https://pardeewiki.du.edu//api.php?action=feedcontributions&user=StellahKwasi&feedformat=atomPardee Wiki - User contributions [en]2024-03-29T13:39:48ZUser contributionsMediaWiki 1.37.4https://pardeewiki.du.edu//index.php?title=Sub-modules&diff=2180Sub-modules2017-03-06T20:56:38Z<p>StellahKwasi: </p>
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<div>[[Agriculture|Agriculture]]<br />
<br />
[[Population|Population]]<br />
<br />
[[Economics|Economics]]<br />
<br />
[[Education|Education]]<br />
<br />
[[Energy|Energy]]<br />
<br />
[[Environment|Environment]]<br />
<br />
[[Governance|Governance]]<br />
<br />
[[Health|Health]]<br />
<br />
[[Infrastructure|Infrastructure]]<br />
<br />
[[Interstate_Politics_(IP)|Interstate Politics (IP)]]<br />
<br />
[[Socio-Political|Socio-Political]]<br />
<br />
[[Transport|Transport]]</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Transport&diff=2179Transport2017-03-06T20:54:08Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete transportation model documentation is available on Pardee'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 Although the text in this interactive system is, for some IFs models, often significantly out of date, you may still find the basic description useful to you.<br />
<br />
<span>The transportation model may eventually consist of a module that represents total demand for transportation services in countries and regions and of additional modules that represent how individual societies meet individual and commercial transportation needs. At this stage, however, the transportation module focuses exclusively on annual sales of cars and trucks.</span><br />
<br />
To read more about the transportation module, see the links below.<br />
<br />
== Transportation Flow Charts ==<br />
<br />
=== Overview ===<br />
<br />
The transportation model may eventually consist of a module that represents total demand for transportation services in countries and regions and of additional modules that represent how individual societies meet individual and commercial transportation needs. At this stage, however, the transportation module focuses exclusively on annual sales of cars and trucks.<br />
<br />
We have developed two approaches to forecasting those sales (a total of four options for the user). GDP per capita at purchasing power parity is the primary driver of sales per capita in&nbsp;[http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars.html most of those options]&nbsp;(in one case total GDP drives total sales, see&nbsp;[http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars2.html Approach 2]). Values of the indices for survival/self-expression and traditional/secular-rational values may join GDP per capita or GDP in determining the annual sales of cars and trucks.<br />
<br />
For more, please read the links below.&nbsp;<br />
<br />
== Cars and Trucks ==<br />
<br />
As the equations elaborate, there are two general approaches and four specific options for determining the annual demand of c[[File:T1.gif|frame|right|Cars and Trucks]]ountries/regions for cars and trucks and therefore the annual new sales of those vehicles.<br />
<br />
GDP per capita at purchasing power parity is the primary driver of sales per capita in three of those options (in one case total GDP drives total sales, see [http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars2.html Approach 2]). Values of the indices for survival/self-expression and traditional/secular-rational values may join GDP per capita in determining the annual per capita sales of total cars and trucks.<br />
<br />
Given per capita sales and population it is easy to compute total annual car and truck sales. Functions that indicate whether individual types of cars or trucks are "inferior" or "superior" goods, that is whether then decrease or increase with incomes, then determine how total car and truck sales are split across individual categories of cars and trucks.<br />
<br />
The following flow is a general representation of the first approach (sales per capita determined by GDP per capita), and the specific formulation depends on the value of the vehicle function switch (vehfuncsw). If the value is 1, GDP/capita alone drives sales per capita. If the value is 3, GDP/capita and values on the traditional/secular-rational dimension drive sales per capita. If the value is 4, GDP/capita and values on the traditional/secular-rational and survival/self-expression dimensions drive sales per capita.<br />
<br />
Future development may focus not just on annual car and truck sales, but on the fleets of each. Variables for fleet size and per capita fleet size are therefore shown above, but not yet used.<br />
<br />
== Cars and Trucks: Approach 2 (Total) ==<br />
<br />
As the equations elaborate, there are two general approaches and four specific options for determining the annual demand of countries/regions for cars and trucks and therefore the annual new sales of those vehicles.<br />
<br />
GDP per capita at purchasing power parity is the primary driver of sales per capita in [http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars.html most of those options]. It is also possible, however, to have car/truck sales driven directly by GDP, which is the logic portrayed below. The specific logic/formulation depends on the value of the vehicle function switch (vehfuncsw). When the value is 2, sales are driven directly by GDP, as shown here. In addition, sales are affected by country/region values on the traditional/secular-rational value dimension. Although the model does not include the survival/self-expression dimension in this particular logic (estimated functions did not show it adding much predictive power), the causal diagram portrays it as a possible driver.<br />
<br />
Functions that indicate whether individual types of cars or trucks are "inferior" or "superior" goods, that is whether then decrease or increase with incomes, then determine how total car and truck sales are split across individual categories of cars and trucks.<br />
<br />
[[File:T2.gif|frame|center|T2.gif]] Future development may focus not just on annual car and truck sales, but on the fleets of each. Variables for fleet size and per capita fleet size are therefore shown above, but not yet used.<br />
<br />
== Transportation Equations ==<br />
<br />
=== Overview ===<br />
<br />
<span>Not available at this time.</span></div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Transport&diff=2178Transport2017-03-06T20:51:29Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete transportation model documentation is available on Pardee'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.<br />
<br />
<span>The transportation model may eventually consist of a module that represents total demand for transportation services in countries and regions and of additional modules that represent how individual societies meet individual and commercial transportation needs. At this stage, however, the transportation module focuses exclusively on annual sales of cars and trucks.</span><br />
<br />
To read more about the transportation module, see the links below.<br />
<br />
== Transportation Flow Charts ==<br />
<br />
=== Overview ===<br />
<br />
The transportation model may eventually consist of a module that represents total demand for transportation services in countries and regions and of additional modules that represent how individual societies meet individual and commercial transportation needs. At this stage, however, the transportation module focuses exclusively on annual sales of cars and trucks.<br />
<br />
We have developed two approaches to forecasting those sales (a total of four options for the user). GDP per capita at purchasing power parity is the primary driver of sales per capita in&nbsp;[http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars.html most of those options]&nbsp;(in one case total GDP drives total sales, see&nbsp;[http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars2.html Approach 2]). Values of the indices for survival/self-expression and traditional/secular-rational values may join GDP per capita or GDP in determining the annual sales of cars and trucks.<br />
<br />
For more, please read the links below.&nbsp;<br />
<br />
== Cars and Trucks ==<br />
<br />
As the equations elaborate, there are two general approaches and four specific options for determining the annual demand of c[[File:T1.gif|frame|right|T1.gif]]ountries/regions for cars and trucks and therefore the annual new sales of those vehicles.<br />
<br />
GDP per capita at purchasing power parity is the primary driver of sales per capita in three of those options (in one case total GDP drives total sales, see [http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars2.html Approach 2]). Values of the indices for survival/self-expression and traditional/secular-rational values may join GDP per capita in determining the annual per capita sales of total cars and trucks.<br />
<br />
Given per capita sales and population it is easy to compute total annual car and truck sales. Functions that indicate whether individual types of cars or trucks are "inferior" or "superior" goods, that is whether then decrease or increase with incomes, then determine how total car and truck sales are split across individual categories of cars and trucks.<br />
<br />
The following flow is a general representation of the first approach (sales per capita determined by GDP per capita), and the specific formulation depends on the value of the vehicle function switch (vehfuncsw). If the value is 1, GDP/capita alone drives sales per capita. If the value is 3, GDP/capita and values on the traditional/secular-rational dimension drive sales per capita. If the value is 4, GDP/capita and values on the traditional/secular-rational and survival/self-expression dimensions drive sales per capita.<br />
<br />
Future development may focus not just on annual car and truck sales, but on the fleets of each. Variables for fleet size and per capita fleet size are therefore shown above, but not yet used.<br />
<br />
== Cars and Trucks: Approach 2 (Total) ==<br />
<br />
As the equations elaborate, there are two general approaches and four specific options for determining the annual demand of countries/regions for cars and trucks and therefore the annual new sales of those vehicles.<br />
<br />
GDP per capita at purchasing power parity is the primary driver of sales per capita in [http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars.html most of those options]. It is also possible, however, to have car/truck sales driven directly by GDP, which is the logic portrayed below. The specific logic/formulation depends on the value of the vehicle function switch (vehfuncsw). When the value is 2, sales are driven directly by GDP, as shown here. In addition, sales are affected by country/region values on the traditional/secular-rational value dimension. Although the model does not include the survival/self-expression dimension in this particular logic (estimated functions did not show it adding much predictive power), the causal diagram portrays it as a possible driver.<br />
<br />
Functions that indicate whether individual types of cars or trucks are "inferior" or "superior" goods, that is whether then decrease or increase with incomes, then determine how total car and truck sales are split across individual categories of cars and trucks.<br />
<br />
[[File:T2.gif|frame|center|T2.gif]] Future development may focus not just on annual car and truck sales, but on the fleets of each. Variables for fleet size and per capita fleet size are therefore shown above, but not yet used.<br />
<br />
== Transportation Equations ==<br />
<br />
=== Overview ===<br />
<br />
<span>Not available at this time.</span></div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=File:Pardee_image.jpg&diff=2174File:Pardee image.jpg2017-03-06T20:38:10Z<p>StellahKwasi: </p>
<hr />
<div></div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Sub-modules&diff=2172Sub-modules2017-03-06T20:30:20Z<p>StellahKwasi: </p>
<hr />
<div>[[Agriculture|Agriculture]]<br />
<br />
[[Population|Population]]<br />
<br />
[[Economics|Economics]]<br />
<br />
[[Education|Education]]<br />
<br />
[[Energy|Energy]]<br />
<br />
[[Environment|Environment]]<br />
<br />
[[Governance|Governance]]<br />
<br />
[[Health|Health]]<br />
<br />
[[Infrastructure|Infrastructure]]<br />
<br />
[[Interstate_Politics_(IP)|Interstate Politics (IP)]]<br />
<br />
[[Socio-Political|Socio-Political]]<br />
<br />
[[Transport|Transport]]<br />
<br />
[[Stellah]]</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Education&diff=2171Education2017-03-05T02:57:58Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete education model documentation is available on Pardee'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.<br />
<br />
=== Overview ===<br />
<br />
<span>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).&nbsp; Its purpose is to serve as a generalized thinking and analysis tool for educational futures within a broader human development context.&nbsp;</span><br />
<br />
<span>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.</span><br />
<br />
<span>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'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.&nbsp;</span><br />
<br />
<span>In the figure below we display the major variables and components that directly determine education demand, supply, and flows in the IFs system.&nbsp; We emphasize again the inter-connectedness of the components and their relationship to the broader human development system.&nbsp; For example, during each year of simulation, the IFs cohort-specific demographic model provides the school age population to the education model.&nbsp; In turn, the education model feeds its calculations of education attainment to the population model’s determination of women’s fertility.&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.</span><br />
<br />
<span>[[File:EduOverview.png|frame|center|EduOverview.png]]</span><br />
<br />
== Structure and Agent System: Education ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>National Education System</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Various Levels of Education; Age Cohorts</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Educational Attainment; Enrollment</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Intake; Graduation; Transition; Spending</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Demand for and achievement in education changes with income, societal change</div><div>&nbsp;</div><div>Public spending available for education rises with income level</div><div>&nbsp;</div><div>Cost of schooling rises with income level</div><div>&nbsp;</div><div>Lack (surplus) of public spending in education hurts (helps) educational access and progression</div><div>&nbsp;</div><div>More education helps economic growth and reduces fertility</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Families send children to school; Government revenue and expenditure in education</div><br />
|}<br />
<br />
<br />
<br />
=== Education Model Coverage ===<br />
<br />
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&nbsp; primary (ISCED level 1), lower secondary (ISCED level 2), upper secondary (ISCED level 3), and tertiary education (ISCED levels 5A, 5B and 6).<br />
<br />
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 [http://www.du.edu/ifs/help/understand/education/system.html#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.<br />
<br />
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.&nbsp;<br />
<br />
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.<br />
<br />
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 "Education Pyramid" or a display of educational attainments mapped over five year age cohorts as is usually done for population pyramids.<br />
<br />
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.<br />
<br />
IFs education model also covers [http://www.du.edu/ifs/help/understand/education/flowcharts/financialflow.html 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.<br />
<br />
=== What the Model Does Not Cover ===<br />
<br />
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.<br />
<br />
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.<br />
<br />
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.&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.<br />
<br />
The IFs education model does not cover private spending in education.<br />
<br />
=== Sources of Education Data ===<br />
<br />
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.<br />
<br />
We collected our [http://www.du.edu/ifs/help/understand/education/flowcharts/studentflow.html student flows] and per student cost data from UNESCO Institute for Statistics' (UIS) [http://stats.uis.unesco.org/unesco/tableviewer/document.aspx?ReportId=143 web data repository]. (Accessed on 05/17/2013)<br />
<br />
For [http://www.du.edu/ifs/help/understand/education/flowcharts/attainment.html educational attainment] data we use estimates by Robert Barro and Jong Wha Lee (2000). They &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.<br />
<br />
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.<br />
<br />
=== Reconciliation of Flow Rates ===<br />
<br />
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.&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).<br />
<br />
The IFs education model uses algorithms to reconcile incongruent flow values.&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.&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.&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.<br />
<br />
=== Variable Naming Convention ===<br />
<br />
All education model variable names start with a two-letter prefix of 'ED' 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.<br />
<br />
=== <span>Education: Dominant Relations</span> ===<br />
<br />
<span>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.</span><br />
<br />
On the financing side, the major dynamic is&nbsp; in the cost of education, e.g., cost per student in primary, EDEXPERPRI, the bulk of which is teachers' salary and which thus goes up with rising income.<br />
<br />
<span>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 -&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.</span><br />
<br />
For diagrams see: [http://www.du.edu/ifs/help/understand/education/flowcharts/studentflow.html Student Flow Charts]; [http://www.du.edu/ifs/help/understand/education/flowcharts/financialflow.html Budget Flow Charts]; [http://www.du.edu/ifs/help/understand/education/flowcharts/attainment.html Attainment Flow Charts]<br />
<br />
<span data-mce-mark="1">For equations see: </span> <span style="background-color: #ffff00" data-mce-mark="1"><span style="background-color: #ffffff" data-mce-mark="1">[http://www.du.edu/ifs/help/understand/education/equations/studentflow.html <span style="background-color: #ffffff" data-mce-mark="1">Student Flow Equations</span> ];&nbsp;[http://www.du.edu/ifs/help/understand/education/equations/budgetflow.html <span style="background-color: #ffffff" data-mce-mark="1">Budget Flow Equations</span> ] </span> </span>; [http://www.du.edu/ifs/help/understand/education/equations/attainment.html Attainment Equations]<br />
<br />
=== '''Key dynamics are directly linked to the dominant relations:''' ===<br />
<br />
*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.<br />
*Each year flow rates are used to update major stocks like enrollment, for the young, and attainment, for the adult.<br />
*Per student expenditure at all levels of education is a function of per capita income.<br />
*Deficit or surplus in public spending on education, GDS(Educ) affects intake, transition and survival rates at all levels of education.<br />
<br />
=== '''Education: Selected Added Value''' ===<br />
<br />
<span>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.</span><br />
<br />
<span>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.</span><br />
<br />
<span>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.</span><br />
<br />
== Education Flow Charts ==<br />
<br />
=== Overview ===<br />
<br />
For each country, the IFs education model represents a multilevel formal education system that starts at primary and ends at tertiary.&nbsp;[http://www.du.edu/ifs/help/understand/education/flowcharts/studentflow.html 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.<br />
<br />
The model represents the dynamics in&nbsp;[http://www.du.edu/ifs/help/understand/education/flowcharts/financialflow.html education financing]&nbsp;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.<br />
<br />
School completion (or dropout) in the education model is carried forward as the&nbsp;[http://www.du.edu/ifs/help/understand/education/flowcharts/attainment.html educational attainment]&nbsp;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.<br />
<br />
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.&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.<br />
<br />
The education model has forward and backward linkages with other parts of the IFs model. During each year of simulation, the IFs cohort-specific&nbsp;[http://www.du.edu/ifs/help/understand/demography/system.html demographic model]&nbsp;provides the school age population to the education model.&nbsp; In turn, the education model feeds its calculations of education attainment to the population model’s determination of women’s fertility.&nbsp; Similarly, the broader economic and socio-political systems provide funding for education, and levels of educational attainment affect&nbsp;[http://www.du.edu/ifs/help/understand/economics/flowcharts/mfp.html economic productivity and growth], and therefore also education spending.&nbsp;<br />
<br />
The figure below shows the major variables and components that directly determine education demand, supply, and flows in the IFs system.&nbsp; The diagram attempts to emphasize on the inter-connectedness of the education model components and their relationship to the broader human development system.<br />
<br />
[[File:Overvieweducation flow.png|border|center|Overvieweducation flow.png]]<br />
<br />
== Education Student Flow ==<br />
<br />
=== Student Flow ===<br />
<br />
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 ''grade-to-grade flow rate'', 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 ''grade-to-grade flow rate''. 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.<br />
<br />
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.<br />
<br />
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.<br />
<br />
[[File:Educationstudentflow.png|center|Educationstudentflow.png]]<br />
<br />
== Education Financial Flow ==<br />
<br />
=== Financial Flow ===<br />
<br />
In addition to [http://www.du.edu/ifs/help/understand/education/flowcharts/studentflow.html 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.<br />
<br />
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 [http://www.du.edu/ifs/help/understand/education/flowcharts/studentflow.html student flows] section.&nbsp; 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.<br />
<br />
The funding needs are sent to the IFs [http://www.du.edu/ifs/help/understand/sociopolitical/system.html sociopolitical model]&nbsp;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 ('''edbudgon''') 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&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&D, defense and an "other" category, using a normalization algorithm.<br />
<br />
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.<br />
<br />
[[File:Edfinancialflows.png|center|Edfinancialflows.png]]<br />
<br />
== Education Attainment ==<br />
<br />
=== Attainment ===<br />
<br />
The algorithm for the tracking of education attainment is very straight-forward.&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.&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.&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&nbsp; (EDYRSAG15, EDYRSAG25) and as share of people 15+ with a certain level of education completed (EDPRIPER, EDSECPER, EDTERPER).<br />
<br />
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).<sup><span style="color: #990000">[1]</span>&nbsp;</sup>This leads, other things equal, to a modest underestimate of adult education attainment, growing with the length of the forecast horizon.&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.<br />
<br />
[[File:Edattainment.png|center|Edattainment.png]] <span style="color: #990000" data-mce-mark="1">1]</span>&nbsp;The multi-state demographic method developed and utilized by IIASA does include education-specific mortality rates. &lt;header&gt;&lt;hgroup&gt;<br />
<br />
== Education Equations ==<br />
<br />
=== <span data-mce-mark="1">Overview</span> ===<br />
<br />
<span data-mce-mark="1">The IFs education model represent two types of educational stocks, [http://www.du.edu/ifs/help/understand/education/equations/studentflow.html stocks of pupils]&nbsp;</span> <span data-mce-mark="1">and stocks of adults with a certain level of [http://www.du.edu/ifs/help/understand/education/equations/attainment.html educational attainment] </span> <span data-mce-mark="1">. </span> <span data-mce-mark="1">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.</span><br />
<br />
<span data-mce-mark="1">The core dynamics of the model is in these [http://www.du.edu/ifs/help/understand/education/equations/studentflow.html flow rates] </span> <span data-mce-mark="1">. These&nbsp;</span> <span data-mce-mark="1">flow</span> <span data-mce-mark="1">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 [http://www.du.edu/ifs/help/understand/education/equations/budgetflow.html budget impact]&nbsp;</span> <span data-mce-mark="1">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.</span><br />
<br />
<span data-mce-mark="1">Intake (or transition), survival, enrollment and completion are some of the rates that IFs model forecast. Rate forecasts [http://www.du.edu/ifs/help/understand/education/system.html cover]&nbsp;elementary</span> <span data-mce-mark="1">, 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.</span><br />
<br />
<span data-mce-mark="1">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 [http://www.du.edu/ifs/help/understand/sociopolitical/system.html socio-political module] </span> <span data-mce-mark="1">is&nbsp;</span> <span data-mce-mark="1">sent back to</span> <span data-mce-mark="1">education model to calculate final enrollments and cost per student as a result of fund shortage or surplus.</span><br />
<br />
<span data-mce-mark="1">The population module provides cohort population to the education model. The [http://www.du.edu/ifs/help/understand/economics/dominant.html economic model] provides&nbsp;</span> <span data-mce-mark="1">per capita income and the socio-political model provides budget allocation. Educational attainment of adults calculated by the education module affects [http://www.du.edu/ifs/help/understand/demography/flowcharts/fertility.html fertility] and [http://www.du.edu/ifs/help/understand/demography/flowcharts/mortality.html mortality] in the [http://www.du.edu/ifs/help/understand/demography/system.html population] and&nbsp;</span> <span data-mce-mark="1">[http://www.du.edu/ifs/help/understand/health/system.html health] modules, affects productivity in the economic module and affects other socio-political outcomes like [http://www.du.edu/ifs/help/understand/governance/flowcharts/inclusiveness.html governance and democracy] levels</span> <span data-mce-mark="1">.</span><br />
<br />
== Equations: Student Flow ==<br />
<br />
=== Econometric Models for Core Inflow and Outflow ===<br />
<br />
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 [http://www.du.edu/ifs/help/understand/education/flowcharts/studentflow.html 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's reciprocal, survival (EDPRISUR) - are some of these rates that are forecast by the model.<br />
<br />
The educational flow rates are best explained by per capita income that serves as a proxy for the families' 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.[[File:EdcrosssectionalGDP.png|right|EdcrosssectionalGDP.png]]<br />
<br />
The educational flow rates are best explained by per capita income that serves as a proxy for the families' 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 figure shows such a regression plot for net intake rate in elementary against per capita income in PPP dollars.<br />
<p style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation1.png http://www.du.edu/ifs/help/media/images/edequation1.png]</p><br />
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 "shift factor" 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.<br />
<div>[http://www.du.edu/ifs/help/media/images/edequation2.png http://www.du.edu/ifs/help/media/images/edequation2.png]</div><p style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation3.png http://www.du.edu/ifs/help/media/images/edequation3.png]</p><br />
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 [http://www.du.edu/ifs/help/understand/education/equations/budgetflow.html budget flow section].<br />
<br />
=== Systemic Shift ===<br />
<br />
Access and participation in education increases with socio-economic developments that bring changes to people'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 a[[File:EdGDPnetintake.png|border|right|EdGDPnetintake.png]]nd 2000.<br />
<br />
IFs education model introduces an algorithm to represent this shift in the regression functions. This "systemic shift" 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., &nbsp;&nbsp;to the other, s, &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.<br />
<p style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation7.png http://www.du.edu/ifs/help/media/images/edequation7.png]</p><p style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation8.png http://www.du.edu/ifs/help/media/images/edequation8.png]</p><p style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation9.png http://www.du.edu/ifs/help/media/images/edequation9.png]</p><br />
[http://www.du.edu/ifs/help/media/images/edequation10.png http://www.du.edu/ifs/help/media/images/edequation10.png]<br />
<br />
As said earlier, [http://www.du.edu/ifs/help/understand/education/flowcharts/studentflow.html 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 [http://www.du.edu/ifs/help/understand/demography/system.html population module]) to forecast enrollment totals.<br />
<br />
=== Grade Flow Algorithm ===<br />
<div><br />
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 ('''EDPRILEN&nbsp;'''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).<br />
<br />
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.&nbsp; Moreover, by setting first grade enrollments to school entrants, we exclude repeating students from the first grade total.&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.&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.&nbsp;<br />
</div><p style="text-align: center" align="left">[http://www.du.edu/ifs/help/media/images/edequation11.png http://www.du.edu/ifs/help/media/images/edequation11.png]</p><p style="text-align: center" align="left">[http://www.du.edu/ifs/help/media/images/edequation12.png http://www.du.edu/ifs/help/media/images/edequation12.png]</p><p style="text-align: center" align="left">[http://www.du.edu/ifs/help/media/images/edequation13.png http://www.du.edu/ifs/help/media/images/edequation13.png]</p><br />
