meat and fish.&lt;br /&gt;
#ProtToCalRatI: the ratio of daily per capita protein to daily per capita calories, by category. It is used to convert future estimates of calorie availability to protein availability. If for some reason the initial estimate of ProtToCalRatI is 0 for any category, the median value for that category based on 2010 is used.&lt;br /&gt;
#GramsToCalRatI: the ratio of daily per capita grams to daily per capita calories, by category. It is used to convert future estimates of calorie availability to a value in grams, which is then used to estimate aggregate demand for food for direct human consumption. If for some reason the initial estimate of GramsToCalRatI is 0 for any category, the median value for that category based on 2010 is used.&lt;br /&gt;
&lt;br /&gt;
===== Forecast years =====&lt;br /&gt;
&lt;br /&gt;
In the forecast years, daily per capita calorie demand begins with a prediction of a total demand, CalPerCap, as a function of average income using the equation above, with a maximum value given by &#039;&#039;&#039;calmax&#039;&#039;&#039;. Two other values are also calculated at this point. First, a base level of calories per capita, CalBase, is also calculated, which is given as the minimum of 3000 or &#039;&#039;&#039;calmax&#039;&#039;&#039;minus 300. Second, because comparative cross sections show a growth of around 7.6 calories per capita per year independent of average income, a factor representing this increase (CaldGr) is calculated as:&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;CaldGr_{r,t} = CaldGr_{r,t-1} +7.638*((calmax-MAX(CalBase,MIN(calmax, CalPerCap_{r} )))/(calmax-CalBase)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Thus, depending on the exact values of &#039;&#039;&#039;calmax&#039;&#039;&#039;, CalBase, and CalPerCap, CaldGr grows each year by a value that centers around 7.6 calories. This value is then added to the predicted value in calculating the total demand for calories.&lt;br /&gt;
&lt;br /&gt;
The equation also takes into account &#039;&#039;&#039;calmax&#039;&#039;&#039;and the multiplicative shift factor on calories per capita calculated in the first year of the model. The latter is named AdjustForinitialDevc, which, as noted previously, is calculate as the value of calactpredrat gradually converging to 1 over a period given by the global parameter &#039;&#039;&#039;agconv&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;TotalCalPerCap_{r} = MIN(&#039;&#039;&#039;&#039;&#039;calmax&#039;&#039;&#039;&#039;&#039;,(CalPerCap_{r} + CaldGr_{r})* AdjustForInitialdevc_{r})* POP_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Finally, a value for the total calories per day, CalDem, is calculated by multiplying TotalCalPerCap times POP.&lt;br /&gt;
&lt;br /&gt;
The next step is to divide the total calories between crops and meat plus fish. First, a predicted value of the share of total calories going to meat and fish, MeatAndFishPctPred, is calculated as a function of GDP per capita, using the equation described earlier. Second, the ratio of between actual share of calories from meat and fish to the predicted value, MeatAndFishActPredRat, calculated in the first year is potentially modified. Specifically, a new variable, AdjustForInitialDevm, is assigned either the intial value of MeatAndFishActPredRat, or a value that reflects convergence of MeatAndFishActPredRat to a value of 1 over a period given by the global parameter &#039;&#039;&#039;agconv&#039;&#039;&#039;. The countries for which convergence does not occur are the South Asian countries – India, Nepal and Mauritius –&amp;amp;nbsp; which are traditionally low meat consuming countries. The actual share of calories from meat and fish is then calculated as:&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;MeatAndFishPctAct_{r} = MeatAndFishPctPred_{r} * AdjustForInitialDevm_r&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
A minimum value of 3.5 percent is also imposed.&lt;br /&gt;
&lt;br /&gt;
With this value for MeatAndFishPctAct, the model can divide the total calories between crops and the combination of meat and fish. Using the value for MeatToMeatFishRatioI, calculated in the first year, the model can then estimate the calories from meat and fish separately. The values are stored in the variable CLPC(&amp;lt;sub&amp;gt;r,f)&amp;lt;/sub&amp;gt;&lt;br /&gt;
&lt;br /&gt;
At this point, these values are adjusted for changes in world food prices and elasticities to demand for these prices.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;CLPC_{r,f=1-3} = CLPC_{r,f=1-3} *(WAP_{f=1-3}/WAP_{f=1-3,t=1} )^{X}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;where&#039;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
WAP&amp;lt;sub&amp;gt;f=1-3&amp;lt;/sub&amp;gt; are the global food prices for crops, meat, and fish&lt;br /&gt;
&lt;br /&gt;
X is the price elasticity of demand and takes on the value of &#039;&#039;&#039;elascd&#039;&#039;&#039;, &#039;&#039;&#039;elasm&#039;&#039;&#039;, and &#039;&#039;&#039;elasfd&#039;&#039;&#039;for crops, meat, and fish, respectively&lt;br /&gt;
&lt;br /&gt;
Given these adjustments, TotalCalPerCap is recalculated as the sum of CLPC for crops, meat, and fish.&lt;br /&gt;
&lt;br /&gt;
Finally, a parameter &#039;&#039;&#039;clpcm&#039;&#039;&#039;is applied to the final value of calories per capita that allows the user to manipulate demand for calories in addition to two parameters (that allow the user to eliminate hunger in a particular country over time) which are described below.&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;The parameters &#039;&#039;&#039;malnelimstartyr&#039;&#039;&#039;and &#039;&#039;&#039;malnelimtargetyr&#039;&#039;&#039;allow the user to reduce hunger in any country over a specific period of time. The activation of these parameters by the user, calculates the required cumulative growth rate in calories to eliminate hunger (reduce the undernourished population to 5 percent of the total population) ClPCcum. This cumulative growth rate is calculated using a logarithmic function that computes the growth rate relative to the household income and unskilled labor in a country.[[#_ftn4|[4]]] &amp;amp;nbsp;Also, the user can activate a switch &#039;&#039;&#039;malelimprecisesw&#039;&#039;&#039;, which calculates the specific number of calories required to eliminate hunger for the most undernourished part of the population. An individual who consumes less than 1000 calories per day but is still alive is assumed to be the most undernourished person in the population.&lt;br /&gt;
&lt;br /&gt;
Therefore the final equation is as follows,&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;CLPC_{r,f} = (CLPC_{r,f} *&#039;&#039;&#039;&#039;&#039;clpcm&#039;&#039;&#039;&#039;&#039;_{r,f} * ClPCcum_{r} )+ Caldef_{r,f} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Where,&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;&#039;clpcm&#039;&#039;&#039;&#039;&#039;is a multiplier that can be used to affect the demand for calories&lt;br /&gt;
&lt;br /&gt;
ClPCcum is the cumulative growth rate required in calories per capita to eliminate hunger over a specific time period determined by malnelimstartyr and malnelimtargetyr&lt;br /&gt;
&lt;br /&gt;
Caldef is the cumulative number of calories required to eliminate hunger for the most undernourished part of the population. This is calculated through the activation of &#039;&#039;&#039;&#039;&#039;malelimprecisesw&#039;&#039;&#039;&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
At this point, i.e., after dealing with the hunger targets, the values for daily grams per capita (GRAMSPC) and daily protein per capita (PROTEINPC) are calculated by multiplying the values for CLPC by GramsToCalRatI and ProtToCalRatI, respectively. Recall that these values were computed in the first year.&lt;br /&gt;
&lt;br /&gt;
A final adjustment to CLPC, PROTEINPC, and GRAMSPC can occur as a result of shortages. This begins with a reduction in FDEM, as described in Section 3.4: Stocks, which is then translated into new values for GRAMSPC, which are then used to recalculate CLPC and PROTEINPC.&lt;br /&gt;
&lt;br /&gt;
One final variable, CLAVAL, which represent the total calories (across all categories) per day for the population as a whole is then calculated as total calories per capita times the population.&lt;br /&gt;
&lt;br /&gt;
==== Agricultural demand for direct human consumption (FDEM&#039;&#039;)&#039;&#039; ====&lt;br /&gt;
&lt;br /&gt;
FDEM represents the amount of agricultural commodities going directly to consumers, presumably for consumption.&lt;br /&gt;
&lt;br /&gt;
===== Pre-processor and first year =====&lt;br /&gt;
&lt;br /&gt;
The pre-processor reads in data from the FAO Food Balance Sheets and initializes values for the amount of agricultural commodities used for direct human consumption, FDEM. If these data are missing for any commodity, a value is calculated by multiplying the daily grams per capita by the size of the population (POP) and the numbers of days in a year (365), and then divided by 100000 to get the units correct. As noted in Box 1, certain adjustments may be made to ensure consistencies between supply and demand in individual countries, as well as between imports and exports across countries.&lt;br /&gt;
&lt;br /&gt;
No adjustments are made to FDEM in the first year.&lt;br /&gt;
&lt;br /&gt;
===== Forecast years =====&lt;br /&gt;
&lt;br /&gt;
In the forecast years, FDEM is initially calculated based upon the calculation of daily grams per capita described in this section below:&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;FDEM_{r,f=1-3} = GRAMSPC_{r,f=1-3} * POP_{r}* 365/100000 &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
There are two situations where the value of FDEM might be adjusted. The first case is where more than 85 percent of consumers’ expenditures are on food stuffs. If this is the case, the values of FDEM for crops and meat and fish are reduced proportionately, as described in this section below.&lt;br /&gt;
&lt;br /&gt;
The second case is when a country faces absolute shortages, i.e., the total domestic supply, AGDEM, is not adequate to meet all of the demands, FDEM + FEDEM + INDEM + AGLOSSTRANS even after drawing down stocks to 0. Here, each of these demands/uses are reduced proportionately to restore the balance as described in Section 3.4: Stocks. In both cases, the decreases in FDEM are fed forward to reduce the actual calories available, as described here.&lt;br /&gt;
&lt;br /&gt;
=== Feed demand for crops, meat and fish ===&lt;br /&gt;
&lt;br /&gt;
Feed demand, FEDDEM, represents: 1) the amount of crops that are used to complement what livestock receive from grazing, and 2) an unspecified use of meat and fish, which appears in the FAO Food Balance Sheets.&lt;br /&gt;
&lt;br /&gt;
===== &#039;&#039;&#039;Pre-processor and first year&#039;&#039;&#039; =====&lt;br /&gt;
&lt;br /&gt;
The pre-processor reads in data from the FAO Food Balance Sheets and initializes values for the amount of agricultural commodities used as feed for other agricultural production, usually meat. If data are missing, a minimum value of 1 ton, or .000001 MMT is used.&lt;br /&gt;
&lt;br /&gt;
An initial adjustment to feed demand for crops can occur in the pre-processor. This occurs when the production from grazing land is not being fully utilized. Specifically, this is when the amount of equivalent feed from grazing land, i.e. grazing land productivity, here named GLandCAP, implies a lower than assumed minimum value of 0.01 tons of crop equivalents per hectare, here named MinLDProd. The implied value of GLandCap is calculated as the difference between the total feed requirement for the number of livestock minus the feed demand divided by the amount of grazing land.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;GLandCAP_{r} = LiveHerd_r* fedreq_r-FEDDEM_{r,f=1}/ LDGraz_{r} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;where,&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
LiveHerd is the size of the livestock herd (discussed in this section&amp;amp;nbsp;)&lt;br /&gt;
&lt;br /&gt;
LDGraz is the amount of grazing land (discussed in this section under Land Dynamics)&lt;br /&gt;
&lt;br /&gt;
FEDDEM&amp;lt;sub&amp;gt;r,f=1&amp;lt;/sub&amp;gt; is the value for demand for crops for feed&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;Fedreq is an estimate of the per animal feed requirements, which is a function of GDP per capita. The function is depicted in the figure below[[#_ftn5|[5]]]:&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
[[File:Feed demand for crops 2.png|frame|center|Feed demand as a function of GDP per capita at PPP]]&amp;lt;br/&amp;gt;If the value of GLandCAP is less than the minimum, MinLDProd—currently hard coded as 0.01 tons of crop equivalents per hectare, based on values for the Saudi desert), then CFEDDEM&amp;lt;sub&amp;gt;r,f=1&amp;lt;/sub&amp;gt; is recalculated as the difference between the total feed requirement for the number of livestock minus the amount of feed equivalent produced by grazing using the minimum productivity.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;CFEDDEM_{r,f=1} = LiveHerd_{r} * fedreq_{r} - MinLDProd* LDGraz_{r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Note that this occurs when the feed from crops meets most, if not all, of the total feed requirements, implying little or no need for feed equivalents from grazing land. Also a minimum value of 0.01 MMT is set for CFEDDEM.&lt;br /&gt;
&lt;br /&gt;
Finally, as noted in Box 1, certain adjustments may be made in the pre-processor to ensure consistencies between supply and demand in individual countries, as well as between imports and exports across countries.&lt;br /&gt;
&lt;br /&gt;
In the first year, the model once again checks to make sure that the grazing land productivity exceeds a minimum value and this time stores this value for future use. A parallel equation to that in the pre-processor is used to get an initial estimate for grazing land productivity, now named GldCap:&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;GLdCAP_{r} = (LVHERD_{r,t=1} * Fedreq_{r,t=1} -FEDDEM_{r,t=1})/LD_{r,l=2,t=1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;where&#039;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
LVHERD&amp;lt;sub&amp;gt;r,t=1&amp;lt;/sub&amp;gt; replaces LiveHerd from the equation in the pre-processor&lt;br /&gt;
&lt;br /&gt;
LD&amp;lt;sub&amp;gt;r,l=2,t=1&amp;lt;/sub&amp;gt; replaces LDGraz from the equation in the pre-processor&lt;br /&gt;
&lt;br /&gt;
FEDDEM&amp;lt;sub&amp;gt;r,f=1&amp;lt;/sub&amp;gt; replaces CFEDDEM&amp;lt;sub&amp;gt;r,f=1&amp;lt;/sub&amp;gt; from the equation in the pre-processor&lt;br /&gt;
&lt;br /&gt;
Fedreq&amp;lt;sub&amp;gt;r&amp;lt;/sub&amp;gt; is the same as in the equation in the pre-processor&lt;br /&gt;
&lt;br /&gt;
Now, if the model estimates that GldCAP is below the minimum level, still called MinLDProd and hard coded to a value of 0.01, a new value of GldCAP&amp;amp;nbsp; calculated:&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;GLdCAP_{r} = LVHERD_{r,t=1} * Fedreq_{r,t=1} -FEDDEM_{r,t=1}/LD_{r,l=2,t=1} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;where&#039;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
LVHERD&amp;lt;sub&amp;gt;r,t=1&amp;lt;/sub&amp;gt;, LD&amp;lt;sub&amp;gt;r,l=2,t=1&amp;lt;/sub&amp;gt;, FEDDEM&amp;lt;sub&amp;gt;r,f=1&amp;lt;/sub&amp;gt;, and fedreq&amp;lt;sub&amp;gt;r&amp;lt;/sub&amp;gt; are defined as above&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;&#039;fedreqm&#039;&#039;&#039;&amp;lt;sub&amp;gt;r&amp;lt;/sub&amp;gt; is a multiplier required to ensure that the grazing land productivity meets the difference between the total feed requirement and that provided by crops in the initial year. It is calculated as:&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;fedreqm_{r} = LD_{r,l=2,t=1} * MinLDProd + FEDDEM_{r,t=1} /(LVHERD_{r,t=1} * fedreq_{r,t=1} )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Note that this value is always greater than or equal to 1 given the condition for making the adjustment. When no adjustment is made, fedreqm is set to 1. These values of GldCAP and fedreqm, calculated in the first year, are held constant for all forecast years&lt;br /&gt;
&lt;br /&gt;
Finally, one other value is calculated in the first year – FeedToFoodRatI, which is the ratio between FEDDEM and FDEM. This is calculated for crops, meat, and fish, but is only used for the latter two categories in the forecast years, as described below.&lt;br /&gt;
&lt;br /&gt;
==== Forecast years ====&lt;br /&gt;
&lt;br /&gt;
In the forecast years, FEDDEM is calculated as a function of the size of the livestock herd (LVHERD), the feed requirements per unit livestock (fedreq), the amount of grazing land (LD&amp;lt;sub&amp;gt;l=2&amp;lt;/sub&amp;gt;), and the productivity of grazing land (GldCAP), but adjustments are also made reflecting the effect of global crop prices on grazing intensity (WAP&amp;lt;sub&amp;gt;f=1&amp;lt;/sub&amp;gt;), changes in the efficiency with which feed is converted into. meat, and the adjustment factor fedreqm calculated in the first year. There is also a parameter with which the user can cause a brute force increase or decrease in FEDDEM (&#039;&#039;&#039;feddemm&#039;&#039;&#039;)&lt;br /&gt;
&lt;br /&gt;
The model first calculates the amount of crop equivalent produced from grazing land using the following equation:&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;GLFeedEq_{r} =(LD_{r,l=2} * GLdCAP_{r} )*( WAP_{f=1} / WAP_{f=1,t-1} )^{elglinpr} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;&#039;&#039;where&#039;&#039;&#039;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;LD&amp;lt;sub&amp;gt;r,l=2&amp;lt;/sub&amp;gt; is the amount of grazing land; the dynamics of this variable is discussed in section 3.10: Land Dynamics&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;GldCAP&amp;lt;sub&amp;gt;r&amp;lt;/sub&amp;gt; is the country value for grazing land capacity initialized in the first year&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;WAP&amp;lt;sub&amp;gt;t,f=1&amp;lt;/sub&amp;gt; is global price for crops; and&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;&#039;&#039;&#039;elglinpr&#039;&#039;&#039; is a global parameter for the elasticity of livestock grazing intensity to annual changes in world crop prices; the basic assumption is that increasing prices should lead to increased grazing intensity and therefore greater productivity of grazing land[[#_ftn6|[6]]]&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;This production of crop equivalents from grazing land is then subtracted from total feed requirement in the following equation:&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&amp;lt;math&amp;gt;FEDDEM_{r,f=1} =(LVHERD_{r} * Fedreq_{r} * fedreqm_{r}*max{0.5,(1-livhdpro/100)^(t-1) }-[GLFeedEq]_r )*feddemm&amp;lt;/math&amp;gt;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;Where&#039;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
LVHERD, fedreq, and fedreqm are as previously described. LVHERD and fedreq are updated each year as described in section 3.11: Livestock Dynamics and as a function of GDP per capita, respectively. fedreqm, determined in the first year, does not change over time.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;&#039;livhdpro&#039;&#039;&#039; is a global parameter related to the rate at which the productivity of crops in producing meat improves over time. This part of the equation implies that the amount of feed needed to produce a unit of meat declines over time to a minimum of half the original amount required&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;&#039;feddemm&#039;&#039;&#039; is a country-specific multiplier that can be used to increase or decrease crop demand for feed purposes&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
For meat and fish, a simpler process is used. The feed to food ratio, FeedToFoodRatI, calculated in the initial years of the model is used to calculate the share of feed demand for meat and fish respectively.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;FEDDEM_{r,f} = FeedToFoodRatI_{r,f} * FDEM_{r,f}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Note that there is no multiplier equivalent to &#039;&#039;&#039;feddemm&#039;&#039;&#039;for meat and fish.&lt;br /&gt;
&lt;br /&gt;
Finally, as with FDEM, FEDDEM may be adjusted to account for excessive consumer spending on food, as described in Box 2 or due to shortages in crops, meat, or fish as described in Section 3.4: Stocks.&lt;br /&gt;
&lt;br /&gt;
=== Industrial demand for crops, meat and fish ===&lt;br /&gt;
&lt;br /&gt;
Industrial demand, INDEM, represents the amount of crops, meat, and fish that are used in industrial processes.&lt;br /&gt;
&lt;br /&gt;
==== &#039;&#039;&#039;Pre-processor and first year&#039;&#039;&#039; ====&lt;br /&gt;
&lt;br /&gt;
The pre-processor reads in data from the FAO Food Balance Sheets and initializes values for the amount of agricultural commodities used in industrial processes. If data are missing, a minimum value of 1 ton, or .000001 MMT is used.&lt;br /&gt;
&lt;br /&gt;
Finally, as noted in Box 1, certain adjustments may be made in the pre-processor to ensure consistencies between supply and demand in individual countries, as well as between imports and exports across countries.&lt;br /&gt;
&lt;br /&gt;
[[File:Industrial demand for crops.png|frame|center|Industrial demand for crops]]&amp;lt;br/&amp;gt;In the first year, two values related to industrial demand for crops are calculated. The first of these is a multiplicative shift factor (INDEMK), which is calculated as the ratio of actual to predicted industrial demand for crops.&amp;amp;nbsp; The predicted value is given by a function that relates per capita industrial demand to GDP per capita, which is shown&amp;lt;br/&amp;gt;below.&amp;amp;nbsp;This multiplicative shift factor remains constant over time. As with FEDDEM, one other value is calculated in the first year – IndToFoodRatI, which is the ratio between INDEM and FDEM. This is calculated for crops, meat, and fish, but is only used for the latter two categories in the forecast years, as described below.&lt;br /&gt;
&lt;br /&gt;
==== &#039;&#039;&#039;Forecast years&#039;&#039;&#039; ====&lt;br /&gt;
&lt;br /&gt;
In the forecast years, for crops, the initial value of industrial demand is updated using the table function above to get a predicted value for industrial demand per capita, which is then multiplied by population (POP) and the multiplicative shift factor (IndemK). At this point, a region-specific multiplier (&#039;&#039;&#039;indemm&#039;&#039;&#039;) can either increase or decrease the initial estimate of INDEM.&lt;br /&gt;
&lt;br /&gt;
A first adjustment to INDEM is related to the world energy price (WEP) and reflects the use of crops for fuel production. Specifically, as the world energy price increases relative to the price in the first year, the industrial demand for crops increases.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;INDEM_{r} = INDEM_{r} *(1+ WEP_{t}/WEP_{t=1}) *FoodforFuel)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Where&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
WEP is world energy price&lt;br /&gt;
&lt;br /&gt;
FoodforFuel is the elasticity of industrial use of crops to world energy prices. It starts at a value given by the global parameter &#039;&#039;&#039;elagind&#039;&#039;&#039;, and declines to a value of 0 over 50 years.&lt;br /&gt;
&lt;br /&gt;
The second adjustment relates to the world crop price (WAP&amp;lt;sub&amp;gt;f=1&amp;lt;/sub&amp;gt;); as this increases relative to the price in the first year, industrial demand for crops declines.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;INDEM_{r} = INDEM_{r} *(WAP_{f=1,t}/WAP_{f=1,t=1} )^{elascd}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;Where&#039;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
WAP is world crop price&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;&#039;elascd&#039;&#039;&#039; is a global parameter specifying the elasticity of crop demand to global food prices&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
A third adjustment is based on an assumed cap on per capita industrial demand for crops (IndemCapperPop—hard coded as 2. Specifically, INDEM is not allowed to exceed IndemCapperPop * POP.&lt;br /&gt;
&lt;br /&gt;
For meat and fish, industrial demand is initially calculated by applying the Industrial demand to food ratio, IndToFoodRatI (calculated in the initial year of the model) to the value of food demand.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;INDEM_{r,f} = IndToFoodRatI_{r} * FDEM_{r,f} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Note that there is no multiplier equivalent to &#039;&#039;&#039;indemm&#039;&#039;&#039;for meat and fish.&lt;br /&gt;
&lt;br /&gt;
Finally, as with FDEM and FEDDEM, INDEM may be adjusted to account for excessive consumer spending on food, as described in section 3.2.5 or due to shortages in crops, meat, or fish as described in this Section below.&lt;br /&gt;
&lt;br /&gt;
=== Food manufacturing demand ===&lt;br /&gt;
&lt;br /&gt;
The final demand category, FMDEM, relates to the use of crops, meat, and fish in food manufacturing and processing.&lt;br /&gt;
&lt;br /&gt;
==== Pre-processor and first year ====&lt;br /&gt;
&lt;br /&gt;
The pre-processor reads in data from the FAO Food Balance Sheets and initializes values for the amount of agricultural commodities used in food manufacturing and processing. Note that If data are missing, a minimum value of 1 ton, or .000001 MMT is used.&lt;br /&gt;
&lt;br /&gt;
As noted in Box 1, certain adjustments may be made in the pre-processor to ensure consistencies between supply and demand in individual countries, as well as between imports and exports across countries.&lt;br /&gt;
&lt;br /&gt;
Paralleling the case for INDEM, FEDDEM, and AGLOSSTRANS, one other value is calculated in the first year –FManToFoodRatI, which is the ratio between INDEM and FDEM. This is calculated for crops, meat, and fish, and used for all three in the forecast years, as described below.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;FMDEM_{r,f} = FManToFoodRatI_{r,f} * FDEM_{r,f} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Forecast years&lt;br /&gt;
&lt;br /&gt;
In the forecast years, for all three categories, demand is calculated using the Food manufacturing to food demand ratio, FManToFoodRatI, calculated in the first year of the model and the value of food demand.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;FMDEM_{r,f} = FManToFoodRatI_{r,f} * FDEM_{r,f} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
As with FDEM, INDEM, and FEDDEM, FMDEM may be adjusted to account for any shortages in crops, meat, or fish as described in Section 3.4: Stocks. It is not currently affected by excessive consumer spending on food, as described in Box 2&lt;br /&gt;
&lt;br /&gt;
=== Total agricultural demand and final adjustment to demand ===&lt;br /&gt;
&lt;br /&gt;
==== Pre-processor and first year ====&lt;br /&gt;
&lt;br /&gt;
AGDEM, which represents the sum of all uses. It is initialized in the first year of the model to ensure the balance with production, imports, and exports:&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;AGDEM_{r,f=1-3,t=1} = AGP_{r,f=1-3,t=1} + AGM_{r,f=1-3,t=1} - AGX_{r,f=1-3,t=1} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
==== Forecast years ====&lt;br /&gt;
&lt;br /&gt;
In the forecast years, AGDEM, is recalculated as the sum of the final values of feed, industry, and food demand and transmission losses:&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;AGDEM_{r,f=1-3} = FEDDEM_{r,f=1-3} + INDEM_{r,f=1-3} + FDEM_{r,f=1-3} + FMDEM_{r,f=1-3} + AGLOSSTRANS_{r,f=1-3} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Note that this occurs after any adjustments to the demand values as a result of excessive consumer spending on food, (described below), but before adjustments as a result of shortages, describe in Section 3.4: Stocks. Thus, it can be the case that the final value of AGDEM may exceed the sum of the individual demand values.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&amp;lt;u&amp;gt;Final agricultural demand adjustment based on levels of consumer spending&amp;lt;/u&amp;gt;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
One final adjustment is made to the agricultural demand variables in the forecast years.&lt;br /&gt;
&lt;br /&gt;
If the preliminary estimate of total food demand in monetary terms (csprelim), is too large of a share of consumption, i.e., if&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;CsPrelim_{r} = CSF_{r} *(FDEM_{r} * WAP_{f=1,t=1} + FDEM_{r,f=2} * WAP_{f=2,t=1} + FDEM_{r,f=3} * WAP_{f=3,t=1} )&amp;gt;0.85*C_{r,t=1}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Where,&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
CSF is the ratio of consumer spending in the agricultural sector in the first year (CS&amp;lt;sub&amp;gt;r,s=1,t=1&amp;lt;/sub&amp;gt;) to DemVal&amp;lt;sub&amp;gt;r&amp;lt;/sub&amp;gt;, a weighted sum of demands for agricultural products for food in the first year;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;DemVal_{r} = FDEM_{r,t-1} *WAP_{f=1,t-1} + FDEM_{r,f=2,t-1} * WAP_{f=2,t-1} + FDEM_{r,f=3,t-1} * WAP_{f=3,t-1} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
C is total household consumption in the first year&lt;br /&gt;
&lt;br /&gt;
When this is the case, a series of steps are taken to bring these values back in line.&lt;br /&gt;
&lt;br /&gt;
#The necessary reduction (NecReduc&amp;lt;sub&amp;gt;r&amp;lt;/sub&amp;gt;), which is in monetary terms, is calculated as CsPrelim&amp;lt;sub&amp;gt;r&amp;lt;/sub&amp;gt; – 0.85*C&amp;lt;sub&amp;gt;r&amp;lt;/sub&amp;gt;&lt;br /&gt;
#A reduction factor (ReducFact) for meat and fish, assuming cuts would disproportionately be there, &amp;amp;nbsp;is calculated as,&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;ReducFact_(r,)=(NecReduc_{r}/csprelim)*2&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
with a maximum value of 1 or full elimination&lt;br /&gt;
&lt;br /&gt;
#The physical demands for crops for meat and fish in tons (FDEM, categories 2 and 3) are reduced by reducfact, and the values of the meat and fish reduction are saved for the next step&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;Meatreduc_{r} = FDEM_{r,f=2} *ReducFact_{r}&amp;lt;/math&amp;gt; &amp;lt;math&amp;gt; Fishreduc_{r} = FDEM_{r,f=3} *ReducFact_{r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;FDEM_{r,f=2,3} = FDEM_{r,f=2,3} *(1-Reducfact)_{r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
#An estimate of the necessary reductions in crops for food, in monetary terms is estimated by subtracting the savings obtained through the reduction in meat demand&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;FoodReduc_{r}= NecReduc_{r} - MeatReduc_{r}* CSF_{r} *WAP_{f=2,t=1} - FishReduc_{r} * CSF_{r} *WAP_{f=3,t=1} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The physical demand for crops for food (FDEM) is then reduced as follows&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;FDEM_{r,f=1} = Max(0.1*FDEM_{r,f=1} , FDEM_{r,f=1} - FoodReduc_{r}/(CSF_{r} *WAP_{f=1,t=1} ))&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Note that this ensures that FDEM is not reduced by more than ninety percent.&lt;br /&gt;
&lt;br /&gt;
Finally, given the changes above, the total demand is recalculated as the sum of the final values of feed, industry, and food demand and transmission losses&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;AGDEM_{r,f} = FEDDEM_{r,f} + INDEM_{r,f} + FDEM_{r,f} + FMDEM_{r,f} + AGLOSSTRANS_{r,f}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{| cellpadding=&amp;quot;0&amp;quot; cellspacing=&amp;quot;0&amp;quot; width=&amp;quot;100%&amp;quot; align=&amp;quot;center&amp;quot; border=&amp;quot;1&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| &amp;lt;div&amp;gt;&lt;br /&gt;
&#039;&#039;&#039;Box 1: Adjustments in the Pre-processor to Ensure Proper Balances&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
The pre-processor reads in data from the FAO Food Balance Sheets and initializes values for the amount of agricultural commodities used for direct human consumption, FDEM, feed (FEDEM), industry (INDEM), food manufacturing (FMDEM), as well as transmission losses (AGLOSSTRANS). All of these are measured in MMT per year. At the same time, it reads in data for production (AGP), imports (AGM), exports (AGX), and total domestic supply (AGDOMSUPP)[1].&lt;br /&gt;
&lt;br /&gt;
A set of conditions should be meet for these variables for each category:&lt;br /&gt;
&lt;br /&gt;
#AGDOMSUPP = AGP + AGM – AGX. This says that total domestic supply equals production plus imports minus exports. This equivalence can be broken if there are changes in stocks, which we will see in forecast years. Currently, however, we assume there are no such changes in the first year. Thus it may be necessary to make adjustment for the equivalence to hold in first year. This is done in the pre-processor, by keeping AGDOMSUPP the same and applying the following three rules:&amp;lt;ol style=&amp;quot;list-style-type:lower-alpha;&amp;quot;&amp;gt;&lt;br /&gt;
&amp;lt;/ol&amp;gt;&amp;lt;/ol&amp;gt;&lt;br /&gt;
&amp;amp;lt;/ol&amp;amp;gt;&lt;br /&gt;
&amp;amp;lt;/ol&amp;amp;gt; &amp;amp;lt;/ol&amp;amp;gt; &amp;amp;lt;/ol&amp;amp;gt; &amp;amp;lt;/ol&amp;amp;gt; &amp;amp;lt;/ol&amp;amp;gt;&lt;br /&gt;
&lt;br /&gt;
*If AGDOMSUPP &amp;gt; AGP + AGM – AGX, i.e., stocks were being drawn down, increase AGP and AGM while reducing AGX.&lt;br /&gt;
*If AGDOMSUPP &amp;lt; AGP + AGM – AGX, i.e., stocks were being added to, decrease AGP and AGM while increasing AGX.&lt;br /&gt;
*Make sure that AGP, AGM, and AGX do not fall below a minimum value.&lt;br /&gt;
*Sum of AGM across countries = Sum of AGX across countries. This says that imports and exports need to match. If they do not, the model calculates the average of the two sums and adjusts AGM and AGX in each country proportionately.&lt;br /&gt;
*AGP + AGM – AGX = FDEM + FEDEM + INDEM + FMDEM + AGLOSSTRANS. This says that the total domestic supply, which accounts for production losses, has to match the total uses (including losses in transmission and distribution).&lt;br /&gt;
&lt;br /&gt;
&amp;amp;lt;/ol&amp;amp;gt;&lt;br /&gt;
&lt;br /&gt;
The pre-processor includes procedures to ensure that these three conditions hold for the initial values in each country. This can lead to minor adjustments in the values for the supply and demand categories. These processes can also lead to changes in related variables, including the production of non-animal meat products (CAGPMILKEGGS), fish catch (AGFISHCATCH), aquaculture production (AQUACUL), the size of the livestock herd (LVHERD), and the breakdown of land areas (LD). The latter occurs because we do not want these processes to change crop yields (YL).&amp;amp;nbsp;&lt;br /&gt;
&amp;lt;/div&amp;gt;&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== Trade ==&lt;br /&gt;
&lt;br /&gt;
Consistent with the approaches within both the economic model and the energy model, trade of agricultural products in IFs uses a pooled approach rather than a bilateral one.&amp;amp;nbsp;&amp;amp;nbsp; That is, we can see the total exports and imports of each country/region, but not the specific volume of trade between any two.&amp;amp;nbsp; Offered exports and demanded imports from each country/region are responsive to the past shares of export and import bases and are summed globally.&amp;amp;nbsp; The average of the totals is taken as the actual level of global trade and the country exports and imports are normalized to that level.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Price differentials across countries do not influence agricultural trade. Although the IFs project has experimented over time with making such trade responsive to prices, there is an increasing tendency globally for food prices to be more closely aligned across countries than was true historically.&amp;amp;nbsp; Moreover, the use within IFs of local relative food surpluses or deficits (as indicated by stock levels) to adjust trade patterns is an effective proxy for the use of prices.&lt;br /&gt;
&lt;br /&gt;
The initial year values of the imports (AGM) and exports (AGX) of the three agricultural commodities in physical quantities are determined in the pre-processor. Since we only have historical data on the imports and exports of fish in monetary terms, these need to be converted to physical terms. This is done by multiplying the monetary values, which are in $billion, by 1000*/2200 to get physical values in million tons. In addition, exports of fish are limited to be less than 70 percent of total fish available and imports less than 1 percent of total fish available. For each of the three agricultural commodity groupings, if there is an imbalance between global imports and global exports in the preprocessor, the latter takes precedence and national imports are adjusted to bring global imports into line with global exports.&lt;br /&gt;
&lt;br /&gt;
In the first year, seven variables are set related to trade for each commodity: XKAVE, MKAVE, XKAVMAX, MKAVMAX at the country level and wxct&amp;lt;sub&amp;gt;=1&amp;lt;/sub&amp;gt;, wmd&amp;lt;sub&amp;gt;t=1&amp;lt;/sub&amp;gt;, and WAP&amp;lt;sub&amp;gt;t=1&amp;lt;/sub&amp;gt; at the global level.&lt;br /&gt;
&lt;br /&gt;
XKAVE and MKAVE are moving average values of export and import propensity, respectively. They are specified as the ratio of agricultural exports and imports to a base value (xbase) for each commodity. For exports, this is basically the sum of production and demand for that commodity; for imports, it is just demand.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;XKAVE_{r,f=1-3,t=1} = AGX_{r,f=1-3,t=1}/(AGP_{r,f=1-3,t=1} + AGDEM_{r,f=1-3,t=1} )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;MKAVE_{r,f=1-3,t=1} = AGM_{r,f=1-3,t=1}/ AGDEM_{r,f=1-3,t=1} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
XKAVMAX and MKAVMAX are maximum values of XKAVE and MKAVE. For crops and meat, XKAVMAX is set to 1.1 times XKAVE, but is not allowed to exceed a value of 0.7; MKAVMAX is set to 1.5 times XKAVE, but also is not allowed to exceed a value of 0.7. For fish, XKAVMAX is set to 1.1 times XKAVE, with a bound of 0.95; MKAVE is set to 1.5 times MKAVE, with a bound of 2. These values are held constant for all future years.&lt;br /&gt;
&lt;br /&gt;
XPriceTermLag, and MPriceTermLag are set to 0 for all commodities. wxc and wmd are the total world agricultural exports and imports; these are set to a value of 1 in the first year. WAP is the initial world price index for each commodity, which is set to 100.&lt;br /&gt;