[http://www.du.edu/ifs/help/media/images/edequation14.png http://www.du.edu/ifs/help/media/images/edequation14.png]<br />
<br />
=== Gross and Net ===<br />
<br />
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).<br />
<p style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation15.png http://www.du.edu/ifs/help/media/images/edequation15.png]</p><p style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation16.png http://www.du.edu/ifs/help/media/images/edequation16.png]</p><p style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation17.png http://www.du.edu/ifs/help/media/images/edequation17.png]</p><p style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation18.png http://www.du.edu/ifs/help/media/images/edequation18.png]</p><p style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation19.png http://www.du.edu/ifs/help/media/images/edequation19.png]</p><br />
[http://www.du.edu/ifs/help/media/images/edequation20.png http://www.du.edu/ifs/help/media/images/edequation20.png]<br />
<br />
&nbsp;<br />
<br />
=== Vocational Education ===<br />
<br />
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.&nbsp;<br />
<br />
IFs also provides two scenario intervention parameters for lower (''edseclowrvocadd) ''and upper secondary (''edsecupprvocadd'') 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).<br />
<br />
[http://www.du.edu/ifs/help/media/images/edequation48.PNG http://www.du.edu/ifs/help/media/images/edequation48.PNG]<br />
<br />
Forecasts of ''EdSecTot_Gen<sub>g,r,t</sub> ''&nbsp;is obtained in the full lower secondary model using transition rates from primary to lower secondary and survival rates of lower secondary.<br />
<br />
=== Science and Engineering Graduates in Tertiary ===<br />
<br />
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 (''edterscienshradd''), 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).<br />
<br />
[http://www.du.edu/ifs/help/media/images/edequation50.PNG http://www.du.edu/ifs/help/media/images/edequation50.PNG]<br />
<br />
== Equations: Budget Flow ==<br />
<br />
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.<br />
<br />
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 ('''edexppconv''') 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).<br />
<div style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation21.png http://www.du.edu/ifs/help/media/images/edequation21.png]</div><div style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation22.png http://www.du.edu/ifs/help/media/images/edequation22.png]</div><div>[http://www.du.edu/ifs/help/media/images/edequation23.png http://www.du.edu/ifs/help/media/images/edequation23.png]</div><div>&nbsp;</div><br />
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.&nbsp;<br />
<br />
[http://www.du.edu/ifs/help/media/images/edequation24.png http://www.du.edu/ifs/help/media/images/edequation24.png]<br />
<br />
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.<br />
<div style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation25.png http://www.du.edu/ifs/help/media/images/edequation25.png]</div><div style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation26.png http://www.du.edu/ifs/help/media/images/edequation26.png]</div><div>[http://www.du.edu/ifs/help/media/images/edequation27.png http://www.du.edu/ifs/help/media/images/edequation27.png]</div><div>&nbsp;</div><br />
[http://www.du.edu/ifs/help/understand/sociopolitical/equations/policygov.html Sociopolitical model]&nbsp;also forecast public spending in other areas of social spending, i.e., military, health, R&D. Another public spending sector, [http://www.du.edu/ifs/help/understand/infrastructure/flowcharts/determining.html infrastructure]&nbsp;is calculated bottom-up, i.e., as an aggregation of demand for construction and maintenance of various types of infrastructure.<br />
<br />
Once all the spending shares are projected, a normalization algorithm is used to distribute the total available government consumption budget (GOVCON) among all sectors.<br />
<div style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation28.png http://www.du.edu/ifs/help/media/images/edequation28.png]</div><div>[http://www.du.edu/ifs/help/media/images/edequation29.png http://www.du.edu/ifs/help/media/images/edequation29.png]</div><div>&nbsp;</div><br />
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 ('''edbudgon''') is used to set aside a certain portion of the projected education investment as shown in the equations below.<br />
<div style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation30.png http://www.du.edu/ifs/help/media/images/edequation30.png]</div><div style="text-align: center">&nbsp;</div><div>[http://www.du.edu/ifs/help/media/images/edequation31.png http://www.du.edu/ifs/help/media/images/edequation31.png]</div><div>&nbsp;</div><br />
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.&nbsp;<br />
<p style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation32.png http://www.du.edu/ifs/help/media/images/edequation32.png]</p><br />
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.<br />
<p style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation33.png http://www.du.edu/ifs/help/media/images/edequation33.png]</p><br />
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.&nbsp;&nbsp;<br />
<br />
A budget impact ratio &nbsp;is then calculated as the ratio of the fund demanded (CalcTotCost) and fund obtained (CalcTotSpend). This budget impact ratio (CalcBudgetImpact) &nbsp;increases or decreases the pre-budget (or demand side as we call it) projection of [http://www.du.edu/ifs/help/understand/education/equations/studentflow.html 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.<br />
<br />
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.<br />
<div style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation34.png http://www.du.edu/ifs/help/media/images/edequation34.png]</div><div>[http://www.du.edu/ifs/help/media/images/edequation35.png http://www.du.edu/ifs/help/media/images/edequation35.png]</div><div>&nbsp;</div><br />
<span>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.</span><br />
<div style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation36.png http://www.du.edu/ifs/help/media/images/edequation36.png]</div><div style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation37.png http://www.du.edu/ifs/help/media/images/edequation37.png]</div><div>[http://www.du.edu/ifs/help/media/images/edequation38.png http://www.du.edu/ifs/help/media/images/edequation38.png]<br/></div><br />
== Equations: Attainment ==<br />
<br />
<span>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.</span><br />
<br />
<span>The basis of calculation for both groups of variables is educational attainment by age cohort and gender as contained in intermediate model variables, EDPriPopPer <sub>r.g,c,t</sub> ,&nbsp; EDSecPopPer<sub>r.g,c,t</sub>, EdTerPopPer<sub>r.g,c,t</sub> (where, r stands for country or region, g for gender, c for cohort and t for time).</span><br />
<br />
<span>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).</span><br />
<p style="text-align: center"><span>[http://www.du.edu/ifs/help/media/images/edequation39.png http://www.du.edu/ifs/help/media/images/edequation39.png]</span></p><br />
<span>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.</span><br />
<br />
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.&nbsp;<br />
<p style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation40.png http://www.du.edu/ifs/help/media/images/edequation40.png]</p><br />
<span>where, pc stands for the five year age cohort where the primary graduates belong. For all other cohorts:</span><br />
<p style="text-align: center"><span>[http://www.du.edu/ifs/help/media/images/edequation41.png http://www.du.edu/ifs/help/media/images/edequation41.png]</span></p><br />
<span>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&nbsp; &nbsp;AGEDST<sub>c,g,r,t</sub> contains the population of five year age cohorts and '''EDPRILEN''' <sub>r,t</sub> </span> &nbsp;<span>&nbsp;is the duration of primary cycle in years.&nbsp;</span><br />
<p style="text-align: center"><span>[http://www.du.edu/ifs/help/media/images/edequation42.png http://www.du.edu/ifs/help/media/images/edequation42.png]</span></p><br />
<span>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.</span><br />
<div style="text-align: center"><span>[http://www.du.edu/ifs/help/media/images/edequation43.png http://www.du.edu/ifs/help/media/images/edequation43.png]</span></div><div style="text-align: center">[http://www.du.edu/ifs/help/media/images/edequation44.png http://www.du.edu/ifs/help/media/images/edequation44.png]</div><div>[http://www.du.edu/ifs/help/media/images/edequation45.png http://www.du.edu/ifs/help/media/images/edequation45.png]</div><div>&nbsp;</div><br />
<span>Here, &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.&nbsp;</span><br />
<br />
<span>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.</span><br />
<p style="text-align: center"><span>[http://www.du.edu/ifs/help/media/images/edequation46.png http://www.du.edu/ifs/help/media/images/edequation46.png]</span></p><br />
<span>Attainments by level, i.e., EDPRIPER, EDSECPER and EDTERPER are also obtained by summing across the corresponding five year cohorts, i.e., EdPriPopPer etc.</span><br />
<p style="text-align: center"><span>[http://www.du.edu/ifs/help/media/images/edequation47.png http://www.du.edu/ifs/help/media/images/edequation47.png]</span></p><br />
<span>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.&nbsp;</span><br />
<br />
== Knowledge Systems ==<br />
<br />
=== Overview ===<br />
<br />
<span data-mce-mark="1">Knowledge and innovation are important drivers of &nbsp;economic growth and human well-being. These activities also &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.</span> [[File:Edknowledge1.png|border|right|Edknowledge1.png]] <span data-mce-mark="1">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.</span><br />
<br />
<span data-mce-mark="1"><span data-mce-mark="1">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).</span></span><br />
<br />
<span data-mce-mark="1">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</span><br />
<br />
== IFs Knowledge Indices ==<br />
<br />
<span>The capacity of a society to tap from and add to the pool of existing knowledge, local and global, depends on</span><br />
<br />
*skills and qualifications of people to assimilate existing and new knowledge,<br />
*an innovation system to facilitate development or adoption of of new knowledge, processes and products<br />
*a technological infrastructure to share, disseminate and regenerate knowledge and information within and across societies<br />
*political and institutional environment conducive to the generation, diffusion and utilization of knowledge<br />
*regulations that offer appropriate incentives towards and remove barriers from international transfer of knowledge<br />
<br />
<span>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 & non-tariff barriers, an indicator of international transfer, in their fourth pillar on economic and institutional environment.&nbsp;</span><br />
<br />
<span>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.&nbsp;</span><br />
<br />
{| class="tableGrid" style="width: 100%; border: 1px solid #cccccc" cellspacing="0" cellpadding="5"<br />
|-<br />
| style="text-align: center; padding-left: 5px; padding-right: 5px" | '''No.'''<br />
| style="text-align: center; padding-left: 5px; padding-right: 5px" | '''Dimension/Pillar'''<br />
| style="text-align: center; padding-left: 5px; padding-right: 5px" | '''World Bank Variables'''<br/><br />
| style="text-align: center; padding-left: 5px; padding-right: 5px" | '''IFs Index'''<br/><br />
| style="text-align: center; padding-left: 5px; padding-right: 5px" | '''IFs Variables'''<br />
|-<br />
| style="text-align: center; padding-left: 5px; padding-right: 5px" | 1<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | Human Capital<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | Adult literacy rate; Secondary enrollment rate; Tertiary enrollment rate<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | KNHCINDEX<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | Adult literacy rate; Adult secondary graduation rate<br />
|-<br />
| style="text-align: center; padding-left: 5px; padding-right: 5px" | 2<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | Innovation<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | R&D researchers, Patent count; Journal articles (all per million people)<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | KNINNOVINDEX<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | Total R&D expenditure (% of GDP); Tertiary graduation rate in science and engineering<br />
|-<br />
| style="text-align: center; padding-left: 5px; padding-right: 5px" | 3<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | ICT<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | Telephones (land + mobile) per 1000 persons; Computers per 1000 persons; Internet users per 10000 persons<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | KNICTINDEX<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | Telephone (fixed); Mobile phone; Personal Computers; Broadband<br />
|-<br />
| style="text-align: center; padding-left: 5px; padding-right: 5px" | 4<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | Economic and Institutional Regime<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" rowspan="2" | Tariff and non-tariff barriers; Regulatory quality; Rule of law<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | KNENVINDEX<br/><br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | Freedom; Economic freedom; Government regulation quality<br/><br />
|-<br />
| style="text-align: center; padding-left: 5px; padding-right: 5px" | 5<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | International Transfer of Knowledge<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | KNEXTINDEX<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | Economic integration index<br />
|-<br />
| style="text-align: center; padding-left: 5px; padding-right: 5px" | 6<br/><br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | Composite Index<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | Knowledge Index, KI (from the first three) and Knowledge Economy Index, KEI (from all 4)<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | KNTOTALINDEX<br />
| style="text-align: left; padding-left: 5px; padding-right: 5px" | <br />
From all of the above<br />
<br />
|}<br />
<br />
[[File:Edknowledge2.png|border|center|Edknowledge2.png]] &lt;header&gt;&lt;hgroup&gt;<br />
<br />
== Knowledge Systems Equations: Total Knowledge Index ==<br />
<br />
&lt;/hgroup&gt;&lt;/header&gt; <span>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.</span><br />
<br />
<span>[http://www.du.edu/ifs/help/media/images/edequation51.PNG http://www.du.edu/ifs/help/media/images/edequation51.PNG]</span><br />
<br />
== Knowledge Systems Equations: Knowledge Sub-Indices ==<br />
<br />
In this section we describe the calculation method for various IFs knowledge indices.&nbsp;<br />
<br />
=== Human capital Index: KNHCINDEX ===<br />
<br />
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.<br />
<br />
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.]<br />
<br />
[http://www.du.edu/ifs/help/media/images/edequation52.PNG http://www.du.edu/ifs/help/media/images/edequation52.PNG]<br />
<br />
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.&nbsp;<br />
<br />
[http://www.du.edu/ifs/help/media/images/edequation53.PNG?ox= http://www.du.edu/ifs/help/media/images/edequation53.PNG?ox=]<br />
<br />
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.&nbsp;<br />
<br />
[http://www.du.edu/ifs/help/media/images/edequation54.PNG http://www.du.edu/ifs/help/media/images/edequation54.PNG]<br />
<br />
LIT and EDSECPER are forecast in the IFs [http://www.du.edu/ifs/help/understand/demography/index.html population] and [http://www.du.edu/ifs/help/understand/education/index.html education] modules.<br />
<br />
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.<br />
<br />
[[File:Edknowledge3.png|border|center|Edknowledge3.png]]<br />
<br />
=== Innovation Index: KNINNOVINDEX ===<br />
<br />
This IFs knowledge sub-index measures the innovation capacity of a nation through its R&D inputs – resources and personnel. It comprises of a total R&D expenditure index and a tertiary science and engineering graduation index as shown in the equations below.<br />
<br />
[http://www.du.edu/ifs/help/media/images/edequation55.PNG http://www.du.edu/ifs/help/media/images/edequation55.PNG]<br />
<br />
For R&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.<br />
<br />
[http://www.du.edu/ifs/help/media/images/edequation56.PNG http://www.du.edu/ifs/help/media/images/edequation56.PNG]<br />
<br />
For science and engineering graduation rate, 25% is used as a maximum. The equations below show the calculation which uses tertiary graduation percentage, EDTERGRATE <sub>Total</sub> and the share of total graduates that obtain a science or engineering degree, EDTERGRSCIEN, both of which are forecast in the IFs education model.<br />
<div><br />
[http://www.du.edu/ifs/help/media/images/edequation57.PNG http://www.du.edu/ifs/help/media/images/edequation57.PNG]<br />
<br />
=== ICT Index: KNICTINDEX ===<br />
<br />
<span data-mce-mark="1">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.</span><br />
</div><br />
<span data-mce-mark="1">[http://www.du.edu/ifs/help/media/images/edequation58.PNG http://www.du.edu/ifs/help/media/images/edequation58.PNG]</span><br />
<br />
=== Governance and Regulatory Environment: KNENVINDEX ===<br />
<br />
<span data-mce-mark="1">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.</span><br />
<br />
<span data-mce-mark="1">For the variables economic freedom, political freedom and governance regulation quality and average them to KNENVINDEX.</span><br />
<div><br />
[http://www.du.edu/ifs/help/media/images/edequation59.PNG http://www.du.edu/ifs/help/media/images/edequation59.PNG]<br />
<br />
=== International Transfer Index: KNEXTINDEX ===<br />
<br />
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.<br />
<br />
[http://www.du.edu/ifs/help/media/images/edequation60.PNG http://www.du.edu/ifs/help/media/images/edequation60.PNG]<br />
<br />
'''Barro-Lee Education Data'''<br />
<br />
<br />
</div><br />
&nbsp;</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Transport&diff=2146Transport2017-02-26T21:39:22Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete transportation model documentation is available on Pardee'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.<br />
<br />
<span>The transportation model may eventually consist of a module that represents total demand for transportation services in countries and regions and of additional modules that represent how individual societies meet individual and commercial transportation needs. At this stage, however, the transportation module focuses exclusively on annual sales of cars and trucks.</span><br />
<br />
To read more about the transportation module, see the links below.<br />
<br />
== Transportation Flow Charts ==<br />
<br />
=== Overview ===<br />
<br />
The transportation model may eventually consist of a module that represents total demand for transportation services in countries and regions and of additional modules that represent how individual societies meet individual and commercial transportation needs. At this stage, however, the transportation module focuses exclusively on annual sales of cars and trucks.<br />
<br />
We have developed two approaches to forecasting those sales (a total of four options for the user). GDP per capita at purchasing power parity is the primary driver of sales per capita in&nbsp;[http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars.html most of those options]&nbsp;(in one case total GDP drives total sales, see&nbsp;[http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars2.html Approach 2]). Values of the indices for survival/self-expression and traditional/secular-rational values may join GDP per capita or GDP in determining the annual sales of cars and trucks.<br />
<br />
For more, please read the links below.&nbsp;<br />
<br />
== Cars and Trucks ==<br />
<br />
As the equations elaborate, there are two general approaches and four specific options for determining the annual demand of c[[File:T1.gif|frame|right|T1.gif]]ountries/regions for cars and trucks and therefore the annual new sales of those vehicles.<br />
<br />
GDP per capita at purchasing power parity is the primary driver of sales per capita in three of those options (in one case total GDP drives total sales, see [http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars2.html Approach 2]). Values of the indices for survival/self-expression and traditional/secular-rational values may join GDP per capita in determining the annual per capita sales of total cars and trucks.<br />
<br />
Given per capita sales and population it is easy to compute total annual car and truck sales. Functions that indicate whether individual types of cars or trucks are "inferior" or "superior" goods, that is whether then decrease or increase with incomes, then determine how total car and truck sales are split across individual categories of cars and trucks.<br />
<br />
The following flow is a general representation of the first approach (sales per capita determined by GDP per capita), and the specific formulation depends on the value of the vehicle function switch (vehfuncsw). If the value is 1, GDP/capita alone drives sales per capita. If the value is 3, GDP/capita and values on the traditional/secular-rational dimension drive sales per capita. If the value is 4, GDP/capita and values on the traditional/secular-rational and survival/self-expression dimensions drive sales per capita.<br />
<br />
Future development may focus not just on annual car and truck sales, but on the fleets of each. Variables for fleet size and per capita fleet size are therefore shown above, but not yet used.<br />
<br />
== Cars and Trucks: Approach 2 (Total) ==<br />
<br />
As the equations elaborate, there are two general approaches and four specific options for determining the annual demand of countries/regions for cars and trucks and therefore the annual new sales of those vehicles.<br />
<br />
GDP per capita at purchasing power parity is the primary driver of sales per capita in [http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars.html most of those options]. It is also possible, however, to have car/truck sales driven directly by GDP, which is the logic portrayed below. The specific logic/formulation depends on the value of the vehicle function switch (vehfuncsw). When the value is 2, sales are driven directly by GDP, as shown here. In addition, sales are affected by country/region values on the traditional/secular-rational value dimension. Although the model does not include the survival/self-expression dimension in this particular logic (estimated functions did not show it adding much predictive power), the causal diagram portrays it as a possible driver.<br />
<br />
Functions that indicate whether individual types of cars or trucks are "inferior" or "superior" goods, that is whether then decrease or increase with incomes, then determine how total car and truck sales are split across individual categories of cars and trucks.<br />
<br />
[[File:T2.gif|frame|center|T2.gif]] Future development may focus not just on annual car and truck sales, but on the fleets of each. Variables for fleet size and per capita fleet size are therefore shown above, but not yet used. <br />
<br />
== Transportation Equations ==<br />
<br />
=== Overview ===<br />
<br />
<span>Not available at this time.</span></div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Transport&diff=2145Transport2017-02-26T21:37:22Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete transportation model documentation is available on Pardee'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.<br />
<br />
<span>The transportation model may eventually consist of a module that represents total demand for transportation services in countries and regions and of additional modules that represent how individual societies meet individual and commercial transportation needs. At this stage, however, the transportation module focuses exclusively on annual sales of cars and trucks.</span><br />
<br />
To read more about the transportation module, see the links below.<br />
<br />
== Transportation Flow Charts ==<br />
<br />
=== Overview ===<br />
<br />
The transportation model may eventually consist of a module that represents total demand for transportation services in countries and regions and of additional modules that represent how individual societies meet individual and commercial transportation needs. At this stage, however, the transportation module focuses exclusively on annual sales of cars and trucks.<br />
<br />
We have developed two approaches to forecasting those sales (a total of four options for the user). GDP per capita at purchasing power parity is the primary driver of sales per capita in&nbsp;[http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars.html most of those options]&nbsp;(in one case total GDP drives total sales, see&nbsp;[http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars2.html Approach 2]). Values of the indices for survival/self-expression and traditional/secular-rational values may join GDP per capita or GDP in determining the annual sales of cars and trucks.<br />
<br />
For more, please read the links below.&nbsp;<br />
<br />
== Cars and Trucks ==<br />
<br />
As the equations elaborate, there are two general approaches and four specific options for determining the annual demand of c[[File:T1.gif|frame|right|T1.gif]]ountries/regions for cars and trucks and therefore the annual new sales of those vehicles.<br />
<br />
GDP per capita at purchasing power parity is the primary driver of sales per capita in three of those options (in one case total GDP drives total sales, see [http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars2.html Approach 2]). Values of the indices for survival/self-expression and traditional/secular-rational values may join GDP per capita in determining the annual per capita sales of total cars and trucks.<br />
<br />
Given per capita sales and population it is easy to compute total annual car and truck sales. Functions that indicate whether individual types of cars or trucks are "inferior" or "superior" goods, that is whether then decrease or increase with incomes, then determine how total car and truck sales are split across individual categories of cars and trucks.<br />
<br />
The following flow is a general representation of the first approach (sales per capita determined by GDP per capita), and the specific formulation depends on the value of the vehicle function switch (vehfuncsw). If the value is 1, GDP/capita alone drives sales per capita. If the value is 3, GDP/capita and values on the traditional/secular-rational dimension drive sales per capita. If the value is 4, GDP/capita and values on the traditional/secular-rational and survival/self-expression dimensions drive sales per capita.<br />
<br />
Future development may focus not just on annual car and truck sales, but on the fleets of each. Variables for fleet size and per capita fleet size are therefore shown above, but not yet used.<br />
<br />
== Cars and Trucks: Approach 2 (Total) ==<br />
<br />
As the equations elaborate, there are two general approaches and four specific options for determining the annual demand of countries/regions for cars and trucks and therefore the annual new sales of those vehicles.<br />
<br />
GDP per capita at purchasing power parity is the primary driver of sales per capita in [http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars.html most of those options]. It is also possible, however, to have car/truck sales driven directly by GDP, which is the logic portrayed below. The specific logic/formulation depends on the value of the vehicle function switch (vehfuncsw). When the value is 2, sales are driven directly by GDP, as shown here. In addition, sales are affected by country/region values on the traditional/secular-rational value dimension. Although the model does not include the survival/self-expression dimension in this particular logic (estimated functions did not show it adding much predictive power), the causal diagram portrays it as a possible driver.<br />
<br />
Functions that indicate whether individual types of cars or trucks are "inferior" or "superior" goods, that is whether then decrease or increase with incomes, then determine how total car and truck sales are split across individual categories of cars and trucks.<br />
<br />
[[File:T2.gif|frame|center]] Future development may focus not just on annual car and truck sales, but on the fleets of each. Variables for fleet size and per capita fleet size are therefore shown above, but not yet used. <br />
<header><hgroup><br />
== Transportation Equations ==<br />
</hgroup></header><br />
=== Overview ===<br />
<br />
<span>Not available at this time.</span></div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=File:T2.gif&diff=2144File:T2.gif2017-02-26T21:28:22Z<p>StellahKwasi: </p>
<hr />
<div></div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Transport&diff=2143Transport2017-02-26T21:27:04Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete transportation model documentation is available on Pardee'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.<br />
<br />
<span>The transportation model may eventually consist of a module that represents total demand for transportation services in countries and regions and of additional modules that represent how individual societies meet individual and commercial transportation needs. At this stage, however, the transportation module focuses exclusively on annual sales of cars and trucks.</span><br />
<br />
To read more about the transportation module, see the links below.<br />
<br />
== Transportation Flow Charts ==<br />
<br />
=== Overview ===<br />
<br />
The transportation model may eventually consist of a module that represents total demand for transportation services in countries and regions and of additional modules that represent how individual societies meet individual and commercial transportation needs. At this stage, however, the transportation module focuses exclusively on annual sales of cars and trucks.<br />
<br />
We have developed two approaches to forecasting those sales (a total of four options for the user). GDP per capita at purchasing power parity is the primary driver of sales per capita in&nbsp;[http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars.html most of those options]&nbsp;(in one case total GDP drives total sales, see&nbsp;[http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars2.html Approach 2]). Values of the indices for survival/self-expression and traditional/secular-rational values may join GDP per capita or GDP in determining the annual sales of cars and trucks.<br />
<br />
For more, please read the links below.&nbsp;<br />
<br />
== Cars and Trucks ==<br />
<br />
As the equations elaborate, there are two general approaches and four specific options for determining the annual demand of c[[File:T1.gif|frame|right|T1.gif]]ountries/regions for cars and trucks and therefore the annual new sales of those vehicles.<br />