&lt;br /&gt;
In the forecast years, the process for determining agricultural imports and exports involves the following steps:&lt;br /&gt;
&lt;br /&gt;
#Estimating the agricultural export capacity and agricultural import demand for each country.&lt;br /&gt;
#Reconciling the differences between global agricultural export capacity and global agricultural import demand.&lt;br /&gt;
#Computing the actual levels of agricultural exports and agricultural imports for each country&lt;br /&gt;
&lt;br /&gt;
The agricultural export capacity is estimated by multiplying the export propensity (XKAVE) by the current year’s production and demand. It is also limited by XKAVMAX:&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;AGX_{r,f=1-3} = MIN(XKAVE_{r,f=1-3}, XKAVMAX_{r,f=1-3} )*(AGP_{r,f=1-3} + AGDEM_{r,f=1-3} )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Similarly, the agricultural import demand is estimated by multiplying the import propensity (MKAVE) by the current year’s demand, with a limit set by MKAVMAX&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;AGM_{r,f=1-3} =MIN(MKAVE_{r,f=1-3}, MKAVMAX_{r,f=1-3} )* AGDEM_{r,f=1-3} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
For each country, values are also estimated for its net surplus or deficit (surpdef) for each commodity. This is based on the following factors: 1) post-loss production, 2) domestic demand, 3) the difference between current and desired stocks, and 4) a trade term&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;surpdef_{r,f=1-3} = AGP_{r,f=1-3} * (1-LOSS_{r,f=1-3}) -AGDEM_{r,f=1-3}&lt;br /&gt;
+ cumstk_{r,f=1-3} - agdstl*(AGP_{r,f=1-3} + AGDEM_{r,f=1-3})&lt;br /&gt;
+TradeTerm_{r,f=1-3}&lt;br /&gt;
 )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The first three factors are straightforward. Production minus demand reflects a basic net surplus, which is then adjusted by any net surplus in stocks. The TradeTerm is related the relative role a country plays in global imports and exports and is given as:&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;TradeTerm_{r,f=1-3} =(AGM_{r,f=1-3}/wmd_{f=1-3,t-1} - AGX_{r,f=1-3}/wxc_{f=1-3,t-1} )*(wmd_{f=1-3,t-1}+ wxc_{f=1-3,t-1})/2&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The TradeTerm is positive (negative) when a country has a larger (smaller) share of the global imports than it does of the global exports of a particular commodity and vice versa. Since the TradeTerm is added to surpdef, it acts as a balancing mechanism; countries that appear as relatively larger (smaller) importers get a positive (negative) boost to their estimated net surplus, which tends to reduce (increase) imports as shown below.&lt;br /&gt;
&lt;br /&gt;
At this point, the global sum of exports and imports across countries will likely differ. Therefore, a procedure is required to balance these. In preparation for this one more global variable and several country-level variables are calculated. The global variable is globalsurdefrate, which is the ratio of the sum across countries of net surplus divided by the sum across countries of demand and production, which is the stock base.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;globalsurdefrate_{f=1-3} =(sum_r(surpdef_{r,f=1-3} )/(sum_r(AGDEM_{r,f=1-3} + AGP_{r,f=1-3}))&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The country-level variables are as follows:&lt;br /&gt;
&lt;br /&gt;
The first term modifies the country’s net surplus, increasing (decreasing) it when the global net surplus is negative (positive).&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;countryextrasurdef_{r,f=1-3} = surpdef_{r,f=1-3} - globalsurdefrate_{f=1-3} *(AGDEM_{r,f=1-3}+ AGP_{r,f=1-3})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The second term modifies how rapidly the net surplus is closed.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;countryextrasurdefadj_{r,f=1-3} = countryextrasurdef_{r,f=1-3}/5&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The third term is simply the ratio of exports to the sum of imports and exports.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;exportshare_{r,f=1-3} = AGX_{r,f=1-3}/(AGX_{r,f=1-3} + AGM_{r,f=1-3} )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The next step is to calculate whether it is necessary to increase (decrease) imports and decrease (increase) exports for each country, and by how much. Whether a country needs to increase its initial estimates of imports and decrease its initial estimates of exports, or vice versa, is determined by the sign of countryextrasurdef. If this value is negative, i.e., the country has a net deficit, it will need to reduce exports and increase imports. The opposite holds for when countryextrasurdef is positive.&lt;br /&gt;
&lt;br /&gt;
As for the amount by which imports and exports need to be increased or decreased, this is a function, in general, of the size of the necessary adjustment and the export share:&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;AGX_{r,f=1-3} = AGX_{r,f=1-3} + countryextrasurfdefadj_{f=1-3} * exportshare_{r,f=1-3}&amp;lt;/math&amp;gt; &amp;lt;math&amp;gt;AGM_{r,f=1-3} = AGM_{r,f=1-3}- countryextrasurfdefadj_{f=1-3} * (1-exportshare_{r,f=1-3})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Note that the sign of countryextrasurdef and the fact that exportshare is a value between 0 and 1 ensure that when exports increases, imports fall, and vice versa.Finally, in this adjustment process, exports and imports are not allowed to fall by more than half or more than double.&lt;br /&gt;
&lt;br /&gt;
This process may not fully reconcile global trade, so a final adjustment is made by setting world trade (WT) as the average of global exports and imports and then adjusting the country values accordingly:&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;WT_{f=1-3} =(sum_r(AGX_{r,f=1-3}) +sum_r(AGM_{r,f=1-3}) )/2&amp;lt;/math&amp;gt; &amp;lt;math&amp;gt; AGX_{r,f=1-3} = AGX_{r,f=1-3} * WT_{f=1-3}/(sum_r(AGX_{r,f=1-3}) )&amp;lt;/math&amp;gt; &amp;lt;math&amp;gt; AGM_{r,f=1-3} = AGM_{r,f=1-3} * WT_{f=1-3}/(sum_r(AGM_{r,f=1-3}) )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
IFs can now update the moving average export (XKAVE) and import (MKAVE) propensities for the next time step. The weights given to history are set by the global parameters &#039;&#039;&#039;xhw&#039;&#039;&#039;and &#039;&#039;&#039;mhw&#039;&#039;&#039;. For small exporters, i.e., where exports are less than one tenth of the sum of production and demand, &#039;&#039;&#039;xhw&#039;&#039;&#039;is reduced by 40 percent, allowing for faster adjustment. XKAVE and MKAVE are updated as&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;XKAVE_{r,f=1-3,t+1} = XKAVE_{r,f=1-3}+(1-xhw)* AGX_{r,f=1-3}/(AGP_{r,f=1-3} + AGDEM_{r,f=1-3} )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;MKAVE_{r,f=1-3,t+1} = XMAVE_{r,f=1-3}+ {1-mhw} * AGM_{r,f=1-3}/AGDEM_{r,f=1-3} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
For crops, the import propensity is bound from below by a factor given by potential GDP (GDPPOT), demand (AGDEM), the conversion factor between agricultural imports in physical terms and dollar values (msf, see section on links to the economic model), and the initial world price for agriculture (WAP).&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;XKAVE_{r,f=1-3,t+1} =&amp;gt; (0.6*GDPPOT_{r})/(AGDEM_{r,f=1-3} * msf_{r}*WAP_{f,t=1})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Finally, XKAVE and MKAVE are bound from above by XKAVMAX and MKAVMAX, respectively.&lt;br /&gt;
&lt;br /&gt;
== Stocks ==&lt;br /&gt;
&lt;br /&gt;
==== &#039;&#039;&#039;First year&#039;&#039;&#039; ====&lt;br /&gt;
&lt;br /&gt;
Due to a lack of good historical data, in the first year, stocks for all three agricultural commodities are assumed to equal desired stocks. These are set to a fraction (agdstl) of total production (AGP) and demand (AGDEM) for each commodity.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;FSTOCK_{r,f=1-3} =(AGP_{r,f=1-3} + AGDEM_{r,f=1-3} )*Agdstl&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Where&lt;br /&gt;
&lt;br /&gt;
Agdstl is a parameter used to set desired stock levels for agricultural commodities.&amp;amp;nbsp; It is set to be 1.5 times &#039;&#039;&#039;dstl&#039;&#039;&#039;, which is a global parameter that can be adjusted by the user&lt;br /&gt;
&lt;br /&gt;
==== &#039;&#039;&#039;Forecast years&#039;&#039;&#039; ====&lt;br /&gt;
&lt;br /&gt;
In future years, basic stock levels (CumStk) increase with production (AGP), decrease with demand or consumption (AGDEM), and adjust for net imports (AGM-AGX).&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;CumStk_{r=1-3} = FSTOCK_{r,f=1-3,t-1} + StkAdj_{r,f=1-3}&amp;lt;/math&amp;gt; &amp;lt;math&amp;gt;StkAdj_{r,f=1-3} = AGP_{r,f=1-3} - AGDEM_{r,f=1-3}+ (AGM_{r,f}- AGX_{r,f=1-3})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Of course, the actual stock values (FSTOCK) are not allowed to go negative. If the basic stock level is negative, stocks are set at zero and a shortage (Sho) exists, which affects calorie availability. If the basic stock level is positive there is no shortage and stocks equal the basic level.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;if cumstk_{r,f=1-3}&amp;lt;0 then Sho_{r,f=1-3} =-StkAdj_{r,f=1-3} and FSTOCK_{r,f=1-3}= 0&amp;lt;/math&amp;gt; &amp;lt;math&amp;gt;if cumstk_{r,f=1-3} &amp;gt; 0 then Sho_{r,f=1-3} = 0 and FSTOCK_{r,f=1-3} = cumstk_{r,f}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Also, if shortages are greater than 0, a reduction factor (ReductionFactor&#039;&#039;&#039;)&#039;&#039;&#039;is computed which is then used to adjust demand and losses.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;if SHO_{r,f}&amp;gt;0, ReductionFactor_{r,f} =(AGDEM_{r,f}- SHO_{r,f})/ AGDEM_{r,f}&amp;lt;/math&amp;gt; &amp;lt;math&amp;gt; FDEM_{r,f}= FDEM_{r,f} * ReductionFactor_{r,f} &amp;lt;/math&amp;gt; &amp;lt;math&amp;gt; FEDDEM_{r,f} = FEDDEM_{r,f} * ReductionFactor_{r,f} &amp;lt;/math&amp;gt; &amp;lt;math&amp;gt; INDEM_{r,f} = INDEM_{r,f} * ReductionFactor_{r,f} &amp;lt;/math&amp;gt; &amp;lt;math&amp;gt; FMDEM_{r,f} = FMDEM_{r,f} * ReductionFactor_{r,f} &amp;lt;/math&amp;gt; &amp;lt;math&amp;gt; AGLOSSTRANS_{r,f} = AGLOSSTRANS_{r,f} * ReductionFactor_{r,f} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== Calorie Availability ==&lt;br /&gt;
&lt;br /&gt;
Daily per capita calorie availability (CLPC) is initialized in the pre-processor. Where available, data is taken from the FAO[[#_ftn10|[10]]] It is multiplied by population (POP) to yield total daily calorie availability and brought into the model with the name CLAVAL. We already saw that this first year value is used in the calculation of two country-specific factors: 1) calactpredrat, which is a shift factor determined as the ratio of calorie availability to predicted calorie demand in the first year, and 2) sclavf, which is a conversion factor relating the total annual demand for food crops and crop equivalents from meat to daily calorie availability.&lt;br /&gt;
&lt;br /&gt;
In the forecast years, CLAVAL is calculated using the final value of calories per capita.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;CLAVAL_r= CLPC_{r,f=4}* POP_{r} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Calorie availability combines with regional calorie need in the population model for the calculation of possible starvation deaths (a seldom used variable because in official death statistics people do not die of starvation but rather of diseases associated with undernutrition); the population and health models therefore look instead to the impact of calorie availability on undernutrition and health.&lt;br /&gt;
&lt;br /&gt;
== Prices ==&lt;br /&gt;
&lt;br /&gt;
IFs keeps track of both national (FPRI) and world (WAP) price indices for each of the three agricultural commodities. All of these are set to an index value of 100 in the building of the base.&lt;br /&gt;
&lt;br /&gt;
The national crop price indices (FPRI, category (1) respond to: 1) changes in global costs of crop production, the latter being expressed as the ratio of global accumulated capital investment in crops to global production and 2) changes in the level of domestic crop stocks. The first factor should provide a long-term basis for rising or falling prices tied to changing technology and other factors of production; the second factor generally should represent shorter-term market variations from that long-term level.&lt;br /&gt;
&lt;br /&gt;
The impact of global costs is given by dividing the ratio of global investment in crops to global production (wkagagpr) in the current year to that same ratio in the first year.&amp;amp;nbsp; The effect of stocks on crop prices (Mul) is calculated using the same ADJSTR function introduced in the description of crop supply, which considers the difference between both the current crop stocks and a desired vale and between current crop stocks and those in the previous year. Two parameters control the degree to which these two ‘differences’ affect the calculation of the adjustment factor. In this case, these are the global, user-controllable parameters &#039;&#039;&#039;fpricr1&#039;&#039;&#039;and &#039;&#039;&#039;fpricr2&#039;&#039;&#039;. All together the equation for domestic crop price indices in the coming year is given as&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;FPRI_{r,f=1,t+1} = WAP_{f=1,t=1} * wkagagpr_{r,t}/wkagagpr_{r,t=1} * Mul_{r} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The domestic crop price indices are also bound between 0.01 and 1000.&lt;br /&gt;
&lt;br /&gt;
The national meat price indices are linked the global crop price. Specifically, they are given as a moving average of the global crop price index&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;FPRI_{r,f=2,t+1} = fprihw* FPRI_{r,f=2,t} +(1-fprihw)* WAP_{f=1,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Where&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;&#039;fprihw&#039;&#039;&#039; is a global parameter used to control the speed at which the domestic meat price changes.&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
The national fish price indices are all set equal to the global fish price index. The determination of the global fish price is similar to that for the national crop price, but here the stock of interest is the global stock and there is no effect related to costs. The ADJSTR function is used once again to calculate the adjustment factor (MUL), this time focusing on the desired global fish stock, the difference between this and the current global fish stock, and the change in the global fish stock in the past year. Again, two parameters control the degree to which these two &amp;quot;differences&amp;quot; affect the calculation of the adjustment factor. In this case, these are the global, user-controllable parameters &#039;&#039;&#039;fprim1&#039;&#039;&#039;and &#039;&#039;&#039;fprim2&#039;&#039;&#039;. The global and national fish prices are thus calculated as&lt;br /&gt;
&lt;br /&gt;
The world price indices for crops and meat are computed, in the following year, as a weighted average of the domestic prices, with the weights given by crop and meat production:&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;WAP_{r,f=1-2,t+1} =(sum_r(FPRI_{r,f=1-2,t+1} * AGP_{r,f=1-2,t+1} ) )/(sum_r(AGP_{r,f=1-2,t+1}) )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== Returns and Profits ==&lt;br /&gt;
&lt;br /&gt;
IFs estimates the net returns in agriculture (AGReturn) for each commodity as the ratio of gross returns (GReturn) to production costs (ProdCost and MProdCost). The agricultural profit ratios (FPROFITR) are then estimated as the ratio of AGReturn in the current year to its value in the initial year. At some points in the evolution of IFs we have used FPROFITR as a guide to rates of investment (see the calculation of mulrprof in All but First 2: Investment); the current formulation for investment does not do so. For completeness, however, we provide a description of these processes in the model, as they still exist as live code.&lt;br /&gt;
&lt;br /&gt;
==== &#039;&#039;&#039;Pre-processor and first year&#039;&#039;&#039; ====&lt;br /&gt;
&lt;br /&gt;
In the first year, values for FPROFITR, sfprofitr, and FPRofitR are all set to 1.&lt;br /&gt;
&lt;br /&gt;
==== &#039;&#039;&#039;Forecast years&#039;&#039;&#039; ====&lt;br /&gt;
&lt;br /&gt;
The production costs for crops are estimated as the cost of cropland, priced at the cost of new land development (CLD), plus the investment in agricultural capital (KAG). The net revenues are given as total yield times the domestic crop price index. This results in&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;ProdCost_{r,f=1,t} = LD_{r,l=1,t} * CLD_{r,t}+ KAG_{r,t} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;GReturn_{r,f=1,t} = (byl_{r,f}* LD_{r,f=1} * FPRI_{r,f=1} * (AGLOSSPROD_{r,f=1} /AGP_{r,f=1}))/ProdCost_{r,f=1,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
For meat, production costs are estimated by the value of the crop equivalents produced by grazing and the cost of feed, where the value is given by the domestic meat price index. The net revenues are based on the size of the herd and the domestic meat price index. This results in&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;MProdCost_{r,f=2,t} =(LD_{r,l=2,t} * GLDCAP_{r,t} + FEDDEM_{r,t} )* FPRI_{r,f=2,t+1} &amp;lt;/math&amp;gt; &amp;lt;math&amp;gt; GReturn_{r,f=2,t}=(LVHERD_{r,t} * FPRI_{r,f=2,t+1})/ProdCost_(r,f=2,t)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
For fish, the production costs are simply estimated by the total production of fish times the domestic meat price index. The net revenues are given as the total production of fish times the domestic fish price index. This implies&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;MProdCost_{r,f=3,t}= FISH_{r,t} * FPRI_{r,f=2,t+1} &amp;lt;/math&amp;gt; &amp;lt;math&amp;gt;GReturn_{r,f=3,t} =(AGP_{r,f=3}* FPRI_{r,f=3,t+1})/ProdCost_{r,f=3,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The net returns for each commodity can then be calculated as&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;AGReturn_{r,f=1-3,t} = GReturn_{r,f=1-3,t}/ProdCost_{r,f=1-3,t} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
These net returns are used to account for changes in profits over time, using the variable FPROFITR, which influences investment in agriculture. This variable is calculated for each commodity as&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;FPROFIT_{r,f=1-3,t} = AGReturn_{r,f=1-3,t}/ARGeturn_{r,f=1-3,t=1} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
A similar variable (wfprofitr) is calculated at the global level as a production weighted average of country/region values, but only for crops.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;u&amp;gt;Investment&amp;lt;/u&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Investment in agriculture is relatively complex in IFs, because changes in investment are the key factor that allows us to clear the agricultural market in the long term. It is very similar to investment in energy, except that we do not need to compute type-specific investments—capital in agriculture is only used for the production function of crops.&lt;br /&gt;
&lt;br /&gt;
We calculate a total agricultural investment need (INAG) to take to the economic model and place into the computation for investment among sectors. This calculation involves multiple factors. &amp;amp;nbsp;These begin with an initial estimate or targeted level of investment (TInAg) that is the product of the ratio of investment to GDP in the previous year times the GDP in the current year.&lt;br /&gt;
&lt;br /&gt;
Three factors modify that basic or target investment level.&amp;amp;nbsp; Two of those are global and one is regional.&amp;amp;nbsp; The first global factor is a multiplier linked to year-to-year change in the ratio of agricultural demand to GDP (WAgDemRMul); typically agricultural demand grows more slowly than GDP.&amp;amp;nbsp; The second is a multiplier responsive to the level of global stocks (MulWSt); if those drop below target levels it would increase production globally and vice versa.&amp;amp;nbsp; The model could use a global price average instead of stocks, but in the recursive structure stocks determine prices and therefore use of stocks accelerates responsiveness of investment.&amp;amp;nbsp; Similarly, the regional factor represents a multiplier tied to regional stock levels (MulSt).&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;TInAg_{r,t} = INAG_{r,t-1}/GDP_{r,t-1} * GDP_{r,t}* WAgDemRMul_{t} * MulWSt_{t}* MulSt_{r,t}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where &amp;lt;math&amp;gt; WAgDemRMul_{t} =((sum_R(AGDEM_{r,t} )/WGDP_{t} )/((sum_R(AGDEM_{r,t-1})/WGDP_{t-1})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
To elaborate, MulWSt and MulSt are adjustment factors related to global and domestic crop stocks, respectively. Both use the PID ADJSTR function described earlier, just as changes in prices use it in order to set prices that change year-to-year so as to chase supply-demand equilibration over time. For MulWSt, the controlling parameters in the PID function for stocks versus targets and changes in stocks are hard coded with values of -0.3 and -0.9, respectively. For MulSt, these parameters are hard coded with values of -0.2 and -0.4, respectively.&lt;br /&gt;
&lt;br /&gt;
Experience with that initial estimate, however, shows that it can be overly responsive to one or more of the multiplicative adjustment factors, thereby setting up behavior that oscillates.&amp;amp;nbsp; Therefore the next step is to compute a smoothed rate of investment as a share of GDP (SmInAgR).&amp;amp;nbsp; That rate gives more weight (60 percent) to the final investment rate in the previous year than it does to the rate that results from the initial target investment calculation.&amp;amp;nbsp; The overall result of this process is to smooth changes in the rate of investment over time.&amp;amp;nbsp; Desired investment (INAG) is the product of that smoothed rate and GDP.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;INAG_{r,t}= SmInAgR_{r,t} * GDP_{r,t} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;SmInAgR_{r,t} = INAG_{r,t-1}/GDP_{r,t-1} *0.6+ TInAg_{r,t}/GDP_{r,t} *0.4)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
To further prevent too rapid of a shift in demand for agricultural investment, INAG is not allowed to increase by more than 30 percent or decrease by more than 25 percent from the actual investment in the current year. A second check ensures that the demand is no less than 0.5 percent and no greater than 40 percent of current agricultural capital (KAG).&lt;br /&gt;
&lt;br /&gt;
At this point a user-controlled country-specific multiplier &#039;&#039;&#039;aginvm&#039;&#039;&#039;can boost or reduce INAG. One final check ensures that as long as GDP in the country is larger than it was in the first year, the demand for agricultural investment is not allowed to decline at an annual rate of more than 1 percent per year from the first year.&lt;br /&gt;
&lt;br /&gt;
Investment need (INAG) then enters the economic model, which returns a value reconciled with all other investment needs and that feeds into further calculations in the agriculture model.&lt;br /&gt;
&lt;br /&gt;
== Economic Linkages ==&lt;br /&gt;
&lt;br /&gt;
Several variables, such as gross production, stocks, consumer spending, trade, prices and investment, are common to both the economic model and the two physical models. But hardly ever will the economic and physical models produce identical values, even during the first time step when both utilize &amp;quot;data.&amp;quot; Thus, although we want the physical model value to override that of the economic model, it cannot simply replace it. Instead IFs extensively uses a procedure of computing an adjustment coefficient during the first time step. That coefficient is the ratio of the value in the economic model to the value in the physical model. In subsequent years IFs uses that coefficient to adjust the value from the physical model before its introduction into the economic model.&lt;br /&gt;
&lt;br /&gt;
Gross production (ZS) in the agricultural sector illustrates this procedure. The value of gross production in the agricultural model is the sum of the products of agricultural production (AGP) and prices (WAP) in each agricultural category. Multiplying that times an adjustment factor (ZSF) computed in the first time stop to assure inter-model consistency produces gross production for the economic (ZS). World average prices (WAP) are used in all the economic/physical model conversions because they assure that global sums (e.g. of exports and imports) will balance.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;ZS_{r,s=1}=ZSF_{r}*sum_f(WAP_{f,t=1}*AGP_{r,f,t} ) &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Where,&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;ZSF_{r} =ZS_{r,s=1}/(sum_f(WAP_{f,t=1}*AGP_{r,f,t=1} ) )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Similarly, food stocks in each category (FSTOCK) and an adjustment factor (FSF) produce stocks (ST) for the economic model.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;ST_{r,s=1}=FSF_{r}*sum_{f}(FSTOCK_{r,f,t}* WAP_{f,t=1} ) &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Where,&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;FSF_{r} = ST_{r,s=1}/(sum_{f}(FSTOCK_{r,f,t=1}* WAP_{f,t=1} ) )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
A similar translation is made for consumer spending on agricultural commodities, recognizing that not all crop demand is directly by consumers.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;CS_{r,s=1} = CSF_{r} *(FDEM_{r,t} * WAP_{f=1,t=1} +sum_{f=2,3}(AGDEM_{r,f,t}* WAP_{f,t=1} ) )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Where&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;CSF_{r} = CS_{r,s=1} /(FDEM_{r,t=1}* WAP_{f=1,t=1} +sum_{f=2,3}(AGDEM_{r,f,t}* WAP_{f,t=1} ) )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
In the same fashion exports (AGX) and imports (AGM) from the agricultural model allow calculation of exports (XS) and imports (MS) for the economic model.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;XS_{r,s=1}= xsf_{r} *sum_f(AGX_{r,f,t}* WAP_{f,t=1} ) &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Where&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;xsf_{r}= XS_{r,s=1}/(sum_{f}(AGX_{r,f,t=1}* WAP_{f,t=1} ) )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
and&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;MS_{r,s=1} = msf_{r} *sum_f(AGX_{r,f,t}* WAP_{f,t=1} ) &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;where&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;msf_{r}= MS_{r,s=1}/(sum_f(AGN_{r,f,t}* WAP_{f,t=1} ) )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
A check and, if necessary, adjustment is made ensure that the monetary values of imports and exports match up at the global level.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;XS_{r,s=1} = XS_{r,s=1} * ((sum_{r}(XS_{r,s=1} ) +sum_{r}(MS_{r,s=1} ) )/2)/(sum_{r}(XS_{r,s=1} ) )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
and&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;MS_{r,s=1}= MS_{r,s=1} *((sum_{r}(XS_{r,s=1} ) +sum_r(MS_{r,s=1} ) )/2)/(sum_{r}(MS_{r,s=1} ) )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
With respect to prices, the agriculture model passes to the economic model a value (PRI), which reflects the ratio of the current domestic crop price index to the initial world crop price index.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;PRI_{r,s=1} = FPRI_{r,f=1}/ WAP_{r,f=1,t=1} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Finally, investment need (INAG) is passed to the economic model under the variable name IDS, category 1 (agriculture).&lt;br /&gt;
&lt;br /&gt;
== Capital Dynamics ==&lt;br /&gt;
&lt;br /&gt;
The economic model of IFs returns a (potentially) modified value of IDS, category 1, reflecting the total amount of capital available for agriculture. This value is assigned to the variable IAval, which overrides the value of INAG calculated earlier (earlier it was basically investment demand; after return from the economic model it becomes investment supply).&amp;amp;nbsp;The agriculture model divides the investment available for agriculture (IAval) into investment for cropland development and investment for other agriculture capital. The coefficient IALK indicates the portion going to cropland development.&lt;br /&gt;
&lt;br /&gt;
IALK is set to a default value of 0.25 for all countries in the pre-processor. In forecast years, IALK changes from this initial value depending on change in the ratio of return on land (RETR) to return on capital (RETK).&lt;br /&gt;
&lt;br /&gt;
IFs calculates the return rate on land as the crop yield (YL) in the first year divided by the current cost of developing a unit of cropland (CLD).&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;RETLD_{r} = YL_{r,t=1}/ CLD_{r,t} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The return on capital depends on the difference between the hypothetical level of crop yield (HYL) that could be obtained from an additional unit investment in agricultural capital and the crop yield without that increment (CompYl). Recalling how crop yield is estimated, the hypothetical crop yield is given as&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;HypothYl_{r} = cD_{r} * agtec_{r} *(KAG_{r+1})^( ALPHA_{r} )*(labagi_{r} )^((1-ALPHA_{r} ) )* satk_{r}&amp;lt;/math&amp;gt; &amp;lt;math&amp;gt; CompYl_{r} = cD_{r} * agtec_{r} *(KAG_{r} )^(ALPHA_{r} )*(labagi_{r} )^((1-ALPHA_{r} ) )* satk_{r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
and the return on capital is given as&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;RETCap_{r} = LD_{r,l=1} *(HypothYLl_{r}- CompYl_{r} )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The ratio of the return to land to the return to capital (RETRAT) is given as&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;RETRAT_{r} = RETLD_{r}/ RETCap_{r} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The adjustment of IALK uses the same first and second order adjustment mechanism that we have seen before with the ADJSTR function. Here the ‘target’ level is the ratio of the return to land to the return to capital in the first year.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;IALK_{r,t+1} = IALK_{r,t=1} *(1+(RETRAT_{r}-RETRAT_{r,t=1}/1))^{eliasp1}*(1+(RETRAT_{r}-RETRAT_{r,t-1}/1))^{eliasp2}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;where,&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;&#039;eliasp1&#039;&#039;&#039; and &#039;&#039;&#039;eliasp2&#039;&#039;&#039; are global parameters&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
Two final checks are made on the value of IALK. First, it is not allowed to exceed a value related to the cost of replacing depreciated investment in land and bringing a portion of grazing or forested land into production.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;IALK_{r,t+1} =&amp;lt;((0.04* LD_{r,l=3} +0.04* LD_{r,l=4} +dkl* LD_{r,l=1} )* CLD_{r})/ IAval_{r} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Second, IALK is bound between 0.1 and 0.8.&lt;br /&gt;
&lt;br /&gt;
Finally the model updates agricultural capital (KAG) for the next year by subtracting depreciation as represented by agricultural capital lifetime (&#039;&#039;&#039;lks&#039;&#039;&#039;), adding the residual (non-land) investment, and adjusting for any civilian damage from warfare (CIVDM – see international politics model documentation).&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;KAG_{r,t+1} = KAG_{r,t}- KAG_{r,t} /lks_{s=1} + IAval_{r}*(1-IALK_{r,t+1} )*(1- CIVDM_{r})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== Land Dynamics ==&lt;br /&gt;
&lt;br /&gt;
&amp;amp;nbsp;Land in IFs is divided into five categories—crop, grazing, forest, urban, and other land. Historical data on total land area (LDTot), crop land (LD&amp;lt;sub&amp;gt;l=1&amp;lt;/sub&amp;gt;), grazing land (LD&amp;lt;sub&amp;gt;l=2&amp;lt;/sub&amp;gt;), forest land (LD&amp;lt;sub&amp;gt;l=3&amp;lt;/sub&amp;gt;), and other land (LD&amp;lt;sub&amp;gt;l=4&amp;lt;/sub&amp;gt;) are taken from FAO data. Historical data on urban land (LD&amp;lt;sub&amp;gt;l=5&amp;lt;/sub&amp;gt;) is taken from WRI.&lt;br /&gt;
&lt;br /&gt;
==== Pre-processor and first year ====&lt;br /&gt;
&lt;br /&gt;
A few adjustments to the historical data are made in the pre-processor.&lt;br /&gt;
&lt;br /&gt;
*In the pre-processor total production of food is reconciled with the total trade. In cases where, demand is greater than domestic supply of crops, crop production is increased to reconcile demand with supply of food production. Crop land is also increased proportionately.&amp;amp;nbsp;&lt;br /&gt;
*If urban land is more than three quarters the area of other land, land is shifted from urban to other land&lt;br /&gt;
*If no data is available for crop land, the same is set to 30 percent of total land area. If no data is available for grazing land, same is set to 5 percent of total land area. If no data is available for other land, same is set to 30 percent of total land area.&lt;br /&gt;
&lt;br /&gt;
After these changes, total land area is recomputed as the sum of the area of the individual land categories.&lt;br /&gt;
&lt;br /&gt;
The pre-processor also reads in a value for potentially arable land (&#039;&#039;&#039;landarablepot&#039;&#039;&#039;), which affects the amount of potential cropland in the model. The share of agricultural capital going to land (IALK) is set to 0.25 in the pre-processor.&lt;br /&gt;
&lt;br /&gt;
One final parameter is estimated related to land in the pre-processor. This is the target rate of growth of cropland (&#039;&#039;&#039;tgrld&#039;&#039;&#039;). When data is available, this is currently estimated as the growth rate of cropland between the year 2015 and the year 2005.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;tgrld_{r} =(LD_{r,l=1,yr=2015}/LD_{r,l=1,yr=2005} )^{1/10}-1&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
When no data are available for cropland in either 2015 or 2005, the target rate of growth of cropland is estimated as a function of average income&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;tgrld_{r} =0.009-0.011*MIN(1,GDPPCP_{r}/30)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
with a maximum growth rate given as a function of cropland as a share of total land&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;tgrld_{r} =&amp;lt; tmaxgrow_{r} =0.015-0.01*MIN(1,0.5* LD_{r,l=1}/LDTot_{r} )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Finally, this target growth rate is restricted to fall between -0.003 and +0.01.&lt;br /&gt;
&lt;br /&gt;
In the first year, IFs estimates an initial unit cost of cropland development (CLD) as&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;CLD_{r,t=1}=(IDS_{r,s=1,t=1} * IALK_{r,t=1})/(LD_{r,l=1,t=1}*(dkl+tgrld_{r} ) )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;where,&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
IDS is the total investment in agriculture&lt;br /&gt;
&lt;br /&gt;
IALK is the share of agricultural investment going to cropland development&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;&#039;dkl&#039;&#039;&#039; is a global parameter indicating the depreciation rate of investment in cropland, essentially a maintenance cost for existing cropland&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;&#039;tgrld&#039;&#039;&#039; is the target growth rate for cropland&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
A related factor (SCLdF), to be used in determining the cost of land development in future years, is also calculated in the first year&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;SCLdF_{r} = CLD_{r,t=1}/LD_{r,l=1,t=1} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
==== Forecast years ====&lt;br /&gt;
&lt;br /&gt;
IFs calculates changes in land use for the coming year as a result of four key dynamic processes. First, changes in urban land may result from income and population changes. Second, economic shifts related to investment, particularly in the agricultural sector, can affect the amount of cropland. Third, IFs there can be expansion or retirement of grazing land for undefined reasons. Finally, in certain scenarios, specific changes in forest land can result from policies related to issues such as conservation and environmental protection.&lt;br /&gt;
&lt;br /&gt;
====== Changes in urban land from income and population changes ======&lt;br /&gt;
&lt;br /&gt;
Changes in urban land result from changes in population and income. IFs first estimates a predicted level of urban land (LandUrbanPred), which is then compared to current urban land. Any changes are assumed to affect all other land types proportionately, unless this leads to not enough land in a particular category. The growth with income is based on an estimated relationship between income and urban land per capita (LandUrbanR)&lt;br /&gt;
&lt;br /&gt;
The predicted level of urban land (LandUrbanPred) is then given as&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;LandUrbanPred_{r} = LD_{r,l=4,t=1} *(POP_{r,t}/POP_{r,t=1} )*(LandUrbanR_{r,t}/LandUrbanR_{r,t=1} )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The change in urban land (NUrbLD) is then calculated as&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;NUrbLD_{r} = LandUrbanPred_{r} - LD_{r,l=4}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Limits are placed on the change in urban land area. First, if urban land is growing, the amount of increase in a single year cannot exceed 1/100&amp;lt;sup&amp;gt;th&amp;lt;/sup&amp;gt; of a variable that is related to the change in the non-urban share of all other land from the base year (NonUrbanShrR)&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;NonUrbanShrR_{r} = (NonUrbanShr_{r,t}/NonUrbanShr_{r,t=1} )^{2}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;where,&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;NonUrbanShr_{r,t=1,t} =(sum_{l}LD_{r,l=1-4,t} )/(sum_{l}LD_{r,l=1-5,t} )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Second, if urban land is declining, it is not permitted to fall below 10,000 hectares. Third, the changes in Urban land are assumed to affect all other land categories proportionately&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;Reduc_{r,l=1-4} = NUrbLD_{r} * LD_{r,l=1-4}/(sum_{l}LD_{r,l=1-4} )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