<br />
GDP per capita at purchasing power parity is the primary driver of sales per capita in three of those options (in one case total GDP drives total sales, see [http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars2.html Approach 2]). Values of the indices for survival/self-expression and traditional/secular-rational values may join GDP per capita in determining the annual per capita sales of total cars and trucks.<br />
<br />
Given per capita sales and population it is easy to compute total annual car and truck sales. Functions that indicate whether individual types of cars or trucks are "inferior" or "superior" goods, that is whether then decrease or increase with incomes, then determine how total car and truck sales are split across individual categories of cars and trucks.<br />
<br />
The following flow is a general representation of the first approach (sales per capita determined by GDP per capita), and the specific formulation depends on the value of the vehicle function switch (vehfuncsw). If the value is 1, GDP/capita alone drives sales per capita. If the value is 3, GDP/capita and values on the traditional/secular-rational dimension drive sales per capita. If the value is 4, GDP/capita and values on the traditional/secular-rational and survival/self-expression dimensions drive sales per capita.<br />
<br />
Future development may focus not just on annual car and truck sales, but on the fleets of each. Variables for fleet size and per capita fleet size are therefore shown above, but not yet used. <br />
<br />
== Cars and Trucks: Approach 2 (Total) ==<br />
<br />
As the equations elaborate, there are two general approaches and four specific options for determining the annual demand of countries/regions for cars and trucks and therefore the annual new sales of those vehicles.<br />
<br />
GDP per capita at purchasing power parity is the primary driver of sales per capita in [http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars.html most of those options]. It is also possible, however, to have car/truck sales driven directly by GDP, which is the logic portrayed below. The specific logic/formulation depends on the value of the vehicle function switch (vehfuncsw). When the value is 2, sales are driven directly by GDP, as shown here. In addition, sales are affected by country/region values on the traditional/secular-rational value dimension. Although the model does not include the survival/self-expression dimension in this particular logic (estimated functions did not show it adding much predictive power), the causal diagram portrays it as a possible driver.<br />
<br />
Functions that indicate whether individual types of cars or trucks are "inferior" or "superior" goods, that is whether then decrease or increase with incomes, then determine how total car and truck sales are split across individual categories of cars and trucks.</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Transport&diff=2142Transport2017-02-26T21:18:47Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete transportation model documentation is available on Pardee'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.<br />
<br />
<span>The transportation model may eventually consist of a module that represents total demand for transportation services in countries and regions and of additional modules that represent how individual societies meet individual and commercial transportation needs. At this stage, however, the transportation module focuses exclusively on annual sales of cars and trucks.</span><br />
<br />
To read more about the transportation module, see the links below. <br />
<br />
== Transportation Flow Charts ==<br />
<br />
=== Overview ===<br />
<br />
The transportation model may eventually consist of a module that represents total demand for transportation services in countries and regions and of additional modules that represent how individual societies meet individual and commercial transportation needs. At this stage, however, the transportation module focuses exclusively on annual sales of cars and trucks.<br />
<br />
We have developed two approaches to forecasting those sales (a total of four options for the user). GDP per capita at purchasing power parity is the primary driver of sales per capita in&nbsp;[http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars.html most of those options]&nbsp;(in one case total GDP drives total sales, see&nbsp;[http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars2.html Approach 2]). Values of the indices for survival/self-expression and traditional/secular-rational values may join GDP per capita or GDP in determining the annual sales of cars and trucks.<br />
<br />
For more, please read the links below.&nbsp; <br />
<br />
== Cars and Trucks ==<br />
<br />
As the equations elaborate, there are two general approaches and four specific options for determining the annual demand of c[[File:T1.gif|frame|right]]ountries/regions for cars and trucks and therefore the annual new sales of those vehicles.<br />
<br />
GDP per capita at purchasing power parity is the primary driver of sales per capita in three of those options (in one case total GDP drives total sales, see [http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars2.html Approach 2]). Values of the indices for survival/self-expression and traditional/secular-rational values may join GDP per capita in determining the annual per capita sales of total cars and trucks.<br />
<br />
Given per capita sales and population it is easy to compute total annual car and truck sales. Functions that indicate whether individual types of cars or trucks are "inferior" or "superior" goods, that is whether then decrease or increase with incomes, then determine how total car and truck sales are split across individual categories of cars and trucks.<br />
<br />
The following flow is a general representation of the first approach (sales per capita determined by GDP per capita), and the specific formulation depends on the value of the vehicle function switch (vehfuncsw). If the value is 1, GDP/capita alone drives sales per capita. If the value is 3, GDP/capita and values on the traditional/secular-rational dimension drive sales per capita. If the value is 4, GDP/capita and values on the traditional/secular-rational and survival/self-expression dimensions drive sales per capita.<br />
<br />
Future development may focus not just on annual car and truck sales, but on the fleets of each. Variables for fleet size and per capita fleet size are therefore shown above, but not yet used. <br />
<header><hgroup><br />
== Cars and Trucks: Approach 2 (Total) ==<br />
</hgroup></header><br />
As the equations elaborate, there are two general approaches and four specific options for determining the annual demand of countries/regions for cars and trucks and therefore the annual new sales of those vehicles.<br />
<br />
GDP per capita at purchasing power parity is the primary driver of sales per capita in [http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars.html most of those options]. It is also possible, however, to have car/truck sales driven directly by GDP, which is the logic portrayed below. The specific logic/formulation depends on the value of the vehicle function switch (vehfuncsw). When the value is 2, sales are driven directly by GDP, as shown here. In addition, sales are affected by country/region values on the traditional/secular-rational value dimension. Although the model does not include the survival/self-expression dimension in this particular logic (estimated functions did not show it adding much predictive power), the causal diagram portrays it as a possible driver.<br />
<br />
Functions that indicate whether individual types of cars or trucks are "inferior" or "superior" goods, that is whether then decrease or increase with incomes, then determine how total car and truck sales are split across individual categories of cars and trucks.</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=File:T1.gif&diff=2141File:T1.gif2017-02-26T21:06:37Z<p>StellahKwasi: </p>
<hr />
<div></div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Transport&diff=2140Transport2017-02-26T21:06:03Z<p>StellahKwasi: Created page with "The most recent and complete transportation model documentation is available on Pardee's [http://pardee.du.edu/ifs-infrastructure-model-documentation website]. Although the te..."</p>
<hr />
<div>The most recent and complete transportation model documentation is available on Pardee'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.<br />
<br />
<span>The transportation model may eventually consist of a module that represents total demand for transportation services in countries and regions and of additional modules that represent how individual societies meet individual and commercial transportation needs. At this stage, however, the transportation module focuses exclusively on annual sales of cars and trucks.</span><br />
<br />
To read more about the transportation module, see the links below.<br />
<header><hgroup><br />
== Transportation Flow Charts ==<br />
</hgroup></header><br />
=== Overview ===<br />
<br />
The transportation model may eventually consist of a module that represents total demand for transportation services in countries and regions and of additional modules that represent how individual societies meet individual and commercial transportation needs. At this stage, however, the transportation module focuses exclusively on annual sales of cars and trucks.<br />
<br />
We have developed two approaches to forecasting those sales (a total of four options for the user). GDP per capita at purchasing power parity is the primary driver of sales per capita in&nbsp;[http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars.html most of those options]&nbsp;(in one case total GDP drives total sales, see&nbsp;[http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars2.html Approach 2]). Values of the indices for survival/self-expression and traditional/secular-rational values may join GDP per capita or GDP in determining the annual sales of cars and trucks.<br />
<br />
For more, please read the links below.&nbsp;<br />
<header><hgroup><br />
== Cars and Trucks ==<br />
</hgroup></header><br />
As the equations elaborate, there are two general approaches and four specific options for determining the annual demand of countries/regions for cars and trucks and therefore the annual new sales of those vehicles.<br />
<br />
GDP per capita at purchasing power parity is the primary driver of sales per capita in three of those options (in one case total GDP drives total sales, see [http://www.du.edu/ifs/help/understand/transportation/flowcharts/cars2.html Approach 2]). Values of the indices for survival/self-expression and traditional/secular-rational values may join GDP per capita in determining the annual per capita sales of total cars and trucks.<br />
<br />
Given per capita sales and population it is easy to compute total annual car and truck sales. Functions that indicate whether individual types of cars or trucks are "inferior" or "superior" goods, that is whether then decrease or increase with incomes, then determine how total car and truck sales are split across individual categories of cars and trucks.<br />
<br />
The following flow is a general representation of the first approach (sales per capita determined by GDP per capita), and the specific formulation depends on the value of the vehicle function switch (vehfuncsw). If the value is 1, GDP/capita alone drives sales per capita. If the value is 3, GDP/capita and values on the traditional/secular-rational dimension drive sales per capita. If the value is 4, GDP/capita and values on the traditional/secular-rational and survival/self-expression dimensions drive sales per capita.</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Sub-modules&diff=2139Sub-modules2017-02-26T20:55:47Z<p>StellahKwasi: </p>
<hr />
<div>[[Agriculture|Agriculture]]<br />
<br />
[[Population|Population]]<br />
<br />
[[Economics|Economics]]<br />
<br />
[[Education|Education]]<br />
<br />
[[Energy|Energy]]<br />
<br />
[[Environment|Environment]]<br />
<br />
[[Governance|Governance]]<br />
<br />
[[Health|Health]]<br />
<br />
[[Infrastructure|Infrastructure]]<br />
<br />
[[Interstate_Politics_(IP)|Interstate Politics (IP)]]<br />
<br />
[[Socio-Political|Socio-Political]]<br />
<br />
[[Transport]]</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Socio-Political&diff=2138Socio-Political2017-02-26T20:50:23Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete socio-political model documentation is available on Pardee'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.<br />
<br />
<span>A substantial portion of the socio-political model of IFs is scattered throughout the other models. There are "policy handles" or intervention points throughout those models. For instance, in the population model, multipliers on the total fertility rate can reflect policy decisions (although they can also reflect the model user's judgment concerning social changes in the country or region, independent of policy). Patterns of regulation, subsidy, tax incidence, and provision of state services are so diffuse and complicated that we resort to looking at their aggregate consequences through various "policy handles" rather than trying to represent them explicitly.</span><br />
<br />
For more information on this module, please use the links below or read more at [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
== Structure and Agent System: Socio-Political ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Socio-political</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social fabric</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Levels of human well-being and institutional development (human and social capital)</div><div>&nbsp;</div><div>Cultural structures</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social expenditures</div><div>&nbsp;</div><div>Value change</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Growth in literacy and human development;</div><div>&nbsp;</div><div>Democratic development, state failure</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Government efforts to develop human capital through spending on health, education, R&D</div><br />
|}<br />
<br />
Unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no standard organizing structure that is widely used for representing socio-political systems. In the context of the TERRA project, IFs developed a multi-component approach to structure that might be called the "social fabric" (a la Robert Pestel).<br />
<br />
Although representation of agent-class behavior would be of special interest in a socio-political module, most relationships in IFs remain at the level of aggregate specifications.<br />
<br />
== Dominant Relations: Socio-political ==<br />
<br />
=== Domestic Socio-Political Change: Dominant Relations ===<br />
<br />
Social and political change occurs on three dimensions (social characteristics or individual life conditions, values, socio-political institutions and process). Although GDP per capita is strongly correlated with all dimensions of change, it might be more appropriate to conceptualize a syndrome or complex of developmental change than to portray an economically-driven process.<br />
<br />
For causal diagram see [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/index.html Socio-Political Flow Charts Overview].<br />
<br />
For equations see, for example, [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
=== Key dynamics are directly linked to the dominant relations: ===<br />
<br />
*The model computes some key social characteristics/life conditions, including life expectancy and fertility rates in the demographic model, but the user can affect them via multipliers (mortm, tfrm). Literacy rate is an endogenous function of education spending, which the user can influence (via gdsm).<br />
*The model computes value or cultural change on three dimensions: traditional versus secular-rational, survival versus self-expression, and modernism versus postmodernism, which the user can affect via additive factors (tradsrateadd, survseadd, matpostradd).<br />
*Freedom, democracy (the POLITY measure), autocracy, economic freedom, and the status of women are all computed endogenously but can all be shifted by the user via multipliers (freedomm, democm, autocm, econfreem, gemm)<br />
<br />
=== Domestic Socio-Political Change: Selected Added Value ===<br />
<br />
The larger socio-political model provides representation and control over government spending on education, health, the military, R&D, foreign aid, and a residual category. Military spending is linked to interstate politics, both as a driver of threat and as a result of action-and-reaction based arms spending. The sub-model provides aggregated indicators of the physical quality of life and the human development index.<br />
<br />
== Socio-political Flow Charts ==<br />
<br />
[[File:SP1.gif|frame|center|SP1.gif]]<br />
<br />
The social and political module represents a complex of interacting structures and processes. These include:<br />
<br />
*The various social characteristics or life conditions of individuals<br />
*Human values, beliefs, and orientations’<br />
*Social and political structures, informal as well as formal<br />
*Social and political processes, both domestic and international<br />
<br />
Cultural foundations frame all of these components. And all of the components interact closely with human demographic and economic systems.<br />
<br />
The socio-political elements of IFs are among the most dynamically evolving aspects of the overall modeling system.&nbsp;Much, but not everything in the above figure has been fully represented yet within IFs; the figure indicates direction of development and shows implemented elements in italics.<br />
<br />
For more, please read the links below.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Social Characteristics: Life Conditions ==<br />
<br />
Individuals are the foundations of society. Many social indicators are actually aggregated indicators of their condition. The Human Development Index (HDI) is a widely-used summary measure of that life condition, based on life expectancy, educational attainment, and GDP per capita.<br />
<br />
[[File:Sp2.gif|frame|center|Sp2.gif]]<br />
<br />
== Physical Quality of Life (PQLI) ==<br />
<br />
The Overseas Development Council (then under the leadership of Jim Grant) developed and pu[[File:Sp3.gif|frame|right|Physical Quality of Life]]blicized a measure of (physical) quality of life (the PQLI) many years ago. It combines literarcy rate, infant mortality rate, and life expectancy, using scales from the lowest to the highest values in the global system. It weights the three scales equally. The literacy rate is, in turn, a function of the per capita spending levels on education, estimated cross-sectionally. In many respects the PQLI was a predecessor of the [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/lifeconditions.html human development index (HDI)].<br />
<br />
Based on country/region-specific Physical Quality of Life, it is possible to compute world quality of life (WPQLI) and the North-South gap in quality of life (NSPQLI). Given country-specific literacy rates, it is also possible to compute world literacy (WLIT).<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Income Distribution ==<br />
<br />
Income distribution is represented by the share of national income earned by the poorest 20 percent of the population. That share is obtained from data whenever possible, but is estimated from a cross-sectional relationship when necessary and changed over time by that relationship (the values tend, however, to be very stable both in the real world and in the model). Because initial conditions of variables affected by income share, such as fertility and mortality rates, already reflect existing income distributions, it is only the changes in that distribution relative to the expected value that the model uses in such relationships. A parameter (incshrm) is available to change income share and thus affect those variables influenced by it.<br />
<br />
[[File:Sp4.gif|frame|center|Sp4.gif]]<br />
<br />
== Social Characteristics: Networking ==<br />
<br />
Being electronically networked is an increasingly important aspect of human life condition. The numb[[File:Sp5.gif|frame|right|444x270px|Sp5.gif]]er of networked persons (NUMNWP) is a function primarily of the growth rate in that number (NUMNWPGR). It is ultimately constrained, however, by the size of the population and by the number of connections and organizational memberships that people can have (numnwplim). The growth in networked person number slows as it approaches the ultimate limit. The model user can affect the growth pattern via a multiplier on the growth rate (numnwpgrm).<br />
<br />
This approach was added to IFs during the TERRA project and draws on the thinking of Tom Tesch and Pol Descamps<br />
<br />
== Social Values and Cultural Evolution ==<br />
<br />
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.<br />
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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.<br />
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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 "wvsagesw," of computing country/region change over time in the three dimensions by functions for each cohort (value of wvsagesw = 1) or by computing change only in the first cohort and then advancting that through time (value of wvsagesw = 2).<br />
<br />
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 (matpostradd, etc.).<br />
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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.<br />
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[[File:Sp6.gif|frame|center|Sp6.gif]]<br />
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== Social Organization and Change ==<br />
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The sociopolitical module computes change in freedom (political and economic) and the status of women. For freedom it uses both the measure of the Freedom House and the combined measure for democracy (building on democracy and autocracy) of the POLITY project. It also computes a measure of economic freedom and of gender equality.<br />
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[[File:Sp7.gif|frame|center|Sp7.gif]]<br />
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== Social Organization: Stability/State Failure ==<br />
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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.<br />
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IFs uses the same primary variables (infant mortality, democracy, and trade openness) as the State Failure project to drive forecasts of the probability of individual events of state failure, of ongoing episodes of it, and of the magnitude of episodes. In addition, it allows the use in the formulation of GDP per capita and years of education. Many other linkages have been and can be explored, including cultural regions.<br />
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[[File:Sp8.gif|frame|center|Sp8.gif]]<br />
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== Government Spending ==<br />
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The economic submodel provides total government spending. Government spending by category begins as a simple pr[[File:Gs1.gif|frame|right|Gs1.gif]]oduct of total government consumption and fractional shares by spending category.<br />
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Spending by type (military, health, education, research and development, other, and foreign aid) is largely specified exogenously, building on the initial conditions for each country/region. In addition, an action-reaction (arms-race) dynamic can be established in military spending if the action-reaction switch is turned on. After adjustments to foreign aid and military spending, spending in all categories is re-normalized to equal total governmental spending.<br />
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Educational spending is further broken out of total educational spending. The user can shift the spending across three educational levels (primary, secondary, and tertiary) through the use of an educational multiplier.<br />
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See also the specifications of [http://www.du.edu/ifs/help/understand/economics/flowcharts/firm.html detailed final demand]&nbsp;and of [http://www.du.edu/ifs/help/understand/economics/flowcharts/finance.html international finance]. <br />
<br />
== Socio-political Equations ==<br />
<br />
=== Overview ===<br />
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A substantial portion of the policy model of IFs is scattered throughout the other models. There are "policy handles" or intervention points throughout those models. For instance, in the population model, multipliers on the total fertility rate can reflect policy decisions (although they can also reflect the model user's judgment concerning social changes in the country or region, independent of policy). Similarly, in the energy model, the multiplier on energy demand can represent conservation policy. Similarly, the ultimate energy resource base and the rate of resource discovery remain uncertain in part because they are subject to a wide range of government interventions - multipliers can introduce assumptions about such interventions. In the economic module, the level of trade protection is very clearly a policy parameter as is the multiplier on the tax rate. Patterns of regulation, subsidy, tax incidence, and provision of state services are so diffuse and complicated that we resort to looking at their aggregate consequences through various "policy handles" rather than trying to represent them explicitly.<br />
<br />
IFs contains other categories of sociol-political activity, however, that it represents in more integrated fashion in the sociopolitical module as a four-dimensional social fabric: social characteristics/life condition, values, social structures (formal and informal), and social processes.<br />
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For help understanding the equations see [http://www.du.edu/ifs/help/understand/equations/notation.html Notation]. <br />
<br />
== Socio-political Equations: Life Conditions ==<br />
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Literacy changes from the initial level for the region because of a multiplier (LITM).<br />
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[http://www.du.edu/ifs/help/media/images/img00494.gif http://www.du.edu/ifs/help/media/images/img00494.gif]<br />
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The function upon which the literacy multiplier is based represents the cross#sectional rela"tionship globally between educational expenditures per capita (EDEX) from the government submodel and literacy rate (LIT). Rather than imposing the typical literacy rate on a region (and thereby being inconsistent with initial empirical values), the literacy multiplier is the ratio of typical literacy at current expenditure levels to the normal literacy level at initial expenditure levels. This formulation predates the development of an educational module that calculates the numbers of those with a primary education (one common definition of literacy). As that module is refined, we will likely derive literacy dynamics from it.<br />
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[http://www.du.edu/ifs/help/media/images/img00495.gif http://www.du.edu/ifs/help/media/images/img00495.gif]<br />
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Educational expenditures (and thus implicitly literacy and labor efficiency) are tied back to the economic model via the economic production function.<br />
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Given life expectancy, literacy, and infant mortality levels from the mortality distribution, it is possible to compute the Physical Quality of Life Index (PQLI) that the Overseas Development Council developed (ODC, 1977: 147#154). This measure averages the three quality of life indicators, first normalizing each indicator so that it ranges from zero to 100. The normaliza"tion is not needed for literacy; for life expectancy it converts the range of approximately 28 (LIFEXPMIN) to 80 (LIFEXPMAX) years into 0 to 100; for infant mortality it converts the range of approximately 229 per thousand (INFMORMAX) to 9 per thousand (INFMORMIN) into 0 to 100.<br />
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[http://www.du.edu/ifs/help/media/images/img00496.gif http://www.du.edu/ifs/help/media/images/img00496.gif]<br />
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For most users, the United Nations Development Program’s human development index (HDI) has replaced the PQLI as an integrated measure of life condition. It is a simple average of three sub-indices for life expectancy, education, and GDP per capita (using purchasing power parity). The life expectancy sub-index is the same as was used for the PQLI. The literacy sub-index is again the literacy rate. 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 measure in IFs differs slightly from the HDI version, because it does not put educational enrollment rates into a broader educational index with literacy; that will be changed as the educational model of IFs is better tested.<br />
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[http://www.du.edu/ifs/help/media/images/img00497.gif http://www.du.edu/ifs/help/media/images/img00497.gif]<br />
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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 has 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.<br />
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[http://www.du.edu/ifs/help/media/images/img00498.gif http://www.du.edu/ifs/help/media/images/img00498.gif]<br />
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The floating measure, in turn, has some limitations because it introduces relative attainment into the equation rather than absolute attainment. IFs therefore uses still a third version of the 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.<br />
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[http://www.du.edu/ifs/help/media/images/img00499.gif http://www.du.edu/ifs/help/media/images/img00499.gif]<br />
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It is useful to compute several additional global indicators, a world physical quality of life index (WPQLI), a world life expectancy (WLIFE), a world literacy rate (WLIT), and a North#South gap index or ratio of quality of life in the "developed -D" regions to the "less developed-L" regions (NSPQLI).<br />
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[http://www.du.edu/ifs/help/media/images/img00500.gif http://www.du.edu/ifs/help/media/images/img00500.gif]<br />
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[http://www.du.edu/ifs/help/media/images/img00501.gif http://www.du.edu/ifs/help/media/images/img00501.gif]<br />
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[http://www.du.edu/ifs/help/media/images/img00502.gif http://www.du.edu/ifs/help/media/images/img00502.gif]<br />
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[http://www.du.edu/ifs/help/media/images/img00503.gif http://www.du.edu/ifs/help/media/images/img00503.gif] <br />
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== Socio-political Equations: Income Distribution ==<br />
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The income share of the poorest 20 percent of the population (INCSHR) depends on the GDP per capita at PPP (GDPPCP) and on an exogenous income share multiplier (incshrm).<br />
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[http://www.du.edu/ifs/help/media/images/img00518.gif http://www.du.edu/ifs/help/media/images/img00518.gif]<br />
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The introduction of different household types into the social accounting matrix structure of IFs made possible the computation of a more sophisticated measure of income distribution tied directly to the model’s computation of household income (HHINC) and household size (HHPOP) by type. A domestic Gini value (GINIDOM) is calculated from a function that uses the normal Lorenz curve foundation for Gini indices. Because that function can calculate values that are quite different from the empirical initial values, a ratio of the empirical value to the initial computed value (GINIDOMRI) is used for scaling purposes. The model’s formulation of the relative household income levels of different household types, and therefore the calculation of a domestic GINI based on those income levels, are in early versions and are still rather crude.<br />
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[http://www.du.edu/ifs/help/media/images/img00519.gif http://www.du.edu/ifs/help/media/images/img00519.gif]<br />
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One value of a domestic Gini calculation is that it, in turn, makes possible the calculation of the percentage of population living on less than one dollar per day (INCOMELT1) or two dollars per day (INCOMELT2). Functions were estimated linking GDP per capita at purchasing power (GDPPCP) and the Gini index to those percentages. Again, IFs uses initial conditions for scaling purposes.<br />
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[http://www.du.edu/ifs/help/media/images/img00520.gif http://www.du.edu/ifs/help/media/images/img00520.gif]<br />
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[http://www.du.edu/ifs/help/media/images/img00521.gif http://www.du.edu/ifs/help/media/images/img00521.gif]<br />
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IFs also calculates a global Gini index across all countries/regions in the model, again using the standard Lorenz curve approach to areas of inequality and equality. It does not yet take into account intra-regional income differentials, but the foundation is now in place to do so.<br />
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[http://www.du.edu/ifs/help/media/images/img00522.gif http://www.du.edu/ifs/help/media/images/img00522.gif]<br />
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The user interface of IFs now uses the same Lorenz-curve approach to allow the user to calculate a specialized-display GINI for any variable that can be represented across all countries/regions of the model. <br />
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== Social Equations Networking ==<br />
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The focal point of this portion of the model is on the computation of the total number of networked persons (NUMNWP). The rate of growth in that number (NUMNWPGR) is subject to several forces. The initial value of that rate is set in the data preprocessor of the model from empirical data. When no data are available for a country or region, the rate is set at a level determined via a cross-sectional relationship between GDP per capita (PPP) and portion of population networked.<br />
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[http://www.du.edu/ifs/help/media/images/img00493.gif http://www.du.edu/ifs/help/media/images/img00493.gif]<br />
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Over time the growth rate of networked persons is subject to a saturating function, as the actual number of networked persons approaches a limit. The limit is set by an exogenous multiplier (numnwplim) on total population; networked persons can exceed total population because of multiple affiliations of individuals (households, NGOs, companies). The user of the model can accelerate or de-accelerate the process of networking via a multiplier on the growth rate (numnwpgrm).<br />
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Although of interest in its own right, the number of networked persons is also carried forward in the model to the production function of the economy. <br />
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== Socio-political Equations: Values ==<br />
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IFs computes change in three cultural dimensions identified by the World Values Survey [http://www.du.edu/ifs/help/intro/support/bibliography.html (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).<br />
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[http://www.du.edu/ifs/help/media/images/img00504.gif http://www.du.edu/ifs/help/media/images/img00504.gif]<br />
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[http://www.du.edu/ifs/help/media/images/img00505.gif http://www.du.edu/ifs/help/media/images/img00505.gif]<br />
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[http://www.du.edu/ifs/help/media/images/img00506.gif http://www.du.edu/ifs/help/media/images/img00506.gif]<br />
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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.<br />
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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 "aging" 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.<br />
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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.<br />
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IFs uses an approach that is similar to the one for literacy in order to estimate the future of another measure created by the United Nations Development Program, one called the Gender Equity Measure (GEM). The closer the values of that measure approach "1", the closer women are to men in political and social power.<br />
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[http://www.du.edu/ifs/help/media/images/img00507.gif http://www.du.edu/ifs/help/media/images/img00507.gif] <br />
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== Socio-political Equations: Structures or Institutions ==<br />
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IFs endogenizes level of freedom (FREEDOM), based on the Freedom House measures, by linking change from initial conditions to GDP per capita at purchasing power parity in an analytic function. For discussion of the relationship between GDP and democracy, see [http://www.du.edu/ifs/help/intro/support/bibliography.html Londregran and Poole (1996)]&nbsp;and [http://www.du.edu/ifs/help/intro/support/bibliography.html Przeworski and Limongi (1997)]. The latter view it as a probabilistic relationship in which there are a variety of reasons (often external pressure) at all levels of economic development for the conversion of dictatorships to democracies and in which the conversion of democracies to dictatorships occurs commonly at low but not high levels of development. That pattern creates a positive correlation between economic development and democratic government. A multiplier in freedom level (freedomm) increases or decreases the level of freedom.<br />
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[http://www.du.edu/ifs/help/media/images/img00508.gif http://www.du.edu/ifs/help/media/images/img00508.gif]<br />
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The Economic Freedom Institute (with leadership from the Fraser Institute; see Gwartney and Lawson with Samida, 2000) have also introduced a measure of economic freedom. IFs represents that in similar fashion.<br />
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&nbsp;[http://www.du.edu/ifs/help/media/images/img00509.gif http://www.du.edu/ifs/help/media/images/img00509.gif]<br />
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The POLITY project provides an alternative to the freedom house measure of freedom or democracy level. In fact, it provides multiple variables related to political system. IFs EARLIER included formations of two of those, democracy (DEMOC) and autocracy (AUTOC). They worked in completely analogous fashion.<br />
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&nbsp;[http://www.du.edu/ifs/help/media/images/img00510.gif http://www.du.edu/ifs/help/media/images/img00510.gif]<br />
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[http://www.du.edu/ifs/help/media/images/img00511.gif http://www.du.edu/ifs/help/media/images/img00511.gif]<br />
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More recently, IFs has (1) combined the two Polity project measures into a single one as is often done with the Polity measures, setting POLITYDEMOC equal to democracy – autocracy + 10, a measure that runs from 0 to 20; (2) introduced a more complicated, multi-level forecast for the new measure.<br />
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Specifically, the project identified three levels of analysis for factors that affect democratic change: domestic, regional, and systemic. At each of the three levels there are multiple factors that can affect democracy within states. At the domestic level we can identify two categories of factors in particular:<br />
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*GDP per capita. This variable correlates highly with almost all measures of social condition; GDP provides the resources for democratization and other social change.<br />
*values/culture. Values clearly do differ across countries and regions of the world and almost certainly affect propensity to democratize.<br />
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At the regional level (or, more accurately, the "swing-states" level) we can also identify three prospective drivers:<br />
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*world average effects. It is possible that the world average exerts a pull-effect on states around the world (for instance, increasingly globalization could lead to homogenization of a wide variety of social structures around the world).<br />
*swing states effects. Some states within regions quite probably affect/lead others (obviously the former Soviet Union was a prime example of such a swing state within its sphere of influence, but there is reason to believe in lesser and less coercive effects elsewhere).<br />
*regional average. States within a region possibly affect each other more generally, such that "swing states" are moved by regional patterns and not simply movers of them.<br />
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At the system level we identify three:<br />
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*systemic leadership impetus. It is often suggested that the United States and other developed countries can affect democratization in less developed countries, either positively or negatively<br />
*snowballing of democracy (Huntington 1991). The wave character of democratization suggests that there may be an internal dynamic, a self-reinforcing positive feedback loop, of the process globally, partially independent of other forces that act on the process. Such a conclusion is consistent with the fact that idea spread and global regime development influence many types of social change (Hughes 2001)<br />
*miscellaneous other forces. Historic analysis would identify world war, economic depression, and other factors to explain the global pattern of democratization, especially the surge or retreat of waves.<br />
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A project document prepared for the CIA’s Strategic Assessment Group (SAG) analyzed historic data and, in cooperation with David Epstein and Larry Diamond, fit an approach to it that cut across these three levels (see Hughes 2002: 59-74 for elaboration and documentation of the empirical work). The empirical work is not documented again here. The work did not find significant and consistent regional level effects, however, and the regional variables are therefore normally turned off.<br />
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The resulting formulation uses the domestic level as an initial base calculation because it is the empirically strongest piece, and later adds (optionally) the regional level effects and the systemic effects. The base calculation is further tied to the actual empirical levels in the initial year of the run, with the impact of the driving variables being felt only in change of those levels. An ‘expected" democracy level (DEMOCEXP) is computed using an analytic function that uses GDP per capita at purchasing power parity (GDPPCP) and the World Value Survey’s survival and self-expression dimension (SURVSE). These were found quite powerful in their level of correlation with democracy and the WVS dimension, interestingly, carries a cultural component into the formulation. The user can further modify this basic formulation with an exogenous multiplier (democm).<br />
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[http://www.du.edu/ifs/help/media/images/img00512.gif http://www.du.edu/ifs/help/media/images/img00512.gif]<br />
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It is also useful to have a separate calculation of the empirically strongest piece of the formulation, namely the domestic effects, but without any adjustment to the initial empirical values. The expected democracy variable (DEMOCEXP) carries that. It can be compared with the fully computed values to see the degree to which there may be tension in countries between democracy levels that GDP per capita and values would predict, on the one hand, and those that are in the initial data. The greatest tension levels tend to be in the Middle Eastern countries, where decmocracy is considerably below "expected" levels.<br />
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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 (polconv) and, in fact, basically shut off convergence by sitting the years very high.<br />
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[http://www.du.edu/ifs/help/media/images/img00514.gif http://www.du.edu/ifs/help/media/images/img00514.gif]<br />
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On top of the country-specific calculation sits the (optional) regional or swing state effect calculation (SwingEffects), turned on by setting the swing states parameter (swseffects) to 1. The swing effects term has three components. The first is a world effect, whereby the democracy level in any given state (the "swingee") is affected by the world average level, with a parameter of impact (swingstdem) and a time adjustment (timeadj) . The second is a regionally powerful state factor, the regional "swinger" effect, with similar parameters. The third is a swing effect based on the average level of democracy in the region (RgDemoc).<br />
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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 and are available for optional use.<br />
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Also on top of the country-level effects sits the effect of global waves (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.<br />
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[http://www.du.edu/ifs/help/media/images/img00515.gif http://www.du.edu/ifs/help/media/images/img00515.gif]<br />
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The democratic wave amplitude is a level that shifts over time (DemocWaveShift) with a normal maximum amplitude (democwvmax) and wave length (democwvlen), 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 (democimpus) compared to the normal impetus level from the U.S. (democimpusn) and the net impetus from other countries/forces (democimpoth).<br />
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[http://www.du.edu/ifs/help/media/images/img00516.gif http://www.du.edu/ifs/help/media/images/img00516.gif]<br />
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Given both the global and regional/swing-state effects, it is possible to add these to the basic country calculation for the final computation of the level of democracy using the Polity scale. The size of the swing effects is constrained by an external parameter (swseffmax).<br />
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[http://www.du.edu/ifs/help/media/images/img00517.gif http://www.du.edu/ifs/help/media/images/img00517.gif] <br />
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== Socio-political Equations: Stability/State Failure ==<br />
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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.<br />
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The extensive database of the project includes many measures of failure. IFs has variables representing three measures in each of the two categories, corresponding to the probability of the first year of a failure event (SFINSTABY1 and SFINTLWARY1), the probability of the first year or a continuing year (SFINSTABALL and SFINTLWARALL), and the magnitude of a first year or continuing event (SFINSTABMAG and SFINTLWARMAG).<br />
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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.<br />
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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’s Strategic Assessment Group (SAG), or to the model for the default values.<br />
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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’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 and that for the other five state failure variables varies with estimation. The analytic function obviously handles various formulations including linear and logarithmic.<br />
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[http://www.du.edu/ifs/help/media/images/img00523.gif http://www.du.edu/ifs/help/media/images/img00523.gif]<br />
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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.<br />
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[http://www.du.edu/ifs/help/media/images/img00524.gif http://www.du.edu/ifs/help/media/images/img00524.gif]<br />
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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.<br />
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[http://www.du.edu/ifs/help/media/images/img00525.gif http://www.du.edu/ifs/help/media/images/img00525.gif] <br />
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== Economic Inequality and Political Conflict ==<br />
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IFs does not yet include this important relationship. See Lichbach (1989) and Moore, Lindstrom, and O’Regan (1996) for analyses of how difficult this relationship is to specify. One critical problem is conceptualization of political conflict, political repression, political instability, political violence, political protest, etc. There are clearly many interacting, but separate dimensions for consideration. As Lichbach (1989: 448) says, "robust EI-PC laws have not been discovered."<br />
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== Policy Equations Government Expenditures ==<br />
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The fiscal model of IFs is quite simple and builds on the computation of government consumption (GOVCON) in the economic model.<br />
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IFs expenditures fall into six categories: military, health, education, research and development, other, and foreign aid. IFs divides total government consumption (GOVCON) into these five destination sectors (GDS) with a vector of government spending coefficients (GK) based on initial conditions. The user can change that default pattern of government spending over time with a multiplier parameter (gdsm). The model normalizes the allocation to assure that the money spent is no more or less than total government consumption.<br />
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The last category of spending complicates the allocation of spending to destination categories. It is traditional not to think of foreign aid in terms of its percentage of the governmental budget (as we often think of defense or educational expenditures), but to think of it in terms of a percentage of the GDP. For instance, the United Nations has called for foreign aid spending equal to 0.7% (earlier 1.0%) of GDP of donor countries. Moreover, for some governments, foreign aid is not an expenditure, but a receipt and an addition to government revenues.<br />
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Therefore IFs actually calculates foreign aid expenditures and receipts first and fixes those amounts (see the [http://www.du.edu/ifs/help/understand/sociopolitical/equations/policyforeign.html foreign aid equations]). It then allocates the amount of government spending that remains in the coffers of aid donors (or the augmented amount available to aid recipients) among the other categories, normalizing the allocation to the sum of the coefficients in those other categories.<br />
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[http://www.du.edu/ifs/help/media/images/img00373.gif http://www.du.edu/ifs/help/media/images/img00373.gif]<br />
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There are several forward linkages of government spending that are important. A mortality multiplier (MORTMG) is computed for the demographic model, using changes in health spending from the initial year and a parameter of the impact of that spending (elashc).<br />
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[http://www.du.edu/ifs/help/media/images/img00376.gif http://www.du.edu/ifs/help/media/images/img00376.gif]<br />
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Three of the forward linkages carry information on spending to the calculation of multifactor productivity in the economic production function, for additive rather than multiplicative use. One variable tracks change in education spending (CNGEDUC), modified by an elasticity of education on MFP (elmfped) and carries it forward. Another tracks changes in health spending (CNGHLTH) using a parameter (elmfphl). The third tracks changes in R&D spending with a parameter of impact (elmfprd). In each case there is a lag involved because of computational sequence.<br />
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[http://www.du.edu/ifs/help/media/images/img00391.gif http://www.du.edu/ifs/help/media/images/img00391.gif]<br />
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Because essentially of an older variable form for the education term that is still used in the agricultural model’s production function, the first of the three terms is transferred to that older variable (LEFMG).<br />
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[http://www.du.edu/ifs/help/media/images/img00474.gif http://www.du.edu/ifs/help/media/images/img00474.gif] <br />
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== Policy Equations Foreign Aid ==<br />
<br />
IFs uses a "pool" approach to aid (AID) rather than indicating bilateral flows from particular donors to particular recipients. That is, all aid from all donors flows into the pool and then all recipients draw proportions of the pool.<br />
<br />
IFs uses the aid value parameter (AIDDON) to calculate the aid (AID) from donors and AIDREC to calculate the targeted aid to recipients. The pool of aid donations determines the actual total level of interstate aid flows, however, and is allocated among potential recipients according to the proportions targeted for each.<br />
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[http://www.du.edu/ifs/help/media/images/img00475.gif http://www.du.edu/ifs/help/media/images/img00475.gif]<br />
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Aid outflows are negative and the total aid pool given (AIDP) is the sum of the negative flows, while the total desired aid of recipients (AIDR) is the sum of positive flows.<br />
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[http://www.du.edu/ifs/help/media/images/img00476.gif http://www.du.edu/ifs/help/media/images/img00476.gif]<br />
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[http://www.du.edu/ifs/help/media/images/img00477.gif http://www.du.edu/ifs/help/media/images/img00477.gif]<br />
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A recomputation of aid for recipients distributes the aid pool across their demands.<br />
<br />
[http://www.du.edu/ifs/help/media/images/img00478.gif http://www.du.edu/ifs/help/media/images/img00478.gif]</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Socio-Political&diff=2137Socio-Political2017-02-26T20:45:47Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete socio-political model documentation is available on Pardee'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.<br />
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<span>A substantial portion of the socio-political model of IFs is scattered throughout the other models. There are "policy handles" or intervention points throughout those models. For instance, in the population model, multipliers on the total fertility rate can reflect policy decisions (although they can also reflect the model user's judgment concerning social changes in the country or region, independent of policy). Patterns of regulation, subsidy, tax incidence, and provision of state services are so diffuse and complicated that we resort to looking at their aggregate consequences through various "policy handles" rather than trying to represent them explicitly.</span><br />
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For more information on this module, please use the links below or read more at [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
== Structure and Agent System: Socio-Political ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Socio-political</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social fabric</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Levels of human well-being and institutional development (human and social capital)</div><div>&nbsp;</div><div>Cultural structures</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social expenditures</div><div>&nbsp;</div><div>Value change</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Growth in literacy and human development;</div><div>&nbsp;</div><div>Democratic development, state failure</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Government efforts to develop human capital through spending on health, education, R&D</div><br />
|}<br />
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Unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no standard organizing structure that is widely used for representing socio-political systems. In the context of the TERRA project, IFs developed a multi-component approach to structure that might be called the "social fabric" (a la Robert Pestel).<br />
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Although representation of agent-class behavior would be of special interest in a socio-political module, most relationships in IFs remain at the level of aggregate specifications.<br />
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== Dominant Relations: Socio-political ==<br />
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=== Domestic Socio-Political Change: Dominant Relations ===<br />
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Social and political change occurs on three dimensions (social characteristics or individual life conditions, values, socio-political institutions and process). Although GDP per capita is strongly correlated with all dimensions of change, it might be more appropriate to conceptualize a syndrome or complex of developmental change than to portray an economically-driven process.<br />
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For causal diagram see [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/index.html Socio-Political Flow Charts Overview].<br />
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For equations see, for example, [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
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=== Key dynamics are directly linked to the dominant relations: ===<br />
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*The model computes some key social characteristics/life conditions, including life expectancy and fertility rates in the demographic model, but the user can affect them via multipliers (mortm, tfrm). Literacy rate is an endogenous function of education spending, which the user can influence (via gdsm).<br />
*The model computes value or cultural change on three dimensions: traditional versus secular-rational, survival versus self-expression, and modernism versus postmodernism, which the user can affect via additive factors (tradsrateadd, survseadd, matpostradd).<br />
*Freedom, democracy (the POLITY measure), autocracy, economic freedom, and the status of women are all computed endogenously but can all be shifted by the user via multipliers (freedomm, democm, autocm, econfreem, gemm)<br />
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=== Domestic Socio-Political Change: Selected Added Value ===<br />
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The larger socio-political model provides representation and control over government spending on education, health, the military, R&D, foreign aid, and a residual category. Military spending is linked to interstate politics, both as a driver of threat and as a result of action-and-reaction based arms spending. The sub-model provides aggregated indicators of the physical quality of life and the human development index.<br />
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== Socio-political Flow Charts ==<br />
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[[File:SP1.gif|frame|center|SP1.gif]]<br />
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The social and political module represents a complex of interacting structures and processes. These include:<br />
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*The various social characteristics or life conditions of individuals<br />
*Human values, beliefs, and orientations’<br />
*Social and political structures, informal as well as formal<br />
*Social and political processes, both domestic and international<br />
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Cultural foundations frame all of these components. And all of the components interact closely with human demographic and economic systems.<br />
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The socio-political elements of IFs are among the most dynamically evolving aspects of the overall modeling system.&nbsp;Much, but not everything in the above figure has been fully represented yet within IFs; the figure indicates direction of development and shows implemented elements in italics.<br />
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For more, please read the links below.<br />