However, this is not allowed to result in the area for a given land category falling below 1,000 hectares. Thus, there may be a slight reduction in the amount of new urban land in certain cases.&lt;br /&gt;
&lt;br /&gt;
====== Changes in cropland due to investment and/or depreciation. ======&lt;br /&gt;
&lt;br /&gt;
The changes in cropland are driven by the economics of land. Specifically, they are a function of the profitability of cropland. Also, they are assumed to affect, at least directly, only the forest and the other land categories.&lt;br /&gt;
&lt;br /&gt;
A maximum amount of cropland expansion each year (MaxLandExpansion) is fixed by the amount of forest land, the amount of other lands, the amount of potential arable land, and the existing amount of cropland. The maximum amount of expansion must be at least 2/100&amp;lt;sup&amp;gt;th&amp;lt;/sup&amp;gt; of the existing cropland, but beyond that it cannot exceed either the total amount of forest and other land or the difference between 110% of the potential arable land (landarablepot) and current cropland.&lt;br /&gt;
&lt;br /&gt;
The change in the amount of cropland and the initially estimated share of agricultural investment going to cropland in the following year are computed differently depending upon the maximum amount of cropland expansion relative to the amount of existing cropland and the current level of average income in a country. Specifically, if the maximum amount of cropland expansion is less than 10 percent of existing cropland then it is assumed that there is no change in cropland (lddev = 0) and that no agricultural investment is targeted for cropland development (IALK = 0).&lt;br /&gt;
&lt;br /&gt;
If the condition mentioned in the previous paragraph is met, i.e., there is an ‘adequate’ amount of land for expanding cropland, the amount of change in cropland (lddev) is initially calculated as&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;LdDev_{r} =(((IAval_{r} * IALK_{r})/CLD_{r} )* ldcropm_{r})-(LD_{r,l=1}*dkl)&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;where&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
IAval is the total amount of funds available for investment in agriculture which is equal to IDS&lt;br /&gt;
&lt;br /&gt;
IALK is the share of agricultural investment going to cropland development&lt;br /&gt;
&lt;br /&gt;
CLD is the unit cost of cropland development&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;&#039;dkl&#039;&#039;&#039; is the depreciation rate of investment in cropland (essential a maintenance cost for existing cropland)&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;&#039;&#039;ldcropm&#039;&#039;&#039; is a country-specific multiplier that can be used to increase or decrease changes in cropland&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
Note that this equation takes into account the need to maintain existing cropland. Also, at this point, the value of LdDev is bound from below to ensure that it does not imply a greater than 10 percent decrease in existing cropland. For relatively poor countries (GDPPCP &amp;lt; 10), the constraint is even stricter. Specifically, IFs calls for a shift in funds to ensure that no cropland is lost. The desired shift in funds is given as&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;DesShift_{r} =-CLD_{r} * LdDev_{r} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The actual shift in funds is limited to 90 percent of the available funds, however, where the available funds are the investment in agriculture not initially designated for cropland development&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;Shift_r = MIN(0.9* IAval_{r} *(1-IALK_{r} ),DesShift_{r} )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The value of lddev given the actual shift in funds is given as&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;LdDev_{r} = LdDev_{r} + Shift_{r}/ CLD_{r} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
In addition, the share of investment in agriculture designated for cropland development is updated to be&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;IALK_{r}= IALK_{r}+ Shift_{r}/IAval_{r} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The changes in cropland are linked to changes in land in the forest and ‘other’ categories. The amount coming from/going to forests reflects the share of forest land relative to ‘other’ land, as well as the current level of development. For countries with a GDP per capita higher than 15,000 dollars and where LdDev is less than 0, more is given back to forest land and the ForShrPar is set to 0.25.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;LDDEVFor_{r}=LdDev_{r}* LD_{r,l=3}/(LD_{r,l=3} + LD_{r,l=4} )* ForShrPar_{r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;where,&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;br/&amp;gt;ForShrPar is given by the function depicted below;&lt;br /&gt;
&lt;br /&gt;
[[File:Changes in cropland due to Investment and or Depreciation.png|frame|center|Changes in crop land due to investment and or depreciation]]&lt;br /&gt;
&lt;br /&gt;
The solid line holds when land is being converted from forests to cropland (lddev &amp;gt; 0) and the dotted line holds when land is being converted from cropland to forests (LdDev &amp;lt; 0). In either case, this implies that the less of the change is related to forest land than would be expected by its share.&lt;br /&gt;
&lt;br /&gt;
Two other qualifiers are that the changes in forest land (LDDEVFor) and the changes in ‘other’ land cannot exceed 90 percent of existing land in these categories and the shifts cannot result in either land category falling below 1,000 hectares. These limits feedback to the change in cropland, finally resulting in the following&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;LdDev_{r}= LDDEVFor_{r} + LDDEVOth_{r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;LD_{r,l=1}= LD_{r,l=1}+ LdDev_{r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;LD_{r,l=3}= LD_{r,l=3} - LDDEVFor_{r} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;LD_{r,l=4}= LD_{r,l=4}- LDDEVOth_{r} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Turning back to the future cost of cropland development, this is estimated differently based only on whether there is ‘adequate’ room for cropland land expansion, defined as when the maximum amount of cropland expansion is greater than 10 percent of existing cropland. If this is the case, the future price of cropland is estimated as&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;CLD_{r,t+1}= CLD_{r,t=1}* LD_{r,l=1,t}/ LD_{r,l=1,t=1} * RemRat_{r}^{0.2}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;where&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
RemRat is the ratio of the maximum land for expansion in the first year to the maximum land for expansion in the current year, with a maximum value of 10&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;RemRat_{r} = MaxLandExpansion_{r,t=1}/MAX(0.1* MaxLandExpansion_{r,t=1},MaxLandExpansion_{r,t} ) &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
This basically states that the price of cropland development grows linearly with growth in cropland and exponentially with declines in available land for cropland expansion.&lt;br /&gt;
&lt;br /&gt;
Alternatively, if the maximum amount of cropland expansion in a given year is less than or equal to10 percent of existing cropland, the cost of bringing new land under cultivation is assumed to grow at the maximum of either 2 percent per year from the cost in the first year or the growth of cropland from the first year. Furthermore, it is not allowed to decline. Thus&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;[CLD]_{r,t+1}=MAX(CLD_{r,t}, CLD_{r,t=1} * LD_{r,l=1,t}/ LD_{r,l=1,t=1} , CLD_{r,t=1}*(1+2*(t-2015)/100))&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
====== Changes in grazing land ======&lt;br /&gt;
&lt;br /&gt;
IFs assumes that relatively poor countries (GDPPCP &amp;amp;lt; 10) will continue to develop additional grazing land, whereas relatively rich countries (GDPPCP &amp;amp;gt; 15) will retire grazing land. No change is expected in countries with average income between $10,000 and $15,000. The annual expansion of grazing land in poor countries is initially estimated as 0.5 percent of the amount of grazing land in the first year. The retirement of grazing land in richer countries is initially estimated as 0.2 percent of current grazing land.&lt;br /&gt;
&lt;br /&gt;
As with cropland, any changes in grazing land will be compensated by changes in forest and ‘other’ land. Each category is initially assumed to be affected proportionately, e.g.,&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;ForestShr_{r}= LD_{r,l=3}/(LD_{r,l=3} + LD_{r,l=4} )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Unlike the case for changes in cropland, there is no adjustment to the forest share as a function of income or the direction of change in grazing land. As with the changes in cropland, however, the changes in forest and ‘other’ land cannot exceed 90 percent of existing land in these categories and the shifts cannot result in either land category falling below 1,000 hectares. Again, these limits feed back to the change in grazing land.&lt;br /&gt;
&lt;br /&gt;
====== Change in forest land due to a policy choice ======&lt;br /&gt;
&lt;br /&gt;
The model user can also force the land in forest area to increase or decrease at the expense of crop and grazing land via a forest multiplier &#039;&#039;&#039;forestm&#039;&#039;&#039;. The change in forestland, LDSHIFT, is bound. In the case of an increase, i.e., &#039;&#039;&#039;forestm&#039;&#039;&#039;&amp;gt; 1, the amount of added land is limited to 20 percent of crop and grazing land; in the case of a decrease, i.e., &#039;&#039;&#039;forestm&#039;&#039;&#039;&amp;lt; 1, the amount of forest land removed is limited to 20 percent of existing forest land.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;- LD_{r,l=3}/5&amp;lt; LANDSHIFT_{r} &amp;lt;(LD_{r,l=1}+ LD_{r,l=2})/5&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;LD_{r,l=3}= LD_{r,l=3}+ LANDSHIFT_{r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The amount of land taken from cropland and grazing land is proportional to the amount of each.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;CropShare_{r}=LD_{r,l=1}/(LD_{r,l=1}+ LD_{r,l=2} )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;LD_{r,l=1}= LD_{r,l=1}+LANDSHIFT_{r}* CROPSHARE_{r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;LD_{r,l=2}= LD_{r,l=2}+ LANDSHIFT_{r} *(1-CROPSHARE_{r})&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
====== Final checks and renormalization of land use ======&lt;br /&gt;
&lt;br /&gt;
Two final adjustments are made to the land area values to clean up any quirks that might have be introduced in the previous processes. First, the values for each category are bound between one thousand and ten billion hectares. Second, the values are normalized so that the sum of the categories equals the total amount of land.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;[LD]_{r,l=1-5,t+1}=LD{r,l=1-5} * LD_{r,l=1-5}/(sum_{l}LD_{r,l=1-5} )&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Finally, a value for world forest area (WFORST) is calculated at the end of this process by summing forestland area across all countries.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;WFORST_{t+1}=sum_{l}LD_{r,l=3} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== Livestock Dynamics ==&lt;br /&gt;
&lt;br /&gt;
In addition to capital and land, the other &amp;quot;stock&amp;quot; or &amp;quot;level&amp;quot; variable with important temporal dynamics is the livestock herd (LVHERD).&lt;br /&gt;
&lt;br /&gt;
==== &#039;&#039;&#039;Pre-processor and first year&#039;&#039;&#039; ====&lt;br /&gt;
&lt;br /&gt;
In the pre-processor, as explained earlier, the values for total meat production and animal meat production are initialized. From these values, IFs calculates the value for livestock by dividing the total animal meat by the slaughter rate (&#039;&#039;&#039;slr&#039;&#039;&#039;)&lt;br /&gt;
&lt;br /&gt;
==== &#039;&#039;&#039;Forecast years&#039;&#039;&#039; ====&lt;br /&gt;
&lt;br /&gt;
The value of LVHERD is calculated by using pre-production loss meat production (AGPppl), adjusting the same for animal products produced (AGPMILKEGGS). This gives total animal meat production. The animal meat production is then divided by the slaughter rate &#039;&#039;&#039;slr&#039;&#039;&#039;&#039;&#039;&#039;.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;LVHERD_{r}=(AGPppl_{r,f=2}- AGPMILKEGGS_{r})/slr&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== Water Dynamics ==&lt;br /&gt;
&lt;br /&gt;
Water use begins with data on total water withdrawals from FAO Aquastat.&amp;amp;nbsp; These are divided by the size of the population to get an estimate of water use per capita.&lt;br /&gt;
&lt;br /&gt;
In future years, water use per capita is forecast to increase in parallel with crop production per capita.&amp;amp;nbsp; Specifically, an expected level of water use per capita as a function of crop production per capita (see figure below) is calculated for crop production in the current year (CropPC) and crop production in the first year (CropPCI).&amp;amp;nbsp; The ratio of these values is multiplied by the water use per capita in the first year (WatUsePCI) to get water use per capita in the current year (WatUsePC).&amp;amp;nbsp; This is multiplied by population (POP) to get total water use (WATUSE)&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;WatUsePC_{r}= WatUsePCI_{r}*f(CropPC_{r} )/f(CropPCI_{r} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;WATUSE_{r}= WatUsePC_{r} * POP_{r}&amp;lt;/math&amp;gt; [[File:Water dynamics.png|Water Use per capita compared to GDP per capita]]&lt;br /&gt;
&lt;br /&gt;
= Data Tables read in Agricultural Pre-Processor – DATAGRI.BAS =&lt;br /&gt;
&lt;br /&gt;
{| border=&amp;quot;1&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;0&amp;quot; width=&amp;quot;0&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| nowrap=&amp;quot;nowrap&amp;quot; style=&amp;quot;width:234px;height:35px;&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;Table&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
| nowrap=&amp;quot;nowrap&amp;quot; style=&amp;quot;width:142px;height:35px;&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;Definition&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
| nowrap=&amp;quot;nowrap&amp;quot; style=&amp;quot;width:164px;height:35px;&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;Original Source&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:35px;&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;Variable to which series relates in PP/How the series is used in the PP&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:20px;&amp;quot; | &lt;br /&gt;
SeriesLandArea&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:20px;&amp;quot; | &lt;br /&gt;
Land Area&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:20px;&amp;quot; | &lt;br /&gt;
WDI&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:20px;&amp;quot; | &lt;br /&gt;
LandArea&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesMalnChil%WeightWB&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Percentage of children under 5 malnourished based on weight; US benchmark&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
World Health Organization.&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Malnourished children&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:35px;&amp;quot; | &lt;br /&gt;
SeriesMalnPop%WB&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:35px;&amp;quot; | &lt;br /&gt;
Percentage of population malnourished&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:35px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:35px;&amp;quot; | &lt;br /&gt;
Malnourished population&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:20px;&amp;quot; | &lt;br /&gt;
SeriesLandCrop&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:20px;&amp;quot; | &lt;br /&gt;
Land, crop&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:20px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:20px;&amp;quot; | &lt;br /&gt;
LDCrop&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:20px;&amp;quot; | &lt;br /&gt;
SeriesLandForest&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:20px;&amp;quot; | &lt;br /&gt;
Land, forest&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:20px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:20px;&amp;quot; | &lt;br /&gt;
LdFor&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:20px;&amp;quot; | &lt;br /&gt;
SeriesLandGrazing&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:20px;&amp;quot; | &lt;br /&gt;
Land, grazing&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:20px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:20px;&amp;quot; | &lt;br /&gt;
LdGraz&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishAquaProdAqAnimalsFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Aquatic Animals Aquaculture Production (tonnes) from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Break down data from FAO into aquaculture and catch stat using data from fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishAquaProdAqPlantsFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Aquatic Plants Aquaculture Production (tonnes) from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Break down data from FAO into aquaculture and catch stat using data from fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishAquaProdCephalopodsFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Cephalopods Aquaculture Production (tonnes) from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Break down data from FAO into aquaculture and catch stat using data from fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishAquaProdCrustaceansFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Crustaceans Aquaculture Production (tonnes) from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Break down data from FAO into aquaculture and catch stat using data from fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishAquaProdDemersalFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total&amp;amp;nbsp; Demersal Aquaculture Production (tonnes) from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Break down data from FAO into aquaculture and catch stat using data from fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishAquaProdFreshwaterFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Freshwater Aquaculture Production (tonnes) from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Break down data from FAO into aquaculture and catch stat using data from fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishAquaProdMarineFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Marine Aquaculture Production (tonnes) from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Break down data from FAO into aquaculture and catch stat using data from fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishAquaProdMolluscsFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Molluscs Aquaculture Production (tonnes) from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Break down data from FAO into aquaculture and catch stat using data from fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishAquaProdPelagicFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Pelagic Aquaculture Production (tonnes) from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Break down data from FAO into aquaculture and catch stat using data from fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCalPerCapPerDayAqAnimalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Animals used for Calories/capita/day (kcal/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCalPerCapPerDayAqPlantsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Plants used for Calories/capita/day (kcal/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCalPerCapPerDayBodyOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Body Oil used for Calories/capita/day (kcal/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCalPerCapPerDayLiverOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Fish Liver Oil used for Calories/capita/day (kcal/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCatchProdAqAnimalsFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Aquatic Animals Catch Production (tonnes) from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Break down data from FAO into aquaculture and catch stat using data from fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCatchProdAqPlantsFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Aquatic Plants Catch Production (tonnes) from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Break down data from FAO into aquaculture and catch stat using data from fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCatchProdCephalopodsFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Cephalopods Capture Production from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Break down data from FAO into aquaculture and catch stat using data from fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCatchProdCrustaceansFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Crustaceans Capture Production from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Break down data from FAO into aquaculture and catch stat using data from fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCatchProdDemersalFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Demersal Capture Production from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Break down data from FAO into aquaculture and catch stat using data from fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCatchProdFreshwaterFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Freshwater Capture Production from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Break down data from FAO into aquaculture and catch stat using data from fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCatchProdMarineFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Marine Capture Production from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Break down data from FAO into aquaculture and catch stat using data from fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCatchProdMolluscsFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Molluscs Capture Production from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Break down data from FAO into aquaculture and catch stat using data from fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCatchProdPelagicFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Pelagic Capture Production (tonnes) from Fishstatj&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Break down data from FAO into aquaculture and catch stat using data from fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishDomesticSupplyAqPlantsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Domestic Supply Quantity of Aquatic Plants (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishDomesticSupplyBodyOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Domestic Supply Quantity ofl Body Oil (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishDomesticSupplyLiverOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Domestic Supply Quantity of Fish Liver Oil (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishDomesticSupplyMealFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Domestic Supply Production of Fish Meal (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishExportsAqPlantsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Plants Exports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishExportsBodyOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Body Oil Exports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishExportsLiverOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Fish Liver Oil EXports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishExportsMealFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Fish Meal Exports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFoodSupplyPerCapPerDayAqAnimalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Animals used for Food Supply/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFoodSupplyPerCapPerDayAqPlantsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Plants used for Food Supply/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFoodSupplyPerCapPerDayBodyOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Body Oil used for Food Supply/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFoodSupplyPerCapPerDayLiverOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Fish Liver Oil used for Food Supply/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishImportsAqPlantsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Plants Imports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishImportsBodyOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Body Oil Imports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishImportsLiverOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Fish Liver Oil Imports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishImportsMealFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Fish Meal Imports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProdAqPlantsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Production Quantity of Aquatic Plants (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProdBodyOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Production Quantity of (Fish) Body Oil (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProdLiverOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Production Quantityt of Fish Liver Oil (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProdMealFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Production Quantity of Fish Meal (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProteinPerCapPerDayAqAnimalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Animals used for Protein/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProteinPerCapPerDayAqPlantsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Plants used for Protein/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProteinPerCapPerDayBodyOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Body Oil used for Protein/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProteinPerCapPerDayLiverOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Fish Liver Oil used for Protein/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFeedAqPlantsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Plants used for Feed (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFeedBodyOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Body Oil used for Feed (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFeedLiverOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Fish Liver Oil used for Feed (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFeedMealFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Fish Meal used for Feed (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFoodAqAnimalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Animals used for Food (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFoodAqPlantsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Plants used for Food (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFoodBodyOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Body Oil for Food (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFoodLiverOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Fish Liver Oil used for Food (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoOtherUtilAqAnimalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Animals used for Other Utilities (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoOtherUtilAqPlantsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Plants used for Other Utilities (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoOtherUtilBodyOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Body Oil used for Other Utilities (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoOtherUtilLiverOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Fish Liver Oil used for Other Utilities (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoOtherUtilMealFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Fish Meal used for Other Utilities (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoSeedAqAnimalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Animals used for Seed (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCalPerCapPerDayCephalopodsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Cephalopods Fish Quantity used for Calories/capita/day (kcal/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCalPerCapPerDayCrustaceansFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Crustaceans Fish used for Calories/capita/day (kcal/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCalPerCapPerDayDemersalFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Demersal Fish consumed for Calories/cap/day (kcal/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCalPerCapPerDayFreshwaterFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Freshwater Fish consumed for calories/cap/day (kcal/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCalPerCapPerDayMarineFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Marine Fish used for Calories/capita/day (kcal/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCalPerCapPerDayMolluscsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Molluscs Fish used for Calories/capita/day (kcal/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCalPerCapPerDayPelagicFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Pelagic Fish consumed for Calories/capita/day (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishDomesticSupplyAqAnimalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Domestic Supply Quantity ofAquatic Animals (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishDomesticSupplyCephalopodsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Domestic Supply Quantity of Cephalopods Fish (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishDomesticSupplyCrustaceansFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Domestic Supply of Crustaceans Fish (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishDomesticSupplyDemersalFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Demersal Fish used for Food(Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishDomesticSupplyFreshwaterFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Domestic Supply of Freshwater Fish (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishDomesticSupplyMarineFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Domestic Supply Quantity of Marine Fish (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishDomesticSupplyMolluscsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Domestic Supply of Molluscs Fish (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishDomesticSupplyPelagicFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Domestic Supply Quantity of Pelagic Fish (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishExportsAqAnimalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Animals Exports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishExportsCephalopodsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Exports of Cephalopods Fish (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishExportsCrustaceansFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Crustaceans Fish Exports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishExportsDemersalFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Demersal Fish Exports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishExportsFreshwaterFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Freshwater Fish Exports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishExportsMarineFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Marine Fish Exports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishExportsMolluscsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Molluscs Fish Exports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishExportsPelagicFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Pelagic Fish Exports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFoodSupplyPerCapPerDayCephalopodsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Cephalopods Fish Quantity used for Food/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFoodSupplyPerCapPerDayCrustaceansFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Crustaceans Fish used for Food Supply/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFoodSupplyPerCapPerDayDemersalFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Demersal Fish for Food Supply/cap/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFoodSupplyPerCapPerDayFreshwaterFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Freshwater Fish used for Food Supply/cap/day&amp;amp;nbsp; (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFoodSupplyPerCapPerDayMarineFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Marine Fish used for Food Suply/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFoodSupplyPerCapPerDayMolluscsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Molluscs Fish used for Food Supply/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFoodSupplyPerCapPerDayPelagicFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Pelagic Fish used for Food Supply/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishImportsAqAnimalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Animals Imports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishImportsCephalopodsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Imports of Cephalopods Fish (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishImportsCrustaceansFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Crustaceans Fish Imports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishImportsDemersalFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Demersal Fish Imports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishImportsFreshwaterFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Freshwater Fish Imports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishImportsMarineFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Marine Fish Imports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishImportsMolluscsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Molluscs Fish Imports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishImportsPelagicFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Pelagic Fish Imports (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProdAqAnimalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Production Quantity Aquatic Animals (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProdCephalopodsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Production Quantity of Cephalopods Fish (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProdCrustaceansFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Production Quantity of Crustaceans Fish (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProdDemersalFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Production quantity of Demersal Fish (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProdFreshwaterFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Domestic Freshwater Fish Production (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProdMarineFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Production Quantity of Marine Fish (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProdMolluscsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Production Quantity of Molluscs Fish (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProdPelagicFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Production quantity of Pelagic Fish (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProteinPerCapPerDayCephalopodsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Cephalopods Fish Quantity used for Protein/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProteinPerCapPerDayCrustaceansFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Crustaceans Fish used for Protein/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProteinPerCapPerDayDemersalFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Demersal Fish consumed for Protein/cap/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProteinPerCapPerDayFreshwaterFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Freshwater Fish consumed for protein/cap/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProteinPerCapPerDayMarineFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Marine Fish used for Protein/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProteinPerCapPerDayMolluscsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Molluscs Fish used for Protein/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProteinPerCapPerDayPelagicFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Pelagic Fish consumed for Protein/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFeedCephalopodsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Cephalopods Fish Quantity used for Feed (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFeedCrustaceansFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Crustaceans Fish used for Feed (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFeedDemersalFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Demersal Fish used for Feed(Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFeedFreshwaterFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Freshwater Fish used for feed&amp;amp;nbsp; (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFeedMarineFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Marine Fish used for Feed (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFeedMolluscsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Molluscs Fish used for Feed (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFeedPelagicFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Pelagic Fish used for Feed (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFoodCephalopodsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Cephalopods Fish Quantity used for Food (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFoodCrustaceansFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Crustaceans Fish used for Food (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFoodDemersalFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Demersal Fish used for Food(Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFoodFreshwaterFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Freshwater Fish for food&amp;amp;nbsp; (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFoodMarineFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Marine Fish used for Food (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFoodMolluscsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Molluscs Fish used for Food (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFoodPelagicFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Pelagic Fish used for Food (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoOtherUtilCephalopodsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Cephalopods Fish Quantity used for Other Utilities (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoOtherUtilCrustaceansFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Crustaceans Fish used for Other Utilities (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoOtherUtilDemersalFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Demersal Fish used for other Utilities(Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoOtherUtilFreshwaterFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Freshwater Fish used for other Utilities (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoOtherUtilMarineFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Marine Fish used for Other Utilities (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoOtherUtilMolluscsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Molluscs Fish used for Other Utilities (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoOtherUtilPelagicFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Pelagic Fish used for Other Utilities (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoSeedCephalopodsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Cephalopods Fish Quantity used for Seed (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoSeedCrustaceansFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Crustaceans Fish used for Seed (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoSeedDemersalFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Demersal Fish used for Feed(Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoSeedFreshwaterFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Freshwater Fish used for Seed&amp;amp;nbsp; (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoSeedMarineFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Marine Fish used for Seed (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoSeedMolluscsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Molluscs Fish used for Seed (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoSeedPelagicFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Pelagic Fish used for Seed (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishExportQuantityFAOTrade&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Quantity of fish exported(Tonnes) from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO, FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
AGXFishQuantTradetbl&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:35px;&amp;quot; | &lt;br /&gt;
SeriesAgFishExportValueFAOTrade&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:35px;&amp;quot; | &lt;br /&gt;
Export value of fish ($1000 US)&amp;amp;nbsp; from FishstatJ software&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:35px;&amp;quot; | &lt;br /&gt;
FAO, FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:35px;&amp;quot; | &lt;br /&gt;
Fish imports and exports&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:35px;&amp;quot; | &lt;br /&gt;
SeriesAgFishImportQuantityFAOTrade&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:35px;&amp;quot; | &lt;br /&gt;
Quantity of fish imported (Tonnes) from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:35px;&amp;quot; | &lt;br /&gt;
FAO, FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:35px;&amp;quot; | &lt;br /&gt;
Fish imports and exports&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:35px;&amp;quot; | &lt;br /&gt;
SeriesAgFishImportValueFAOTrade&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:35px;&amp;quot; | &lt;br /&gt;
Import value of fish ($1000 US)&amp;amp;nbsp; from FishstatJ software&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:35px;&amp;quot; | &lt;br /&gt;
FAO, FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:35px;&amp;quot; | &lt;br /&gt;
Fish imports and exports&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgCropExportQuantityFAOTrade&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Quantity of Crops exported (Tonnes) from FAO Trade Domain&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Crop trade&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgCropExportValueFAOTrade&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Value of Crops exported (1000$ US) from FAO Trade Domain&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Crop trade&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgCropImportQuantityFAOTrade&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Quantity of Crops Imported (Tonnes) from FAO Trade Domain&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Crop trade&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgCropImportValueFAOTrade&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Value of Crops Imported(1000$ USD) from FAO Trade Domain&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Crop trade&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgMeatExportQuantityFAOTrade&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Quantity of meat exported (Tonnes) from FAO Trade Domain&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Meat trade&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgMeatExportValueFAOTrade&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Value of meat exported (1000$ US) from FAO Trade Domain&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Meat trade&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgMeatImportQuantityFAOTrade&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Quantity of Meat Imported (Tonnes) from FAO Trade Domain&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Meat trade&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgMeatImportValueFAOTrade&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Value of Meat Imported (1000$ US) from FAO Trade Domain&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Meat trade&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:20px;&amp;quot; | &lt;br /&gt;
SeriesAgProdCereals&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:20px;&amp;quot; | &lt;br /&gt;
Cereal production&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:20px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:20px;&amp;quot; | &lt;br /&gt;
Crop production (AGP)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:35px;&amp;quot; | &lt;br /&gt;
SeriesAgProdFruitsExclMelons&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:35px;&amp;quot; | &lt;br /&gt;
Production of fruit, excluding melons&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:35px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:35px;&amp;quot; | &lt;br /&gt;
Crop production (AGP)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:20px;&amp;quot; | &lt;br /&gt;
SeriesAgProdPulses&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:20px;&amp;quot; | &lt;br /&gt;
Pulses production&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:20px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:20px;&amp;quot; | &lt;br /&gt;
Crop production (AGP)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:35px;&amp;quot; | &lt;br /&gt;
SeriesAgProdRootsTub&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:35px;&amp;quot; | &lt;br /&gt;
Root and tuber production&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:35px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:35px;&amp;quot; | &lt;br /&gt;
Crop production (AGP)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:35px;&amp;quot; | &lt;br /&gt;
SeriesAgProdVegMel&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:35px;&amp;quot; | &lt;br /&gt;
Vegetable, melon production&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:35px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:35px;&amp;quot; | &lt;br /&gt;
Crop production (AGP)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:20px;&amp;quot; | &lt;br /&gt;
SeriesLandOther&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:20px;&amp;quot; | &lt;br /&gt;
Land, other&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:20px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:20px;&amp;quot; | &lt;br /&gt;
LdOth&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:35px;&amp;quot; | &lt;br /&gt;
SeriesLandBuiltGFNcorine&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:35px;&amp;quot; | &lt;br /&gt;
Land Area, Artificial Land&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:35px;&amp;quot; | &lt;br /&gt;
CORINE Land Cover&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:35px;&amp;quot; | &lt;br /&gt;
LdUrbTbl&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesLandBuiltGFNgaez&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Land Area, Settlement and Infrastructure&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
Global Agro-Ecological Zones (GAEZ) Model&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
LdUrbTbl&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:35px;&amp;quot; | &lt;br /&gt;
SeriesLandBuiltGFNglc&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:35px;&amp;quot; | &lt;br /&gt;
Land Area, Infrastructure aggregated&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:35px;&amp;quot; | &lt;br /&gt;
Global Land Cover (GLC) 2000&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:35px;&amp;quot; | &lt;br /&gt;
LdUrbTbl&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:103px;&amp;quot; | &lt;br /&gt;
SeriesLandBuiltGFNsage&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:103px;&amp;quot; | &lt;br /&gt;
Land Area, Buit area&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:103px;&amp;quot; | &lt;br /&gt;
Sustainability and the Global Environment (SAGE) at University of Wisconsin&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:103px;&amp;quot; | &lt;br /&gt;
LdUrbTbl&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:20px;&amp;quot; | &lt;br /&gt;
SeriesAgProdMeat&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:20px;&amp;quot; | &lt;br /&gt;
Meat production&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:20px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:20px;&amp;quot; | &lt;br /&gt;
Meat production (AGP)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:20px;&amp;quot; | &lt;br /&gt;
SeriesAgFruVegEx&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:20px;&amp;quot; | &lt;br /&gt;
Fruit, vegetable exports&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:20px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:20px;&amp;quot; | &lt;br /&gt;
Food imports&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:20px;&amp;quot; | &lt;br /&gt;
SeriesAgFruVegIm&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:20px;&amp;quot; | &lt;br /&gt;
Fruit, vegetable imports&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:20px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:20px;&amp;quot; | &lt;br /&gt;
Food imports&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:35px;&amp;quot; | &lt;br /&gt;
SeriesLandPotentialArable&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:35px;&amp;quot; | &lt;br /&gt;
total potential arable land&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:35px;&amp;quot; | &lt;br /&gt;
FAOTERRASTAT&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:35px;&amp;quot; | &lt;br /&gt;
LandArablePot&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:341px;&amp;quot; | &lt;br /&gt;
SeriesWaterAnRenResources&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:341px;&amp;quot; | &lt;br /&gt;
Annually renewable water resources&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:341px;&amp;quot; | &lt;br /&gt;
FAO: Water Resources, Development and Management Service. AQUASTAT Information System on Water in Agriculture: Review of Water Resource Statistics by Country.&amp;amp;nbsp; [http://www.fao.org/waicent/faoinfo/agricult/agl/aglw/aquastat/water_res/index.htm http://www.fao.org/waicent/faoinfo/agricult/agl/aglw/aquastat/water_res/index.htm].&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:341px;&amp;quot; | &lt;br /&gt;
Water resources&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:341px;&amp;quot; | &lt;br /&gt;
SeriesWaterAnWithdrawals&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:341px;&amp;quot; | &lt;br /&gt;
Annual water withdrawals/use (1990=70-99;2000=update, mostly 2000)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:341px;&amp;quot; | &lt;br /&gt;
FAO: Water Resources, Development and Management Service. AQUASTAT Information System on Water in Agriculture: Review of Water Resource Statistics by Country.&amp;amp;nbsp; [http://www.fao.org/waicent/faoinfo/agricult/agl/aglw/aquastat/water_res/index.htm http://www.fao.org/waicent/faoinfo/agricult/agl/aglw/aquastat/water_res/index.htm].&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:341px;&amp;quot; | &lt;br /&gt;
Water use&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:341px;&amp;quot; | &lt;br /&gt;
SeriesWaterAnRenResourcesOld&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:341px;&amp;quot; | &lt;br /&gt;
Annually renewable water resources&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:341px;&amp;quot; | &lt;br /&gt;
FAO: Water Resources, Development and Management Service. AQUASTAT Information System on Water in Agriculture: Review of Water Resource Statistics by Country.&amp;amp;nbsp; [http://www.fao.org/waicent/faoinfo/agricult/agl/aglw/aquastat/water_res/index.htm http://www.fao.org/waicent/faoinfo/agricult/agl/aglw/aquastat/water_res/index.htm].&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:341px;&amp;quot; | &lt;br /&gt;
Water resources&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:222px;&amp;quot; | &lt;br /&gt;
SeriesLandUrban&amp;amp;Built&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:222px;&amp;quot; | &lt;br /&gt;
Land, urban and built-up areas&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:222px;&amp;quot; | &lt;br /&gt;
Loveland, T.R., Reed, B.C., J.F., Brown, J.F., Ohlen, D.O., Zhu, Z., Yang, L.&amp;amp;nbsp; Merchant. J. 2000. &amp;amp;lt;i&amp;amp;gt;Global Land Cover Characteristics Database&amp;amp;nbsp; V 2.0. [http://edcdaac.usgs.gov/glcc/globdoc2_0.html http://edcdaac.usgs.gov/glcc/globdoc2_0.html]&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:222px;&amp;quot; | &lt;br /&gt;
LdUrbTbl&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAgBovineMeatProductionFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total Domestic Bovine Meat Production (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Meat production (AGP)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAgCerealsEx&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Cereal exports&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAgCerealsIm&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Cereal imports&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAgCerealSupply&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Cereal, domestic supply quantity&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAgCerealWaste&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
FAO Cereal Waste&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAgMeatEx&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Meat exports&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAgMeatIm&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Meat imports&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAgMeatOtherProductionFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total Domestic Meat (Other) Production (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Meat production (AGP)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgMuttonandGoatMeatProductionFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Domestic Mutton and Goat Meat Production (million metric tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Meat production (AGP)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAgPigMeatProductionFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total Domestic Pigmeat Production (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Meat production (AGP)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAgPoultryMeatProductionFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total Domestic Poultry Meat Production (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Meat production (AGP)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAgPulsesEx&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Pulse exports&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAgPulsesIm&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Pulseimports&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAgVegetableSupply&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
FAO Vegetable Supply&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAgVegetableWaste&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
FAO Vegetable Waste&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAGCropCalPerCapPerDayFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total calories consumed from crops per capita per day (kcal/capita/day).&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
CLPC&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGCropDomesticSupplyFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Domestic Supply Quantity of Crops (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGCropExportsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total Crop Exports (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAGCropFatPerCapPerDayFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total grams of fat consumed from crops per capita per day (g/capita/day).&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
GRAMSPC&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAGCropFoodSupplyPerCapPerDayFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total food supply per capita per day from crops (g/capita/day).&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
GRAMSPC&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGCropImportsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total Crop Imports (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGCropProductionFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total Domestic Crop Production (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Crop production (AGP)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAGCropProteinPerCapPerDayFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total protein consumption per capita per day from crops (g/capita/day).&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
PROTEINPC&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGCroptoFeedFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total crop quantity used for feed (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
FEDDEM&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGCroptoFoodFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total crop quantity used for food (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
FDEM&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGCroptoFoodManuFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total crop quantity used for food manufacture (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
FDEM&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGCroptoOtherUtilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total crop quantity used for other utilities (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
INDEM&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGCroptoSeedFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total crops used for seeds (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
FMDEM&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGCroptoWasteFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total crops that go to waste(tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
AGLOSSTRANS&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:86px;&amp;quot; | &lt;br /&gt;
SeriesAGFishCalPerCapPerDayFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:86px;&amp;quot; | &lt;br /&gt;
Total per capita per day caloric supplies derived from fish for human consumption (kcal/capita/day).&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:86px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:86px;&amp;quot; | &lt;br /&gt;
CLPC&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGFishDomesticSupplyFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Domestic Supply Quantity of Fish&amp;amp;nbsp; (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGFishExportsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total Fish Exports (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAGFishFatPerCapPerDayFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total grams of fat consumed from fish per capita per day (g/capita/day).&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
GRAMSPC&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAGFishFoodSupplyPerCapPerDayFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total food supply per capita per day from fish (g/capita/day).&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
GRAMSPC&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGFishImportsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total Fish Imports (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAGFishProteinPerCapPerDayFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total protein consumption per capita per day from fish (g/capita/day).&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
PROTEINPC&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGFishtoFeedFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total fish quantity used for feed (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
FEDDEM&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGFishtoFoodFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total fish quantity used for food (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
FDEM&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGFishtoOtherUtilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total fish quantity used for other utilities (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
INDEM&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGFishtoSeedFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total fish used for seeds i.e. reproduction (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
FMDEM&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAGMeatCalPerCapPerDayFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total calories consumed from meat per capita per day (kcal/capita/day).&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
CLPC&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGMeatDomesticSupplyFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Domestic Supply Quantity of Meat (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGMeatExportsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total Meat Exports (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAGMeatFatPerCapPerDayFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total grams of fat consumed from meat per capita per day (g/capita/day).&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
GRAMSPC&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAGMeatFoodSupplyPerCapPerDayFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total food supply per capita per day from meat (g/capita/day).&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
GRAMSPC&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGMeatImportsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total Meat Imports (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGMeatProductionFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total Domestic Meat Production (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Meat production (AGP)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAGMeatProteinPerCapPerDayFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total protein consumption per capita per day from meat (g/capita/day).&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
PROTEINPC&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGMeattoFeedFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total meat quantity used for feed (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
FEDDEM&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGMeattoFoodFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total meat quantity used for food (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
FDEM&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAGMeattoFoodManuFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total meat quantity used for food manufacture (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
FDEM&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGMeattoOtherUtilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total meat quantity used for other utilities (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
INDEM&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGMeattoSeedFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total meat used for seeds i.e. reproduction (tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
FMDEM&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAGMeattoWasteFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Total meat that goes to waste (tonnes).&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
AGLOSSTRANS&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishAquaProdOthersFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Others Aquaculture Production (tonnes) from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Data from Fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCatchProdAqMammalsFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Aquatic Mammals Catch Production (tonnes) from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Data from Fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCatchProdOthersFSJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Others Capture Production from FishstatJ&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&amp;amp;nbsp; Global Aquaculture Production Quantity data&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Data from Fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFatPerCapPerDayAqAnimalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Animals used for Fat/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFatPerCapPerDayAqPlantsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Plants used for Fat/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFatPerCapPerDayBodyOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Body Oil used for Fat/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFatPerCapPerDayLiverOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Fish Liver Oil used for Fat/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishStockVarAqPlantsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Plants Stock Variations (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishStockVarBodyOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Body Oil Stock Variations (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishStockVarLiverOilFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Fish Liver Oil Stock Variations (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishStockVarMealFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Fish Meal Stock Variations (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishCalPerCapPerDayAqMammalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Aquatic Mammals used for Calories/capita/day (kcal/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
CLPC&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishDomesticSupplyAqMammalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Domestic Supply of Aquatic Mammals (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishExportsAqMammalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Mammals Exports(Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFatPerCapPerDayAqMammalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Mammals used for Fat/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFatPerCapPerDayCephalopodsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Cephalopods Fish Quantity used for Fat/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFatPerCapPerDayCrustaceansFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Crustaceans Fish used for Fat/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFatPerCapPerDayDemersalFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Demersal Fish consumed for Fat/cap/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFatPerCapPerDayFreshwaterFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Freshwater Fish consumed for Fat/cap/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFatPerCapPerDayMarineFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Marine Fish used for Fat/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFatPerCapPerDayMolluscsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Molluscs Fish used for Fat/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFatPerCapPerDayPelagicFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Pelagic Fish consumed for Fat/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:69px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFoodSupplyPerCapPerDayAqMammalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:69px;&amp;quot; | &lt;br /&gt;
Total Aquatic Mammals used for Food Supply/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:69px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:69px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProdAqMammalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Production Quantity of Aquatic Mammals (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProteinPerCapPerDayAqMammalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Mammals used for Protein/capita/day (g/capita/day)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishStockVarAqAnimalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Animals Stock Variation (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishStockVarCephalopodsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Cephalopods Fish Stock Variations (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishStockVarCrustaceansFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Crustaceans Fish Stock Variations (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishStockVarDemersalFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Demersal Fish Stock VariationTonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishStockVarFreshwaterFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Freshwater Stock Variation (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishStockVarMarineFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Marine Fish Stock Variations (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishStockVarMolluscsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Molluscs Fish Stock Variations (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishStockVarPelagicFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Pelagic Fish Stock Variations (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoFoodAqMammalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Mammals used for Food (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishtoOtherUtilAqMammalsFAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Total Aquatic Mammals used for Other Utilities (Tonnes)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Read in all fish series from FAO Food Balance Sheets&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProdAquaInland&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Fish,Production, Inland Aquaculture&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Data from Fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProdAquaMarine&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Fish, production, Marine Aquaculture&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Data from Fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProdCatchInland&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Fish, Production, Inland Catch&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Data from Fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:40px;&amp;quot; | &lt;br /&gt;
SeriesAgFishProdCatchMarine&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:40px;&amp;quot; | &lt;br /&gt;
Fish, Production, Marine Catch&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:40px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:40px;&amp;quot; | &lt;br /&gt;
Data from Fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishExportVal&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Global Commodities Production and Trade Value of Fish Exports (USD)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:52px;&amp;quot; | &lt;br /&gt;
SeriesAgFishImportVal&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:52px;&amp;quot; | &lt;br /&gt;
Global Commodities Production and Trade Value of Fish Imports (USD)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:52px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:52px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:35px;&amp;quot; | &lt;br /&gt;
SeriesAgFishAquaOther&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:35px;&amp;quot; | &lt;br /&gt;
Aquaculture, other (plants, frogs, crocodiles, turtles)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:35px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:35px;&amp;quot; | &lt;br /&gt;
Data from Fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:20px;&amp;quot; | &lt;br /&gt;
SeriesAgFishImpt&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:20px;&amp;quot; | &lt;br /&gt;
Fish, import value&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:20px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:20px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:20px;&amp;quot; | &lt;br /&gt;
SeriesAidCerealDon&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:20px;&amp;quot; | &lt;br /&gt;
Cereal donations&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:20px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:20px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:20px;&amp;quot; | &lt;br /&gt;
SeriesAidCerealRec&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:20px;&amp;quot; | &lt;br /&gt;
Cereal gifts/aid received&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:20px;&amp;quot; | &lt;br /&gt;
FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:20px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:256px;&amp;quot; | &lt;br /&gt;
SeriesAgFishAquaInland&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:256px;&amp;quot; | &lt;br /&gt;
Aquaculture, inland&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:256px;&amp;quot; | &lt;br /&gt;
FAO, Aquaculture Quantities Dataset 1984-1997, Fishery Statistics Database downloadable with Fishstat-Plus software at: ([http://www.fao.org/WAICENT/FAOINFO/FISHERY/statist/FISOFT/FISHPLUS.HTM http://www.fao.org/WAICENT/FAOINFO/FISHERY/statist/FISOFT/FISHPLUS.HTM]&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:256px;&amp;quot; | &lt;br /&gt;
Data from Fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:256px;&amp;quot; | &lt;br /&gt;
SeriesAgFishAquaMarine&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:256px;&amp;quot; | &lt;br /&gt;
Aquaculture, marine fish catch&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:256px;&amp;quot; | &lt;br /&gt;
FAO, Aquaculture Quantities Dataset 1984-1997, Fishery Statistics Database downloadable with Fishstat-Plus software at: ([http://www.fao.org/WAICENT/FAOINFO/FISHERY/statist/FISOFT/FISHPLUS.HTM http://www.fao.org/WAICENT/FAOINFO/FISHERY/statist/FISOFT/FISHPLUS.HTM]&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:256px;&amp;quot; | &lt;br /&gt;
Data from Fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:256px;&amp;quot; | &lt;br /&gt;
SeriesAgFishAquaCatchTot&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:256px;&amp;quot; | &lt;br /&gt;
Fish production totals, aquaculture and capture&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:256px;&amp;quot; | &lt;br /&gt;
FAO, Aquaculture Quantities Dataset 1984-1997, Fishery Statistics Database downloadable with Fishstat-Plus software at: ([http://www.fao.org/WAICENT/FAOINFO/FISHERY/statist/FISOFT/FISHPLUS.HTM http://www.fao.org/WAICENT/FAOINFO/FISHERY/statist/FISOFT/FISHPLUS.HTM]&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:256px;&amp;quot; | &lt;br /&gt;
Data from Fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:273px;&amp;quot; | &lt;br /&gt;
SeriesAgFishAquaTotal&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:273px;&amp;quot; | &lt;br /&gt;
Aquaculture, coastal and marine total fish production&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:273px;&amp;quot; | &lt;br /&gt;
Fishery Information, Data and Statistics Unit,&amp;amp;nbsp; FAO. 2004. FISHSTAT Plus:&amp;amp;nbsp; Version 2.3 (available on-line at [http://www.fao.org/fi/statist/FISOFT/FISHPLUS.asp http://www.fao.org/fi/statist/FISOFT/FISHPLUS.asp]); Capture production 1950-2002 dataset. Rome: FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:273px;&amp;quot; | &lt;br /&gt;
Data from Fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:256px;&amp;quot; | &lt;br /&gt;
SeriesAgFishExpt&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:256px;&amp;quot; | &lt;br /&gt;
Fish, export value&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:256px;&amp;quot; | &lt;br /&gt;
FAO, Aquaculture Quantities Dataset 1984-1997, Fishery Statistics Database downloadable with Fishstat-Plus software at: ([http://www.fao.org/WAICENT/FAOINFO/FISHERY/statist/FISOFT/FISHPLUS.HTM http://www.fao.org/WAICENT/FAOINFO/FISHERY/statist/FISOFT/FISHPLUS.HTM]&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:256px;&amp;quot; | &lt;br /&gt;
Trade data&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:273px;&amp;quot; | &lt;br /&gt;
SeriesAgFishInlandProd&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:273px;&amp;quot; | &lt;br /&gt;
Fish capture, inland&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:273px;&amp;quot; | &lt;br /&gt;