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== Social Characteristics: Life Conditions ==<br />
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Individuals are the foundations of society. Many social indicators are actually aggregated indicators of their condition. The Human Development Index (HDI) is a widely-used summary measure of that life condition, based on life expectancy, educational attainment, and GDP per capita.<br />
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[[File:Sp2.gif|frame|center|Sp2.gif]]<br />
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== Physical Quality of Life (PQLI) ==<br />
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The Overseas Development Council (then under the leadership of Jim Grant) developed and pu[[File:Sp3.gif|frame|right|Physical Quality of Life]]blicized a measure of (physical) quality of life (the PQLI) many years ago. It combines literarcy rate, infant mortality rate, and life expectancy, using scales from the lowest to the highest values in the global system. It weights the three scales equally. The literacy rate is, in turn, a function of the per capita spending levels on education, estimated cross-sectionally. In many respects the PQLI was a predecessor of the [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/lifeconditions.html human development index (HDI)].<br />
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Based on country/region-specific Physical Quality of Life, it is possible to compute world quality of life (WPQLI) and the North-South gap in quality of life (NSPQLI). Given country-specific literacy rates, it is also possible to compute world literacy (WLIT).<br />
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== Income Distribution ==<br />
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Income distribution is represented by the share of national income earned by the poorest 20 percent of the population. That share is obtained from data whenever possible, but is estimated from a cross-sectional relationship when necessary and changed over time by that relationship (the values tend, however, to be very stable both in the real world and in the model). Because initial conditions of variables affected by income share, such as fertility and mortality rates, already reflect existing income distributions, it is only the changes in that distribution relative to the expected value that the model uses in such relationships. A parameter (incshrm) is available to change income share and thus affect those variables influenced by it.<br />
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[[File:Sp4.gif|frame|center|Sp4.gif]]<br />
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== Social Characteristics: Networking ==<br />
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Being electronically networked is an increasingly important aspect of human life condition. The numb[[File:Sp5.gif|frame|right|444x270px|Sp5.gif]]er of networked persons (NUMNWP) is a function primarily of the growth rate in that number (NUMNWPGR). It is ultimately constrained, however, by the size of the population and by the number of connections and organizational memberships that people can have (numnwplim). The growth in networked person number slows as it approaches the ultimate limit. The model user can affect the growth pattern via a multiplier on the growth rate (numnwpgrm).<br />
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This approach was added to IFs during the TERRA project and draws on the thinking of Tom Tesch and Pol Descamps<br />
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== Social Values and Cultural Evolution ==<br />
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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.<br />
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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.<br />
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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 "wvsagesw," of computing country/region change over time in the three dimensions by functions for each cohort (value of wvsagesw = 1) or by computing change only in the first cohort and then advancting that through time (value of wvsagesw = 2).<br />
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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 (matpostradd, etc.).<br />
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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.<br />
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[[File:Sp6.gif|frame|center|Sp6.gif]]<br />
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== Social Organization and Change ==<br />
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The sociopolitical module computes change in freedom (political and economic) and the status of women. For freedom it uses both the measure of the Freedom House and the combined measure for democracy (building on democracy and autocracy) of the POLITY project. It also computes a measure of economic freedom and of gender equality.<br />
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[[File:Sp7.gif|frame|center|Sp7.gif]]<br />
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== Social Organization: Stability/State Failure ==<br />
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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.<br />
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IFs uses the same primary variables (infant mortality, democracy, and trade openness) as the State Failure project to drive forecasts of the probability of individual events of state failure, of ongoing episodes of it, and of the magnitude of episodes. In addition, it allows the use in the formulation of GDP per capita and years of education. Many other linkages have been and can be explored, including cultural regions.<br />
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[[File:Sp8.gif|frame|center|Sp8.gif]]<br />
<header><hgroup><br />
== Government Spending ==<br />
</hgroup></header><br />
The economic submodel provides total government spending. Government spending by category begins as a simple pr[[File:Gs1.gif|frame|right]]oduct of total government consumption and fractional shares by spending category.<br />
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Spending by type (military, health, education, research and development, other, and foreign aid) is largely specified exogenously, building on the initial conditions for each country/region. In addition, an action-reaction (arms-race) dynamic can be established in military spending if the action-reaction switch is turned on. After adjustments to foreign aid and military spending, spending in all categories is re-normalized to equal total governmental spending.<br />
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Educational spending is further broken out of total educational spending. The user can shift the spending across three educational levels (primary, secondary, and tertiary) through the use of an educational multiplier.<br />
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See also the specifications of [http://www.du.edu/ifs/help/understand/economics/flowcharts/firm.html detailed final demand]&nbsp;and of [http://www.du.edu/ifs/help/understand/economics/flowcharts/finance.html international finance]. <br />
<header><hgroup><br />
== Socio-political Equations ==<br />
</hgroup></header><br />
=== Overview ===<br />
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A substantial portion of the policy model of IFs is scattered throughout the other models. There are "policy handles" or intervention points throughout those models. For instance, in the population model, multipliers on the total fertility rate can reflect policy decisions (although they can also reflect the model user's judgment concerning social changes in the country or region, independent of policy). Similarly, in the energy model, the multiplier on energy demand can represent conservation policy. Similarly, the ultimate energy resource base and the rate of resource discovery remain uncertain in part because they are subject to a wide range of government interventions - multipliers can introduce assumptions about such interventions. In the economic module, the level of trade protection is very clearly a policy parameter as is the multiplier on the tax rate. Patterns of regulation, subsidy, tax incidence, and provision of state services are so diffuse and complicated that we resort to looking at their aggregate consequences through various "policy handles" rather than trying to represent them explicitly.<br />
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IFs contains other categories of sociol-political activity, however, that it represents in more integrated fashion in the sociopolitical module as a four-dimensional social fabric: social characteristics/life condition, values, social structures (formal and informal), and social processes.<br />
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For help understanding the equations see [http://www.du.edu/ifs/help/understand/equations/notation.html Notation].<br />
<header><hgroup><br />
== Socio-political Equations: Life Conditions ==<br />
</hgroup></header><br />
Literacy changes from the initial level for the region because of a multiplier (LITM).<br />
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http://www.du.edu/ifs/help/media/images/img00494.gif<br />
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The function upon which the literacy multiplier is based represents the cross#sectional rela"tionship globally between educational expenditures per capita (EDEX) from the government submodel and literacy rate (LIT). Rather than imposing the typical literacy rate on a region (and thereby being inconsistent with initial empirical values), the literacy multiplier is the ratio of typical literacy at current expenditure levels to the normal literacy level at initial expenditure levels. This formulation predates the development of an educational module that calculates the numbers of those with a primary education (one common definition of literacy). As that module is refined, we will likely derive literacy dynamics from it.<br />
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http://www.du.edu/ifs/help/media/images/img00495.gif<br />
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Educational expenditures (and thus implicitly literacy and labor efficiency) are tied back to the economic model via the economic production function.<br />
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Given life expectancy, literacy, and infant mortality levels from the mortality distribution, it is possible to compute the Physical Quality of Life Index (PQLI) that the Overseas Development Council developed (ODC, 1977: 147#154). This measure averages the three quality of life indicators, first normalizing each indicator so that it ranges from zero to 100. The normaliza"tion is not needed for literacy; for life expectancy it converts the range of approximately 28 (LIFEXPMIN) to 80 (LIFEXPMAX) years into 0 to 100; for infant mortality it converts the range of approximately 229 per thousand (INFMORMAX) to 9 per thousand (INFMORMIN) into 0 to 100.<br />
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http://www.du.edu/ifs/help/media/images/img00496.gif<br />
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For most users, the United Nations Development Program’s human development index (HDI) has replaced the PQLI as an integrated measure of life condition. It is a simple average of three sub-indices for life expectancy, education, and GDP per capita (using purchasing power parity). The life expectancy sub-index is the same as was used for the PQLI. The literacy sub-index is again the literacy rate. 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 measure in IFs differs slightly from the HDI version, because it does not put educational enrollment rates into a broader educational index with literacy; that will be changed as the educational model of IFs is better tested.<br />
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http://www.du.edu/ifs/help/media/images/img00497.gif<br />
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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 has 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.<br />
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http://www.du.edu/ifs/help/media/images/img00498.gif<br />
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The floating measure, in turn, has some limitations because it introduces relative attainment into the equation rather than absolute attainment. IFs therefore uses still a third version of the 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.<br />
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http://www.du.edu/ifs/help/media/images/img00499.gif<br />
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It is useful to compute several additional global indicators, a world physical quality of life index (WPQLI), a world life expectancy (WLIFE), a world literacy rate (WLIT), and a North#South gap index or ratio of quality of life in the "developed -D" regions to the "less developed-L" regions (NSPQLI).<br />
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http://www.du.edu/ifs/help/media/images/img00500.gif<br />
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== Socio-political Equations: Income Distribution ==<br />
</hgroup></header><br />
The income share of the poorest 20 percent of the population (INCSHR) depends on the GDP per capita at PPP (GDPPCP) and on an exogenous income share multiplier (incshrm).<br />
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http://www.du.edu/ifs/help/media/images/img00518.gif<br />
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The introduction of different household types into the social accounting matrix structure of IFs made possible the computation of a more sophisticated measure of income distribution tied directly to the model’s computation of household income (HHINC) and household size (HHPOP) by type. A domestic Gini value (GINIDOM) is calculated from a function that uses the normal Lorenz curve foundation for Gini indices. Because that function can calculate values that are quite different from the empirical initial values, a ratio of the empirical value to the initial computed value (GINIDOMRI) is used for scaling purposes. The model’s formulation of the relative household income levels of different household types, and therefore the calculation of a domestic GINI based on those income levels, are in early versions and are still rather crude.<br />
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http://www.du.edu/ifs/help/media/images/img00519.gif<br />
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One value of a domestic Gini calculation is that it, in turn, makes possible the calculation of the percentage of population living on less than one dollar per day (INCOMELT1) or two dollars per day (INCOMELT2). Functions were estimated linking GDP per capita at purchasing power (GDPPCP) and the Gini index to those percentages. Again, IFs uses initial conditions for scaling purposes.<br />
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http://www.du.edu/ifs/help/media/images/img00520.gif<br />
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IFs also calculates a global Gini index across all countries/regions in the model, again using the standard Lorenz curve approach to areas of inequality and equality. It does not yet take into account intra-regional income differentials, but the foundation is now in place to do so.<br />
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http://www.du.edu/ifs/help/media/images/img00522.gif<br />
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The user interface of IFs now uses the same Lorenz-curve approach to allow the user to calculate a specialized-display GINI for any variable that can be represented across all countries/regions of the model.<br />
<header><hgroup><br />
== Social Equations Networking ==<br />
</hgroup></header><br />
The focal point of this portion of the model is on the computation of the total number of networked persons (NUMNWP). The rate of growth in that number (NUMNWPGR) is subject to several forces. The initial value of that rate is set in the data preprocessor of the model from empirical data. When no data are available for a country or region, the rate is set at a level determined via a cross-sectional relationship between GDP per capita (PPP) and portion of population networked.<br />
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http://www.du.edu/ifs/help/media/images/img00493.gif<br />
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Over time the growth rate of networked persons is subject to a saturating function, as the actual number of networked persons approaches a limit. The limit is set by an exogenous multiplier (numnwplim) on total population; networked persons can exceed total population because of multiple affiliations of individuals (households, NGOs, companies). The user of the model can accelerate or de-accelerate the process of networking via a multiplier on the growth rate (numnwpgrm).<br />
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Although of interest in its own right, the number of networked persons is also carried forward in the model to the production function of the economy.<br />
<header><hgroup><br />
== Socio-political Equations: Values ==<br />
</hgroup></header><br />
IFs computes change in three cultural dimensions identified by the World Values Survey [http://www.du.edu/ifs/help/intro/support/bibliography.html (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).<br />
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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.<br />
<br />
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 "aging" 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.<br />
<br />
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.<br />
<br />
IFs uses an approach that is similar to the one for literacy in order to estimate the future of another measure created by the United Nations Development Program, one called the Gender Equity Measure (GEM). The closer the values of that measure approach "1", the closer women are to men in political and social power.<br />
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<header><hgroup><br />
== Socio-political Equations: Structures or Institutions ==<br />
</hgroup></header><br />
IFs endogenizes level of freedom (FREEDOM), based on the Freedom House measures, by linking change from initial conditions to GDP per capita at purchasing power parity in an analytic function. For discussion of the relationship between GDP and democracy, see [http://www.du.edu/ifs/help/intro/support/bibliography.html Londregran and Poole (1996)]&nbsp;and [http://www.du.edu/ifs/help/intro/support/bibliography.html Przeworski and Limongi (1997)]. The latter view it as a probabilistic relationship in which there are a variety of reasons (often external pressure) at all levels of economic development for the conversion of dictatorships to democracies and in which the conversion of democracies to dictatorships occurs commonly at low but not high levels of development. That pattern creates a positive correlation between economic development and democratic government. A multiplier in freedom level (freedomm) increases or decreases the level of freedom.<br />
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The Economic Freedom Institute (with leadership from the Fraser Institute; see Gwartney and Lawson with Samida, 2000) have also introduced a measure of economic freedom. IFs represents that in similar fashion.<br />
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The POLITY project provides an alternative to the freedom house measure of freedom or democracy level. In fact, it provides multiple variables related to political system. IFs EARLIER included formations of two of those, democracy (DEMOC) and autocracy (AUTOC). They worked in completely analogous fashion.<br />
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More recently, IFs has (1) combined the two Polity project measures into a single one as is often done with the Polity measures, setting POLITYDEMOC equal to democracy – autocracy + 10, a measure that runs from 0 to 20; (2) introduced a more complicated, multi-level forecast for the new measure.<br />
<br />
Specifically, the project identified three levels of analysis for factors that affect democratic change: domestic, regional, and systemic. At each of the three levels there are multiple factors that can affect democracy within states. At the domestic level we can identify two categories of factors in particular:<br />
<br />
*GDP per capita. This variable correlates highly with almost all measures of social condition; GDP provides the resources for democratization and other social change.<br />
*values/culture. Values clearly do differ across countries and regions of the world and almost certainly affect propensity to democratize.<br />
<br />
At the regional level (or, more accurately, the "swing-states" level) we can also identify three prospective drivers:<br />
<br />
*world average effects. It is possible that the world average exerts a pull-effect on states around the world (for instance, increasingly globalization could lead to homogenization of a wide variety of social structures around the world).<br />
*swing states effects. Some states within regions quite probably affect/lead others (obviously the former Soviet Union was a prime example of such a swing state within its sphere of influence, but there is reason to believe in lesser and less coercive effects elsewhere).<br />
*regional average. States within a region possibly affect each other more generally, such that "swing states" are moved by regional patterns and not simply movers of them.<br />
<br />
At the system level we identify three:<br />
<br />
*systemic leadership impetus. It is often suggested that the United States and other developed countries can affect democratization in less developed countries, either positively or negatively<br />
*snowballing of democracy (Huntington 1991). The wave character of democratization suggests that there may be an internal dynamic, a self-reinforcing positive feedback loop, of the process globally, partially independent of other forces that act on the process. Such a conclusion is consistent with the fact that idea spread and global regime development influence many types of social change (Hughes 2001)<br />
*miscellaneous other forces. Historic analysis would identify world war, economic depression, and other factors to explain the global pattern of democratization, especially the surge or retreat of waves.<br />
<br />
A project document prepared for the CIA’s Strategic Assessment Group (SAG) analyzed historic data and, in cooperation with David Epstein and Larry Diamond, fit an approach to it that cut across these three levels (see Hughes 2002: 59-74 for elaboration and documentation of the empirical work). The empirical work is not documented again here. The work did not find significant and consistent regional level effects, however, and the regional variables are therefore normally turned off.<br />
<br />
The resulting formulation uses the domestic level as an initial base calculation because it is the empirically strongest piece, and later adds (optionally) the regional level effects and the systemic effects. The base calculation is further tied to the actual empirical levels in the initial year of the run, with the impact of the driving variables being felt only in change of those levels. An ‘expected" democracy level (DEMOCEXP) is computed using an analytic function that uses GDP per capita at purchasing power parity (GDPPCP) and the World Value Survey’s survival and self-expression dimension (SURVSE). These were found quite powerful in their level of correlation with democracy and the WVS dimension, interestingly, carries a cultural component into the formulation. The user can further modify this basic formulation with an exogenous multiplier (democm).<br />
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It is also useful to have a separate calculation of the empirically strongest piece of the formulation, namely the domestic effects, but without any adjustment to the initial empirical values. The expected democracy variable (DEMOCEXP) carries that. It can be compared with the fully computed values to see the degree to which there may be tension in countries between democracy levels that GDP per capita and values would predict, on the one hand, and those that are in the initial data. The greatest tension levels tend to be in the Middle Eastern countries, where decmocracy is considerably below "expected" levels.<br />
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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 (polconv) and, in fact, basically shut off convergence by sitting the years very high.<br />
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On top of the country-specific calculation sits the (optional) regional or swing state effect calculation (SwingEffects), turned on by setting the swing states parameter (swseffects) to 1. The swing effects term has three components. The first is a world effect, whereby the democracy level in any given state (the "swingee") is affected by the world average level, with a parameter of impact (swingstdem) and a time adjustment (timeadj) . The second is a regionally powerful state factor, the regional "swinger" effect, with similar parameters. The third is a swing effect based on the average level of democracy in the region (RgDemoc).<br />
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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 and are available for optional use.<br />
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Also on top of the country-level effects sits the effect of global waves (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.<br />
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The democratic wave amplitude is a level that shifts over time (DemocWaveShift) with a normal maximum amplitude (democwvmax) and wave length (democwvlen), 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 (democimpus) compared to the normal impetus level from the U.S. (democimpusn) and the net impetus from other countries/forces (democimpoth).<br />
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Given both the global and regional/swing-state effects, it is possible to add these to the basic country calculation for the final computation of the level of democracy using the Polity scale. The size of the swing effects is constrained by an external parameter (swseffmax).<br />
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<header><hgroup><br />
== Socio-political Equations: Stability/State Failure ==<br />
</hgroup></header><br />
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.<br />
<br />
The extensive database of the project includes many measures of failure. IFs has variables representing three measures in each of the two categories, corresponding to the probability of the first year of a failure event (SFINSTABY1 and SFINTLWARY1), the probability of the first year or a continuing year (SFINSTABALL and SFINTLWARALL), and the magnitude of a first year or continuing event (SFINSTABMAG and SFINTLWARMAG).<br />
<br />
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.<br />
<br />
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’s Strategic Assessment Group (SAG), or to the model for the default values.<br />
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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’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 and that for the other five state failure variables varies with estimation. The analytic function obviously handles various formulations including linear and logarithmic.<br />
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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.<br />
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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.<br />
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<header><hgroup><br />
== Economic Inequality and Political Conflict ==<br />
</hgroup></header><br />
IFs does not yet include this important relationship. See Lichbach (1989) and Moore, Lindstrom, and O’Regan (1996) for analyses of how difficult this relationship is to specify. One critical problem is conceptualization of political conflict, political repression, political instability, political violence, political protest, etc. There are clearly many interacting, but separate dimensions for consideration. As Lichbach (1989: 448) says, "robust EI-PC laws have not been discovered."<br />
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&nbsp; &nbsp;<br />
<header><hgroup><br />
== Policy Equations Government Expenditures ==<br />
</hgroup></header><br />
The fiscal model of IFs is quite simple and builds on the computation of government consumption (GOVCON) in the economic model.<br />
<br />
IFs expenditures fall into six categories: military, health, education, research and development, other, and foreign aid. IFs divides total government consumption (GOVCON) into these five destination sectors (GDS) with a vector of government spending coefficients (GK) based on initial conditions. The user can change that default pattern of government spending over time with a multiplier parameter (gdsm). The model normalizes the allocation to assure that the money spent is no more or less than total government consumption.<br />
<br />
The last category of spending complicates the allocation of spending to destination categories. It is traditional not to think of foreign aid in terms of its percentage of the governmental budget (as we often think of defense or educational expenditures), but to think of it in terms of a percentage of the GDP. For instance, the United Nations has called for foreign aid spending equal to 0.7% (earlier 1.0%) of GDP of donor countries. Moreover, for some governments, foreign aid is not an expenditure, but a receipt and an addition to government revenues.<br />
<br />
Therefore IFs actually calculates foreign aid expenditures and receipts first and fixes those amounts (see the [http://www.du.edu/ifs/help/understand/sociopolitical/equations/policyforeign.html foreign aid equations]). It then allocates the amount of government spending that remains in the coffers of aid donors (or the augmented amount available to aid recipients) among the other categories, normalizing the allocation to the sum of the coefficients in those other categories.<br />
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There are several forward linkages of government spending that are important. A mortality multiplier (MORTMG) is computed for the demographic model, using changes in health spending from the initial year and a parameter of the impact of that spending (elashc).<br />
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Three of the forward linkages carry information on spending to the calculation of multifactor productivity in the economic production function, for additive rather than multiplicative use. One variable tracks change in education spending (CNGEDUC), modified by an elasticity of education on MFP (elmfped) and carries it forward. Another tracks changes in health spending (CNGHLTH) using a parameter (elmfphl). The third tracks changes in R&D spending with a parameter of impact (elmfprd). In each case there is a lag involved because of computational sequence.<br />