Fishery Information, Data and Statistics Unit,&amp;amp;nbsp; FAO. 2004. FISHSTAT Plus:&amp;amp;nbsp; Version 2.3 (available on-line at [http://www.fao.org/fi/statist/FISOFT/FISHPLUS.asp http://www.fao.org/fi/statist/FISOFT/FISHPLUS.asp]); Capture production 1950-2002 dataset. Rome: FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:273px;&amp;quot; | &lt;br /&gt;
Data from Fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:256px;&amp;quot; | &lt;br /&gt;
SeriesAgFish%Protein&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:256px;&amp;quot; | &lt;br /&gt;
Fish protein as percent of total supply&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:256px;&amp;quot; | &lt;br /&gt;
FAO, Aquaculture Quantities Dataset 1984-1997, Fishery Statistics Database downloadable with Fishstat-Plus software at: ([http://www.fao.org/WAICENT/FAOINFO/FISHERY/statist/FISOFT/FISHPLUS.HTM http://www.fao.org/WAICENT/FAOINFO/FISHERY/statist/FISOFT/FISHPLUS.HTM])&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:256px;&amp;quot; | &lt;br /&gt;
PROTEINPC&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:273px;&amp;quot; | &lt;br /&gt;
SeriesAgFishFreshwaterCatch&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:273px;&amp;quot; | &lt;br /&gt;
Freshwater fish catch&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:273px;&amp;quot; | &lt;br /&gt;
Fishery Information, Data and Statistics Unit,&amp;amp;nbsp; FAO. 2004. FISHSTAT Plus:&amp;amp;nbsp; Version 2.3 (available on-line at [http://www.fao.org/fi/statist/FISOFT/FISHPLUS.asp http://www.fao.org/fi/statist/FISOFT/FISHPLUS.asp]); Capture production 1950-2002 dataset. Rome: FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:273px;&amp;quot; | &lt;br /&gt;
Data from Fish stat j&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:234px;height:273px;&amp;quot; | &lt;br /&gt;
SeriesAgFishMarineCatch&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:142px;height:273px;&amp;quot; | &lt;br /&gt;
Marine fish catch&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:164px;height:273px;&amp;quot; | &lt;br /&gt;
Fishery Information, Data and Statistics Unit,&amp;amp;nbsp; FAO. 2004. FISHSTAT Plus:&amp;amp;nbsp; Version 2.3 (available on-line at [http://www.fao.org/fi/statist/FISOFT/FISHPLUS.asp http://www.fao.org/fi/statist/FISOFT/FISHPLUS.asp]); Capture production 1950-2002 dataset. Rome: FAO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:180px;height:273px;&amp;quot; | &lt;br /&gt;
Data from Fish stat j&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
= Key variables in the agricultural model =&lt;br /&gt;
&lt;br /&gt;
{| border=&amp;quot;1&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;0&amp;quot; width=&amp;quot;0&amp;quot; style=&amp;quot;width:528px;&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:52px;&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;Name&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:52px;&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;Unit&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:52px;&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;Dimensionality&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:52px;&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;Description&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:52px;&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;Where Initialized*&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:137px;&amp;quot; | &lt;br /&gt;
AGDEM&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:137px;&amp;quot; | &lt;br /&gt;
10^6 tons&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:137px;&amp;quot; | &lt;br /&gt;
country, food type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:137px;&amp;quot; | &lt;br /&gt;
total agricultural demand/apparent consumption by food type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:137px;&amp;quot; | &lt;br /&gt;
FY&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:137px;&amp;quot; | &lt;br /&gt;
AGLOSSCONS&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:137px;&amp;quot; | &lt;br /&gt;
10^6 tons&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:137px;&amp;quot; | &lt;br /&gt;
country, food type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:137px;&amp;quot; | &lt;br /&gt;
Consumption losses&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:137px;&amp;quot; | &lt;br /&gt;
FY&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:137px;&amp;quot; | &lt;br /&gt;
AGLOSSPROD&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:137px;&amp;quot; | &lt;br /&gt;
10^6 tons&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:137px;&amp;quot; | &lt;br /&gt;
country, food type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:137px;&amp;quot; | &lt;br /&gt;
Production losses&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:137px;&amp;quot; | &lt;br /&gt;
FY&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:137px;&amp;quot; | &lt;br /&gt;
AGLOSSTRANS&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:137px;&amp;quot; | &lt;br /&gt;
10^6 tons&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:137px;&amp;quot; | &lt;br /&gt;
country, food type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:137px;&amp;quot; | &lt;br /&gt;
Transmission and distribution losses&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:137px;&amp;quot; | &lt;br /&gt;
PP&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:35px;&amp;quot; | &lt;br /&gt;
AGM&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:35px;&amp;quot; | &lt;br /&gt;
10^6 tons&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:35px;&amp;quot; | &lt;br /&gt;
country, food type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:35px;&amp;quot; | &lt;br /&gt;
food imports&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:35px;&amp;quot; | &lt;br /&gt;
PP&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:69px;&amp;quot; | &lt;br /&gt;
AGP&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:69px;&amp;quot; | &lt;br /&gt;
10^6 tons&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:69px;&amp;quot; | &lt;br /&gt;
food type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:69px;&amp;quot; | &lt;br /&gt;
total food production by food type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:69px;&amp;quot; | &lt;br /&gt;
PP for crops, FY for meat and fish&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:69px;&amp;quot; | &lt;br /&gt;
AGPMILKAND EGGS&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:69px;&amp;quot; | &lt;br /&gt;
10^6 tons&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:69px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:69px;&amp;quot; | &lt;br /&gt;
Total non-meat animal products&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:69px;&amp;quot; | &lt;br /&gt;
PP&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:35px;&amp;quot; | &lt;br /&gt;
AGX&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:35px;&amp;quot; | &lt;br /&gt;
10^6 tons&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:35px;&amp;quot; | &lt;br /&gt;
country, food type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:35px;&amp;quot; | &lt;br /&gt;
food exports&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:35px;&amp;quot; | &lt;br /&gt;
PP&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:86px;&amp;quot; | &lt;br /&gt;
AQUACUL&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:86px;&amp;quot; | &lt;br /&gt;
10^6 tons fish&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:86px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:86px;&amp;quot; | &lt;br /&gt;
total fish production in aquaculture&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:86px;&amp;quot; | &lt;br /&gt;
PP&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:86px;&amp;quot; | &lt;br /&gt;
CDALF&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:86px;&amp;quot; | &lt;br /&gt;
dimensionless (0-1)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:86px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:86px;&amp;quot; | &lt;br /&gt;
Cobb-Douglas alpha&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:86px;&amp;quot; | &lt;br /&gt;
FY (ECONOMY)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:52px;&amp;quot; | &lt;br /&gt;
CIVDM&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:52px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:52px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:52px;&amp;quot; | &lt;br /&gt;
civilian damage from war&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:52px;&amp;quot; | &lt;br /&gt;
FY (SOCIOPOL)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:52px;&amp;quot; | &lt;br /&gt;
CLAVAL&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:52px;&amp;quot; | &lt;br /&gt;
10^6 Calories/day&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:52px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:52px;&amp;quot; | &lt;br /&gt;
actual calorie availability&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:52px;&amp;quot; | &lt;br /&gt;
PP (DATAAGRI)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:69px;&amp;quot; | &lt;br /&gt;
CLD&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:69px;&amp;quot; | &lt;br /&gt;
thousand $/hectare&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:69px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:69px;&amp;quot; | &lt;br /&gt;
cost of land development&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:69px;&amp;quot; | &lt;br /&gt;
FY&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:69px;&amp;quot; | &lt;br /&gt;
CLPC&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:69px;&amp;quot; | &lt;br /&gt;
calories&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:69px;&amp;quot; | &lt;br /&gt;
country, food type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:69px;&amp;quot; | &lt;br /&gt;
Calories per capita per day&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:69px;&amp;quot; | &lt;br /&gt;
PP&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:137px;&amp;quot; | &lt;br /&gt;
CO2PER&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:137px;&amp;quot; | &lt;br /&gt;
Percent&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:137px;&amp;quot; | &lt;br /&gt;
none&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:137px;&amp;quot; | &lt;br /&gt;
CO2, percentage increase in atmosphere, pre-industrial base&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:137px;&amp;quot; | &lt;br /&gt;
FY (ENVIRONMENT)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:69px;&amp;quot; | &lt;br /&gt;
CS&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:69px;&amp;quot; | &lt;br /&gt;
billion $&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:69px;&amp;quot; | &lt;br /&gt;
country, economic sector&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:69px;&amp;quot; | &lt;br /&gt;
value of HH consumption&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:69px;&amp;quot; | &lt;br /&gt;
PP (DATAECON)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:120px;&amp;quot; | &lt;br /&gt;
CULTREG&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:120px;&amp;quot; | &lt;br /&gt;
Index&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:120px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:120px;&amp;quot; | &lt;br /&gt;
Culutrual region: CULTREG 6 includes India, Nepal, Mauritius&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:120px;&amp;quot; | &lt;br /&gt;
PP (DataValues)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:103px;&amp;quot; | &lt;br /&gt;
ENVYLCHG&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:103px;&amp;quot; | &lt;br /&gt;
Percent&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:103px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:103px;&amp;quot; | &lt;br /&gt;
annual change in agricultural yield due to climate change&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:103px;&amp;quot; | &lt;br /&gt;
FY (ENVIRONMENT)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:86px;&amp;quot; | &lt;br /&gt;
FDEM&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:86px;&amp;quot; | &lt;br /&gt;
10^6 tons&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:86px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:86px;&amp;quot; | &lt;br /&gt;
food production going directly to food&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:86px;&amp;quot; | &lt;br /&gt;
PP&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:69px;&amp;quot; | &lt;br /&gt;
FEDDEM&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:69px;&amp;quot; | &lt;br /&gt;
10^6 tons&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:69px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:69px;&amp;quot; | &lt;br /&gt;
food production going to livestock&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:69px;&amp;quot; | &lt;br /&gt;
PP&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:69px;&amp;quot; | &lt;br /&gt;
FISH&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:69px;&amp;quot; | &lt;br /&gt;
10^6 tons fish&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:69px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:69px;&amp;quot; | &lt;br /&gt;
total fish production&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:69px;&amp;quot; | &lt;br /&gt;
FY&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:52px;&amp;quot; | &lt;br /&gt;
FMDEM&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:52px;&amp;quot; | &lt;br /&gt;
10^6 tons&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:52px;&amp;quot; | &lt;br /&gt;
country,food type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:52px;&amp;quot; | &lt;br /&gt;
food production going to food manufacturing&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:52px;&amp;quot; | &lt;br /&gt;
PP&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:120px;&amp;quot; | &lt;br /&gt;
FPRI&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:120px;&amp;quot; | &lt;br /&gt;
Base 100&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:120px;&amp;quot; | &lt;br /&gt;
country, food type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:120px;&amp;quot; | &lt;br /&gt;
country specific food price by food type (all 100 in base year)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:120px;&amp;quot; | &lt;br /&gt;
FY&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:52px;&amp;quot; | &lt;br /&gt;
FPROFITR&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:52px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:52px;&amp;quot; | &lt;br /&gt;
country, food type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:52px;&amp;quot; | &lt;br /&gt;
food profit ratio to initial year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:52px;&amp;quot; | &lt;br /&gt;
FY&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:52px;&amp;quot; | &lt;br /&gt;
FSTOCK&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:52px;&amp;quot; | &lt;br /&gt;
10^6 tons&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:52px;&amp;quot; | &lt;br /&gt;
country, food type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:52px;&amp;quot; | &lt;br /&gt;
food stocks, by food type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:52px;&amp;quot; | &lt;br /&gt;
FY&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:52px;&amp;quot; | &lt;br /&gt;
GRAMSPC&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:52px;&amp;quot; | &lt;br /&gt;
grams&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:52px;&amp;quot; | &lt;br /&gt;
country, food type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:52px;&amp;quot; | &lt;br /&gt;
Food supply per capita per day in grams&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:52px;&amp;quot; | &lt;br /&gt;
PP&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:52px;&amp;quot; | &lt;br /&gt;
I&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:52px;&amp;quot; | &lt;br /&gt;
billion$&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:52px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:52px;&amp;quot; | &lt;br /&gt;
total investment&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:52px;&amp;quot; | &lt;br /&gt;
FY (ECONOMY)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:86px;&amp;quot; | &lt;br /&gt;
IALK&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:86px;&amp;quot; | &lt;br /&gt;
ratio (fraction)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:86px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:86px;&amp;quot; | &lt;br /&gt;
investment in agriculture, land share&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:86px;&amp;quot; | &lt;br /&gt;
PP&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:69px;&amp;quot; | &lt;br /&gt;
IDS&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:69px;&amp;quot; | &lt;br /&gt;
billion$&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:69px;&amp;quot; | &lt;br /&gt;
country, economic sector&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:69px;&amp;quot; | &lt;br /&gt;
investment by economic sector&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:69px;&amp;quot; | &lt;br /&gt;
FY (ECONOMY)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:137px;&amp;quot; | &lt;br /&gt;
INDEM&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:137px;&amp;quot; | &lt;br /&gt;
10^6 tons crops&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:137px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:137px;&amp;quot; | &lt;br /&gt;
industrial crop demand (crop production going directly to industry)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:137px;&amp;quot; | &lt;br /&gt;
PP&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:52px;&amp;quot; | &lt;br /&gt;
KAG&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:52px;&amp;quot; | &lt;br /&gt;
billion$&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:52px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:52px;&amp;quot; | &lt;br /&gt;
value of agricultural capital&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:52px;&amp;quot; | &lt;br /&gt;
FY&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:52px;&amp;quot; | &lt;br /&gt;
LABS&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:52px;&amp;quot; | &lt;br /&gt;
million people&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:52px;&amp;quot; | &lt;br /&gt;
country, economic sector&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:52px;&amp;quot; | &lt;br /&gt;
labor supply&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:52px;&amp;quot; | &lt;br /&gt;
FY (ECONOMY)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:52px;&amp;quot; | &lt;br /&gt;
landarablepot&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:52px;&amp;quot; | &lt;br /&gt;
10^6 ha&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:52px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:52px;&amp;quot; | &lt;br /&gt;
potential arable land&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:52px;&amp;quot; | &lt;br /&gt;
PP&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:35px;&amp;quot; | &lt;br /&gt;
LD&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:35px;&amp;quot; | &lt;br /&gt;
10^6 ha&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:35px;&amp;quot; | &lt;br /&gt;
country, land type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:35px;&amp;quot; | &lt;br /&gt;
Amount of land&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:35px;&amp;quot; | &lt;br /&gt;
PP&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:52px;&amp;quot; | &lt;br /&gt;
LVHERD&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:52px;&amp;quot; | &lt;br /&gt;
10^6 tons meat&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:52px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:52px;&amp;quot; | &lt;br /&gt;
Size of livestock herd&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:52px;&amp;quot; | &lt;br /&gt;
PP&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:52px;&amp;quot; | &lt;br /&gt;
MFPRATE&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:52px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:52px;&amp;quot; | &lt;br /&gt;
country, economic sector&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:52px;&amp;quot; | &lt;br /&gt;
multifactor productivity rate&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:52px;&amp;quot; | &lt;br /&gt;
FY (ECONOMY)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:52px;&amp;quot; | &lt;br /&gt;
MS&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:52px;&amp;quot; | &lt;br /&gt;
billion $&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:52px;&amp;quot; | &lt;br /&gt;
country, economic sector&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:52px;&amp;quot; | &lt;br /&gt;
value of imports&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:52px;&amp;quot; | &lt;br /&gt;
PP (DATAECON)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:52px;&amp;quot; | &lt;br /&gt;
PROTEINPC&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:52px;&amp;quot; | &lt;br /&gt;
per capita per day&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:52px;&amp;quot; | &lt;br /&gt;
country, food type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:52px;&amp;quot; | &lt;br /&gt;
Proteins per capita per day&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:52px;&amp;quot; | &lt;br /&gt;
PP&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:69px;&amp;quot; | &lt;br /&gt;
TGRYL&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:69px;&amp;quot; | &lt;br /&gt;
growth rate in decimal form&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:69px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:69px;&amp;quot; | &lt;br /&gt;
target growth rate in yield&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:69px;&amp;quot; | &lt;br /&gt;
FY&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:103px;&amp;quot; | &lt;br /&gt;
WAP&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:103px;&amp;quot; | &lt;br /&gt;
Base 100&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:103px;&amp;quot; | &lt;br /&gt;
food type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:103px;&amp;quot; | &lt;br /&gt;
global food price by food type (all 100 in base year)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:103px;&amp;quot; | &lt;br /&gt;
?&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:86px;&amp;quot; | &lt;br /&gt;
WAPRO&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:86px;&amp;quot; | &lt;br /&gt;
10^6 tons&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:86px;&amp;quot; | &lt;br /&gt;
food type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:86px;&amp;quot; | &lt;br /&gt;
world agricultural production&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:86px;&amp;quot; | &lt;br /&gt;
FY&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:52px;&amp;quot; | &lt;br /&gt;
WATUSE&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:52px;&amp;quot; | &lt;br /&gt;
cubic km&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:52px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:52px;&amp;quot; | &lt;br /&gt;
water usage, annual&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:52px;&amp;quot; | &lt;br /&gt;
FY&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:69px;&amp;quot; | &lt;br /&gt;
WATUSEPC&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:69px;&amp;quot; | &lt;br /&gt;
cubic km/10^6 persons&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:69px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:69px;&amp;quot; | &lt;br /&gt;
water usage per capita, annual&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:69px;&amp;quot; | &lt;br /&gt;
PP&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:103px;&amp;quot; | &lt;br /&gt;
WEP&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:103px;&amp;quot; | &lt;br /&gt;
Base 100&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:103px;&amp;quot; | &lt;br /&gt;
none&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:103px;&amp;quot; | &lt;br /&gt;
World Energy Price per Barrel Oil Equivalent (Base 100)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:103px;&amp;quot; | &lt;br /&gt;
FY (ENERGY)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:35px;&amp;quot; | &lt;br /&gt;
WFORST&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:35px;&amp;quot; | &lt;br /&gt;
10^6 ha forest land&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:35px;&amp;quot; | &lt;br /&gt;
none&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:35px;&amp;quot; | &lt;br /&gt;
world forest area&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:35px;&amp;quot; | &lt;br /&gt;
FY&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:35px;&amp;quot; | &lt;br /&gt;
WGDP&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:35px;&amp;quot; | &lt;br /&gt;
billion $&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:35px;&amp;quot; | &lt;br /&gt;
none&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:35px;&amp;quot; | &lt;br /&gt;
global GDP&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:35px;&amp;quot; | &lt;br /&gt;
FY&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:52px;&amp;quot; | &lt;br /&gt;
WSTK&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:52px;&amp;quot; | &lt;br /&gt;
10^6 tons&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:52px;&amp;quot; | &lt;br /&gt;
food type&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:52px;&amp;quot; | &lt;br /&gt;
world agricultural stocks&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:52px;&amp;quot; | &lt;br /&gt;
FY&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:52px;&amp;quot; | &lt;br /&gt;
XS&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:52px;&amp;quot; | &lt;br /&gt;
billion $&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:52px;&amp;quot; | &lt;br /&gt;
country, economic sector&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:52px;&amp;quot; | &lt;br /&gt;
value of exports&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:52px;&amp;quot; | &lt;br /&gt;
PP (DATAECON)&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:86px;&amp;quot; | &lt;br /&gt;
YL&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:86px;&amp;quot; | &lt;br /&gt;
10^6 tons crops/10^6 ha crop land&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:86px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:86px;&amp;quot; | &lt;br /&gt;
productivity of crop land in terms of crops&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:86px;&amp;quot; | &lt;br /&gt;
FY&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:110px;height:69px;&amp;quot; | &lt;br /&gt;
ZS&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:90px;height:69px;&amp;quot; | &lt;br /&gt;
billion $&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:100px;height:69px;&amp;quot; | &lt;br /&gt;
country, economic sector&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:106px;height:69px;&amp;quot; | &lt;br /&gt;
value of gross production&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:121px;height:69px;&amp;quot; | &lt;br /&gt;
PP (DATAECON)&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
= Key User controllable parameters in the IFs agricultural model =&lt;br /&gt;
&lt;br /&gt;
{| border=&amp;quot;1&amp;quot; cellspacing=&amp;quot;0&amp;quot; cellpadding=&amp;quot;0&amp;quot; width=&amp;quot;0&amp;quot; style=&amp;quot;width:388px;&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:34px;&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;Name&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;Unit&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;Dimensionality&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;Description&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;Default Value&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:85px;&amp;quot; | &lt;br /&gt;
Agconv&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
years&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
agricultural demand convergence time to function&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
75&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:85px;&amp;quot; | &lt;br /&gt;
Aginvm&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
Multiplier Base 1&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
year, country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
multiplier on investment in agriculture&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
1&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:102px;&amp;quot; | &lt;br /&gt;
aglossconsperc&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
percentage&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
year, country, food type (crop, meat, fish)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
waste rate of agricultural consumption&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;10&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:136px;&amp;quot; | &lt;br /&gt;
aglossprodperc&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:136px;&amp;quot; | &lt;br /&gt;
percentage&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:136px;&amp;quot; | &lt;br /&gt;
year, country, food type (crop, meat, aquaculture, fish catch)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:136px;&amp;quot; | &lt;br /&gt;
loss rate at point of production&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:136px;&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;10&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:102px;&amp;quot; | &lt;br /&gt;
aglosstransm&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
Multiplier Base 1&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
year, country, food type (crop, meat, fish)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
loss rate from producer to consumer, multiplier&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
&#039;&#039;&#039;1&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:170px;&amp;quot; | &lt;br /&gt;
Agon&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:170px;&amp;quot; | &lt;br /&gt;
switch (0-1)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:170px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:170px;&amp;quot; | &lt;br /&gt;
switch to turn off or on linkages between ag module and other modules; default is on&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:170px;&amp;quot; | &lt;br /&gt;
1&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:85px;&amp;quot; | &lt;br /&gt;
aquaculconv&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
years&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
time over which aquaculture growth falls to 0&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
50&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:51px;&amp;quot; | &lt;br /&gt;
Aquaculgr&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:51px;&amp;quot; | &lt;br /&gt;
growth rate in percent&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:51px;&amp;quot; | &lt;br /&gt;
year, country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:51px;&amp;quot; | &lt;br /&gt;
aquaculture growth rate, initial&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:51px;&amp;quot; | &lt;br /&gt;
3.5&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:102px;&amp;quot; | &lt;br /&gt;
Aquaculm&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
year, country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
multiplier on aquaculture production&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
1&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:203px;&amp;quot; | &lt;br /&gt;
Calmax&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:203px;&amp;quot; | &lt;br /&gt;
Calories/person/day&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:203px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:203px;&amp;quot; | &lt;br /&gt;
maximum kilocalories needed per day per person.&amp;amp;nbsp; This value should be a biologically-determined number that you will not normally change over time.&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:203px;&amp;quot; | &lt;br /&gt;
3800&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:374px;&amp;quot; | &lt;br /&gt;
Calmeatm&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:374px;&amp;quot; | &lt;br /&gt;
dimensionless (0-1)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:374px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:374px;&amp;quot; | &lt;br /&gt;
the maximum portion of calories that will come from meat.&amp;amp;nbsp; The model increases the portion of calories taken in the form of meat with income up to this level (a value between 0 and 1).&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:374px;&amp;quot; | &lt;br /&gt;
0.7&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:51px;&amp;quot; | &lt;br /&gt;
clpcm&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:51px;&amp;quot; | &lt;br /&gt;
Multiplier Base1&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:51px;&amp;quot; | &lt;br /&gt;
year,country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:51px;&amp;quot; | &lt;br /&gt;
Per capita calorie multiplier&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:51px;&amp;quot; | &lt;br /&gt;
1&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:68px;&amp;quot; | &lt;br /&gt;
Dkl&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
depreciation rate in decimal form&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
depreciation rate of investment in land&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
0.01&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:546px;&amp;quot; | &lt;br /&gt;
Dstl&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:546px;&amp;quot; | &lt;br /&gt;
dimensionless (0-1)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:546px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:546px;&amp;quot; | &lt;br /&gt;
Desired stock (inventory) level in the economy.&amp;amp;nbsp; It is in proportional terms so that 0.1 represents a 10% target stock level (of a base that usually includes annual production and may include demand, exports, or imports).&amp;amp;nbsp; There is little reason for most users to want to change this parameter.&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:546px;&amp;quot; | &lt;br /&gt;
0.1&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:153px;&amp;quot; | &lt;br /&gt;
Elagind&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:153px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:153px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:153px;&amp;quot; | &lt;br /&gt;
elasticity of industrial (incl. energy) use of crops with energy price&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:153px;&amp;quot; | &lt;br /&gt;
0.2&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:85px;&amp;quot; | &lt;br /&gt;
elagmpr1&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
elasticity of agricultural imports to prices&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:102px;&amp;quot; | &lt;br /&gt;
elagmpr2&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
elasticity of agricultural imports to change in prices&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:85px;&amp;quot; | &lt;br /&gt;
elagxpr1&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
elasticity of agricultural exports to prices&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:102px;&amp;quot; | &lt;br /&gt;
elagxpri2&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
elasticity of agricultural exports to change in prices&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:85px;&amp;quot; | &lt;br /&gt;
Elascd&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
elasticity of crop food demand to prices&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
-0.15&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:68px;&amp;quot; | &lt;br /&gt;
Elasfd&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
elasticity of fish demand to prices&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
-0.3&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:68px;&amp;quot; | &lt;br /&gt;
Elasmd&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
elasticity of meat demand to prices&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
-0.3&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:68px;&amp;quot; | &lt;br /&gt;
elfdpr1&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
elasticity of yield to stocks/inventories&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
-0.5&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:102px;&amp;quot; | &lt;br /&gt;
elfdpr2&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
elasticity of yield to changes in stocks/inventories&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
-1&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:102px;&amp;quot; | &lt;br /&gt;
Elglinpr&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
elasticity of livestock grazing intensity to prices&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
0.5&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:85px;&amp;quot; | &lt;br /&gt;
eliasp1&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
elasticity of ag investment in land to return&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
0.2&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:102px;&amp;quot; | &lt;br /&gt;
eliasp2&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
elasticity of ag investment in land to change in return&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
0.4&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:68px;&amp;quot; | &lt;br /&gt;
elinag1&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
elasticity of ag investment to profit&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
0.15&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:102px;&amp;quot; | &lt;br /&gt;
elinag2&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
elasticity of ag investment to change in profit&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
0.3&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:102px;&amp;quot; | &lt;br /&gt;
ellvhpr1&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
elasticity of livestock herd size to stock level&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:136px;&amp;quot; | &lt;br /&gt;
ellvhpr2&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:136px;&amp;quot; | &lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:136px;&amp;quot; | &lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:136px;&amp;quot; | &lt;br /&gt;
elasticity of livestock herd size to changes in stock level&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:136px;&amp;quot; | &lt;br /&gt;
&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:34px;&amp;quot; | &lt;br /&gt;
envco2fert&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
No unit&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
No unit&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
Crop CO2 sensitivity&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
0.1365&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:85px;&amp;quot; | &lt;br /&gt;
envylchgadd&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
percentage&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
additive factor for effect of climate on yield&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
0&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:68px;&amp;quot; | &lt;br /&gt;
envylchgm&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
Multiplier Base 1&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
year, country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
multiplier on effect of climate on yield&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
1&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:68px;&amp;quot; | &lt;br /&gt;
feddemm&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
Multiplier Base 1&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
year,country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
Livestock feed demand multiplier&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
1&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:34px;&amp;quot; | &lt;br /&gt;
fishcatchm&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
Multiplier Base 1&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
year, country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
fish catch multiplier&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
1&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:34px;&amp;quot; | &lt;br /&gt;
Forest&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
Multiplier Base 1&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
year, country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
forest land multiplier&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
1&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:85px;&amp;quot; | &lt;br /&gt;
fpricr1&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
food prices, response to stock level&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
-0.3&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:102px;&amp;quot; | &lt;br /&gt;
fpricr2&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
food prices, response to change in stock level&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
-0.6&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:85px;&amp;quot; | &lt;br /&gt;
Fprihw&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
ratio&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
food prices (inertial delay) in change&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
0.8&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:85px;&amp;quot; | &lt;br /&gt;
fprimt1&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
fish prices, response to stock level&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:85px;&amp;quot; | &lt;br /&gt;
-0.3&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:102px;&amp;quot; | &lt;br /&gt;
fprimt2&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
fish prices, response to change in stock level&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
-0.6&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:68px;&amp;quot; | &lt;br /&gt;
indemm&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
Multiplier Base 1&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
year,country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
Industrial agricultural demand multiplier&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
1&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:102px;&amp;quot; | &lt;br /&gt;
Ldcropm&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
year, country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
multiplier on land to be developed for cropland&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
1&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:102px;&amp;quot; | &lt;br /&gt;
Ldwf&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
hectares/person&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
land withdrawal factor with population growth&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
0.05&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:102px;&amp;quot; | &lt;br /&gt;
Livhdpro&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
livestock herd productivity with grain feeding&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
0.5&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:51px;&amp;quot; | &lt;br /&gt;
Lks&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:51px;&amp;quot; | &lt;br /&gt;
years&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:51px;&amp;quot; | &lt;br /&gt;
country, economic sector&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:51px;&amp;quot; | &lt;br /&gt;
lifetime of capital&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:51px;&amp;quot; | &lt;br /&gt;
30&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:119px;&amp;quot; | &lt;br /&gt;
Lvcf&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:119px;&amp;quot; | &lt;br /&gt;
tons crops/tons meat&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:119px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:119px;&amp;quot; | &lt;br /&gt;
global livestock to calorie conversion factor, compared to crops&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:119px;&amp;quot; | &lt;br /&gt;
2&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:136px;&amp;quot; | &lt;br /&gt;
malelimprecisesw&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:136px;&amp;quot; | &lt;br /&gt;
switch (0-1)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:136px;&amp;quot; | &lt;br /&gt;
year,country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:136px;&amp;quot; | &lt;br /&gt;
elimination of hunger for only the undernoursihed population&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:136px;&amp;quot; | &lt;br /&gt;
0&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:102px;&amp;quot; | &lt;br /&gt;
malnelimstartyr&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
year,country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
start year for an elimination of hunger scenario&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
0&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:102px;&amp;quot; | &lt;br /&gt;
malnelimtargetyr&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
year,country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
Target year for an elimination of hunger scenario&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
0&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:546px;&amp;quot; | &lt;br /&gt;
Meatmax&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:546px;&amp;quot; | &lt;br /&gt;
tons meat/person/yr&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:546px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:546px;&amp;quot; | &lt;br /&gt;
The maximum meat consumption per person, in tons per person per year.&amp;amp;nbsp; This parameter is only used to restrict meat consumption calculations in the initial year, in case of unreasonable data.&amp;amp;nbsp; If you wish to introduce scenarios around dietary patterns (for instance, to reduce meat consumption), use the parameter calmeatm.&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:546px;&amp;quot; | &lt;br /&gt;
0.12&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:459px;&amp;quot; | &lt;br /&gt;
Mhw&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:459px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:459px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:459px;&amp;quot; | &lt;br /&gt;
iMport propensity, historical (inertial) delay in change.&amp;amp;nbsp; Values near 1.0 imply very rapid adjustment and values near 0 imply little or no adjustment.&amp;amp;nbsp; Significant changes in this parameter could destabilize model behavior.&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:459px;&amp;quot; | &lt;br /&gt;
0.5&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:102px;&amp;quot; | &lt;br /&gt;
Ofscth&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
10^6 tons fish&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
total global non-aquaculture fish production&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
80&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:493px;&amp;quot; | &lt;br /&gt;
Protecm&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:493px;&amp;quot; | &lt;br /&gt;
Multiplier Base 1&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:493px;&amp;quot; | &lt;br /&gt;
year, country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:493px;&amp;quot; | &lt;br /&gt;
Trade protection multiplier.&amp;amp;nbsp; A multiplier on the price of imported goods, unit-less, by region.&amp;amp;nbsp; A value of 1 implies no change, while higher values proportionately increase the prices of imported goods and lower values decrease them.&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:493px;&amp;quot; | &lt;br /&gt;
1&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:34px;&amp;quot; | &lt;br /&gt;
Slr&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
fraction (0-1)&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
slaughter rate&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
0.7&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:68px;&amp;quot; | &lt;br /&gt;
Tgrld&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
growth rate in decimal form&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
target growth in cultivated land&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
0.1&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:408px;&amp;quot; | &lt;br /&gt;
Xhw&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:408px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:408px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:408px;&amp;quot; | &lt;br /&gt;
eXport propensity, inertial delay in change.&amp;amp;nbsp; This parameter computes a moving average of export propensity.&amp;amp;nbsp; A value of 0.7 would weight the historical or moving average by 0.7 and the newly computed value by 1-0.7 or 0.3.&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:408px;&amp;quot; | &lt;br /&gt;
0.7&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:102px;&amp;quot; | &lt;br /&gt;
Ylexp&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
dimensionless&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
yield, exponent controlling saturation speed&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:102px;&amp;quot; | &lt;br /&gt;
0.5&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:68px;&amp;quot; | &lt;br /&gt;
Ylhw&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
ratio&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
yield, inertial delay in change&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
0.2&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:34px;&amp;quot; | &lt;br /&gt;
Ylm&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
Multiplier Base 1&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
year, country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
yield multiplier&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:34px;&amp;quot; | &lt;br /&gt;
1&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:68px;&amp;quot; | &lt;br /&gt;
Ylmax&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
10^6 tons crops/10^6 ha crop land&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
year, country&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
crop yield, maximum&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
15&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;width:132px;height:68px;&amp;quot; | &lt;br /&gt;
Ylmaxgr&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
growth rate in decimal form&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
year&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
maximum growth in yield&lt;br /&gt;
&lt;br /&gt;
| style=&amp;quot;width:64px;height:68px;&amp;quot; | &lt;br /&gt;
0.075&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
= References =&lt;br /&gt;
&lt;br /&gt;
&amp;lt;references /&amp;gt;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Agriculture&amp;amp;nbsp;Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1995. &#039;&#039;Who Will Feed China?&#039;&#039; New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Cline, William R. 2007.&#039;&#039;Global warming and agriculture: Impact estimates by country. &#039;&#039;Washington, DC: Peterson Institute for International Economics.&lt;br /&gt;
&lt;br /&gt;
Herrera, Amilcar O., et al. 1976. C&#039;&#039;atastrophe or New Society? A Latin American World Model. &#039;&#039;Ottawa: International Development Research Centre.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander H., and Elizabeth R. Ducot. 1976. &amp;quot;AGRIMOD: A Simulation Model for the Analysis of U.S. Food Policies.&amp;quot; Paper delivered at Conference on Systems Analysis of Grain Reserves, Joint Annual Meeting of GRSA and TIMS, Philadelphia, Pa., March 31-April 2.&lt;br /&gt;
&lt;br /&gt;
Meadows, Dennis L. et al. 1974&#039;&#039;. Dynamics of Growth in a Finite World.&#039;&#039; Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Rosegrant, Mark W., Mercedita Agcaoili-Sombilla, and Nicostrato D. Perez. 1995. &amp;quot;Global Food Projections to 2020: Implications for Investment.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 5.&lt;br /&gt;
&lt;br /&gt;
Systems Analysis Research Unit (SARU). 1977. &#039;&#039;SARUM 76 Global Modeling Project.&#039;&#039; Departments of the Environment and Transport, 2 Marsham Street, London, 3WIP 3EB.&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
	</entry>
	<entry>
		<id>https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8307</id>
		<title>Introduction to IFs</title>
		<link rel="alternate" type="text/html" href="https://pardeewiki.du.edu//index.php?title=Introduction_to_IFs&amp;diff=8307"/>
		<updated>2017-09-07T21:32:30Z</updated>

		<summary type="html">&lt;p&gt;JessRettig: Just cleaned this up a little and added links.&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Purposes&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;International Futures (IFs) is a tool for thinking about long-term global trends and planning more strategically for the&amp;amp;nbsp;future.&amp;amp;nbsp;&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
IFs can help you:&lt;br /&gt;
&lt;br /&gt;
*Understand&amp;amp;nbsp;the state of major&amp;amp;nbsp;global systems&lt;br /&gt;
*Explore&amp;amp;nbsp;long-term trends and consider where they might take&amp;amp;nbsp;us&lt;br /&gt;
*Learn about the dynamic&amp;amp;nbsp;interactions between global systems&lt;br /&gt;
*Clarify long-term organizational goals/priorities&lt;br /&gt;
*Develop alternative scenarios (if-then statements) about the future&lt;br /&gt;
*Investigate how different groups (households, firms or governments) can shape the future&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;IFs development and analysis depend&amp;amp;nbsp;on core, underlying assumptions, including the following.&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*Global issues are becoming&amp;amp;nbsp;more significant as the scope of human interaction and human impact on the broader environment grow.&lt;br /&gt;
*Goals and priorities for human systems are becoming clearer and are more frequently and consistently communicated.&lt;br /&gt;
*Understanding of the dynamics of human systems is improving&amp;amp;nbsp;rapidly.&lt;br /&gt;
*The domain of human choice and action is broadening.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;What issues can you&amp;amp;nbsp;investigate with IFs?&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
*[[Environment]]: Atmospheric carbon dioxide levels, world forest area, fossil fuel usage&lt;br /&gt;
*[[Socio-Political|Socio-Political Change]]: Life expectancy, literacy rate, democracy level, status of women, value change&lt;br /&gt;
*[[Population|Demographics]]: Population levels and growth, fertility, mortality, migration&lt;br /&gt;
*[[Agriculture|Food and Agriculture]]: Land use and production levels, calorie availability, malnutrition rates&lt;br /&gt;
*[[Energy]]: Resource and production levels, demand patterns, renewable energy share&lt;br /&gt;
*[[Economics]]: Sectoral production, consumption, and trade patterns and structural change&lt;br /&gt;
*[[Interstate_Politics_(IP)|Geopolitics]]: Country and regional power levels&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Visual Representation&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
[[File:IFsOverviewChart.jpg|frame|center|Visual representation of IFs structure]]&lt;br /&gt;
&lt;br /&gt;
Among the philosophical premises of the International Futures (IFs) project is that the model cannot be a &amp;quot;black box&amp;quot; to users and be truly useful. Model users must be able to examine the structures of IFs in order (1) to have confidence in them, and (2) learn from them.&lt;br /&gt;
&lt;br /&gt;
There is available (see topics under Understanding the Mode in the contents of this help system):&lt;br /&gt;
&lt;br /&gt;
*[[Understand_IFs#Dominant_Relations|Dominant Relations]] of the model structure&lt;br /&gt;
*[[Understand_IFs#2.2|Structure-Based and Agent-Class Driven Modeling]]&lt;br /&gt;
*[[Understand_IFs#Equation_Notation|Equation Notation]]&lt;br /&gt;
*[[Introduction_to_IFs#IFs_Bibliography|IFs Bibliography]] of data and data sources&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Issues and Modules: Quick Survey&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;population&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents 22 age-sex cohorts to age 100+&lt;br /&gt;
*calculates change in fertility and mortality rates in response to income, income distribution, and analysis multipliers&lt;br /&gt;
*computes average life expectancy at birth, literacy rate, and overall measures of human development (HDI) and physical quality of life&lt;br /&gt;
*represents migration and HIV/AIDS&lt;br /&gt;
*includes a newly developing submodel of formal education across primary, secondary, and tertiary levels&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;economic&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents the economy in six sectors: agriculture, materials, energy, industry, services, and ICT (other sectors could be configured, using raw data from the GTAP project)&lt;br /&gt;
*computes and uses input-output matrices that change dynamically with development level&lt;br /&gt;
*is a general equilibrium-seeking model that does not assume exact equilibrium will exist in any given year; rather it uses inventories as buffer stocks and to provide price signals so that the model chases equilibrium over time&lt;br /&gt;
*contains an endogenous production function that represents contributions to growth in multifactor productivity from R&amp;amp;D, education, worker health, economic policies (&amp;quot;freedom&amp;quot;), and energy prices (the &amp;quot;quality&amp;quot; of capital)&lt;br /&gt;
*uses a Linear Expenditure System to represent changing consumption patterns&lt;br /&gt;
*utilizes a &amp;quot;pooled&amp;quot; rather than the bilateral trade approach for international trade&lt;br /&gt;
*is being imbedded during 2002 in a social accounting matrix (SAM) envelope that will tie economic production and consumption to intra-actor financial flows&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;agricultural&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*represents production, consumption and trade of crops and meat; it also carries ocean fish catch and aquaculture in less detail&lt;br /&gt;
*maintains land use in crop, grazing, forest, urban, and &amp;quot;other&amp;quot; categories&lt;br /&gt;
*represents demand for food, for livestock feed, and for industrial use of agricultural products&lt;br /&gt;
*is a partial equilibrium model in which food stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the agricultural sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The &#039;&#039;&#039;energy&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*portrays production of six energy types: oil, gas, coal, nuclear, hydroelectric, and other renewable&lt;br /&gt;
*represents consumption and trade of energy in the aggregate&lt;br /&gt;
*represents known reserves and ultimate resources of the fossil fuels&lt;br /&gt;
*portrays changing capital costs of each energy type with technological change as well as with draw-downs of resources&lt;br /&gt;
*is a partial equilibrium model in which energy stocks buffer imbalances between production and consumption and determine price changes&lt;br /&gt;
*overrides the energy sector in the economic module unless the user chooses otherwise&lt;br /&gt;
&lt;br /&gt;
The two &#039;&#039;&#039;socio-political&#039;&#039;&#039; sub-modules:&lt;br /&gt;
&lt;br /&gt;
Within countries or geographic groupings&lt;br /&gt;
&lt;br /&gt;
*represents fiscal policy through taxing and spending decisions&lt;br /&gt;
*shows six categories of government spending: military, health, education, R&amp;amp;D, foreign aid, and a residual category&lt;br /&gt;
*represents changes in social conditions of individuals (like fertility rates or literacy levels), attitudes of individuals (such as the level of materialism/postmaterialism of a society from the World Value Survey), and the social organization of people (such as the status of women)&lt;br /&gt;
*represents the evolution of democracy&lt;br /&gt;
*represents the prospects for state instability or failure&lt;br /&gt;
&lt;br /&gt;
Between countries or groupings of countries&lt;br /&gt;
&lt;br /&gt;
*traces changes in power balances across states and regions&lt;br /&gt;
*allows exploration of changes in the level of interstate threat&lt;br /&gt;
*represents possible action-reaction processes and arms races with associated potential for conflict among countries&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;environmental&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows tracking of remaining resources of fossil fuels, of the area of forested land, of water usage, and of atmospheric carbon dioxide emissions&lt;br /&gt;
&lt;br /&gt;
The implicit &#039;&#039;&#039;technology&#039;&#039;&#039; module:&lt;br /&gt;
&lt;br /&gt;
*is distributed throughout the overall model&lt;br /&gt;
*allows changes in assumptions about rates of technological advance in agriculture, energy, and the broader economy&lt;br /&gt;
*explicitly represents the extent of electronic networking of individuals in societies&lt;br /&gt;
*is tied to the governmental spending model with respect to R&amp;amp;D spending&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Background&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
International Futures (IFs) has evolved since 1980 through three &amp;quot;generations,&amp;quot; with a fourth generation now taking form.&lt;br /&gt;
&lt;br /&gt;
The first generation had deep roots in the world models of the 1970s, including those of the Club of Rome. In particular, IFs drew on the Mesarovic-Pestel or World Integrated Model (Mesarovic and Pestel 1974). The author of IFs had contributed to that project, including the construction of the energy submodel. IFs consciously also drew on the Leontief World Model (Leontief et al. 1977), the Bariloche Foundation’s world model (Herrera et al. 1976), and Systems Analysis Research Unit Model (SARU 1977), following comparative analysis of those models by Hughes (1980). That generation was written in FORTRAN and available for use on main-frame computers through CONDUIT, an educational software distribution center at the University of Iowa. Although the primary use of that and subsequent generations was by students, IFs has always had some policy analysis capability that has appealed to specialists. For example, the U.S. Foreign Service Institute used the first generation of IFs in a mid-career training program.&lt;br /&gt;
&lt;br /&gt;
The second generation of International Futures moved to early microcomputers in 1985, using the DOS platform. It was a very simplified version of the original IFs without regional or country differentiation.&lt;br /&gt;
&lt;br /&gt;
The third generation, first available in 1993, became a full-scale microcomputer model. The third generation improved earlier representations of demographic, energy, and food systems, but added new environmental and socio-political content. It built upon the collaboration of the author with the GLOBUS project, and it adopted the economic submodel of GLOBUS (developed by the author). GLOBUS had been created with the inspiration of Karl Deutsch and under the leadership of Stuart Bremer (1987) at the Wissenschaftszentrum in Berlin.&lt;br /&gt;
&lt;br /&gt;
The third generation has produced three editions/major releases of IFs, each accompanied by a book also called International Futures (Hughes 1993, 1996, 1999). The second edition moved to a Visual Basic platform that allowed a much improved menu-driven interface, running under Windows. The third edition incorporated an early global mapping capability and an initial ability to do cross-sectional and longitudinal data analysis.&lt;br /&gt;
&lt;br /&gt;
The fourth generation has been taking shape since early 2000. It has been heavily influenced by the usage of the model in an increasingly policy-analysis mode by several important organizations. First, General Motors commissioned a specialized version of IFs named CoVaTrA (Consumer Values Trends Analysis) with a need for updated and extended demographic modeling and representation of value change. An alliance was established with the World Values Survey, directed by Ronald Inglehart, to create that version. Second, the Strategic Assessments Group of the Central Intelligence Agency commissioned a specialized version named IFs for SAG. The work involved in preparing that greatly extended and enhanced the socio-political representations of the model, both domestic and international. Third, the European Commission sponsored a project named TERRA which has led to a specialized version named IFs for TERRA. IFs for TERRA work led to enhancements across the model, including improved representation of economic sectors, updated IO matrices and a basic Social Accounting Matrix, GINI and Lorenz curves, and preparing for extended environmental impact representation (drawing upon the Advanced Sustainability Analysis framework of the Finland Futures Research Center).&lt;br /&gt;
&lt;br /&gt;
Throughout this emergence of a fourth generation IFs (incorporating all of the above elements for additional users) there has been also a heavy emphasis on enhanced usability. Ideas from Robert Pestel in the TERRA project led to the creation of a new tree-structure for scenario creation and management. Ideas from Ronald Inglehart led to the development of the Guided Use structure and a somewhat more game-like character within that structure. Inglehart also help arrange funding to support the programming of Guided Use through the European Union Center of the University of Michigan.&lt;br /&gt;
&lt;br /&gt;
The fifth version of IFs is currently in use and represents broad strides to improving the model and its usability. It is the first version of this software to be placed online due to the help of the National Intelligence Council ([http://www.ifs.du.edu http://www.ifs.du.edu]). Also, usability has been increased as Packaged Displays and Flex Packaged Displays were introduced that allowed for the creation of very specific lists of countries/regions, groups or Glists. A new education model has also been incorporated into the broader IFs model. New scenarios were created for UNEP (focusing on environmental change) and Pardee (focusing on poverty). Finally, one of the largest changes made was incorporating 182 countries into the Base-Case scenario used by IFs. Previous versions of IFs used broader regions to forecast global trends. This change also did away with the Student and Professional versions.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Geographic Representation of the World&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
186 countries underpin the functioning of IFs and these countries can be displayed separately or as parts of larger groups that users can determine.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;Below is a visual representation of how different entities are organized into Countries/Regions, Groups or Glists:&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
[[File:Geo 186.png|frame|right|Visual representation of IF&#039;s definition of regions/countries/groups/glists]]&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;*Note: In older versions of IFs, Regions were used as intermediaries between Countries and Groups. In the future, they, or some similarly named unit, will be a sub-unit of Countries. Regions, acting as a sub-unit of Countries, are currently not a feature of IFs. See the image located at the bottom of this Help topic.&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
When using IFs, there are many occasions where the user is asked whether or not they would like to display their results as a product of single countries, or larger groups. This is typically a toggle switch that moves between Country/Region and Groups, however, it might be a three-way-toggle that includes Country/Region, Group and Glist.&lt;br /&gt;
&lt;br /&gt;
Countries/Regions are currently the smallest geographical unit that users can represent. The ability to split countries down into smaller regions, or states, is under development. There are 186 different countries/regions that users can display.&lt;br /&gt;
&lt;br /&gt;
Groups are variably organized geographically or by memberships in international institutions/regimes. You can find out who is represented in each group and add or delete members by exploring the [[Extended_Features#Manage_Groups/Regions|Managing Regionalization]] function.&lt;br /&gt;
&lt;br /&gt;
Glists merge both Groups and Countries/Regions. These lists are mostly geographically bound. In the future, the Glist distinction will become more important as some users may want to place, for example, both the Indian state of Kerala in a Glist with Sri Lanka and Nepal.&lt;br /&gt;
&lt;br /&gt;
Users may also want to [[Extended_Features#Change_Grouping/Regionalization|create]] their own groups or [[Extended_Features#Identify_Groups_or_Country/Region_Members|explore]] what countries are members of what groups.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;IFs Time Horizon&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Future Forecasts.&#039;&#039;&#039; IFs begins computation with data from 2000 and can dynamically calculate values for all variables annually through 2100.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Historical Analysis and &amp;quot;Forecasts.&amp;quot;&#039;&#039;&#039; IFs also includes an extensive and growing historical data base starting in 1960. The data basis allows analysis of relationships among variables across countries and across time.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Instructional Use&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
The standard modes for using IFs in a classroom are:&lt;br /&gt;
&lt;br /&gt;
1. Assigning class members to an issue area or topic. Consider identifying specific questions for them to address.&lt;br /&gt;
&lt;br /&gt;
2. Assigning class members to a country/geographic region. Again, specificity helps.&lt;br /&gt;
&lt;br /&gt;
Most often, students will work independently or in groups on projects and share information after completing them. It is possible, however, to have students work interactively, by assigning them topics or regions, letting them begin work, and then have the interacting groups (or individuals) create a collective model run with the changes that each group proposes by topic or region. That process, although more difficult to organize, allows the class as whole to investigate the interaction of their topics or regions (and to share learning about model use).&lt;br /&gt;
&lt;br /&gt;
There is a&amp;amp;nbsp;[http://portfolio.du.edu/bhughes web site]&amp;amp;nbsp;available in support of the educational use of IFs. You will find syllabi at that site. There are several [[Introduction_to_IFs#Publications_on_IFs|publications]] on IFs, including a book structured specifically for educational use.&lt;br /&gt;
&lt;br /&gt;
Donald Borock has described his classroom use of IFs in print. Borock, Donald. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot; Advances in Social Science and Computers 4, 103-127.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Acknowledgements&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
The author gratefully recognizes critical contributions in the forms of:&lt;br /&gt;
&lt;br /&gt;
:1. Testing and suggestions for development of IFs in one or more of multiple generations. By Donald Borock, Richard Chadwick, William Dixon, Dale Rothman, Phil Schrodt, Douglas Stuart, Donald Sylvan, Jonathan Wilkenfeld, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
:2. Computer assistance across many releases. By Michael Niemann, Terrance Peet-Lukes, Douglas McClure, Mohammod Irfan, and Jose Solorzano.&lt;br /&gt;
&lt;br /&gt;
:3. Data gathering and general assistance. By James Chung, Padma Padula, Shannon Brady, David Horan, Michael Ferrier, Kay Drucker, Warren Christopher, and Anwar Hossain.&lt;br /&gt;
&lt;br /&gt;
:4. Long-term encouragement and support. By Harold Guetzkow, Karl Deutsch, Richard Chadwick, Gerald Barney, and Ronald Inglehart.&lt;br /&gt;
&lt;br /&gt;
:5. Association in related world modeling projects and projects building upon IFs. By Mihajlo Mesarovic, Aldo Barsotti, Juan Huerta, John Richardson, Thomas Shook, Patricia Strauch, and other members of the World Integrated Model (WIM) team. By Stuart Bremer, Peter Brecke, Thomas Cusack, Wolf Dieter-Eberwein, Brian Pollins, and Dale Smith of the GLOBUS modeling project. By Evan Hillebrand, Paul Herman, and others of the IFs for SAG project. By Rob Lempert and Steve Bankes at RAND, Santa Monica. By Robert Pestel, Jonathan Cave, Ronald Inglehart, Sergei Parinov, Pentti Malaska, and many others in the IFs for TERRA project.&lt;br /&gt;
&lt;br /&gt;
:6. Financial assistance (without responsibility for the form of the evolving product). By the National Science Foundation, the Cleveland Foundation, the Exxon Education Foundation, the Kettering Family Foundation, the Pacific Cultural Foundation, the United States Institute of Peace, General Motors, the Strategic Assessments Group of the Central Intelligence Agency, the European Commission (Information Society Technology) Programme, the European Union Center of the University of Michigan, the National Intelligence Council (for web conversion), and Frederick S. Pardee. &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp;&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Feedback&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
Feedback on how to improve IFs is always appreciated, especially if you find something that is not working. Compliments are also accepted. Please contact. To send the IFs team an e-mail, click on&amp;amp;nbsp;[mailto:pardee.center@du.edu Pardee Center]&amp;amp;nbsp;in stand-alone versions or on the web.&lt;br /&gt;
&lt;br /&gt;
= &amp;lt;span style=&amp;quot;font-size:xx-large;&amp;quot;&amp;gt;Support for IFs Use&amp;lt;/span&amp;gt; =&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Publications on IFs&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
To obtain additional information about IFs and its use, consult:&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes and Evan E. Hillebrand, &#039;&#039;&#039;Exploring and Shaping International Futures.&#039;&#039;&#039; Boulder, CO: Paradigm Publishers, 2006. Specifically, see chapter 4.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &#039;&#039;&#039;International Futures: Choices in the Face of Uncertainty,&#039;&#039;&#039; 3rd ed. Boulder, CO: Westview Press, 1999. This volume is built around IFs and contains detailed suggestions for its use. Version 3.17 of IFs, which runs under Windows 95, is distributed with the third edition of the book. The second edition contained a version for Windows 3.1, and the first edition ran under DOS. Chapter 4 of the 2nd edition of IFs included Flow Charts of Worldviews , reproduced now in this Help system.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &#039;&#039;&#039;Continuity and Change in World Politics,&#039;&#039;&#039; 4th ed. Englewood Cliffs, N.J.: Prentice Hall, 2000. IFs can also usefully supplement this textbook on global politics.&lt;br /&gt;
&lt;br /&gt;
Barry B. Hughes, &amp;quot;The International Futures (IFs) Modeling Project. 1999. &#039;&#039;&#039;Simulation and Gaming&#039;&#039;&#039; 30, No. 3 (September): 304-326.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;IFs Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Alcamo, Joseph, Rik Leemans and Eric Kreileman, eds. 1998.&amp;amp;nbsp;&#039;&#039;Global Change Scenarios of the 21st Century: Results from the IMAGE 2.1 Model&#039;&#039;. The Netherlands: Pergamon.&lt;br /&gt;
&lt;br /&gt;
Alcamo, Joseph. 1994.&amp;amp;nbsp;&#039;&#039;IMAGE 2.0: Integrated Modeling of Global Climate Change&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
&lt;br /&gt;
Alexandratos, Nikos, ed. 1995.&amp;amp;nbsp;&#039;&#039;World Agriculture: Towards 2010&#039;&#039;&amp;amp;nbsp;(An FAO Study). New York: FAO and John Wiley and Sons.&lt;br /&gt;
&lt;br /&gt;
Allen, R. G. D. 1968.&amp;amp;nbsp;&#039;&#039;Macro-Economic Theory: A Mathematical Treatment&#039;&#039;. New York: St. Martin&#039;s Press.&lt;br /&gt;
&lt;br /&gt;
Avery, Dennis. 1995. &amp;quot;Saving the Planet with Pesticides,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 50-82.&lt;br /&gt;
&lt;br /&gt;
Bailey, Ronald, ed. 1995.&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;. New York: The Free Press.&lt;br /&gt;
&lt;br /&gt;
Barbieri, Kathleen. 1996. &amp;quot;Economic Interdependence: A Path to Peace or a Source of Interstate Conflict?&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Peace Research&#039;&#039;&amp;amp;nbsp;33: 29-50.&lt;br /&gt;
&lt;br /&gt;