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Because essentially of an older variable form for the education term that is still used in the agricultural model’s production function, the first of the three terms is transferred to that older variable (LEFMG).<br />
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<header><hgroup><br />
== Policy Equations Foreign Aid ==<br />
</hgroup></header><br />
IFs uses a "pool" approach to aid (AID) rather than indicating bilateral flows from particular donors to particular recipients. That is, all aid from all donors flows into the pool and then all recipients draw proportions of the pool.<br />
<br />
IFs uses the aid value parameter (AIDDON) to calculate the aid (AID) from donors and AIDREC to calculate the targeted aid to recipients. The pool of aid donations determines the actual total level of interstate aid flows, however, and is allocated among potential recipients according to the proportions targeted for each.<br />
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Aid outflows are negative and the total aid pool given (AIDP) is the sum of the negative flows, while the total desired aid of recipients (AIDR) is the sum of positive flows.<br />
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A recomputation of aid for recipients distributes the aid pool across their demands.<br />
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http://www.du.edu/ifs/help/media/images/img00478.gif</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Sub-modules&diff=2136Sub-modules2017-02-26T19:35:39Z<p>StellahKwasi: </p>
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<div>[[Agriculture|Agriculture]]<br />
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[[Population|Population]]<br />
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[[Economics|Economics]]<br />
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[[Education|Education]]<br />
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[[Energy|Energy]]<br />
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[[Environment|Environment]]<br />
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[[Governance|Governance]]<br />
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[[Health|Health]]<br />
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[[Infrastructure|Infrastructure]]<br />
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[[Interstate_Politics_(IP)|Interstate Politics (IP)]]<br />
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[[Socio-Political|Socio-Political]]</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=File:Gs1.gif&diff=2135File:Gs1.gif2017-02-26T19:08:06Z<p>StellahKwasi: </p>
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<div></div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Government_Spending&diff=2134Government Spending2017-02-26T19:04:38Z<p>StellahKwasi: Created page with "The economic submodel provides total government spending. Government spending by category begins as a simple product of total government consumption and fractional shares by s..."</p>
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<div>The economic submodel provides total government spending. Government spending by category begins as a simple product of total government consumption and fractional shares by spending category.<br />
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Spending by type (military, health, education, research and development, other, and foreign aid) is largely specified exogenously, building on the initial conditions for each country/region. In addition, an action-reaction (arms-race) dynamic can be established in military spending if the action-reaction switch is turned on. After adjustments to foreign aid and military spending, spending in all categories is re-normalized to equal total governmental spending.<br />
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Educational spending is further broken out of total educational spending. The user can shift the spending across three educational levels (primary, secondary, and tertiary) through the use of an educational multiplier.</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Sub-modules&diff=2133Sub-modules2017-02-26T19:02:23Z<p>StellahKwasi: </p>
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<div>[[Agriculture|Agriculture]]<br />
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[[Population|Population]]<br />
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[[Economics|Economics]]<br />
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[[Education|Education]]<br />
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[[Energy|Energy]]<br />
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[[Environment|Environment]]<br />
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[[Governance|Governance]]<br />
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[[Health|Health]]<br />
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<div>The most recent and complete socio-political model documentation is available on Pardee'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.<br />
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<span>A substantial portion of the socio-political model of IFs is scattered throughout the other models. There are "policy handles" or intervention points throughout those models. For instance, in the population model, multipliers on the total fertility rate can reflect policy decisions (although they can also reflect the model user's judgment concerning social changes in the country or region, independent of policy). Patterns of regulation, subsidy, tax incidence, and provision of state services are so diffuse and complicated that we resort to looking at their aggregate consequences through various "policy handles" rather than trying to represent them explicitly.</span><br />
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For more information on this module, please use the links below or read more at [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
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== Structure and Agent System: Socio-Political ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Socio-political</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social fabric</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Levels of human well-being and institutional development (human and social capital)</div><div>&nbsp;</div><div>Cultural structures</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social expenditures</div><div>&nbsp;</div><div>Value change</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Growth in literacy and human development;</div><div>&nbsp;</div><div>Democratic development, state failure</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Government efforts to develop human capital through spending on health, education, R&D</div><br />
|}<br />
<br />
Unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no standard organizing structure that is widely used for representing socio-political systems. In the context of the TERRA project, IFs developed a multi-component approach to structure that might be called the "social fabric" (a la Robert Pestel).<br />
<br />
Although representation of agent-class behavior would be of special interest in a socio-political module, most relationships in IFs remain at the level of aggregate specifications.<br />
<br />
== Dominant Relations: Socio-political ==<br />
<br />
=== Domestic Socio-Political Change: Dominant Relations ===<br />
<br />
Social and political change occurs on three dimensions (social characteristics or individual life conditions, values, socio-political institutions and process). Although GDP per capita is strongly correlated with all dimensions of change, it might be more appropriate to conceptualize a syndrome or complex of developmental change than to portray an economically-driven process.<br />
<br />
For causal diagram see [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/index.html Socio-Political Flow Charts Overview].<br />
<br />
For equations see, for example, [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
=== Key dynamics are directly linked to the dominant relations: ===<br />
<br />
*The model computes some key social characteristics/life conditions, including life expectancy and fertility rates in the demographic model, but the user can affect them via multipliers (mortm, tfrm). Literacy rate is an endogenous function of education spending, which the user can influence (via gdsm).<br />
*The model computes value or cultural change on three dimensions: traditional versus secular-rational, survival versus self-expression, and modernism versus postmodernism, which the user can affect via additive factors (tradsrateadd, survseadd, matpostradd).<br />
*Freedom, democracy (the POLITY measure), autocracy, economic freedom, and the status of women are all computed endogenously but can all be shifted by the user via multipliers (freedomm, democm, autocm, econfreem, gemm)<br />
<br />
=== Domestic Socio-Political Change: Selected Added Value ===<br />
<br />
The larger socio-political model provides representation and control over government spending on education, health, the military, R&D, foreign aid, and a residual category. Military spending is linked to interstate politics, both as a driver of threat and as a result of action-and-reaction based arms spending. The sub-model provides aggregated indicators of the physical quality of life and the human development index.<br />
<br />
== Socio-political Flow Charts ==<br />
<br />
[[File:SP1.gif|frame|center|SP1.gif]]<br />
<br />
The social and political module represents a complex of interacting structures and processes. These include:<br />
<br />
*The various social characteristics or life conditions of individuals<br />
*Human values, beliefs, and orientations’<br />
*Social and political structures, informal as well as formal<br />
*Social and political processes, both domestic and international<br />
<br />
Cultural foundations frame all of these components. And all of the components interact closely with human demographic and economic systems.<br />
<br />
The socio-political elements of IFs are among the most dynamically evolving aspects of the overall modeling system.&nbsp;Much, but not everything in the above figure has been fully represented yet within IFs; the figure indicates direction of development and shows implemented elements in italics.<br />
<br />
For more, please read the links below.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Social Characteristics: Life Conditions ==<br />
<br />
Individuals are the foundations of society. Many social indicators are actually aggregated indicators of their condition. The Human Development Index (HDI) is a widely-used summary measure of that life condition, based on life expectancy, educational attainment, and GDP per capita.<br />
<br />
[[File:Sp2.gif|frame|center|Sp2.gif]]<br />
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== Physical Quality of Life (PQLI) ==<br />
<br />
The Overseas Development Council (then under the leadership of Jim Grant) developed and pu[[File:Sp3.gif|frame|right|Physical Quality of Life]]blicized a measure of (physical) quality of life (the PQLI) many years ago. It combines literarcy rate, infant mortality rate, and life expectancy, using scales from the lowest to the highest values in the global system. It weights the three scales equally. The literacy rate is, in turn, a function of the per capita spending levels on education, estimated cross-sectionally. In many respects the PQLI was a predecessor of the [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/lifeconditions.html human development index (HDI)].<br />
<br />
Based on country/region-specific Physical Quality of Life, it is possible to compute world quality of life (WPQLI) and the North-South gap in quality of life (NSPQLI). Given country-specific literacy rates, it is also possible to compute world literacy (WLIT).<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Income Distribution ==<br />
<br />
Income distribution is represented by the share of national income earned by the poorest 20 percent of the population. That share is obtained from data whenever possible, but is estimated from a cross-sectional relationship when necessary and changed over time by that relationship (the values tend, however, to be very stable both in the real world and in the model). Because initial conditions of variables affected by income share, such as fertility and mortality rates, already reflect existing income distributions, it is only the changes in that distribution relative to the expected value that the model uses in such relationships. A parameter (incshrm) is available to change income share and thus affect those variables influenced by it.<br />
<br />
[[File:Sp4.gif|frame|center|Sp4.gif]]<br />
<br />
== Social Characteristics: Networking ==<br />
<br />
Being electronically networked is an increasingly important aspect of human life condition. The numb[[File:Sp5.gif|frame|right|444x270px|Sp5.gif]]er of networked persons (NUMNWP) is a function primarily of the growth rate in that number (NUMNWPGR). It is ultimately constrained, however, by the size of the population and by the number of connections and organizational memberships that people can have (numnwplim). The growth in networked person number slows as it approaches the ultimate limit. The model user can affect the growth pattern via a multiplier on the growth rate (numnwpgrm).<br />
<br />
This approach was added to IFs during the TERRA project and draws on the thinking of Tom Tesch and Pol Descamps<br />
<br />
== Social Values and Cultural Evolution ==<br />
<br />
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.<br />
<br />
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.<br />
<br />
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 "wvsagesw," of computing country/region change over time in the three dimensions by functions for each cohort (value of wvsagesw = 1) or by computing change only in the first cohort and then advancting that through time (value of wvsagesw = 2).<br />
<br />
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 (matpostradd, etc.).<br />
<br />
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.<br />
<br />
[[File:Sp6.gif|frame|center|Sp6.gif]]<br />
<br />
== Social Organization and Change ==<br />
<br />
The sociopolitical module computes change in freedom (political and economic) and the status of women. For freedom it uses both the measure of the Freedom House and the combined measure for democracy (building on democracy and autocracy) of the POLITY project. It also computes a measure of economic freedom and of gender equality.<br />
<br />
[[File:Sp7.gif|frame|center|Sp7.gif]]<br />
<br />
== Social Organization: Stability/State Failure ==<br />
<br />
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.<br />
<br />
IFs uses the same primary variables (infant mortality, democracy, and trade openness) as the State Failure project to drive forecasts of the probability of individual events of state failure, of ongoing episodes of it, and of the magnitude of episodes. In addition, it allows the use in the formulation of GDP per capita and years of education. Many other linkages have been and can be explored, including cultural regions.<br />
<br />
[[File:Sp8.gif|frame|center]]</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=File:Sp8.gif&diff=2131File:Sp8.gif2017-02-26T18:51:24Z<p>StellahKwasi: </p>
<hr />
<div></div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Socio-Political&diff=2130Socio-Political2017-02-26T18:50:54Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete socio-political model documentation is available on Pardee'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.<br />
<br />
<span>A substantial portion of the socio-political model of IFs is scattered throughout the other models. There are "policy handles" or intervention points throughout those models. For instance, in the population model, multipliers on the total fertility rate can reflect policy decisions (although they can also reflect the model user's judgment concerning social changes in the country or region, independent of policy). Patterns of regulation, subsidy, tax incidence, and provision of state services are so diffuse and complicated that we resort to looking at their aggregate consequences through various "policy handles" rather than trying to represent them explicitly.</span><br />
<br />
For more information on this module, please use the links below or read more at [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
== Structure and Agent System: Socio-Political ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Socio-political</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social fabric</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Levels of human well-being and institutional development (human and social capital)</div><div>&nbsp;</div><div>Cultural structures</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social expenditures</div><div>&nbsp;</div><div>Value change</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Growth in literacy and human development;</div><div>&nbsp;</div><div>Democratic development, state failure</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Government efforts to develop human capital through spending on health, education, R&D</div><br />
|}<br />
<br />
Unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no standard organizing structure that is widely used for representing socio-political systems. In the context of the TERRA project, IFs developed a multi-component approach to structure that might be called the "social fabric" (a la Robert Pestel).<br />
<br />
Although representation of agent-class behavior would be of special interest in a socio-political module, most relationships in IFs remain at the level of aggregate specifications.<br />
<br />
== Dominant Relations: Socio-political ==<br />
<br />
=== Domestic Socio-Political Change: Dominant Relations ===<br />
<br />
Social and political change occurs on three dimensions (social characteristics or individual life conditions, values, socio-political institutions and process). Although GDP per capita is strongly correlated with all dimensions of change, it might be more appropriate to conceptualize a syndrome or complex of developmental change than to portray an economically-driven process.<br />
<br />
For causal diagram see [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/index.html Socio-Political Flow Charts Overview].<br />
<br />
For equations see, for example, [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
=== Key dynamics are directly linked to the dominant relations: ===<br />
<br />
*The model computes some key social characteristics/life conditions, including life expectancy and fertility rates in the demographic model, but the user can affect them via multipliers (mortm, tfrm). Literacy rate is an endogenous function of education spending, which the user can influence (via gdsm).<br />
*The model computes value or cultural change on three dimensions: traditional versus secular-rational, survival versus self-expression, and modernism versus postmodernism, which the user can affect via additive factors (tradsrateadd, survseadd, matpostradd).<br />
*Freedom, democracy (the POLITY measure), autocracy, economic freedom, and the status of women are all computed endogenously but can all be shifted by the user via multipliers (freedomm, democm, autocm, econfreem, gemm)<br />
<br />
=== Domestic Socio-Political Change: Selected Added Value ===<br />
<br />
The larger socio-political model provides representation and control over government spending on education, health, the military, R&D, foreign aid, and a residual category. Military spending is linked to interstate politics, both as a driver of threat and as a result of action-and-reaction based arms spending. The sub-model provides aggregated indicators of the physical quality of life and the human development index.<br />
<br />
== Socio-political Flow Charts ==<br />
<br />
[[File:SP1.gif|frame|center|SP1.gif]]<br />
<br />
The social and political module represents a complex of interacting structures and processes. These include:<br />
<br />
*The various social characteristics or life conditions of individuals<br />
*Human values, beliefs, and orientations’<br />
*Social and political structures, informal as well as formal<br />
*Social and political processes, both domestic and international<br />
<br />
Cultural foundations frame all of these components. And all of the components interact closely with human demographic and economic systems.<br />
<br />
The socio-political elements of IFs are among the most dynamically evolving aspects of the overall modeling system.&nbsp;Much, but not everything in the above figure has been fully represented yet within IFs; the figure indicates direction of development and shows implemented elements in italics.<br />
<br />
For more, please read the links below.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Social Characteristics: Life Conditions ==<br />
<br />
Individuals are the foundations of society. Many social indicators are actually aggregated indicators of their condition. The Human Development Index (HDI) is a widely-used summary measure of that life condition, based on life expectancy, educational attainment, and GDP per capita.<br />
<br />
[[File:Sp2.gif|frame|center|Sp2.gif]]<br />
<br />
== Physical Quality of Life (PQLI) ==<br />
<br />
The Overseas Development Council (then under the leadership of Jim Grant) developed and pu[[File:Sp3.gif|frame|right|Physical Quality of Life]]blicized a measure of (physical) quality of life (the PQLI) many years ago. It combines literarcy rate, infant mortality rate, and life expectancy, using scales from the lowest to the highest values in the global system. It weights the three scales equally. The literacy rate is, in turn, a function of the per capita spending levels on education, estimated cross-sectionally. In many respects the PQLI was a predecessor of the [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/lifeconditions.html human development index (HDI)].<br />
<br />
Based on country/region-specific Physical Quality of Life, it is possible to compute world quality of life (WPQLI) and the North-South gap in quality of life (NSPQLI). Given country-specific literacy rates, it is also possible to compute world literacy (WLIT).<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Income Distribution ==<br />
<br />
Income distribution is represented by the share of national income earned by the poorest 20 percent of the population. That share is obtained from data whenever possible, but is estimated from a cross-sectional relationship when necessary and changed over time by that relationship (the values tend, however, to be very stable both in the real world and in the model). Because initial conditions of variables affected by income share, such as fertility and mortality rates, already reflect existing income distributions, it is only the changes in that distribution relative to the expected value that the model uses in such relationships. A parameter (incshrm) is available to change income share and thus affect those variables influenced by it.<br />
<br />
[[File:Sp4.gif|frame|center|Sp4.gif]]<br />
<br />
== Social Characteristics: Networking ==<br />
<br />
Being electronically networked is an increasingly important aspect of human life condition. The numb[[File:Sp5.gif|frame|right|444x270px|Sp5.gif]]er of networked persons (NUMNWP) is a function primarily of the growth rate in that number (NUMNWPGR). It is ultimately constrained, however, by the size of the population and by the number of connections and organizational memberships that people can have (numnwplim). The growth in networked person number slows as it approaches the ultimate limit. The model user can affect the growth pattern via a multiplier on the growth rate (numnwpgrm).<br />
<br />
This approach was added to IFs during the TERRA project and draws on the thinking of Tom Tesch and Pol Descamps<br />
<br />
== Social Values and Cultural Evolution ==<br />
<br />
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.<br />
<br />
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.<br />
<br />
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 "wvsagesw," of computing country/region change over time in the three dimensions by functions for each cohort (value of wvsagesw = 1) or by computing change only in the first cohort and then advancting that through time (value of wvsagesw = 2).<br />
<br />
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 (matpostradd, etc.).<br />
<br />
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.<br />
<br />
[[File:Sp6.gif|frame|center|Sp6.gif]]<br />
<br />
== Social Organization and Change ==<br />
<br />
The sociopolitical module computes change in freedom (political and economic) and the status of women. For freedom it uses both the measure of the Freedom House and the combined measure for democracy (building on democracy and autocracy) of the POLITY project. It also computes a measure of economic freedom and of gender equality.<br />
<br />
[[File:Sp7.gif|frame|center|Sp7.gif]]<br />
<br />
== Social Organization: Stability/State Failure ==<br />
<br />
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.<br />
<br />
IFs uses the same primary variables (infant mortality, democracy, and trade openness) as the State Failure project to drive forecasts of the probability of individual events of state failure, of ongoing episodes of it, and of the magnitude of episodes. In addition, it allows the use in the formulation of GDP per capita and years of education. Many other linkages have been and can be explored, including cultural regions.</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Socio-Political&diff=2129Socio-Political2017-02-26T18:00:54Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete socio-political model documentation is available on Pardee'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.<br />
<br />
<span>A substantial portion of the socio-political model of IFs is scattered throughout the other models. There are "policy handles" or intervention points throughout those models. For instance, in the population model, multipliers on the total fertility rate can reflect policy decisions (although they can also reflect the model user's judgment concerning social changes in the country or region, independent of policy). Patterns of regulation, subsidy, tax incidence, and provision of state services are so diffuse and complicated that we resort to looking at their aggregate consequences through various "policy handles" rather than trying to represent them explicitly.</span><br />
<br />
For more information on this module, please use the links below or read more at [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
== Structure and Agent System: Socio-Political ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Socio-political</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social fabric</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Levels of human well-being and institutional development (human and social capital)</div><div>&nbsp;</div><div>Cultural structures</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social expenditures</div><div>&nbsp;</div><div>Value change</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Growth in literacy and human development;</div><div>&nbsp;</div><div>Democratic development, state failure</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Government efforts to develop human capital through spending on health, education, R&D</div><br />
|}<br />
<br />
Unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no standard organizing structure that is widely used for representing socio-political systems. In the context of the TERRA project, IFs developed a multi-component approach to structure that might be called the "social fabric" (a la Robert Pestel).<br />
<br />
Although representation of agent-class behavior would be of special interest in a socio-political module, most relationships in IFs remain at the level of aggregate specifications.<br />
<br />
== Dominant Relations: Socio-political ==<br />
<br />
=== Domestic Socio-Political Change: Dominant Relations ===<br />
<br />
Social and political change occurs on three dimensions (social characteristics or individual life conditions, values, socio-political institutions and process). Although GDP per capita is strongly correlated with all dimensions of change, it might be more appropriate to conceptualize a syndrome or complex of developmental change than to portray an economically-driven process.<br />
<br />
For causal diagram see [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/index.html Socio-Political Flow Charts Overview].<br />
<br />
For equations see, for example, [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
=== Key dynamics are directly linked to the dominant relations: ===<br />
<br />
*The model computes some key social characteristics/life conditions, including life expectancy and fertility rates in the demographic model, but the user can affect them via multipliers (mortm, tfrm). Literacy rate is an endogenous function of education spending, which the user can influence (via gdsm).<br />
*The model computes value or cultural change on three dimensions: traditional versus secular-rational, survival versus self-expression, and modernism versus postmodernism, which the user can affect via additive factors (tradsrateadd, survseadd, matpostradd).<br />
*Freedom, democracy (the POLITY measure), autocracy, economic freedom, and the status of women are all computed endogenously but can all be shifted by the user via multipliers (freedomm, democm, autocm, econfreem, gemm)<br />
<br />
=== Domestic Socio-Political Change: Selected Added Value ===<br />
<br />
The larger socio-political model provides representation and control over government spending on education, health, the military, R&D, foreign aid, and a residual category. Military spending is linked to interstate politics, both as a driver of threat and as a result of action-and-reaction based arms spending. The sub-model provides aggregated indicators of the physical quality of life and the human development index.<br />
<br />
== Socio-political Flow Charts ==<br />
<br />
[[File:SP1.gif|frame|center|SP1.gif]]<br />
<br />
The social and political module represents a complex of interacting structures and processes. These include:<br />
<br />
*The various social characteristics or life conditions of individuals<br />
*Human values, beliefs, and orientations’<br />
*Social and political structures, informal as well as formal<br />
*Social and political processes, both domestic and international<br />
<br />
Cultural foundations frame all of these components. And all of the components interact closely with human demographic and economic systems.<br />
<br />
The socio-political elements of IFs are among the most dynamically evolving aspects of the overall modeling system.&nbsp;Much, but not everything in the above figure has been fully represented yet within IFs; the figure indicates direction of development and shows implemented elements in italics.<br />