Barker, T.S. and A.W.A. Peterson, eds. 1987.&amp;amp;nbsp;&#039;&#039;The Cambridge Multisectoral Dynamic Model of the British Economy&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Barney, Gerald O., W. Brian Kreutzer, and Martha J. Garrett, eds. 1991.&amp;amp;nbsp;&#039;&#039;Managing a Nation&#039;&#039;, 2nd ed. Boulder: Westview Press.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. 1997.&amp;amp;nbsp;&#039;&#039;Determinants of Economic Growth: A Cross-Country Empirical Study&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. and Xavier Sala-i-Martin. 1999.&amp;amp;nbsp;&#039;&#039;Economic Growth&#039;&#039;. Cambridge, Mass: MIT Press.&lt;br /&gt;
&lt;br /&gt;
Bennett, D. Scott, and Allan Stam. 2003.&amp;amp;nbsp;&#039;&#039;The Behavioral Origins of War: Cumulation and Limits to Knowledge in Understanding International Conflict&#039;&#039;. Ann Arbor: University of Michigan Press&lt;br /&gt;
&lt;br /&gt;
Birg, Herwig. 1995.&amp;amp;nbsp;&#039;&#039;World Population Projections for the 21st Century&#039;&#039;. Frankfurt: Campus Verlag.&lt;br /&gt;
&lt;br /&gt;
Borock, Donald M. 1996. &amp;quot;Using Computer Assisted Instruction to Enhance the Understanding of Policymaking,&amp;quot;&amp;amp;nbsp;&#039;&#039;Advances in Social Science and Computers&#039;&#039;&amp;amp;nbsp;4, 103-127.&lt;br /&gt;
&lt;br /&gt;
Bos, Eduard, My T. Vu, Ernest Massiah, and Rodolfo A. Bulatao. 1994.&amp;amp;nbsp;&#039;&#039;World Population Projections 1994-95 Edition&#039;&#039;&amp;amp;nbsp;[editions are biannual] Baltimore: Johns Hopkins Press.&lt;br /&gt;
&lt;br /&gt;
Boulding, Elise and Kenneth E. Boulding. 1995.&amp;amp;nbsp;&#039;&#039;The Future: Images and Processes&#039;&#039;. Thousand Oaks, CA: Sage Publications.&lt;br /&gt;
&lt;br /&gt;
Brecke, Peter. 1993. &amp;quot;Integrated Global Models that Run on Personal Computers,&amp;quot;&amp;amp;nbsp;&#039;&#039;Simulation&#039;&#039;60 (2).&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. 1977.&amp;amp;nbsp;&#039;&#039;Simulated Worlds: A Computer Model of National Decision-Making&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A., ed. 1987.&amp;amp;nbsp;&#039;&#039;The GLOBUS Model: Computer Simulation of World-wide Political and Economic Developments&#039;&#039;. Boulder, CO: Westview.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. and Walter Gruhn. 1988.&amp;amp;nbsp;&#039;&#039;Micro GLOBUS: A Computer Model of Long-Term Global Political and Economic Processes&#039;&#039;. Berlin: edition sigma.&lt;br /&gt;
&lt;br /&gt;
Bremer, Stuart A. and Barry B. Hughes. 1990.&amp;amp;nbsp;&#039;&#039;Disarmament and Development: A Design for the Future?&#039;&#039;&amp;amp;nbsp;Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Brockmeier, Martina and Channing Arndt (presentor). 2002. Social Accounting Matrices. Powerpoint presentation on GTAP and SAMs (June 21). Found on the web.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1981.&amp;amp;nbsp;&#039;&#039;Building a Sustainable Society&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1988. &amp;quot;Analyzing the Demographic Trap,&amp;quot; in&amp;amp;nbsp;&#039;&#039;State of the World 1987&#039;&#039;, eds. Lester R. Brown and others. New York: W.W. Norton, pp. 20-37.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1995.&amp;amp;nbsp;&#039;&#039;Who Will Feed China?&#039;&#039;&amp;amp;nbsp;New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1996.&amp;amp;nbsp;&#039;&#039;Tough Choices: Facing the Challenge of Food Scarcity&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., et al. 1996&amp;amp;nbsp;&#039;&#039;State of the World 1996&#039;&#039;. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., Nicholas Lenssen, and Hal Kane. 1995.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1995. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R., Christopher Flavin, and Hal Kane. 1996.&amp;amp;nbsp;&#039;&#039;Vital Signs&#039;&#039;&amp;amp;nbsp;1996. New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Burkhardt, Helmut. 1995. &amp;quot;Priorities for a Sustainable Civilization,&amp;quot; unpublished conference paper. Department of Physics, Ryerson Polytechnic University, Toronto, Canada.&lt;br /&gt;
&lt;br /&gt;
Bussolo, Maurizio, Mohamed Chemingui and David O’Connor. 2002. A Multi-Region Social Accounting Matrix (1995) and Regional Environmental General Equilibrium Model for India (REGEMI). Paris: OECD Development Centre (February). Available at&amp;amp;nbsp;[http://www.oecd.org/dev/technics www.oecd.org/dev/technics].&lt;br /&gt;
&lt;br /&gt;
British Petroleum Company. 1995.&amp;amp;nbsp;&#039;&#039;BP Statistical Review of World Energy&#039;&#039;. London: British Petroleum Company.&lt;br /&gt;
&lt;br /&gt;
Central Intelligence Agency (CIA). 1991.&amp;amp;nbsp;&#039;&#039;Handbook of Economic Statistics, 1991&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
&lt;br /&gt;
Central Intelligence Agency (CIA). 1994.&#039;&#039;&amp;amp;nbsp;The World Factbook 1994&#039;&#039;. Washington, D.C.: Central Intelligence Agency.&lt;br /&gt;
&lt;br /&gt;
Chang, Sheldon S. L. 1961.&amp;amp;nbsp;&#039;&#039;Synthesis of Optimum Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Chenery, Hollis and Moises Syrquin. 1975.&amp;amp;nbsp;&#039;&#039;Patterns of Development 1950-1970&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Cipolla, Carlo M. 1962.&amp;amp;nbsp;&#039;&#039;The Economic History of World Population&#039;&#039;. Baltimore: Penguin.&lt;br /&gt;
&lt;br /&gt;
Cook, Earl. 1976.&amp;amp;nbsp;&#039;&#039;Man, Energy, Society&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
&lt;br /&gt;
Committee on the Strategic Assessment of the U.S. Department of Energy’s Coal Program. 1995.&amp;amp;nbsp;&#039;&#039;Coal: Energy for the Future&#039;&#039;. Washington, D.C.: National Academy Press.&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1981.&amp;amp;nbsp;&#039;&#039;The Global 2000 Report to the President&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1981b.&amp;amp;nbsp;&#039;&#039;Environmental Trends&#039;&#039;. Washington, D.C. (July).&lt;br /&gt;
&lt;br /&gt;
Council on Environmental Quality (CEQ). 1991.&amp;amp;nbsp;&#039;&#039;21st Annual Report&#039;&#039;. Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Crescenzi, Mark J.C. and Andrew J. Enterline. 2001. &amp;quot;Time Remembered: A Dynamic Model of Interstate Interaction,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;45, no. 3 (September): 409-431.&lt;br /&gt;
&lt;br /&gt;
Crosson, Pierre, and Jock R. Anderson. 1992.&amp;amp;nbsp;&#039;&#039;Resources and Global Food Prospects&#039;&#039;. Washington, D.C.: The World Bank. World Bank Technical Paper Number 184.&lt;br /&gt;
&lt;br /&gt;
Cusack, Thomas R. and Richard J. Stoll. 1990.&amp;amp;nbsp;&#039;&#039;Exploring Realpolitik: Probing International Relations with Computer Simulatio&#039;&#039;n. Boulder: Lynne Rienner Publishers.&lt;br /&gt;
&lt;br /&gt;
Dargay, Joyce and Dermot Gately. 1999. &amp;quot;Income’s Effect on Car and Vehicle Ownership, Worldwide: 1960-2015,&amp;quot;&amp;amp;nbsp;&#039;&#039;Transportation Research Part A&#039;&#039;&amp;amp;nbsp;33: 101-138.&lt;br /&gt;
&lt;br /&gt;
Dall, P., Kaspar, F. and Alcamo, J. 1998. &amp;quot;Modeling World-wide Water Availability and Water Use Under the Influence of Climate Change,&amp;quot;&amp;amp;nbsp;&#039;&#039;Proceedings of the Second International Conference on Climate and Water&#039;&#039;, July 17-20, Espoo, Finland.&lt;br /&gt;
&lt;br /&gt;
Dimaranan, Betina V. and Robert A. McDougall, eds. 2002.&amp;amp;nbsp;&#039;&#039;Global Trade, Assistance, and Production: The GTAP 5 Data Base&#039;&#039;. Center for Global Trade Analysis, Purdue University. Available at [http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp http://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp].&lt;br /&gt;
&lt;br /&gt;
Dowlatabadi, H., and Morgan, M.G. 1993. &amp;quot;A Model Framework for Integrated Studies of the Climate Problem,&amp;quot;&amp;amp;nbsp;&#039;&#039;Energy Policy&#039;&#039;&amp;amp;nbsp;(March): 209-221.&lt;br /&gt;
&lt;br /&gt;
Duchin, Faye. 1998.&amp;amp;nbsp;&#039;&#039;Structural Economics: Measuring Change in Technology, Lifestyles, and the Environment&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
&lt;br /&gt;
Edwards, Stephen R. 1995. &amp;quot;Conserving Biodiversity,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 212-265.&lt;br /&gt;
&lt;br /&gt;
Edmonds, J., and Reilly, J.M. 1985.&amp;amp;nbsp;&#039;&#039;Global Energy: Assessing the Future&#039;&#039;. Oxford, UK: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Edmonds, J., Pitcher, H. Rosenberg, N., and Wigley, T. &amp;quot;Design for the Global Change Assessment Model.&amp;quot;&amp;amp;nbsp;&#039;&#039;Integrative Assessment of Mitigation, Impacts and Adaptation to Climate Change&#039;&#039;. Laxenburg, Austria.&lt;br /&gt;
&lt;br /&gt;
Ehrlich, Paul R. and Anne H. Ehrlich. 1972.&amp;amp;nbsp;&#039;&#039;Population, Resources, Environment&#039;&#039;. San Francisco: W.H. Freeman.&lt;br /&gt;
&lt;br /&gt;
Eicher, Carl. 1982. &amp;quot;Facing up to Africa&#039;s Food Crisis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Foreign Affairs&#039;&#039;&amp;amp;nbsp;61, no. 1 (Fall): 151-74.&lt;br /&gt;
&lt;br /&gt;
Eberstadt, Nicholas. 1995. &amp;quot;Population, Food, and Income,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 8-47.&lt;br /&gt;
&lt;br /&gt;
Esty, Daniel C., Jack A. Goldstone, Ted Robert Gurr, Barbara Harff, Marc Levy, Geoffrey D. Dabelko, Pamela T. Surko, and Alan N. Unger. 1998. State Failure Task Force Report: Phase II Findings. Volume provided courtesy of Ted Robert Gurr.&lt;br /&gt;
&lt;br /&gt;
Flavin, Christopher. 1996. &amp;quot;Facing Up to the Risks of Climate Change,&amp;quot; in Lester R. Brown and others, eds., State of the World 1996 (New York: W.W. Norton), pp. 21-39.&lt;br /&gt;
&lt;br /&gt;
Forrester, Jay W. 1968.&amp;amp;nbsp;&#039;&#039;Principles of Systems&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Gilpin, Robert. 1981.&amp;amp;nbsp;&#039;&#039;War and Change in World Politics&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Globerman, Steven. 2000 (May). Linkages Between Technological Change and Productivity Growth. Industry Canada Research Publications Program: Occasional Paper 23.&lt;br /&gt;
&lt;br /&gt;
Grant, Lindsey. 1982.&amp;amp;nbsp;&#039;&#039;The Cornucopian Fallacies&#039;&#039;. Washington, D.C.: Environmental Fund.&lt;br /&gt;
&lt;br /&gt;
Griffith, Rachel, Stephen Redding, and John Van Reenen. 2000.&amp;amp;nbsp;&#039;&#039;Mapping the Two Faces of R&amp;amp;D: Productivity Growth in a Panel of OECD Industries&#039;&#039;. Institute for Fiscal Studies (January)&lt;br /&gt;
&lt;br /&gt;
Gwartney, James and Robert Lawson with Dexter Samida. 2000.&amp;amp;nbsp;&#039;&#039;Economic Freedom of the World: 2000 Annual Report&#039;&#039;. Vancouver, B.C.: the Fraser Institute.&lt;br /&gt;
&lt;br /&gt;
Hammond, Allen. 1998.&amp;amp;nbsp;&#039;&#039;Which World? Scenarios for the 21st Century&#039;&#039;. Washington, D.C.: Island Press.&lt;br /&gt;
&lt;br /&gt;
Harff, Barbara, with Ted Robert Gurr and Alan Unger. 1999. Preconditions of Genocide and Politicide: 1955-1998. Paper prepared for the State Failure Task Force and provided courtesy of Barbara Harff and Ted Gurr.&lt;br /&gt;
&lt;br /&gt;
Henderson, Hazel. 1996. &amp;quot;Changing Paradigms and Indicators: Implementing Equitable, Sustainable and Participatory Development,&amp;quot; in Jo Marie Griesgraber and Bernhard G. Gunter,&amp;amp;nbsp;&#039;&#039;Development: New Paradigms and Principles for the 21st Century&#039;&#039;. East Haven, CT: Pluto Press, pp. 103-136.&lt;br /&gt;
&lt;br /&gt;
Herrera, Amilcar O., et al. 1976.&#039;&#039;&amp;amp;nbsp;Catastrophe or New Society? A Latin American World Model&#039;&#039;. Ottawa: International Development Research Centre.&lt;br /&gt;
&lt;br /&gt;
Hoekstra, A.Y. 1998.&amp;amp;nbsp;&#039;&#039;Perspectives on Water: An Integrated Model-Based Exploration of the Future&#039;&#039;. Utrecht, the Netherlands: International Books.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1980.&amp;amp;nbsp;&#039;&#039;World Modeling&#039;&#039;. Lexington, Mass: Lexington Books.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1982.&amp;amp;nbsp;&#039;&#039;International Futures Simulation: User&#039;s Manual&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985a.&amp;amp;nbsp;&#039;&#039;International Futures Simulation&#039;&#039;. Iowa City: CONDUIT, University of Iowa.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985b. &amp;quot;World Models: The Bases of Difference,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;29, 77-101.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1985c.&amp;amp;nbsp;&#039;&#039;World Futures: A Critical Analysis of Alternatives&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1987. &amp;quot;Domestic Economic Processes,&amp;quot; in Stuart A. Bremer, ed.,&amp;amp;nbsp;&#039;&#039;The Globus Model: Computer Simulation of Worldwide Political Economic Development&#039;&#039;&amp;amp;nbsp;(Frankfurt and Boulder: Campus and Westview), pp. 39-158.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1988. &amp;quot;International Futures: History and Status,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Science Microcomputer Review&#039;&#039;&amp;amp;nbsp;6, 43-48.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1999. &amp;quot;The International Futures (IFs) Modeling Project.&#039;&#039;&amp;amp;nbsp;Simulation and Gaming&#039;&#039;&amp;amp;nbsp;Vol 30, No. 3 (September): 304-326.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 1999.&amp;amp;nbsp;&#039;&#039;International Futures&#039;&#039;, 3rd edition Boulder: Westview Press, 1999.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2000.&amp;amp;nbsp;&#039;&#039;Continuity and Change in World Politics&#039;&#039;. Englewood Cliffs, N.J.: Prentice-Hall, fourth edition.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2001. &amp;quot;Global Social Transformation: The Sweet Spot, the Steady Slog, and the Systemic Shift,&amp;quot;&amp;amp;nbsp;&#039;&#039;Economic Development and Cultural Change&#039;&#039;&amp;amp;nbsp;49, No. 2 (January): 423-458.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2002.&amp;amp;nbsp;&#039;&#039;Theats and Opportunities Analysis&#039;&#039;. Living document prepared for the Strategic Assessments Group, Office of Transnational Issues, Central Intelligence Agency, August 2002.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. and Anwar Hossain. 2003. Long-Term Socio-Economic Modeling: With Universal, Globally-Integrated Social Accounting Matrices (SAMs) in a General Equilibrium Model Structure. IFs Project Living Document, University of Denver.&lt;br /&gt;
&lt;br /&gt;
Huth, Paul. 1996.&amp;amp;nbsp;&#039;&#039;Standing Your Ground: Territorial Disputes and International Conflict&#039;&#039;. Ann Arbor, MI: University of Michigan Press.&lt;br /&gt;
&lt;br /&gt;
Inglehart, Ronald. 1997.&amp;amp;nbsp;&#039;&#039;Modernization and Postmodernization: Cultural, Economic, and Political Change in 43 Societies&#039;&#039;. Ewing, NJ: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1995.&amp;amp;nbsp;&#039;&#039;Oil, Gas, and Coal Supply Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996.&amp;amp;nbsp;&#039;&#039;World Energy Outlook&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Energy Agency (IEA). 1996b.&amp;amp;nbsp;&#039;&#039;The Strategic Value of Fossil Fuels: Challenges and Responses&#039;&#039;. Paris: International Energy Agency.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;International Financial Statistics&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
International Monetary Fund (IMF). 1995.&amp;amp;nbsp;&#039;&#039;World Economic Outlook&#039;&#039;. Washington, D.C.: International Monetary Fund.&lt;br /&gt;
&lt;br /&gt;
Intergovernmental Panel on Climate Change (IPCC). 1995. Several volumes by various working groups. Published by Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Jansen, Karel and Rob Vos, eds. 1997.&amp;amp;nbsp;&#039;&#039;External Finance and Adjustment: Failure and Success in the Developing World&#039;&#039;. London: Macmillan Press Ltd.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1998.&amp;amp;nbsp;&#039;&#039;Modeling Global Change: The Art of Integrated Assessment Modelling&#039;&#039;. Cheltenham, UK: Edward Elgar.&lt;br /&gt;
&lt;br /&gt;
Janssen, Marco. 1996.&amp;amp;nbsp;&#039;&#039;Meeting Targets: Tools to Support Integrated Modelling of Global Change&#039;&#039;. Den Haag: CIP-Gegevens Koninklijke Bibliotheek.&lt;br /&gt;
&lt;br /&gt;
Jansson, Kurt, Michael Harris, Angela Penrose. 1987.&amp;amp;nbsp;&#039;&#039;The Ethiopian Famine&#039;&#039;. London: Zed Books Ltd.&lt;br /&gt;
&lt;br /&gt;
Jeffreys, Kent. 1995. &amp;quot;Rescuing the Oceans,&amp;quot; in&#039;&#039;&amp;amp;nbsp;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 296-338.&lt;br /&gt;
&lt;br /&gt;
Jones, Daniel M., Stuart A. Bremer, and J. David Singer. 1996. &amp;quot;Militarized Interstate Disputes, 1816-1992: Rationale, Coding Rules, and Empirical Patterns,&amp;quot;&amp;amp;nbsp;&#039;&#039;Conflict Management and Peace Science&#039;&#039;&amp;amp;nbsp;XV, No. 2: 163-215.&lt;br /&gt;
&lt;br /&gt;
Khan, Haider A. 1998.&amp;amp;nbsp;&#039;&#039;Technology, Development and Democracy&#039;&#039;. Northhampton, Mass: Edward Elgar Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Kahn, Herman, William Brown, and Leon Martel. 1976.&amp;amp;nbsp;&#039;&#039;The Next 200 Years&#039;&#039;. New York: William Morrow.&lt;br /&gt;
&lt;br /&gt;
Kalymon, Basil A. 1975. &amp;quot;Economic Incentives in OPEC Oil Pricing Policy.&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Development Economics&#039;&#039;&amp;amp;nbsp;2: 337-362.&lt;br /&gt;
&lt;br /&gt;
Kaplan, Robert. 1994. &amp;quot;The Coming Anarchy,&amp;quot;&amp;amp;nbsp;&#039;&#039;The Atlantic Monthly&#039;&#039;&amp;amp;nbsp;273 (February): .&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999a. &amp;quot;Aggregating Governance Indicators&amp;quot;. World Bank Policy Research Department Working Paper No. 2195.&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton. 1999b. &amp;quot;Governance Matters&amp;quot;. World Bank Policy Research Department Working Paper No. 2196.&lt;br /&gt;
&lt;br /&gt;
Keepin, B. and B. Wynne. 1984. &amp;quot;Technical Analysis of the IIASA Energy Scenarios,&amp;quot;&amp;amp;nbsp;&#039;&#039;Nature&#039;&#039;312: 691-695.&lt;br /&gt;
&lt;br /&gt;
Kehoe, Timothy J. 1996. Social Accounting Matrices and Applied General Equilibrium Models. Federal Reserve Bank of Minneapolis, Working Paper 563.&lt;br /&gt;
&lt;br /&gt;
Kennedy, Paul. 1993.&amp;amp;nbsp;&#039;&#039;Preparing for the Twenty-First Century&#039;&#039;. New York: Random House.&lt;br /&gt;
&lt;br /&gt;
Klein, Lawrence R. and Fu-chen Lo, eds. 1995.&amp;amp;nbsp;&#039;&#039;Modeling Global Change&#039;&#039;. Tokyo: United Nations University Press.&lt;br /&gt;
&lt;br /&gt;
Kornai, J. 1971.&amp;amp;nbsp;&#039;&#039;Anti-Equilibrium&#039;&#039;. Amsterdam: North Holland.&lt;br /&gt;
&lt;br /&gt;
Kwasnicki, Witold and Halina Kwasnicka. 1996. &amp;quot;Long-Term Diffusion Factors of Technological Development: An Evolutionary Model and Case Study,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 31-57.&lt;br /&gt;
&lt;br /&gt;
Leontief, Wassily, Anne Carter and Peter Petri. 1977.&amp;amp;nbsp;&#039;&#039;The Future of the World Economy&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander H., and Elizabeth R. Ducot. 1976. &amp;quot;AGRIMOD: A Simulation Model for the Analysis of U.S. Food Policies.&amp;quot; Paper delivered at Conference on Systems Analysis of Grain Reserves, Joint Annual Meeting of GRSA and TIMS, Philadelphia, Pa., March 31-April 2.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander, H., et al. 1977. Energy in Agriculture: On Modeling Inputs in AGRIMOD. Final Report to U.S. Department of Energy. Palo Alto: Systems Control, Inc., August, available through NTIS.&lt;br /&gt;
&lt;br /&gt;
Lichbach, Mark Irving. 1989. &amp;quot;An Evaluation of ‘Does Economic Inequality Breed Political Conflict?,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;, Vol 41 , No. 4 (July 1989): 431-470.&lt;br /&gt;
&lt;br /&gt;
Liverman, Dianne. 1983.&amp;amp;nbsp;&#039;&#039;The Use of Global Simulation Models in Assessing Climate Impacts on the World Food System&#039;&#039;. Dissertation, University of California, Los Angeles.&lt;br /&gt;
&lt;br /&gt;
Londregan, John B. and Keith T. Poole. 1996. &amp;quot;Does High Income Promote Democrary?&amp;quot;,&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 1 (October): 1-30.&lt;br /&gt;
&lt;br /&gt;
MacKenzie, James J. 1996. &amp;quot;Oil as a Finite Resource: When is Global Production Likely to Peak?&amp;quot; Paper of the World Resources Institute. Washington, D.C.: WRI.&lt;br /&gt;
&lt;br /&gt;
Maddison, Angus. 1995.&amp;amp;nbsp;&#039;&#039;Monitoring the World Economy 1820-1992&#039;&#039;. Paris: OECD.&lt;br /&gt;
&lt;br /&gt;
Malthus, Thomas. 1798.&amp;amp;nbsp;&#039;&#039;An Essay on the Principle of Population as It Affects the Future Improvement of Society&#039;&#039;. London (reprinted many times).&lt;br /&gt;
&lt;br /&gt;
Mansfield, Edward D. 1994.&amp;amp;nbsp;&#039;&#039;Power, Trade, and War&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Marchetti, Cesare, Perrin S. Meyer, and Jesse H. Ausubel. 1996. &amp;quot;Human Population Dynamics Revisited with the Logistic Model: How Much Can be Modeled and Predicted?,&amp;quot;&amp;amp;nbsp;&#039;&#039;Technological Forecasting and Social Change&#039;&#039;&amp;amp;nbsp;52 (May): 1-30.&lt;br /&gt;
&lt;br /&gt;
Martens, Pim and Jan Rotmans, eds. 1999.&amp;amp;nbsp;&#039;&#039;Climate Change: An Integrated Perspective&#039;&#039;. Dordrecht, The Netherlands: Kluwer Academic Publishers.&lt;br /&gt;
&lt;br /&gt;
Martens, W.J.M. 1997. &amp;quot;Health Impacts of Climate Change and Ozone Depletion: An Eco-Epidemiological Approach,&amp;quot; Maastricht, the Netherlands: Maastricht University.&lt;br /&gt;
&lt;br /&gt;
Mason, Andrew. 1997. &amp;quot;The Role of Population Change in the Asian Economic Miracle,&amp;quot; Honolulu, Hawaii: East-West Center, AsiaPacific Issues, No. 33 (October), 8 pages.&lt;br /&gt;
&lt;br /&gt;
McMahon, Walter W. 1997.&amp;amp;nbsp;&#039;&#039;Education and Development: Measuring the Social Benefits&#039;&#039;. Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, Jorgen Randers, and William K. Behrens, III. 1972.&amp;amp;nbsp;&#039;&#039;Limits to Growth&#039;&#039;. New York: Universe Books.&lt;br /&gt;
&lt;br /&gt;
Meadows, Donnela H., Dennis L. Meadows, and Jorgen Randers. 1992.&amp;amp;nbsp;&#039;&#039;Beyond the Limits&#039;&#039;. Post Mills, Vermont: Chelsea Green Publishing Company.&lt;br /&gt;
&lt;br /&gt;
Meadows, Dennis L. et al. 1974.&amp;amp;nbsp;&#039;&#039;Dynamics of Growth in a Finite World&#039;&#039;. Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Mesarovic, Mihajlo D. and Eduard Pestel. 1974.&amp;amp;nbsp;&#039;&#039;Mankind at the Turning Point&#039;&#039;. New York: E.P. Dutton &amp;amp; Co.&lt;br /&gt;
&lt;br /&gt;
Mishkin, Eli. And Ludwig Braun, ed. 1961.&amp;amp;nbsp;&#039;&#039;Adaptive Control Systems&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Moore, Will H., Ronny Lindstrom, and Valerie O’Regan. 1996. &amp;quot;Land Reform, Political Violence and the Economic Inequality-Political Conflict Nexus: A Longitudinal Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;International Interactions&#039;&#039;&amp;amp;nbsp;21, No. 4: 335-363.&lt;br /&gt;
&lt;br /&gt;
Mori, Shunsuke and Masato Takahaashi, 1997. An Integrated Assessment Model for the Evaluation of New Energy Technologies and Food Production, accepted by&amp;amp;nbsp;&#039;&#039;International Journal of Global Energy Issues&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
Naill, Roger F. 1977.&amp;amp;nbsp;&#039;&#039;Managing the Energy Transition&#039;&#039;. Vols. 1 and 2. Cambridge, Mass: Ballinger Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1992. &amp;quot;The DICE Model: Background and Structure of a Dynamic Integrated Climate Economy,&amp;quot; New Haven: Yale University.&lt;br /&gt;
&lt;br /&gt;
Nordhaus, William D. 1979.&amp;amp;nbsp;&#039;&#039;The Efficient Use of Energy Resources&#039;&#039;. New Haven, CT: Yale University Press.&lt;br /&gt;
&lt;br /&gt;
Oneal, John R. and Bruce M. Russett. 1997. The Classical Liberals were Right: Democracy, Interdependence, and Conflict, 1950-1985.&amp;amp;nbsp;&#039;&#039;International Studies Quarterly&#039;&#039;&amp;amp;nbsp;41, no. 2 (June): 267-294.&lt;br /&gt;
&lt;br /&gt;
Pan, Xiaoming. 2000 (January). &amp;quot;Social and Ecological Accounting Matrix: an Empirical Study for China,&amp;quot; paper submitted for the Thirteenth International Conference on Input-Output Techniques, Macerata, Italy, August 21-25, 2000.&lt;br /&gt;
&lt;br /&gt;
Pesaran, M. Hashem and G. C. Harcourt. 1999. Life and Work of John Richard Nicholas Stone.&lt;br /&gt;
&lt;br /&gt;
Pirages, Dennis. 1989.&amp;amp;nbsp;&#039;&#039;Global Technopolitics&#039;&#039;. Pacific Grove, Calif: Brooks/Cole Publishing.&lt;br /&gt;
&lt;br /&gt;
Prinn, R. H.J., A. Sokolov, C. Wand, X. Xiao, Z. Yang, R. Eckhaus, P. Stone, D. Ellerman, J Melilo, J. Fitzmaurice, D. Kicklighter, and Y. Liu. 1996. &amp;quot;Integrated Global System Model for Climate Policy Analysis: Model Framework and Sensitivity Analysis.&amp;quot; Cambridge, Mass: Global Change Center, Massachusetts Institute of Technology.&lt;br /&gt;
&lt;br /&gt;
Przeworski, Adam and Fernando Limongi. 1997. &amp;quot;Modernization: Theories and Facts,&amp;quot;&amp;amp;nbsp;&#039;&#039;World Politics&#039;&#039;&amp;amp;nbsp;49, no. 2 (January): 155-183.&lt;br /&gt;
&lt;br /&gt;
Population Reference Bureau. 1996. World Population Data Sheet 1996. Washington, D.C.: Population Reference Bureau.&lt;br /&gt;
&lt;br /&gt;
Postel, Sandra. 1996.&amp;amp;nbsp;&#039;&#039;Dividing the Waters: Food Security, Ecosystem Health, and the New Politics of Scarcity&#039;&#039;. Worldwatch Paper 132. Washington, D.C.: Worldwatch Institute, September.&lt;br /&gt;
&lt;br /&gt;
Pyatt, G. and J.I. Round, eds. 1985.&amp;amp;nbsp;&#039;&#039;Social Accounting Matrices: A Basis for Planning&#039;&#039;. Washington, D.C.: The World Bank.&lt;br /&gt;
&lt;br /&gt;
Raskin, P., T. Banuri, G. Gallopín, P. Gutman, A. Hammond, R. Kates, and R. Swart. 2001. Great Transition:&amp;amp;nbsp;&#039;&#039;The Promise and Lure of the Times Ahead&#039;&#039;. Forthcoming.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1990.&amp;amp;nbsp;&#039;&#039;Global Politics&#039;&#039;, 4th edition. Boston: Houghton Mifflin.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee. 1995.&amp;amp;nbsp;&#039;&#039;Democracy and International Conflict&#039;&#039;. Columbia: University of South Carolina Press.&lt;br /&gt;
&lt;br /&gt;
Ray, James Lee and J. David Singer. 1973. &amp;quot; Measuring the Concentration of Power in the International System,&amp;quot;&#039;&#039;&amp;amp;nbsp;Sociological Methods and Research&#039;&#039;&amp;amp;nbsp;1, no. 4: 403-436. Reprinted in&amp;amp;nbsp;&#039;&#039;Measuring the Correlates of War&#039;&#039;, edited by J. David Singer and Paul Diehl. Ann Arbor: University of Michigan Press, 1990.&lt;br /&gt;
&lt;br /&gt;
Rayner. S. 1992. &amp;quot;Cultural Theory and Risk Analysis,&amp;quot;&amp;amp;nbsp;&#039;&#039;Social Theory of Risk&#039;&#039;, ed. G. D. Preagor. Westport, USA.&lt;br /&gt;
&lt;br /&gt;
Repetto, Robert and Duncan Austin. 1997.&amp;amp;nbsp;&#039;&#039;The Costs of Climate Protection&#039;&#039;. Washington, D.C.: World Resources Institute.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis Fry. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Chicago: Quadrangle Books.&lt;br /&gt;
&lt;br /&gt;
Richardson, Lewis F. 1960.&amp;amp;nbsp;&#039;&#039;Arms and Insecurity&#039;&#039;. Pittsburgh: Boxwood Press.&lt;br /&gt;
&lt;br /&gt;
Romer, Paul M. 1994. &amp;quot;The Origins of Endogenous Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Journal of Economic Perspectives&#039;&#039;Vol 8, No. 1 (Winter): 3-22.&lt;br /&gt;
&lt;br /&gt;
Root T. and Stephen Schneider. 1995. &amp;quot;Ecology and Climate: Research Strategies and Implications,&amp;quot; Science 269 (52): 334-341.&lt;br /&gt;
&lt;br /&gt;
Rosegrant, Mark W., Mercedita Agcaoili-Sombilla, and Nicostrato D. Perez. 1995. &amp;quot;Global Food Projections to 2020: Implications for Investment.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 5.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1999. Integrated Assessment Models: Uncertainty, Quality and Use. Maastricht, the Netherlands: Maastricht University, International Centre for Integrative Studies (ICIS), Working Paper 199-E005.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and Burt de Vries, eds. 1997.&amp;amp;nbsp;&#039;&#039;Perspectives on Global Change: The Targets Approach&#039;&#039;. Cambridge, UK: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan and M.B.A. van Asselt. 1996. &amp;quot;Integrated Assessment: A Growing Child on its Way to Maturity,&amp;quot;&amp;amp;nbsp;&#039;&#039;Climatic Change&#039;&#039;&amp;amp;nbsp;34 (3-4): 327-336.&lt;br /&gt;
&lt;br /&gt;
Rotmans, Jan. 1990.&amp;amp;nbsp;&#039;&#039;IMAGE: An Integrated Model to Assess the Greenhouse Effect&#039;&#039;. Dordrecht, the Netherlands: Kluwer Academics.&lt;br /&gt;
&lt;br /&gt;
Saaty, Thomas L. 1996. The Analytic Network Process: Decision Making with Dependence and Feedback. Pittsburgh: RWS Publications.&lt;br /&gt;
&lt;br /&gt;
Schafer, Andreas and David G. Victor. 1997. The Future Mobility of the World Population. Massachusetts Institute of Technology and International Institute for Applied Systems Analysis, Discussion Paper 97-6-4 (revision 2, September).&lt;br /&gt;
&lt;br /&gt;
Scheer, Sara J. and Satya Yadav. 1996. &amp;quot;Land Degradation in the Developing World: Implications for Food, Agriculture, and the Environment to 2020.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 14.&lt;br /&gt;
&lt;br /&gt;
Schneider, Stephen. 1997. &amp;quot;Integrated Assessment Modeling of Climate Change: Transparent Rational Tool for Policy Making or Opaque Screen Hiding Value-Laden Assumptions?&amp;quot;&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(4): 229-250.&lt;br /&gt;
&lt;br /&gt;
Schwartz, Peter. 1996.&#039;&#039;&amp;amp;nbsp;The Art of the Long View.&#039;&#039;&amp;amp;nbsp;New York: Doubleday.&lt;br /&gt;
&lt;br /&gt;
Sedjo, Roger A. 1995. &amp;quot;Forests: Conflicting Signals,&amp;quot; in&amp;amp;nbsp;&#039;&#039;The True State of the Planet&#039;&#039;, ed. Ronald Bailey. New York: The Free Press, pp. 178-209.&lt;br /&gt;
&lt;br /&gt;
Shane, Harold G. and Gary A. Sojka. 1990. &amp;quot;John Elfreth Watkins, Jr.: Forgotten Genius of Forecasting,&amp;quot; in Edward Cornish, ed.,&#039;&#039;&amp;amp;nbsp;The 1990s and Beyond&#039;&#039;. Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Shaw, Timothy W. and Clement E. Adibe. 1995-96. &amp;quot;Africa and Global Developments in the Twenty-First Century,&amp;quot; International Journal 51 (Winter): 1-26.&lt;br /&gt;
&lt;br /&gt;
Siegmann, Heinrich. 1985.&amp;amp;nbsp;&#039;&#039;Recent Developments in World Modeling&#039;&#039;. Berlin: Science Center.&lt;br /&gt;
&lt;br /&gt;
Simon, Julian. 1981.&amp;amp;nbsp;&#039;&#039;The Ultimate Resource&#039;&#039;. Princeton: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Singer, J. David, Stuart Bremer, and John Stuckey. 1972. &amp;quot;Capability Distribution, Uncertainty, and Major Power Wars, 1820-1965.&amp;quot; In Bruce Russett, ed.,&amp;amp;nbsp;&#039;&#039;Peace, War, and Numbers.&#039;&#039;&amp;amp;nbsp;Beverly Hills: Sage.&lt;br /&gt;
&lt;br /&gt;
Sivard, Ruth Leger. 1993.&amp;amp;nbsp;&#039;&#039;World Military and Social Expenditures 1993.&#039;&#039;&amp;amp;nbsp;Washington, D.C. 20007: World Priorities, Box 25140.&lt;br /&gt;
&lt;br /&gt;
Solow, Robert M. 1956. &amp;quot;A Contribution to the Theory of Economic Growth,&amp;quot;&amp;amp;nbsp;&#039;&#039;Quarterly Journal of Economics&#039;&#039;&amp;amp;nbsp;70, 1 (February): 65-94.&lt;br /&gt;
&lt;br /&gt;
Stanford University. 1978.&amp;amp;nbsp;&#039;&#039;Stanford Pilot Energy/Economic Model&#039;&#039;. Stanford: Department of Research, Interim Report, Vol. 1.&lt;br /&gt;
&lt;br /&gt;
Stockholm International Peace Research Institute (SIPRI). 1994.&amp;amp;nbsp;&#039;&#039;SIPRI Yearbook&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Stone, Richard. 1986. &amp;quot;The Accounts of Society,&amp;quot;&#039;&#039;&amp;amp;nbsp;Journal of Applied Econometrics&#039;&#039;&amp;amp;nbsp;1, no. 1 (January): 5-28.&lt;br /&gt;
&lt;br /&gt;
Strategic Assessments Group (SAG), Office of Transnational Issues, Directorate of Intelligence. 2001 (February). The Global Economy in the Long Term. OTI IR 2001-013.&lt;br /&gt;
&lt;br /&gt;
Systems Analysis Research Unit (SARU). 1977.&amp;amp;nbsp;&#039;&#039;SARUM 76 Global Modeling Project&#039;&#039;. Departments of the Environment and Transport, 2 Marsham Street, London, 3WIP 3EB.&lt;br /&gt;
&lt;br /&gt;
Tammen, Ronald L, Jacek Kugler, Douglas Lemke, Allan C. Stam III, Carole Alsharabati, Mark Andrew Abdollahian, Brian Efird, and A.F.K. Organski. 2000. Power Transitions: Strategies for the 21st Century. New York: Chatham House Publishers.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1975. &amp;quot;Theoretical Foundations and Technical Implications.&amp;quot; in Charles Blitzer, Peter Clark and Lance Taylor, eds.,&amp;amp;nbsp;&#039;&#039;Economy-Wide Models and Development Planning.&#039;&#039;&amp;amp;nbsp;Oxford: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Taylor, Lance. 1979.&amp;amp;nbsp;&#039;&#039;Macro Models for Developing Countries&#039;&#039;. New York: McGraw-Hill.&lt;br /&gt;
&lt;br /&gt;
Thirlwall, A. P. 1977.&amp;amp;nbsp;&#039;&#039;Growth and Development&#039;&#039;. New York: John Wiley and Sons.&lt;br /&gt;
&lt;br /&gt;
Thompson, M. 1997. Cultural Theory and Integrated Assessment.&amp;amp;nbsp;&#039;&#039;Environmental Modelling and Assessment&#039;&#039;&amp;amp;nbsp;2(3): 139-150.&lt;br /&gt;
&lt;br /&gt;
Thompson, M., R. Ellis and A. Wildavsky. 1990.&amp;amp;nbsp;&#039;&#039;Cultural Theory&#039;&#039;. Boulder, Co: Westview Press.&lt;br /&gt;
&lt;br /&gt;
Thorbecke, Erik. 2001. &amp;quot;The Social Accounting Matrix: Deterministic or Stochastic Concept?&amp;quot;, paper prepared for a conference in honor of Graham Pyatt&#039;s retirement, at the Institute of Social Studies, The Hague, Netherlands (November 29 and 30). Available at [http://people.cornell.edu/pages/et17/etpapers.html http://people.cornell.edu/pages/et17/etpapers.html].&lt;br /&gt;
&lt;br /&gt;
United Nations, Department of Economic and Social Affairs. 1956.&amp;amp;nbsp;&#039;&#039;Methods of Population Projections by Sex and Age&#039;&#039;. New York: United Nations, ST/SOA Series A.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1992.&amp;amp;nbsp;&#039;&#039;Long-Range World Population Projections. Two Centuries of Population Growth: 1950-2150&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations (UN). 1993.&amp;amp;nbsp;&#039;&#039;World Population Prospects - the 1992 Revision&#039;&#039;. New York: United Nations.&lt;br /&gt;
&lt;br /&gt;
United Nations Development Program (UNDP). 1995.&amp;amp;nbsp;&#039;&#039;Human Development Report&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1992.&amp;amp;nbsp;&#039;&#039;Production Yearbook.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Food and Agricultural Organization (FAO). 1995.&#039;&#039;&amp;amp;nbsp;World Agriculture: Towards 2010.&#039;&#039;&amp;amp;nbsp;Rome: FAO.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 1999. The World at Six Billion New York: UN.&lt;br /&gt;
&lt;br /&gt;
United Nations Population Division. 2000. Replacement Migration: Is it a Solution to Declining and Ageing Populations? New York: UN.&lt;br /&gt;
&lt;br /&gt;
United States Arms Control and Disarmament Agency (ACDA). 1995.&amp;amp;nbsp;&#039;&#039;World Military Expenditures and Arms Transfers 1995&#039;&#039;. Washington, D.C.: Arms Control and Disarmament Agency.&lt;br /&gt;
&lt;br /&gt;
United States Bureau of the Census. 1991.&amp;amp;nbsp;&#039;&#039;World Population Profile: 1991&#039;&#039;. Report WP/91 Washington, D.C.: Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Walters, Robert S. and David H. Blake. 1992.&amp;amp;nbsp;&#039;&#039;The Politics of Global Economic Relations&#039;&#039;, 4th edition. Englewood Cliffs, N.J.: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Waltz, Kenneth N. 1959. Man, the State, and War: A Theoretical Analysis. New York: Columbia University Press.&lt;br /&gt;
&lt;br /&gt;
Watkins, John Elfreth, Jr. 1990. &amp;quot;What May Happen in the Next Hundred Years,&amp;quot; in Edward Cornish, ed.,&amp;amp;nbsp;&#039;&#039;The 1990s and Beyond.&#039;&#039;&amp;amp;nbsp;Bethesda, Maryland: World Future Society, pp. 150-155.&lt;br /&gt;
&lt;br /&gt;
Wildavsky, Aaron, and Ellen Tenenbaum. 1981.&amp;amp;nbsp;&#039;&#039;The Politics of Mistrust&#039;&#039;. Beverly Hills: Sage Publications.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1991b.&amp;amp;nbsp;&#039;&#039;World Tables 1991&#039;&#039;. New York: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
World Bank. 1995&amp;amp;nbsp;&#039;&#039;World Development Report 1995&#039;&#039;. New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
World Energy Council (WEC) Commission. 1993.&amp;amp;nbsp;&#039;&#039;Energy for Tomorrow’s World.&#039;&#039;&amp;amp;nbsp;New York: St. Martin’s Press.&lt;br /&gt;
&lt;br /&gt;
World Resources Institute (WRI). 1994.&amp;amp;nbsp;&#039;&#039;World Resources 1994-95.&#039;&#039;&amp;amp;nbsp;New York: Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Wortman, Sterling and Ralph W. Cummings, Jr. 1978.&#039;&#039;&amp;amp;nbsp;To Feed This World&#039;&#039;. Baltimore: Johns Hopkins University Press.&lt;br /&gt;
&lt;br /&gt;
Zinnes, Dina A. and John W. Gillespie, eds. 1976.&amp;amp;nbsp;&#039;&#039;Mathematical Models in International Relations&#039;&#039;&amp;amp;nbsp;(New York: Preaeger).&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Agriculture&amp;amp;nbsp;Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Brown, Lester R. 1995. &#039;&#039;Who Will Feed China?&#039;&#039; New York: W.W. Norton.&lt;br /&gt;
&lt;br /&gt;
Cline, William R. 2007.&#039;&#039;Global warming and agriculture: Impact estimates by country. &#039;&#039;Washington, DC: Peterson Institute for International Economics.&lt;br /&gt;
&lt;br /&gt;
Herrera, Amilcar O., et al. 1976. C&#039;&#039;atastrophe or New Society? A Latin American World Model. &#039;&#039;Ottawa: International Development Research Centre.&lt;br /&gt;
&lt;br /&gt;
Levis, Alexander H., and Elizabeth R. Ducot. 1976. &amp;quot;AGRIMOD: A Simulation Model for the Analysis of U.S. Food Policies.&amp;quot; Paper delivered at Conference on Systems Analysis of Grain Reserves, Joint Annual Meeting of GRSA and TIMS, Philadelphia, Pa., March 31-April 2.&lt;br /&gt;
&lt;br /&gt;
Meadows, Dennis L. et al. 1974&#039;&#039;. Dynamics of Growth in a Finite World.&#039;&#039; Cambridge, Mass: Wright-Allen Press.&lt;br /&gt;
&lt;br /&gt;
Rosegrant, Mark W., Mercedita Agcaoili-Sombilla, and Nicostrato D. Perez. 1995. &amp;quot;Global Food Projections to 2020: Implications for Investment.&amp;quot; Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 5.&lt;br /&gt;
&lt;br /&gt;
Systems Analysis Research Unit (SARU). 1977. &#039;&#039;SARUM 76 Global Modeling Project.&#039;&#039; Departments of the Environment and Transport, 2 Marsham Street, London, 3WIP 3EB.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Education Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Archibugi, Daniele, and Alberto Coco. 2005. “Measuring Technological Capabilities at the Country Level: A Survey and a Menu for Choice.” Research Policy 34(2). Research Policy: 175–194.&lt;br /&gt;
&lt;br /&gt;
Bush, Vannevar. 1945. Science: The Endless Frontier. Washington: United States Government Printing Office.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert and Jong-Wha Lee. 2010. &amp;quot;A New Data Set of Educational Attainment in the World, 1950-2010.&amp;quot; NBER Working Paper No. 15902. National Bureau of Economic Research, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
Barro, Robert and Jong-Wha Lee. 2000. “International Data on Educational Attainment: Updates and Implications.” NBER Working Paper No. 7911. National Bureau of Economic Research, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
Bruns, Barbara, Alain Mingat, and Ramahatra Rakotomalala. 2003. Achieving Universal Primary Education by 2015: A Chance for Every Child. Washington, DC: World Bank.&lt;br /&gt;
&lt;br /&gt;
Chen, Derek H. C., and Carl J. Dahlman. 2005. The Knowledge Economy, the KAM Methodology and World Bank Operations. The World Bank, October 19.&lt;br /&gt;
&lt;br /&gt;
Clemens, Michael A. 2004. The Long Walk to School: International education goals in historical perspective. Econ WPA, March.&amp;amp;nbsp;[http://ideas.repec.org/p/wpa/wuwpdc/0403007.html http://ideas.repec.org/p/wpa/wuwpdc/0403007.html].&lt;br /&gt;
&lt;br /&gt;
Cohen, Daniel, and Marcelo Soto. 2001. “Growth and Human Capital: Good Data, Good Results.” Technical Paper 179.&amp;amp;nbsp; Paris: OECD.&lt;br /&gt;
&lt;br /&gt;
Cuaresma, Jesus Crespo, and Wolfgang Lutz. 2007 (April).&amp;amp;nbsp; “Human Capital, Age Structure and Economic Growth:&amp;amp;nbsp; Evidence from a New Dataset.” Interim Report IR-07-011. Laxenburg, Austria:&amp;amp;nbsp; International Institute for Applied Systems Analysis.&lt;br /&gt;
&lt;br /&gt;
Delamonica, Enrique, Santosh Mehrotra, and Jan Vandemoortele.&amp;amp;nbsp;2001 (August).&amp;amp;nbsp; “Is EFA Affordable? Estimating the Global Minimum Cost of ‘Education for All’”. Innocenti Working Paper No. 87.&amp;amp;nbsp; Florence: UNICEF Innocenti Research Centre.&amp;amp;nbsp;[http://www.unicef-irc.org/publications/pdf/iwp87.pdf http://www.unicef-irc.org/publications/pdf/iwp87.pdf].&lt;br /&gt;
&lt;br /&gt;
Dickson, Janet R., Barry B. Hughes, and Mohammod T. Irfan. 2010. Advancing Global Education. Vol 2, Patterns of Potential Human Progress series.&amp;amp;nbsp; Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.&amp;amp;nbsp;[http://www.ifs.du.edu/documents http://www.ifs.du.edu/documents].&lt;br /&gt;
&lt;br /&gt;
Dutta, Soumitra (Ed.). 2013. The Global Innovation Index 2013. The Local Dynamics of Innovation.&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2004b (March).&amp;amp;nbsp; “International Futures (IFs): An Overview of Structural Design.” Pardee Center for International Futures Working Paper, Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx http://www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. and Evan E. Hillebrand. 2006.&amp;amp;nbsp;&amp;amp;nbsp;&#039;&#039;Exploring and Shaping International Futures&#039;&#039;.&amp;amp;nbsp; Boulder, Co:&amp;amp;nbsp; Paradigm Publishers.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. with Anwar Hossain and Mohammod T. Irfan. 2004 (May).&amp;amp;nbsp; “The Structure of IFs.” Pardee Center for International Futures Working Paper, Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx http://www.ifs.du.edu/documents/reports.aspx].&amp;amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
Irfan, Mohammod T. 2008.&amp;amp;nbsp; “A Global Education Transition: Computer Simulation of Alternative Paths in Universal Basic Education,” Ph.D. dissertation presented to the Josef Korbel School of International Studies, University of Denver, Denver, Colorado.&amp;amp;nbsp;&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx http://www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
Juma, Calestous, and Lee Yee-Cheong. 2005. Innovation: Applying Knowledge in Development. London: Earthscan. (Available online at&amp;amp;nbsp;[http://www.unmillenniumproject.org/documents/Science-complete.pdf http://www.unmillenniumproject.org/documents/Science-complete.pdf&amp;amp;nbsp;])&lt;br /&gt;
&lt;br /&gt;
McMahon, Walter W. 1999 (first published in paperback in 2002).&amp;amp;nbsp; Education and Development: Measuring the Social Benefits. Oxford:&amp;amp;nbsp; Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Wils, Annababette and Raymond O&#039;Connor. 2003. “The causes and dynamics of the global education transition.” AED Working Paper. Washington, DC: Academy for Educational Development&lt;br /&gt;
&lt;br /&gt;
UNESCO. 2010. UNESCO Science Report 2010. The Current Status of Science around the World. UNESCO. Paris.&lt;br /&gt;
&lt;br /&gt;
World Bank. 2010. Innovation Policy: A Guide for Developing Countries. (Available online at&amp;amp;nbsp;[https://openknowledge.worldbank.org/bitstream/handle/10986/2460/548930PUB0EPI11C10Dislosed061312010.pdf?sequence=1 https://openknowledge.worldbank.org/bitstream/handle/10986/2460/548930PUB0EPI11C10Dislosed061312010.pdf?sequence=1])&lt;br /&gt;
&lt;br /&gt;
World Bank. 2007. Building Knowledge Economies: Advanced Strategies for Development. WBI Development Studies. Washington, D.C: World Bank. (Available online at&amp;amp;nbsp;[http://siteresources.worldbank.org/KFDLP/Resources/461197-1199907090464/BuildingKEbook.pdf http://siteresources.worldbank.org/KFDLP/Resources/461197-1199907090464/BuildingKEbook.pdf])&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Energy Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Kalymon, Basil A. 1975. &amp;quot;Economic Incentives in OPEC Oil Pricing Policy.&amp;quot; &#039;&#039;Journal of Development Economics&#039;&#039; 2: 337-362.&lt;br /&gt;
&lt;br /&gt;
Naill, Roger F. 1977.&#039;&#039;Managing the Energy Transition.&#039;&#039; Vols. 1 and 2. Cambridge, Mass: Ballinger Publishing Co.&lt;br /&gt;
&lt;br /&gt;
Stanford University. 1978. &#039;&#039;Stanford Pilot Energy/Economic Model.&#039;&#039; Stanford: Department of Research, Interim Report, Vol. 1.&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Governance Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Barro, Robert J. and Jong-Wha Lee. 2001. &amp;quot;International Data on Educational Attainment: Updates and Implications,&amp;quot;&amp;amp;nbsp;&#039;&#039;Oxford Economic Papers&#039;&#039;&amp;amp;nbsp;53(3): 541-563.&lt;br /&gt;
&lt;br /&gt;
Cilliers, Jakkie, Barry Hughes, and Jonathan Moyer. 2011.&amp;amp;nbsp;&#039;&#039;African Futures 2050: The Next 40 Years&#039;&#039;. Pretoria, South Africa and Denver, Colorado: Institute for Security Studies and Frederick S. Pardee Center for International Futures.&lt;br /&gt;
&lt;br /&gt;
Correlates of War Project. 2011. “State System Membership List, v2011.” Online,&amp;amp;nbsp;[http://correlatesofwar.org/ http://correlatesofwar.org&amp;amp;nbsp;].&lt;br /&gt;
&lt;br /&gt;
Diamond, Larry. 1992. “Economic Development and Democracy Reconsidered.”&amp;amp;nbsp;&#039;&#039;American Behavioral Scientist&#039;&#039;&amp;amp;nbsp;35(4/5): 450-499.&lt;br /&gt;
&lt;br /&gt;
Diehl, Paul F., ed. 1999.&amp;amp;nbsp;&#039;&#039;A Roadmap to War: Territorial Dimensions of International Conflict&#039;&#039;, 1&amp;lt;sup&amp;gt;st&amp;lt;/sup&amp;gt;&amp;amp;nbsp;ed. Nashville: Vanderbilt University Press.&lt;br /&gt;
&lt;br /&gt;
Easton, David. 1965.&amp;amp;nbsp;&#039;&#039;A Framework for Political Analysis&#039;&#039;. Englewood Cliffs, New Jersey: Prentice-Hall.&lt;br /&gt;
&lt;br /&gt;
Esty, Daniel C., Jack A. Goldstone, Ted Robert Gurr, Barbara Harff, Marc Levy, Geoffrey D. Dabelko, Pamela Surko, and Alan N. Unger. 1998. “State Failure Task Force Report: Phase II Findings.” Study Commissioned by the Central Intelligence Agency and George Mason University School of Public Policy. Political Instability Task Force, Arlington VA.&lt;br /&gt;
&lt;br /&gt;
Freedom House, Inc. 2009.&amp;amp;nbsp;&#039;&#039;Freedom in the World 2009: The Annual Survey of Political Rights and Civil Liberties&#039;&#039;. Washington, DC: Freedom House, Inc.\&lt;br /&gt;
&lt;br /&gt;
Goldstone, Jack A. 2010. “The New Population Bomb”&amp;amp;nbsp;&#039;&#039;Foreign Affairs&#039;&#039;&amp;amp;nbsp;(January/February): 31-43.&lt;br /&gt;
&lt;br /&gt;
Goldstone, Jack A., Robert H. Bates, David L. Epstein, Ted Robert Gurr, Michael B. Lustik, Monty G. Marshall, Jay Ulfelder, and Mark Woodward. 2010. “A Global Model for Forecasting Political Instability.”&amp;amp;nbsp;&#039;&#039;American Journal of Political Science&#039;&#039;&amp;amp;nbsp;54(1): 190-208. doi: 10.1111/j.1540-5907.2009.00426.x.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2001. “Global Social Transformation: The Sweet Spot, the Steady Slog, and the Systemic Shift.”&amp;amp;nbsp;&#039;&#039;Economic Development and Cultural Change&#039;&#039;&amp;amp;nbsp;49(2): 423-458. doi: 10.1086/452510.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2002. &amp;quot;Threats and Opportunities Analysis,&amp;quot; working document prepared for the Strategic Assessments Group, Office of Transnational Issues, Central Intelligence Agency.&amp;amp;nbsp; Available on the IFs project web site at&amp;amp;nbsp;[http://www.ifs.du.edu/ www.ifs.du.edu].&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., and Anwar Hossain. 2003. “Long-Term Socio-Economic Modeling: With Universal, Globally-Integrated Social Accounting Matrices (SAMs) in a General Equilibrium Model Structure.” Working Paper, University of Denver, Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/assets/documents/economyandsamdocument46.pdf http://www.ifs.du.edu/assets/documents/economyandsamdocument46.pdf]&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., Devin Joshi, Jonathan Moyer, Timothy Sisk and José Roberto Solórzano. 2014.&amp;amp;nbsp;&#039;&#039;Strengthening Governance Globally.&amp;amp;nbsp;&#039;&#039;vol. 5, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.&lt;br /&gt;
&lt;br /&gt;
Huntington, Samuel P. 1991.&amp;amp;nbsp;&#039;&#039;The Third Wave: Democratization in the Late Twentieth Century&#039;&#039;. Norman, OK: University of Oklahoma.&lt;br /&gt;
&lt;br /&gt;
Inglehart, Ronald. 1997.&amp;amp;nbsp;&amp;amp;nbsp;&#039;&#039;Modernization and Postmodernization&#039;&#039;.&amp;amp;nbsp; Princeton: PrincetonUniversity Press.&lt;br /&gt;
&lt;br /&gt;
Joshi, Devin. 2011a. “Good Governance, State Capacity, and the Millennium Development Goals.”&amp;amp;nbsp;&#039;&#039;Perspectives on Global Development and Technology&amp;amp;nbsp;&#039;&#039;10(2): 339-360. doi: 10.1163/156914911X5824.68.&lt;br /&gt;
&lt;br /&gt;
Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi. 2010. “The Worldwide Governance Indicators: Methodology and Analytical Issues.” World Bank Policy Research Working Paper no. 5430. World Bank, Washington, DC.&lt;br /&gt;
&lt;br /&gt;
Marshall, Monty G. and Benjamin R. Cole. 2008. “Global Report on Conflict, Governance and State Fragility 2008.”&amp;amp;nbsp;&#039;&#039;Foreign Policy Bulletin&#039;&#039;&amp;amp;nbsp;18: 3-21. doi: 10.1017/S1052703608000014.&lt;br /&gt;
&lt;br /&gt;
Marshall, Monty G., and Benjamin R. Cole. 2009. “Global Report 2009: Conflict, Governance, and State Fragility.” Vienna, VA.: Center for Systemic Peace and Center for Global Policy.&lt;br /&gt;
&lt;br /&gt;
Marshall, Monty G., and Benjamin R. Cole. 2011. &amp;quot;Global Report 2011: Conflict, Governance, and State Fragility.&amp;quot; Vienna, VA. Center for Systemic Peace.&lt;br /&gt;
&lt;br /&gt;
Marshall, Monty G., and Keith Jaggers. 2011. “Polity IV Project: Political Regime Characteristics and Transitions 1800-2010.”&amp;amp;nbsp;[http://www.systemicpeace.org/polity/polity4.htm http://www.systemicpeace.org/polity/polity4.htm]&amp;amp;nbsp;[accessed December 22 2012]&lt;br /&gt;
&lt;br /&gt;
Mauro, Paolo. 1995. “Corruption and Growth.”&amp;amp;nbsp;&#039;&#039;The Quarterly Journal of Economics&#039;&#039;&amp;amp;nbsp;110(3) (August): 681-712.&lt;br /&gt;
&lt;br /&gt;
Migdal, Joel. 1988.&amp;amp;nbsp;&#039;&#039;Strong Societies and Weak Sates: State-Society Relations and State Capabilities in the&amp;amp;nbsp;Third World&#039;&#039;. Princeton: Princeton University Press&lt;br /&gt;
&lt;br /&gt;
Mo, Pak Hung. 2001. “Corruption and Economic Growth.”&amp;amp;nbsp;&#039;&#039;Journal of Comparative Economics&amp;amp;nbsp;&#039;&#039;29(1) (March): 66-79. doi:10.1006/jcec.2000.1703.&lt;br /&gt;
&lt;br /&gt;
North, Douglass C., John Joseph Wallis, and Barry R. Weingast. 2009.&amp;amp;nbsp;&#039;&#039;Violence and Social Orders: A Conceptual Framework for Interpreting Recorded Human History&#039;&#039;. Cambridge: Cambridge University Press.&lt;br /&gt;
&lt;br /&gt;
Pierson, Paul. 2004.&amp;amp;nbsp;&#039;&#039;Politics in Time: History, Institutions, and Social Analysis&#039;&#039;. Princeton, NJ: Princeton University Press.&lt;br /&gt;
&lt;br /&gt;
Rice, Susan E., and Stewart Patrick. 2008.&amp;amp;nbsp;&#039;&#039;Index of State Weakness in the Developing World.&#039;&#039;&amp;amp;nbsp;Washington, DC: The Brookings Institution.&lt;br /&gt;
&lt;br /&gt;
Shihata, Ibrahim F. I. 1996. “Corruption - A General Review with an Emphasis on the Role of the World Bank.”&amp;amp;nbsp;&#039;&#039;Dickinson Journal of International Law&#039;&#039;&amp;amp;nbsp;15: 451.&lt;br /&gt;
&lt;br /&gt;
Tanzi, Vito. 1998. “Corruption Around the World: Causes, Consequences, Scope, and Cures.” Staff Papers - International Monetary Fund 45(4) (December): 559-594.&lt;br /&gt;
&lt;br /&gt;
Urdal, H. 2004. “The devil in the demographics: the effect of youth bulges on domestic armed conflict, 1950-2000.” Social Development Papers: Conflict and Reconstruction Paper 14.&lt;br /&gt;
&lt;br /&gt;
Ware, H. 2004. “Pacific instability and youth bulges: the devil in the demography and the economy.” Paper delivered at the 12th Biennial Conference of the Australian Population Association, 15-17.&lt;br /&gt;
&lt;br /&gt;
Wagner, Adolph. 1892.&amp;amp;nbsp;&#039;&#039;Grundlegung der Politischen Ökonomie&#039;&#039;. Leipzig: C.F. Winter Publishing Firm.&lt;br /&gt;
&lt;br /&gt;
World Bank. 2011.&amp;amp;nbsp;&#039;&#039;World Development Indicators 2011.&#039;&#039;&amp;amp;nbsp;Washington, DC: World Bank. Available at&amp;amp;nbsp;[http://data.worldbank.org/data-catalog/world-development-indicators http://data.worldbank.org/data-catalog/world-development-indicators].&lt;br /&gt;
&lt;br /&gt;
== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Health Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
&lt;br /&gt;
Adams 1987.&amp;amp;nbsp;[http://www.geog.ucl.ac.uk/~jadams/PDFs/smeed&#039;s%20law.pdf &amp;quot;Smeed&#039;s Law: some further thoughts.&amp;quot;]&amp;amp;nbsp;&#039;&#039;Traffic Engineering and Control&#039;&#039;&amp;amp;nbsp;(Feb) 70-73.&lt;br /&gt;
&lt;br /&gt;
Alsan, Marcella, David E. Bloom, and David Canning. 2006. “The Effects of Population Health on Foreign Direct Investment Inflows to Low- and Middle-Income Countries,”&amp;amp;nbsp;&#039;&#039;World Development&#039;&#039;&amp;amp;nbsp;34(4): 613-630.&lt;br /&gt;
&lt;br /&gt;
Anand, Sudhir and Martin Ravallion. 1993. “Human development in poor countries: on the role of private incomes and public services,”&amp;amp;nbsp;&#039;&#039;Journal of Economic Perspectives&#039;&#039;&amp;amp;nbsp;7(1): 133–150.&lt;br /&gt;
&lt;br /&gt;
Ashraf, Quamrul H., Ashley Lester, and David N. Weil. 2008. “When Does Improving Health Raise GDP?”&amp;amp;nbsp; NBER Working Paper No. 14449. National Bureau of Economic Research, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
Bidani, Benu and Martin Ravallion. 1997. “Decomposing social indicators using distributional data.”&amp;amp;nbsp;&#039;&#039;Journal of Econometrics&#039;&#039;&amp;amp;nbsp;77: 125–139.&lt;br /&gt;
&lt;br /&gt;
Bloom, David E., and David Canning. 2004. “Global Demographic Change: Dimensions and Economic Significance.” NBER Working Paper No. 10817.&amp;amp;nbsp; National Bureau of Economic Research, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
Blössner, Monika, and Mercedes de Onis. 2005.&amp;amp;nbsp;&#039;&#039;Malnutrition: quantifying the health impact at national and local levels.&#039;&#039;&amp;amp;nbsp;Geneva, World Health Organization. (WHO Environmental Burden of Disease Series, No. 12).&lt;br /&gt;
&lt;br /&gt;
Dargay, Gately, and Sommer 2007. “Vehicle Ownership and Income Growth, Worldwide: 1960-2030”. Joyce Dargay, Dermot Gately and Martin Sommer, January 2007.&lt;br /&gt;
&lt;br /&gt;
Deaton, Angus, and Christina Paxson. 2000 (May). “Growth and Savings Among Individuals and Households.”&amp;amp;nbsp;&#039;&#039;The Review of Economics and Statistics&#039;&#039;&amp;amp;nbsp;82(2): 212-225.&lt;br /&gt;
&lt;br /&gt;
Desai, Manish A., Sumi Mehta, and Kirk R. Smith. 2004. “Indoor smoke from solid fuels: Assessing the environmental burden of disease.”WHOEnvironmental Burden of Disease Series No. 4&#039;&#039;.&amp;amp;nbsp;&#039;&#039;Annette Pruss-Üstun, Diamid Campbell-Lendrum, Carlos Corvalán, and Alistair Woodward, series eds. World Health Organization, Geneva.&lt;br /&gt;
&lt;br /&gt;
Ezzati, Majid and Alan D. Lopez. 2004. “Smoking and oral tobacco use.” In Majid Ezzati, Alan D. Lopez, Anthony Rodgers, and Cristopher J.L. Murray, eds.,&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors&#039;&#039;. Geneva: World Health Organization, 883-957.&amp;amp;nbsp; Retrieved 4 Feb 2009, from&amp;amp;nbsp;[http://www.who.int/publications/cra/chapters/volume1/part4/en/index.html http://www.who.int/publications/cra/chapters/volume1/part4/en/index.html].&lt;br /&gt;
&lt;br /&gt;
Ezzati, Majid, Alan D. Lopez, Anthony Rodgers, Christopher J.L. Murray, eds. 2004.&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors&#039;&#039;. Geneva: World Health Organization.&lt;br /&gt;
&lt;br /&gt;
Fernández-Villaverde, Jesús, and Dirk Kruegger. 2004 (September 14). “Consumption over the Life Cycle: Facts from Consumer Expenditure Survey Data,” unpublished manuscript, University of Pennsylvania and University of Frankfort.&amp;amp;nbsp;&amp;amp;nbsp;[http://www.dklevine.com/archive/refs4506439000000000304.pdf http://www.dklevine.com/archive/refs4506439000000000304.pdf]&lt;br /&gt;
&lt;br /&gt;
Fernández-Villaverde, Jesús, and Dirk Kruegger. 2005 (December 19). “Consumption over the Life Cycle: How Important are Consumer Durables?,” unpublished manuscript, University of Pennsylvania and Goethe University.&amp;amp;nbsp;&amp;amp;nbsp;[http://journals.cambridge.org/action/displayAbstract?fromPage=online&amp;amp;aid=8466457 http://journals.cambridge.org/action/displayAbstract?fromPage=online&amp;amp;aid=8466457]&lt;br /&gt;
&lt;br /&gt;
Gakidou, Emmanuela, Shefali Oza, Cecilia Vidal Fuertes, Amy Y. Li, Diana K. Lee, Angelica Sousa, Margaret C. Hogan, Stephen Vander Hoorn, and Majid Ezzati. 2007.” Improving Child Survival Through Environmental and Nutritional Interventions: The Importance of Targeting Interventions Toward the Poor.”&amp;amp;nbsp;&#039;&#039;Journal of the American Medical Association&#039;&#039;&amp;amp;nbsp;298(16): 1876-1887.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. and Hillebrand, Evan E. 2006. “Exploring and shaping International Futures”. Boulder, CO: Paradigm Publishers.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B., Randall Kuhn, Cecilia Peterson, Dale Rothman, and Jose Solorzano. 2011.&amp;amp;nbsp;&amp;amp;nbsp;&#039;&#039;Improving Global Health: Patterns of Potential Human Progress, Volume 3&#039;&#039;.&amp;amp;nbsp; Paradigm Publishing and Oxford India.&lt;br /&gt;
&lt;br /&gt;
Hughes, Barry B. 2005.&amp;amp;nbsp; “Productivity in IFs.” Pardee Center for International Futures Working Paper, University of Denver, Denver, CO.&amp;amp;nbsp;&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx http://www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
&lt;br /&gt;
James, W. Philip T., Rachel Jackson-Leach , Cliona Ni Mhurchu, Eleni Kalamara, Maryam Shayeghi, Neville J. Rigby, Chizuru Nishida, and Anthony Rodgers. 2004.&amp;amp;nbsp; “Overweight and obesity (high body mass index).” In Majid Ezzati, Alan D. Lopez, Anthony Rodgers and Christopher J.L. Murray, eds.,&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors.&#039;&#039;&amp;amp;nbsp;Geneva: World Health Organization, 959-1108.&lt;br /&gt;
&lt;br /&gt;
Jamison, Dean T., Jia Wang, Kenneth Hill, and Juan-Luis Londono. 1996. “Income, Mortality and Fertility in Latin America: Country-Level Performance, 1960 - 90.”&amp;amp;nbsp;&#039;&#039;Analisis Economico&#039;&#039;11(2): 219-261.&lt;br /&gt;
&lt;br /&gt;
Kelly, Christopher, Nora Pashayan, Sreetharan Munisamy, and Joshn W. Powles. 2009.&amp;amp;nbsp; “Mortality attributable to excess adiposity in England and Wales in 2003 and 2015: explorations with a spreadsheet implementation of the Comparative Risk Assessment mentodology.”&amp;amp;nbsp;&#039;&#039;Population Health Metrics&#039;&#039;&amp;amp;nbsp;7(11): 1-7.&lt;br /&gt;
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Lopez, Alan D., Neil E. Collishaw, and Tapani Piha. 1994. “A descriptive model of the cigarette epidemic in developed countries.”&amp;amp;nbsp;&#039;&#039;Tobacco Control&#039;&#039;&amp;amp;nbsp;3(3): 242-247. &amp;amp;nbsp;&lt;br /&gt;
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Mathers, Colin D., and Dejan Loncar. 2005. &amp;quot;Updated Projections of Global Mortality and Burden of Disease, 2002-2030: Data Sources, Methods and Results.&amp;quot; Evidence and Information for Policy Working Paper. World Health Organization, Geneva.&lt;br /&gt;
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Mathers, Colin D., and Dejan Loncar. 2006. &amp;quot;Projections of Global Mortality and Burden of Disease from 2002 to 2030.&amp;quot;&amp;amp;nbsp;&#039;&#039;PLoS Medicine&#039;&#039;&amp;amp;nbsp;3(11): e442, 2011-2030.&amp;amp;nbsp; Retrieved 13 March 2009. doi:10.1371/journal.pmed.0030442.&lt;br /&gt;
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Mathers, Colin D., and Dejan Loncar. 2006b. “New projections of global mortality and burden of disease from 2002 to 2030.” Protocol S1. Technical Appendix to Mathers and Loncar 2006.&amp;amp;nbsp;&lt;br /&gt;
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Mathers, Colin D., and Dejan Loncar. 2006c. “Results of Regressions of Age–Sex-Specific Mortality for Detailed Causes on the Respective Cause Cluster Based on the Full Country Panel Dataset, 1950–2002.” Technical Appendix to Mathers and Loncar 2006.&amp;amp;nbsp;&lt;br /&gt;
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Nixon, John, and Philippe Ulmann. 2006. “The Relationship Between Health Care Expenditure and Health Outcomes: Evidence and caveats for a Causal Link.”&amp;amp;nbsp;&#039;&#039;European Journal of Health Economics&#039;&#039;&amp;amp;nbsp;7: 7-18.&lt;br /&gt;
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Peto, Richard, Jillian Boreham, Alan D. Lopez, Michael Thun, and Clark Heath, Jr. 1992. “Mortality from Tobacco in Developed Countries: Indirect Estimation from National Vital Statistics.”&amp;amp;nbsp;&#039;&#039;Lancet&amp;amp;nbsp;&#039;&#039;339(8804): 1268–1278. doi:10.1016/0140- 6736(92)91600-D.&lt;br /&gt;
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Ploeg, Martine, Katja K. H. Aben, and Lambertus A. Kiemeney. 2009. “The Present and Future Burden of Urinary Bladder Cancer in the World.”&amp;amp;nbsp;&#039;&#039;World Journal of Urology&#039;&#039;&amp;amp;nbsp;27(3): 289-293. doi:[http://dx.doi.org/10.1007/s00345-009-0383-3 &amp;amp;nbsp;10.1007/s00345-009-0383-3&amp;amp;nbsp;]. &amp;amp;nbsp;&lt;br /&gt;
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Shibuya, Kenji, Mie Inoue, and Alan D. Lopez. 2005. “Statistical Modeling and Projections of Lung Cancer Mortality in 4 Industrialized Countries.”&amp;amp;nbsp;&#039;&#039;International Journal of Cancer&#039;&#039;&amp;amp;nbsp;117(3): 476-485. doi:[http://dx.doi.org/10.1002/ijc.21078 &amp;amp;nbsp;10.1002/ijc.21078&amp;amp;nbsp;]. &amp;amp;nbsp;&lt;br /&gt;
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Smeed, RJ 1949. &amp;quot;Some statistical aspects of road safety research&amp;quot;.&amp;amp;nbsp;[http://en.wikipedia.org/wiki/Royal_Statistical_Society &#039;&#039;Royal Statistical Society&#039;&#039;], Journal (A) CXII (Part I, series 4). 1-24.&lt;br /&gt;
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Smith, Lisa C. and Lawrence Haddad. 2000. “Explaining Child Malnutrition in Developing Countries: A Cross-Sectional Analysis.” Washington, D.C.: International Food Policy Research Institute.&lt;br /&gt;
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Soares, Rodrigo R. 2007. “On the Determinants of Mortality Reductions in the Developing World.”&amp;amp;nbsp;&#039;&#039;Population and Development Review&amp;amp;nbsp;&#039;&#039;33(2): 247-287.&lt;br /&gt;
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United Nations Population Division. 2003.&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;World Population Prospects: The 2002 Revision, Highlight.&#039;&#039;&amp;amp;nbsp; New York:&amp;amp;nbsp; United Nations. Department of Economics and Social Affairs.&lt;br /&gt;
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United Nations Population Division. 2009.&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;&amp;amp;nbsp;&#039;&#039;World Population Prospects: The 2008 Revision, Highlights.&#039;&#039;&amp;amp;nbsp; New York:&amp;amp;nbsp; United Nations. Department of Economics and Social Affairs.&lt;br /&gt;
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Wagstaff, Adam. 2002. “Inequalities in Health in Developing Countries: Swimming Against the Tide?” Unpublished Manuscript&lt;br /&gt;
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== &amp;lt;span style=&amp;quot;font-size:x-large;&amp;quot;&amp;gt;Infrastructure Bibliography&amp;lt;/span&amp;gt; ==&lt;br /&gt;
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Agénor, Pierre-Richard, Mustapha Kamel Nabli, and Tarik M. Yousef. 2007. “Public Infrastructure and Private Investment in the Middle East and North Africa.” In Mustapha Kamel Nabli, ed.,. Breaking the Barriers to Higher Economic Growth: Better Governance and Deeper Reforms in the Middle East and North Africa. Washington, DC: World Bank Publications, 399–422.&lt;br /&gt;
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Asian Development Bank, Japan Bank for International Cooperation, and World Bank. 2005.&amp;amp;nbsp;&#039;&#039;Connecting East Asia: A New Framework for Infrastructure&#039;&#039;. Tokyo: Asian Development Bank, Japan Bank for International Cooperation, and World Bank.&amp;amp;nbsp;[http://siteresources.worldbank.org/INTEASTASIAPACIFIC/Resources/Connecting-East-Asia.pdf http://siteresources.worldbank.org/INTEASTASIAPACIFIC/Resources/Connecting-East-Asia.pdf].&lt;br /&gt;
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Bhattacharyay, Biswa Nath. 2010. “Estimating Demand for Infrastructure in Energy, Transport, Telecommunications, Water and Sanitation in Asia and the Pacific: 2010-2020”. Working Paper no. 248. Asian Development Bank Institute, Tokyo.&amp;amp;nbsp;[http://www.adbi.org/working-paper/2010/09/09/4062.infrastructure.demand.asia.pacific/ http://www.adbi.org/working-paper/2010/09/09/4062.infrastructure.demand.asia.pacific/].&lt;br /&gt;
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Bruinsma, Jelle. 2011. “The Resources Outlook: By How Much Do Land, Water and Crop Yields Need to Increase by 2050?” In Piero Conforti, ed.,.&amp;amp;nbsp;&#039;&#039;Looking Ahead in World Food and Agriculture: Perspectives to 2050&#039;&#039;. Rome: Food and Agriculture Organization of the United Nations (FAO), 233–275.&amp;amp;nbsp;[http://www.fao.org/docrep/014/i2280e/i2280e.pdf http://www.fao.org/docrep/014/i2280e/i2280e.pdf].&lt;br /&gt;
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Calderón, César, and Luis Servén. 2010a. “Infrastructure and Economic Development in Sub-Saharan Africa.”&amp;amp;nbsp;&#039;&#039;Journal of African Economies&#039;&#039;&amp;amp;nbsp;19(Supplement 1): i13–i87. doi:10.1093/jae/ejp022.&amp;amp;nbsp;[http://jae.oxfordjournals.org/cgi/content/abstract/19/suppl_1/i13 http://jae.oxfordjournals.org/cgi/content/abstract/19/suppl_1/i13].&lt;br /&gt;
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Calderón, César, and Luis Servén. 2010b. “Infrastructure in Latin America”. World Bank Policy Research Working Paper. Report Number 5317. World Bank, Washington, DC.&lt;br /&gt;
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Canning, David. 1998. “A Database of World Stocks of Infrastructure, 1950-1995.”&amp;amp;nbsp;&#039;&#039;The World Bank Economic Review&#039;&#039;&amp;amp;nbsp;12(3): 529–548.&lt;br /&gt;
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Canning, David, and Mansour Farahani. 2007. “A Database of World Stocks of Infrastructure: Update 1950-2005”. Harvard School of Public Health, Boston, MA.&amp;amp;nbsp;[http://www.hsph.harvard.edu/faculty/david-canning/data-sets/ http://www.hsph.harvard.edu/faculty/david-canning/data-sets/].&lt;br /&gt;
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Cavallo, Eduardo Alfredo, and Christian Daude. 2008. “Public Investment in Developing Countries: A Blessing or a Curse?” RES Working Paper #4597. Inter-American Development Bank (IADB) - Research Department, OECD, Washington, DC.&lt;br /&gt;
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Chatterton, Isabe, and Olga S. Puerto. 2006.&amp;amp;nbsp;&#039;&#039;Estimation of Infrastructure Investment Needs in the South Asia Region: Executive Summary&#039;&#039;. Washington, DC: World Bank.&amp;amp;nbsp;[http://siteresources.worldbank.org/INTSARREGTOPTRANSPORT/Resources/Inf_Investment_Needs_IC_version4.pdf http://siteresources.worldbank.org/INTSARREGTOPTRANSPORT/Resources/Inf_Investment_Needs_IC_version4.pdf].&lt;br /&gt;
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Congressional Budget Office. 2010.&amp;amp;nbsp;&#039;&#039;Public Spending on Transportation and Water Infrastructure&#039;&#039;. Washington, DC: Congressional Budget Office.&amp;amp;nbsp;[http://www.cbo.gov/publication/21902 http://www.cbo.gov/publication/21902].&lt;br /&gt;
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Estache, Antonio, and Ana Goicoechea. 2005. “A Research Database on Infrastructure Economic Performance”. Policy Research Working Paper no. 3643. World Bank, Washington, DC.&amp;amp;nbsp;[http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2005/06/16/000016406_20050616100502/Rendered/PDF/wps3643.pdf http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2005/06/16/000016406_20050616100502/Rendered/PDF/wps3643.pdf].&lt;br /&gt;
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Ezzati, Majid, Alan D. Lopez, Anthony Rodgers, and Christopher J. L. Murray, eds. 2004.&amp;amp;nbsp;&#039;&#039;Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors&#039;&#039;. Geneva, Switzerland: World Health Organization (WHO).&lt;br /&gt;
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Fay, Marianne. 2001. “Financing the Future: Infrastructure Needs in Latin America, 2000-05”. Policy Research Working Paper no. 2545. World Bank, Washington, DC.&amp;amp;nbsp;[http://elibrary.worldbank.org/docserver/download/2545.pdf?expires=1375200693&amp;amp;id=id&amp;amp;accname=guest&amp;amp;checksum=DB7E5FB146EE28C93511B5ADAB2FD3CB http://elibrary.worldbank.org/docserver/download/2545.pdf?expires=1375200693&amp;amp;id=id&amp;amp;accname=guest&amp;amp;checksum=DB7E5FB146EE28C93511B5ADAB2FD3CB].&lt;br /&gt;
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Fay, Marianne, and Tito Yepes. 2003. “Investing in Infrastructure: What Is Needed from 2000 to 2010?” Policy Research Working Paper no. 3102. World Bank, Washington, DC. RePEc.&amp;amp;nbsp;[http://ideas.repec.org/p/wbk/wbrwps/3102.html http://ideas.repec.org/p/wbk/wbrwps/3102.html].&lt;br /&gt;
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Hughes, Barry B. 2007. “Forecasting Global Economic Growth with Endogenous Multifactor Productivity: The International Futures (IFs) Approach”. Pardee Center for International Futures Working Paper, University of Denver. Denver, CO.&amp;amp;nbsp;[http://www.ifs.du.edu/documents/reports.aspx www.ifs.du.edu/documents/reports.aspx].&lt;br /&gt;
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Hughes, Barry B., Devin Joshi, Jonathan Moyer, Timothy Sisk and José Roberto Solórzano. 2014. Strengthening Governance Globally. vol. 5, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.&lt;br /&gt;
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Hughes, Gordon, Paul Chinowsky, and Ken Strzepek. 2009. “The Costs of Adapting to Climate Change for Infrastructure”. Economics of Adaptation to Climate Change Discussion Paper no. 2. World Bank, Washington, DC.&lt;br /&gt;
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International Transport Forum, and Organisation for Economic Cooperation and Development (OECD). 2011. “Trends in Transport Infrastructure Investment 1995-2009”. Paris.&lt;br /&gt;
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Kohli, Harpaul Alberto, and Phillip Basil. 2011. “Requirements for Infrastructure Investment in Latin America Under Alternate Growth Scenarios.”&amp;amp;nbsp;&#039;&#039;Global Journal of Emerging Market Economies&#039;&#039;&amp;amp;nbsp;3(1): 59 –110. doi:10.1177/097491011000300103.&amp;amp;nbsp;[http://eme.sagepub.com/content/3/1/59.abstract http://eme.sagepub.com/content/3/1/59.abstract].&lt;br /&gt;
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Kim, M. Julie, and Rita Nangia. 2010. “Infrastructure Development in India and China—A Comparative Analysis.” In William Ascher and Corinne Krupp, eds.,.&amp;amp;nbsp;&#039;&#039;Physical Infrastructure Development: Balancing The Growth, Equity, and Environmental Imperatives&#039;&#039;. New York, NY: Palgrave Macmillan, 97–140.&lt;br /&gt;
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Lora, Eduardo A. 2007.&amp;amp;nbsp;&#039;&#039;Public Investment in Infrastructure in Latin America: Is Debt the Culprit?&#039;&#039;&amp;amp;nbsp;Inter-American Development Bank Working Paper. Washington, DC: Inter-American Development Bank (IADB) - Research Department.&lt;br /&gt;
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Nelson, Gerald C., Mark W. Rosegrant, Amanda Palazzo, Ian Gray, Christina Ingersoll, Richard Robertson, Simla Tokgoz, Tingju Zhu, Timothy B. Sulser, Claudia Ringler, Siwa Msangi, and Liangzhi You. 2010.&amp;amp;nbsp;&#039;&#039;Food Security, Farming, and Climate Change to 2050: Scenarios, Results, Policy Options&#039;&#039;. Washington, DC: International Food Policy Research Institute.&amp;amp;nbsp;[http://www.ifpri.org/publication/food-security-farming-and-climate-change-2050 http://www.ifpri.org/publication/food-security-farming-and-climate-change-2050].&lt;br /&gt;
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Organisation for Economic Co-operation and Development. 2006.&amp;amp;nbsp;&#039;&#039;Infrastructure to 2030 Volume 1: Telecom, Land Transport, Water and Electricity&#039;&#039;. Infrastructure to 2030. Paris: Organisation for Economic Cooperation and Development.&lt;br /&gt;
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Organisation for Economic Co-operation and Development. 2009.&amp;amp;nbsp;&#039;&#039;Going for Growth: Economic Policy Reforms&#039;&#039;. Paris: Organisation for Economic Cooperation and Development (OECD).&lt;br /&gt;
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Qiang, Christine Zhen-Wei, Carlo M. Rossotto, and Kaoru Kimura. 2009. “Economic Impacts of Broadband.” In World Bank, ed.,.&amp;amp;nbsp;&#039;&#039;2009 Information and Communications for Development: Extending Reach and Increasing Impact&#039;&#039;. Washington, DC: World Bank, 35–50.&lt;br /&gt;
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Rothman, Dale S. Mohammod T. Irfan, Eli Margolese-Malin, Barry B. Hughes, Jonathan Moyer, and Janet Dickson. 2013.&amp;amp;nbsp;&#039;&#039;Building Global Infrastructure.&amp;amp;nbsp;&#039;&#039;vol. 4, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press. Stambrook, David. 2006. “Key Factors Driving the Future Demand for Surface Transport Infrastructure and Services.” In Organisation for Economic Cooperation and Development (OECD), ed.,.&amp;amp;nbsp;&#039;&#039;Infrastructure to 2030 Volume 1: Telecom, Land Transport, Water and Electricity&#039;&#039;. Infrastructure to 2030. Paris: Organisation for Economic Cooperation and Development (OECD), 185–239.&lt;br /&gt;
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World Health Organization, and UNICEF. 2013.&amp;amp;nbsp;&#039;&#039;Progress on Sanitation and Drinking-Water - 2013 Update&#039;&#039;. Geneva: World Health Organization.&lt;br /&gt;
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Yepes, Tito. 2008. “Investment Needs for Infrastructure in Developing Countries 2008-15”. Draft. World Bank, Washington, DC.&lt;br /&gt;
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Yepes, Tito. 2005.&amp;amp;nbsp;&#039;&#039;Expenditure on Infrastructure in East Asia Region, 2006–2010&#039;&#039;. East Asia Pacific Infrastructure Flagship Study. Manila: Asian Development Bank (ADB), Japan Bank for International Cooperation (JBIC), World Bank.&lt;br /&gt;
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You, Liangzhi, Claudia Ringler, Ulrike Wood-Sichra, Richard Robertson, Stanley Wood, Tingju Zhu, Gerald Nelson, Zhe Guo, and Yan Sun. 2011. “What Is the Irrigation Potential for Africa? A Combined Biophysical and Socioeconomic Approach.”&amp;amp;nbsp;&#039;&#039;Food Policy&#039;&#039;&amp;amp;nbsp;36(6): 770–782. doi:10.1016/j.foodpol.2011.09.001.&amp;amp;nbsp;[http://www.sciencedirect.com/science/article/pii/S030691921100114X http://www.sciencedirect.com/science/article/pii/S030691921100114X].&lt;br /&gt;
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== [[Development_Mode_Features|Development Mode Features]] ==&lt;/div&gt;</summary>
		<author><name>JessRettig</name></author>
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