<br />
For more, please read the links below.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Social Characteristics: Life Conditions ==<br />
<br />
Individuals are the foundations of society. Many social indicators are actually aggregated indicators of their condition. The Human Development Index (HDI) is a widely-used summary measure of that life condition, based on life expectancy, educational attainment, and GDP per capita.<br />
<br />
[[File:Sp2.gif|frame|center|Sp2.gif]]<br />
<br />
== Physical Quality of Life (PQLI) ==<br />
<br />
The Overseas Development Council (then under the leadership of Jim Grant) developed and pu[[File:Sp3.gif|frame|right|Physical Quality of Life]]blicized a measure of (physical) quality of life (the PQLI) many years ago. It combines literarcy rate, infant mortality rate, and life expectancy, using scales from the lowest to the highest values in the global system. It weights the three scales equally. The literacy rate is, in turn, a function of the per capita spending levels on education, estimated cross-sectionally. In many respects the PQLI was a predecessor of the [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/lifeconditions.html human development index (HDI)].<br />
<br />
Based on country/region-specific Physical Quality of Life, it is possible to compute world quality of life (WPQLI) and the North-South gap in quality of life (NSPQLI). Given country-specific literacy rates, it is also possible to compute world literacy (WLIT).<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Income Distribution ==<br />
<br />
Income distribution is represented by the share of national income earned by the poorest 20 percent of the population. That share is obtained from data whenever possible, but is estimated from a cross-sectional relationship when necessary and changed over time by that relationship (the values tend, however, to be very stable both in the real world and in the model). Because initial conditions of variables affected by income share, such as fertility and mortality rates, already reflect existing income distributions, it is only the changes in that distribution relative to the expected value that the model uses in such relationships. A parameter (incshrm) is available to change income share and thus affect those variables influenced by it.<br />
<br />
[[File:Sp4.gif|frame|center|Sp4.gif]]<br />
<br />
== Social Characteristics: Networking ==<br />
<br />
Being electronically networked is an increasingly important aspect of human life condition. The numb[[File:Sp5.gif|frame|right|444x270px|Sp5.gif]]er of networked persons (NUMNWP) is a function primarily of the growth rate in that number (NUMNWPGR). It is ultimately constrained, however, by the size of the population and by the number of connections and organizational memberships that people can have (numnwplim). The growth in networked person number slows as it approaches the ultimate limit. The model user can affect the growth pattern via a multiplier on the growth rate (numnwpgrm).<br />
<br />
This approach was added to IFs during the TERRA project and draws on the thinking of Tom Tesch and Pol Descamps<br />
<br />
== Social Values and Cultural Evolution ==<br />
<br />
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.<br />
<br />
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.<br />
<br />
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 "wvsagesw," of computing country/region change over time in the three dimensions by functions for each cohort (value of wvsagesw = 1) or by computing change only in the first cohort and then advancting that through time (value of wvsagesw = 2).<br />
<br />
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 (matpostradd, etc.).<br />
<br />
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.<br />
<br />
[[File:Sp6.gif|frame|center|Sp6.gif]]<br />
<br />
== Social Organization and Change ==<br />
<br />
The sociopolitical module computes change in freedom (political and economic) and the status of women. For freedom it uses both the measure of the Freedom House and the combined measure for democracy (building on democracy and autocracy) of the POLITY project. It also computes a measure of economic freedom and of gender equality.<br />
<br />
[[File:Sp7.gif|frame|center|Sp7.gif]] <br />
<br />
== Social Organization: Stability/State Failure ==<br />
<br />
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.<br />
<br />
IFs uses the same primary variables (infant mortality, democracy, and trade openness) as the State Failure project to drive forecasts of the probability of individual events of state failure, of ongoing episodes of it, and of the magnitude of episodes. In addition, it allows the use in the formulation of GDP per capita and years of education. Many other linkages have been and can be explored, including cultural regions.</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Socio-Political&diff=2128Socio-Political2017-02-26T00:08:32Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete socio-political model documentation is available on Pardee'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.<br />
<br />
<span>A substantial portion of the socio-political model of IFs is scattered throughout the other models. There are "policy handles" or intervention points throughout those models. For instance, in the population model, multipliers on the total fertility rate can reflect policy decisions (although they can also reflect the model user's judgment concerning social changes in the country or region, independent of policy). Patterns of regulation, subsidy, tax incidence, and provision of state services are so diffuse and complicated that we resort to looking at their aggregate consequences through various "policy handles" rather than trying to represent them explicitly.</span><br />
<br />
For more information on this module, please use the links below or read more at [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
== Structure and Agent System: Socio-Political ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Socio-political</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social fabric</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Levels of human well-being and institutional development (human and social capital)</div><div>&nbsp;</div><div>Cultural structures</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social expenditures</div><div>&nbsp;</div><div>Value change</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Growth in literacy and human development;</div><div>&nbsp;</div><div>Democratic development, state failure</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Government efforts to develop human capital through spending on health, education, R&D</div><br />
|}<br />
<br />
Unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no standard organizing structure that is widely used for representing socio-political systems. In the context of the TERRA project, IFs developed a multi-component approach to structure that might be called the "social fabric" (a la Robert Pestel).<br />
<br />
Although representation of agent-class behavior would be of special interest in a socio-political module, most relationships in IFs remain at the level of aggregate specifications.<br />
<br />
== Dominant Relations: Socio-political ==<br />
<br />
=== Domestic Socio-Political Change: Dominant Relations ===<br />
<br />
Social and political change occurs on three dimensions (social characteristics or individual life conditions, values, socio-political institutions and process). Although GDP per capita is strongly correlated with all dimensions of change, it might be more appropriate to conceptualize a syndrome or complex of developmental change than to portray an economically-driven process.<br />
<br />
For causal diagram see [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/index.html Socio-Political Flow Charts Overview].<br />
<br />
For equations see, for example, [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
=== Key dynamics are directly linked to the dominant relations: ===<br />
<br />
*The model computes some key social characteristics/life conditions, including life expectancy and fertility rates in the demographic model, but the user can affect them via multipliers (mortm, tfrm). Literacy rate is an endogenous function of education spending, which the user can influence (via gdsm).<br />
*The model computes value or cultural change on three dimensions: traditional versus secular-rational, survival versus self-expression, and modernism versus postmodernism, which the user can affect via additive factors (tradsrateadd, survseadd, matpostradd).<br />
*Freedom, democracy (the POLITY measure), autocracy, economic freedom, and the status of women are all computed endogenously but can all be shifted by the user via multipliers (freedomm, democm, autocm, econfreem, gemm)<br />
<br />
=== Domestic Socio-Political Change: Selected Added Value ===<br />
<br />
The larger socio-political model provides representation and control over government spending on education, health, the military, R&D, foreign aid, and a residual category. Military spending is linked to interstate politics, both as a driver of threat and as a result of action-and-reaction based arms spending. The sub-model provides aggregated indicators of the physical quality of life and the human development index.<br />
<br />
== Socio-political Flow Charts ==<br />
<br />
[[File:SP1.gif|frame|center|SP1.gif]]<br />
<br />
The social and political module represents a complex of interacting structures and processes. These include:<br />
<br />
*The various social characteristics or life conditions of individuals<br />
*Human values, beliefs, and orientations’<br />
*Social and political structures, informal as well as formal<br />
*Social and political processes, both domestic and international<br />
<br />
Cultural foundations frame all of these components. And all of the components interact closely with human demographic and economic systems.<br />
<br />
The socio-political elements of IFs are among the most dynamically evolving aspects of the overall modeling system.&nbsp;Much, but not everything in the above figure has been fully represented yet within IFs; the figure indicates direction of development and shows implemented elements in italics.<br />
<br />
For more, please read the links below.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Social Characteristics: Life Conditions ==<br />
<br />
Individuals are the foundations of society. Many social indicators are actually aggregated indicators of their condition. The Human Development Index (HDI) is a widely-used summary measure of that life condition, based on life expectancy, educational attainment, and GDP per capita.<br />
<br />
[[File:Sp2.gif|frame|center|Sp2.gif]]<br />
<br />
== Physical Quality of Life (PQLI) ==<br />
<br />
The Overseas Development Council (then under the leadership of Jim Grant) developed and pu[[File:Sp3.gif|frame|right|Physical Quality of Life]]blicized a measure of (physical) quality of life (the PQLI) many years ago. It combines literarcy rate, infant mortality rate, and life expectancy, using scales from the lowest to the highest values in the global system. It weights the three scales equally. The literacy rate is, in turn, a function of the per capita spending levels on education, estimated cross-sectionally. In many respects the PQLI was a predecessor of the [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/lifeconditions.html human development index (HDI)].<br />
<br />
Based on country/region-specific Physical Quality of Life, it is possible to compute world quality of life (WPQLI) and the North-South gap in quality of life (NSPQLI). Given country-specific literacy rates, it is also possible to compute world literacy (WLIT).<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Income Distribution ==<br />
<br />
Income distribution is represented by the share of national income earned by the poorest 20 percent of the population. That share is obtained from data whenever possible, but is estimated from a cross-sectional relationship when necessary and changed over time by that relationship (the values tend, however, to be very stable both in the real world and in the model). Because initial conditions of variables affected by income share, such as fertility and mortality rates, already reflect existing income distributions, it is only the changes in that distribution relative to the expected value that the model uses in such relationships. A parameter (incshrm) is available to change income share and thus affect those variables influenced by it.<br />
<br />
[[File:Sp4.gif|frame|center|Sp4.gif]]<br />
<br />
== Social Characteristics: Networking ==<br />
<br />
Being electronically networked is an increasingly important aspect of human life condition. The numb[[File:Sp5.gif|frame|right|444x270px|Sp5.gif]]er of networked persons (NUMNWP) is a function primarily of the growth rate in that number (NUMNWPGR). It is ultimately constrained, however, by the size of the population and by the number of connections and organizational memberships that people can have (numnwplim). The growth in networked person number slows as it approaches the ultimate limit. The model user can affect the growth pattern via a multiplier on the growth rate (numnwpgrm).<br />
<br />
This approach was added to IFs during the TERRA project and draws on the thinking of Tom Tesch and Pol Descamps<br />
<br />
== Social Values and Cultural Evolution ==<br />
<br />
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.<br />
<br />
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.<br />
<br />
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 "wvsagesw," of computing country/region change over time in the three dimensions by functions for each cohort (value of wvsagesw = 1) or by computing change only in the first cohort and then advancting that through time (value of wvsagesw = 2).<br />
<br />
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 (matpostradd, etc.).<br />
<br />
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.<br />
<br />
[[File:Sp6.gif|frame|center|Sp6.gif]]<br />
<br />
== Social Organization and Change ==<br />
<br />
The sociopolitical module computes change in freedom (political and economic) and the status of women. For freedom it uses both the measure of the Freedom House and the combined measure for democracy (building on democracy and autocracy) of the POLITY project. It also computes a measure of economic freedom and of gender equality.<br />
<br />
[[File:Sp7.gif|frame|center]]<br />
<header><hgroup><br />
== Social Organization: Stability/State Failure ==<br />
</hgroup></header><br />
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.<br />
<br />
IFs uses the same primary variables (infant mortality, democracy, and trade openness) as the State Failure project to drive forecasts of the probability of individual events of state failure, of ongoing episodes of it, and of the magnitude of episodes. In addition, it allows the use in the formulation of GDP per capita and years of education. Many other linkages have been and can be explored, including cultural regions.</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=File:Sp7.gif&diff=2127File:Sp7.gif2017-02-26T00:04:11Z<p>StellahKwasi: </p>
<hr />
<div></div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Socio-Political&diff=2126Socio-Political2017-02-26T00:03:34Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete socio-political model documentation is available on Pardee'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.<br />
<br />
<span>A substantial portion of the socio-political model of IFs is scattered throughout the other models. There are "policy handles" or intervention points throughout those models. For instance, in the population model, multipliers on the total fertility rate can reflect policy decisions (although they can also reflect the model user's judgment concerning social changes in the country or region, independent of policy). Patterns of regulation, subsidy, tax incidence, and provision of state services are so diffuse and complicated that we resort to looking at their aggregate consequences through various "policy handles" rather than trying to represent them explicitly.</span><br />
<br />
For more information on this module, please use the links below or read more at [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
== Structure and Agent System: Socio-Political ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Socio-political</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social fabric</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Levels of human well-being and institutional development (human and social capital)</div><div>&nbsp;</div><div>Cultural structures</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social expenditures</div><div>&nbsp;</div><div>Value change</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Growth in literacy and human development;</div><div>&nbsp;</div><div>Democratic development, state failure</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Government efforts to develop human capital through spending on health, education, R&D</div><br />
|}<br />
<br />
Unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no standard organizing structure that is widely used for representing socio-political systems. In the context of the TERRA project, IFs developed a multi-component approach to structure that might be called the "social fabric" (a la Robert Pestel).<br />
<br />
Although representation of agent-class behavior would be of special interest in a socio-political module, most relationships in IFs remain at the level of aggregate specifications.<br />
<br />
== Dominant Relations: Socio-political ==<br />
<br />
=== Domestic Socio-Political Change: Dominant Relations ===<br />
<br />
Social and political change occurs on three dimensions (social characteristics or individual life conditions, values, socio-political institutions and process). Although GDP per capita is strongly correlated with all dimensions of change, it might be more appropriate to conceptualize a syndrome or complex of developmental change than to portray an economically-driven process.<br />
<br />
For causal diagram see [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/index.html Socio-Political Flow Charts Overview].<br />
<br />
For equations see, for example, [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
=== Key dynamics are directly linked to the dominant relations: ===<br />
<br />
*The model computes some key social characteristics/life conditions, including life expectancy and fertility rates in the demographic model, but the user can affect them via multipliers (mortm, tfrm). Literacy rate is an endogenous function of education spending, which the user can influence (via gdsm).<br />
*The model computes value or cultural change on three dimensions: traditional versus secular-rational, survival versus self-expression, and modernism versus postmodernism, which the user can affect via additive factors (tradsrateadd, survseadd, matpostradd).<br />
*Freedom, democracy (the POLITY measure), autocracy, economic freedom, and the status of women are all computed endogenously but can all be shifted by the user via multipliers (freedomm, democm, autocm, econfreem, gemm)<br />
<br />
=== Domestic Socio-Political Change: Selected Added Value ===<br />
<br />
The larger socio-political model provides representation and control over government spending on education, health, the military, R&D, foreign aid, and a residual category. Military spending is linked to interstate politics, both as a driver of threat and as a result of action-and-reaction based arms spending. The sub-model provides aggregated indicators of the physical quality of life and the human development index.<br />
<br />
== Socio-political Flow Charts ==<br />
<br />
[[File:SP1.gif|frame|center|SP1.gif]]<br />
<br />
The social and political module represents a complex of interacting structures and processes. These include:<br />
<br />
*The various social characteristics or life conditions of individuals<br />
*Human values, beliefs, and orientations’<br />
*Social and political structures, informal as well as formal<br />
*Social and political processes, both domestic and international<br />
<br />
Cultural foundations frame all of these components. And all of the components interact closely with human demographic and economic systems.<br />
<br />
The socio-political elements of IFs are among the most dynamically evolving aspects of the overall modeling system.&nbsp;Much, but not everything in the above figure has been fully represented yet within IFs; the figure indicates direction of development and shows implemented elements in italics.<br />
<br />
For more, please read the links below.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Social Characteristics: Life Conditions ==<br />
<br />
Individuals are the foundations of society. Many social indicators are actually aggregated indicators of their condition. The Human Development Index (HDI) is a widely-used summary measure of that life condition, based on life expectancy, educational attainment, and GDP per capita.<br />
<br />
[[File:Sp2.gif|frame|center|Sp2.gif]]<br />
<br />
== Physical Quality of Life (PQLI) ==<br />
<br />
The Overseas Development Council (then under the leadership of Jim Grant) developed and pu[[File:Sp3.gif|frame|right|Physical Quality of Life]]blicized a measure of (physical) quality of life (the PQLI) many years ago. It combines literarcy rate, infant mortality rate, and life expectancy, using scales from the lowest to the highest values in the global system. It weights the three scales equally. The literacy rate is, in turn, a function of the per capita spending levels on education, estimated cross-sectionally. In many respects the PQLI was a predecessor of the [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/lifeconditions.html human development index (HDI)].<br />
<br />
Based on country/region-specific Physical Quality of Life, it is possible to compute world quality of life (WPQLI) and the North-South gap in quality of life (NSPQLI). Given country-specific literacy rates, it is also possible to compute world literacy (WLIT).<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Income Distribution ==<br />
<br />
Income distribution is represented by the share of national income earned by the poorest 20 percent of the population. That share is obtained from data whenever possible, but is estimated from a cross-sectional relationship when necessary and changed over time by that relationship (the values tend, however, to be very stable both in the real world and in the model). Because initial conditions of variables affected by income share, such as fertility and mortality rates, already reflect existing income distributions, it is only the changes in that distribution relative to the expected value that the model uses in such relationships. A parameter (incshrm) is available to change income share and thus affect those variables influenced by it.<br />
<br />
[[File:Sp4.gif|frame|center|Sp4.gif]]<br />
<br />
== Social Characteristics: Networking ==<br />
<br />
Being electronically networked is an increasingly important aspect of human life condition. The numb[[File:Sp5.gif|frame|right|444x270px|Sp5.gif]]er of networked persons (NUMNWP) is a function primarily of the growth rate in that number (NUMNWPGR). It is ultimately constrained, however, by the size of the population and by the number of connections and organizational memberships that people can have (numnwplim). The growth in networked person number slows as it approaches the ultimate limit. The model user can affect the growth pattern via a multiplier on the growth rate (numnwpgrm).<br />
<br />
This approach was added to IFs during the TERRA project and draws on the thinking of Tom Tesch and Pol Descamps<br />
<br />
== Social Values and Cultural Evolution ==<br />
<br />
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.<br />
<br />
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.<br />
<br />
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 "wvsagesw," of computing country/region change over time in the three dimensions by functions for each cohort (value of wvsagesw = 1) or by computing change only in the first cohort and then advancting that through time (value of wvsagesw = 2).<br />
<br />
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 (matpostradd, etc.).<br />
<br />
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.<br />
<br />
[[File:Sp6.gif|frame|center|Sp6.gif]] <br />
<br />
== Social Organization and Change ==<br />
<br />
The sociopolitical module computes change in freedom (political and economic) and the status of women. For freedom it uses both the measure of the Freedom House and the combined measure for democracy (building on democracy and autocracy) of the POLITY project. It also computes a measure of economic freedom and of gender equality.</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Socio-Political&diff=2125Socio-Political2017-02-26T00:01:55Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete socio-political model documentation is available on Pardee'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.<br />
<br />
<span>A substantial portion of the socio-political model of IFs is scattered throughout the other models. There are "policy handles" or intervention points throughout those models. For instance, in the population model, multipliers on the total fertility rate can reflect policy decisions (although they can also reflect the model user's judgment concerning social changes in the country or region, independent of policy). Patterns of regulation, subsidy, tax incidence, and provision of state services are so diffuse and complicated that we resort to looking at their aggregate consequences through various "policy handles" rather than trying to represent them explicitly.</span><br />
<br />
For more information on this module, please use the links below or read more at [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
== Structure and Agent System: Socio-Political ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Socio-political</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social fabric</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Levels of human well-being and institutional development (human and social capital)</div><div>&nbsp;</div><div>Cultural structures</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social expenditures</div><div>&nbsp;</div><div>Value change</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Growth in literacy and human development;</div><div>&nbsp;</div><div>Democratic development, state failure</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Government efforts to develop human capital through spending on health, education, R&D</div><br />
|}<br />
<br />
Unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no standard organizing structure that is widely used for representing socio-political systems. In the context of the TERRA project, IFs developed a multi-component approach to structure that might be called the "social fabric" (a la Robert Pestel).<br />
<br />
Although representation of agent-class behavior would be of special interest in a socio-political module, most relationships in IFs remain at the level of aggregate specifications.<br />
<br />
== Dominant Relations: Socio-political ==<br />
<br />
=== Domestic Socio-Political Change: Dominant Relations ===<br />
<br />
Social and political change occurs on three dimensions (social characteristics or individual life conditions, values, socio-political institutions and process). Although GDP per capita is strongly correlated with all dimensions of change, it might be more appropriate to conceptualize a syndrome or complex of developmental change than to portray an economically-driven process.<br />
<br />
For causal diagram see [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/index.html Socio-Political Flow Charts Overview].<br />
<br />
For equations see, for example, [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
=== Key dynamics are directly linked to the dominant relations: ===<br />
<br />
*The model computes some key social characteristics/life conditions, including life expectancy and fertility rates in the demographic model, but the user can affect them via multipliers (mortm, tfrm). Literacy rate is an endogenous function of education spending, which the user can influence (via gdsm).<br />
*The model computes value or cultural change on three dimensions: traditional versus secular-rational, survival versus self-expression, and modernism versus postmodernism, which the user can affect via additive factors (tradsrateadd, survseadd, matpostradd).<br />
*Freedom, democracy (the POLITY measure), autocracy, economic freedom, and the status of women are all computed endogenously but can all be shifted by the user via multipliers (freedomm, democm, autocm, econfreem, gemm)<br />
<br />
=== Domestic Socio-Political Change: Selected Added Value ===<br />
<br />
The larger socio-political model provides representation and control over government spending on education, health, the military, R&D, foreign aid, and a residual category. Military spending is linked to interstate politics, both as a driver of threat and as a result of action-and-reaction based arms spending. The sub-model provides aggregated indicators of the physical quality of life and the human development index.<br />
<br />
== Socio-political Flow Charts ==<br />
<br />
[[File:SP1.gif|frame|center|SP1.gif]]<br />
<br />
The social and political module represents a complex of interacting structures and processes. These include:<br />
<br />
*The various social characteristics or life conditions of individuals<br />
*Human values, beliefs, and orientations’<br />
*Social and political structures, informal as well as formal<br />
*Social and political processes, both domestic and international<br />
<br />
Cultural foundations frame all of these components. And all of the components interact closely with human demographic and economic systems.<br />
<br />
The socio-political elements of IFs are among the most dynamically evolving aspects of the overall modeling system.&nbsp;Much, but not everything in the above figure has been fully represented yet within IFs; the figure indicates direction of development and shows implemented elements in italics.<br />
<br />
For more, please read the links below.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Social Characteristics: Life Conditions ==<br />
<br />
Individuals are the foundations of society. Many social indicators are actually aggregated indicators of their condition. The Human Development Index (HDI) is a widely-used summary measure of that life condition, based on life expectancy, educational attainment, and GDP per capita.<br />
<br />
[[File:Sp2.gif|frame|center|Sp2.gif]]<br />
<br />
== Physical Quality of Life (PQLI) ==<br />
<br />
The Overseas Development Council (then under the leadership of Jim Grant) developed and pu[[File:Sp3.gif|frame|right|Physical Quality of Life]]blicized a measure of (physical) quality of life (the PQLI) many years ago. It combines literarcy rate, infant mortality rate, and life expectancy, using scales from the lowest to the highest values in the global system. It weights the three scales equally. The literacy rate is, in turn, a function of the per capita spending levels on education, estimated cross-sectionally. In many respects the PQLI was a predecessor of the [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/lifeconditions.html human development index (HDI)].<br />
<br />
Based on country/region-specific Physical Quality of Life, it is possible to compute world quality of life (WPQLI) and the North-South gap in quality of life (NSPQLI). Given country-specific literacy rates, it is also possible to compute world literacy (WLIT).<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Income Distribution ==<br />
<br />
Income distribution is represented by the share of national income earned by the poorest 20 percent of the population. That share is obtained from data whenever possible, but is estimated from a cross-sectional relationship when necessary and changed over time by that relationship (the values tend, however, to be very stable both in the real world and in the model). Because initial conditions of variables affected by income share, such as fertility and mortality rates, already reflect existing income distributions, it is only the changes in that distribution relative to the expected value that the model uses in such relationships. A parameter (incshrm) is available to change income share and thus affect those variables influenced by it.<br />
<br />
[[File:Sp4.gif|frame|center|Sp4.gif]]<br />
<br />
== Social Characteristics: Networking ==<br />
<br />
Being electronically networked is an increasingly important aspect of human life condition. The numb[[File:Sp5.gif|frame|right|444x270px|Sp5.gif]]er of networked persons (NUMNWP) is a function primarily of the growth rate in that number (NUMNWPGR). It is ultimately constrained, however, by the size of the population and by the number of connections and organizational memberships that people can have (numnwplim). The growth in networked person number slows as it approaches the ultimate limit. The model user can affect the growth pattern via a multiplier on the growth rate (numnwpgrm).<br />
<br />
This approach was added to IFs during the TERRA project and draws on the thinking of Tom Tesch and Pol Descamps <br />
<br />
== Social Values and Cultural Evolution ==<br />
<br />
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.<br />
<br />
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.<br />
<br />
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 "wvsagesw," of computing country/region change over time in the three dimensions by functions for each cohort (value of wvsagesw = 1) or by computing change only in the first cohort and then advancting that through time (value of wvsagesw = 2).<br />
<br />
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 (matpostradd, etc.).<br />
<br />
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.<br />
<br />
[[File:Sp6.gif|frame|center]]<br />
<header><hgroup><br />
== Social Organization and Change ==<br />
</hgroup></header><br />
The sociopolitical module computes change in freedom (political and economic) and the status of women. For freedom it uses both the measure of the Freedom House and the combined measure for democracy (building on democracy and autocracy) of the POLITY project. It also computes a measure of economic freedom and of gender equality.</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=File:Sp6.gif&diff=2124File:Sp6.gif2017-02-25T23:37:55Z<p>StellahKwasi: </p>
<hr />
<div></div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Socio-Political&diff=2123Socio-Political2017-02-25T23:34:16Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete socio-political model documentation is available on Pardee'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.<br />
<br />
<span>A substantial portion of the socio-political model of IFs is scattered throughout the other models. There are "policy handles" or intervention points throughout those models. For instance, in the population model, multipliers on the total fertility rate can reflect policy decisions (although they can also reflect the model user's judgment concerning social changes in the country or region, independent of policy). Patterns of regulation, subsidy, tax incidence, and provision of state services are so diffuse and complicated that we resort to looking at their aggregate consequences through various "policy handles" rather than trying to represent them explicitly.</span><br />
<br />
For more information on this module, please use the links below or read more at [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
== Structure and Agent System: Socio-Political ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Socio-political</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social fabric</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Levels of human well-being and institutional development (human and social capital)</div><div>&nbsp;</div><div>Cultural structures</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social expenditures</div><div>&nbsp;</div><div>Value change</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Growth in literacy and human development;</div><div>&nbsp;</div><div>Democratic development, state failure</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Government efforts to develop human capital through spending on health, education, R&D</div><br />
|}<br />
<br />
Unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no standard organizing structure that is widely used for representing socio-political systems. In the context of the TERRA project, IFs developed a multi-component approach to structure that might be called the "social fabric" (a la Robert Pestel).<br />
<br />
Although representation of agent-class behavior would be of special interest in a socio-political module, most relationships in IFs remain at the level of aggregate specifications.<br />
<br />
== Dominant Relations: Socio-political ==<br />
<br />
=== Domestic Socio-Political Change: Dominant Relations ===<br />
<br />
Social and political change occurs on three dimensions (social characteristics or individual life conditions, values, socio-political institutions and process). Although GDP per capita is strongly correlated with all dimensions of change, it might be more appropriate to conceptualize a syndrome or complex of developmental change than to portray an economically-driven process.<br />
<br />
For causal diagram see [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/index.html Socio-Political Flow Charts Overview].<br />
<br />
For equations see, for example, [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
=== Key dynamics are directly linked to the dominant relations: ===<br />
<br />
*The model computes some key social characteristics/life conditions, including life expectancy and fertility rates in the demographic model, but the user can affect them via multipliers (mortm, tfrm). Literacy rate is an endogenous function of education spending, which the user can influence (via gdsm).<br />
*The model computes value or cultural change on three dimensions: traditional versus secular-rational, survival versus self-expression, and modernism versus postmodernism, which the user can affect via additive factors (tradsrateadd, survseadd, matpostradd).<br />
*Freedom, democracy (the POLITY measure), autocracy, economic freedom, and the status of women are all computed endogenously but can all be shifted by the user via multipliers (freedomm, democm, autocm, econfreem, gemm)<br />
<br />
=== Domestic Socio-Political Change: Selected Added Value ===<br />
<br />
The larger socio-political model provides representation and control over government spending on education, health, the military, R&D, foreign aid, and a residual category. Military spending is linked to interstate politics, both as a driver of threat and as a result of action-and-reaction based arms spending. The sub-model provides aggregated indicators of the physical quality of life and the human development index.<br />
<br />
== Socio-political Flow Charts ==<br />
<br />
[[File:SP1.gif|frame|center|SP1.gif]]<br />
<br />
The social and political module represents a complex of interacting structures and processes. These include:<br />
<br />
*The various social characteristics or life conditions of individuals<br />
*Human values, beliefs, and orientations’<br />
*Social and political structures, informal as well as formal<br />
*Social and political processes, both domestic and international<br />
<br />
Cultural foundations frame all of these components. And all of the components interact closely with human demographic and economic systems.<br />
<br />
The socio-political elements of IFs are among the most dynamically evolving aspects of the overall modeling system.&nbsp;Much, but not everything in the above figure has been fully represented yet within IFs; the figure indicates direction of development and shows implemented elements in italics.<br />
<br />
For more, please read the links below.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Social Characteristics: Life Conditions ==<br />
<br />
Individuals are the foundations of society. Many social indicators are actually aggregated indicators of their condition. The Human Development Index (HDI) is a widely-used summary measure of that life condition, based on life expectancy, educational attainment, and GDP per capita.<br />
<br />
[[File:Sp2.gif|frame|center|Sp2.gif]]<br />
<br />
== Physical Quality of Life (PQLI) ==<br />
<br />
The Overseas Development Council (then under the leadership of Jim Grant) developed and pu[[File:Sp3.gif|frame|right|Physical Quality of Life]]blicized a measure of (physical) quality of life (the PQLI) many years ago. It combines literarcy rate, infant mortality rate, and life expectancy, using scales from the lowest to the highest values in the global system. It weights the three scales equally. The literacy rate is, in turn, a function of the per capita spending levels on education, estimated cross-sectionally. In many respects the PQLI was a predecessor of the [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/lifeconditions.html human development index (HDI)].<br />
<br />
Based on country/region-specific Physical Quality of Life, it is possible to compute world quality of life (WPQLI) and the North-South gap in quality of life (NSPQLI). Given country-specific literacy rates, it is also possible to compute world literacy (WLIT).<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Income Distribution ==<br />
<br />
Income distribution is represented by the share of national income earned by the poorest 20 percent of the population. That share is obtained from data whenever possible, but is estimated from a cross-sectional relationship when necessary and changed over time by that relationship (the values tend, however, to be very stable both in the real world and in the model). Because initial conditions of variables affected by income share, such as fertility and mortality rates, already reflect existing income distributions, it is only the changes in that distribution relative to the expected value that the model uses in such relationships. A parameter (incshrm) is available to change income share and thus affect those variables influenced by it.<br />
<br />
[[File:Sp4.gif|frame|center|Sp4.gif]]<br />
<br />
== Social Characteristics: Networking ==<br />
<br />
Being electronically networked is an increasingly important aspect of human life condition. The numb[[File:Sp5.gif|frame|right|444x270px]]er of networked persons (NUMNWP) is a function primarily of the growth rate in that number (NUMNWPGR). It is ultimately constrained, however, by the size of the population and by the number of connections and organizational memberships that people can have (numnwplim). The growth in networked person number slows as it approaches the ultimate limit. The model user can affect the growth pattern via a multiplier on the growth rate (numnwpgrm).<br />
<br />
This approach was added to IFs during the TERRA project and draws on the thinking of Tom Tesch and Pol Descamps<br />
<header><hgroup><br />
== Social Values and Cultural Evolution ==<br />
</hgroup></header><br />
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.<br />
<br />
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.<br />
<br />
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 "wvsagesw," of computing country/region change over time in the three dimensions by functions for each cohort (value of wvsagesw = 1) or by computing change only in the first cohort and then advancting that through time (value of wvsagesw = 2).<br />
<br />
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 (matpostradd, etc.).<br />
<br />
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.</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=File:Sp5.gif&diff=2122File:Sp5.gif2017-02-25T23:22:31Z<p>StellahKwasi: </p>
<hr />
<div></div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Socio-Political&diff=2121Socio-Political2017-02-25T23:21:01Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete socio-political model documentation is available on Pardee'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.<br />
<br />
<span>A substantial portion of the socio-political model of IFs is scattered throughout the other models. There are "policy handles" or intervention points throughout those models. For instance, in the population model, multipliers on the total fertility rate can reflect policy decisions (although they can also reflect the model user's judgment concerning social changes in the country or region, independent of policy). Patterns of regulation, subsidy, tax incidence, and provision of state services are so diffuse and complicated that we resort to looking at their aggregate consequences through various "policy handles" rather than trying to represent them explicitly.</span><br />
<br />
For more information on this module, please use the links below or read more at [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
== Structure and Agent System: Socio-Political ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Socio-political</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social fabric</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Levels of human well-being and institutional development (human and social capital)</div><div>&nbsp;</div><div>Cultural structures</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social expenditures</div><div>&nbsp;</div><div>Value change</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Growth in literacy and human development;</div><div>&nbsp;</div><div>Democratic development, state failure</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Government efforts to develop human capital through spending on health, education, R&D</div><br />
|}<br />
<br />
Unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no standard organizing structure that is widely used for representing socio-political systems. In the context of the TERRA project, IFs developed a multi-component approach to structure that might be called the "social fabric" (a la Robert Pestel).<br />
<br />
Although representation of agent-class behavior would be of special interest in a socio-political module, most relationships in IFs remain at the level of aggregate specifications.<br />
<br />
== Dominant Relations: Socio-political ==<br />
<br />
=== Domestic Socio-Political Change: Dominant Relations ===<br />
<br />
Social and political change occurs on three dimensions (social characteristics or individual life conditions, values, socio-political institutions and process). Although GDP per capita is strongly correlated with all dimensions of change, it might be more appropriate to conceptualize a syndrome or complex of developmental change than to portray an economically-driven process.<br />
<br />
For causal diagram see [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/index.html Socio-Political Flow Charts Overview].<br />
<br />
For equations see, for example, [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
=== Key dynamics are directly linked to the dominant relations: ===<br />
<br />
*The model computes some key social characteristics/life conditions, including life expectancy and fertility rates in the demographic model, but the user can affect them via multipliers (mortm, tfrm). Literacy rate is an endogenous function of education spending, which the user can influence (via gdsm).<br />
*The model computes value or cultural change on three dimensions: traditional versus secular-rational, survival versus self-expression, and modernism versus postmodernism, which the user can affect via additive factors (tradsrateadd, survseadd, matpostradd).<br />
*Freedom, democracy (the POLITY measure), autocracy, economic freedom, and the status of women are all computed endogenously but can all be shifted by the user via multipliers (freedomm, democm, autocm, econfreem, gemm)<br />
<br />
=== Domestic Socio-Political Change: Selected Added Value ===<br />
<br />
The larger socio-political model provides representation and control over government spending on education, health, the military, R&D, foreign aid, and a residual category. Military spending is linked to interstate politics, both as a driver of threat and as a result of action-and-reaction based arms spending. The sub-model provides aggregated indicators of the physical quality of life and the human development index.<br />
<br />
== Socio-political Flow Charts ==<br />
<br />
[[File:SP1.gif|frame|center|SP1.gif]]<br />
<br />
The social and political module represents a complex of interacting structures and processes. These include:<br />
<br />
*The various social characteristics or life conditions of individuals<br />
*Human values, beliefs, and orientations’<br />
*Social and political structures, informal as well as formal<br />
*Social and political processes, both domestic and international<br />
<br />
Cultural foundations frame all of these components. And all of the components interact closely with human demographic and economic systems.<br />
<br />
The socio-political elements of IFs are among the most dynamically evolving aspects of the overall modeling system.&nbsp;Much, but not everything in the above figure has been fully represented yet within IFs; the figure indicates direction of development and shows implemented elements in italics.<br />
<br />
For more, please read the links below.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Social Characteristics: Life Conditions ==<br />
<br />
Individuals are the foundations of society. Many social indicators are actually aggregated indicators of their condition. The Human Development Index (HDI) is a widely-used summary measure of that life condition, based on life expectancy, educational attainment, and GDP per capita.<br />
<br />
[[File:Sp2.gif|frame|center|Sp2.gif]] <br />
<br />
== Physical Quality of Life (PQLI) ==<br />
<br />
The Overseas Development Council (then under the leadership of Jim Grant) developed and pu[[File:Sp3.gif|frame|right|Physical Quality of Life]]blicized a measure of (physical) quality of life (the PQLI) many years ago. It combines literarcy rate, infant mortality rate, and life expectancy, using scales from the lowest to the highest values in the global system. It weights the three scales equally. The literacy rate is, in turn, a function of the per capita spending levels on education, estimated cross-sectionally. In many respects the PQLI was a predecessor of the [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/lifeconditions.html human development index (HDI)].<br />
<br />
Based on country/region-specific Physical Quality of Life, it is possible to compute world quality of life (WPQLI) and the North-South gap in quality of life (NSPQLI). Given country-specific literacy rates, it is also possible to compute world literacy (WLIT).<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Income Distribution ==<br />
<br />
Income distribution is represented by the share of national income earned by the poorest 20 percent of the population. That share is obtained from data whenever possible, but is estimated from a cross-sectional relationship when necessary and changed over time by that relationship (the values tend, however, to be very stable both in the real world and in the model). Because initial conditions of variables affected by income share, such as fertility and mortality rates, already reflect existing income distributions, it is only the changes in that distribution relative to the expected value that the model uses in such relationships. A parameter (incshrm) is available to change income share and thus affect those variables influenced by it.<br />
<br />
[[File:Sp4.gif|frame|center|Sp4.gif]]<br />
<br />
== Social Characteristics: Networking ==</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Socio-Political&diff=2120Socio-Political2017-02-25T22:40:03Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete socio-political model documentation is available on Pardee'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.<br />
<br />
<span>A substantial portion of the socio-political model of IFs is scattered throughout the other models. There are "policy handles" or intervention points throughout those models. For instance, in the population model, multipliers on the total fertility rate can reflect policy decisions (although they can also reflect the model user's judgment concerning social changes in the country or region, independent of policy). Patterns of regulation, subsidy, tax incidence, and provision of state services are so diffuse and complicated that we resort to looking at their aggregate consequences through various "policy handles" rather than trying to represent them explicitly.</span><br />
<br />
For more information on this module, please use the links below or read more at [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
== Structure and Agent System: Socio-Political ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Socio-political</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social fabric</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Levels of human well-being and institutional development (human and social capital)</div><div>&nbsp;</div><div>Cultural structures</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social expenditures</div><div>&nbsp;</div><div>Value change</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Growth in literacy and human development;</div><div>&nbsp;</div><div>Democratic development, state failure</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Government efforts to develop human capital through spending on health, education, R&D</div><br />
|}<br />
<br />
Unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no standard organizing structure that is widely used for representing socio-political systems. In the context of the TERRA project, IFs developed a multi-component approach to structure that might be called the "social fabric" (a la Robert Pestel).<br />
<br />
Although representation of agent-class behavior would be of special interest in a socio-political module, most relationships in IFs remain at the level of aggregate specifications.<br />
<br />
== Dominant Relations: Socio-political ==<br />
<br />
=== Domestic Socio-Political Change: Dominant Relations ===<br />
<br />
Social and political change occurs on three dimensions (social characteristics or individual life conditions, values, socio-political institutions and process). Although GDP per capita is strongly correlated with all dimensions of change, it might be more appropriate to conceptualize a syndrome or complex of developmental change than to portray an economically-driven process.<br />
<br />
For causal diagram see [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/index.html Socio-Political Flow Charts Overview].<br />
<br />
For equations see, for example, [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
=== Key dynamics are directly linked to the dominant relations: ===<br />
<br />
*The model computes some key social characteristics/life conditions, including life expectancy and fertility rates in the demographic model, but the user can affect them via multipliers (mortm, tfrm). Literacy rate is an endogenous function of education spending, which the user can influence (via gdsm).<br />
*The model computes value or cultural change on three dimensions: traditional versus secular-rational, survival versus self-expression, and modernism versus postmodernism, which the user can affect via additive factors (tradsrateadd, survseadd, matpostradd).<br />
*Freedom, democracy (the POLITY measure), autocracy, economic freedom, and the status of women are all computed endogenously but can all be shifted by the user via multipliers (freedomm, democm, autocm, econfreem, gemm)<br />
<br />
=== Domestic Socio-Political Change: Selected Added Value ===<br />
<br />
The larger socio-political model provides representation and control over government spending on education, health, the military, R&D, foreign aid, and a residual category. Military spending is linked to interstate politics, both as a driver of threat and as a result of action-and-reaction based arms spending. The sub-model provides aggregated indicators of the physical quality of life and the human development index.<br />
<br />
== Socio-political Flow Charts ==<br />
<br />
[[File:SP1.gif|frame|center|SP1.gif]]<br />
<br />
The social and political module represents a complex of interacting structures and processes. These include:<br />
<br />
*The various social characteristics or life conditions of individuals<br />
*Human values, beliefs, and orientations’<br />
*Social and political structures, informal as well as formal<br />
*Social and political processes, both domestic and international<br />
<br />
Cultural foundations frame all of these components. And all of the components interact closely with human demographic and economic systems.<br />
<br />
The socio-political elements of IFs are among the most dynamically evolving aspects of the overall modeling system.&nbsp;Much, but not everything in the above figure has been fully represented yet within IFs; the figure indicates direction of development and shows implemented elements in italics.<br />
<br />
For more, please read the links below.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Social Characteristics: Life Conditions ==<br />
<br />
Individuals are the foundations of society. Many social indicators are actually aggregated indicators of their condition. The Human Development Index (HDI) is a widely-used summary measure of that life condition, based on life expectancy, educational attainment, and GDP per capita.<br />
<br />
[[File:Sp2.gif|frame|center|Sp2.gif]]<br />
<header><hgroup><br />
== Physical Quality of Life (PQLI) ==<br />
</hgroup></header><br />
The Overseas Development Council (then under the leadership of Jim Grant) developed and pu[[File:Sp3.gif|frame|right|Physical Quality of Life]]blicized a measure of (physical) quality of life (the PQLI) many years ago. It combines literarcy rate, infant mortality rate, and life expectancy, using scales from the lowest to the highest values in the global system. It weights the three scales equally. The literacy rate is, in turn, a function of the per capita spending levels on education, estimated cross-sectionally. In many respects the PQLI was a predecessor of the [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/lifeconditions.html human development index (HDI)].<br />
<br />
Based on country/region-specific Physical Quality of Life, it is possible to compute world quality of life (WPQLI) and the North-South gap in quality of life (NSPQLI). Given country-specific literacy rates, it is also possible to compute world literacy (WLIT). <br />
<br />
<br />
<br />
<br />
<br />
<br />
<header><hgroup><br />
== Income Distribution ==<br />
</hgroup></header><br />
Income distribution is represented by the share of national income earned by the poorest 20 percent of the population. That share is obtained from data whenever possible, but is estimated from a cross-sectional relationship when necessary and changed over time by that relationship (the values tend, however, to be very stable both in the real world and in the model). Because initial conditions of variables affected by income share, such as fertility and mortality rates, already reflect existing income distributions, it is only the changes in that distribution relative to the expected value that the model uses in such relationships. A parameter (incshrm) is available to change income share and thus affect those variables influenced by it.<br />
<br />
[[File:Sp4.gif|frame|center]]<br />
<br />
<br />
<br/></div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Socio-Political&diff=2119Socio-Political2017-02-25T22:17:14Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete socio-political model documentation is available on Pardee'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.<br />
<br />
<span>A substantial portion of the socio-political model of IFs is scattered throughout the other models. There are "policy handles" or intervention points throughout those models. For instance, in the population model, multipliers on the total fertility rate can reflect policy decisions (although they can also reflect the model user's judgment concerning social changes in the country or region, independent of policy). Patterns of regulation, subsidy, tax incidence, and provision of state services are so diffuse and complicated that we resort to looking at their aggregate consequences through various "policy handles" rather than trying to represent them explicitly.</span><br />
<br />
For more information on this module, please use the links below or read more at [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
== Structure and Agent System: Socio-Political ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Socio-political</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social fabric</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Levels of human well-being and institutional development (human and social capital)</div><div>&nbsp;</div><div>Cultural structures</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social expenditures</div><div>&nbsp;</div><div>Value change</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Growth in literacy and human development;</div><div>&nbsp;</div><div>Democratic development, state failure</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Government efforts to develop human capital through spending on health, education, R&D</div><br />
|}<br />
<br />
Unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no standard organizing structure that is widely used for representing socio-political systems. In the context of the TERRA project, IFs developed a multi-component approach to structure that might be called the "social fabric" (a la Robert Pestel).<br />
<br />
Although representation of agent-class behavior would be of special interest in a socio-political module, most relationships in IFs remain at the level of aggregate specifications.<br />
<br />
== Dominant Relations: Socio-political ==<br />
<br />
=== Domestic Socio-Political Change: Dominant Relations ===<br />
<br />
Social and political change occurs on three dimensions (social characteristics or individual life conditions, values, socio-political institutions and process). Although GDP per capita is strongly correlated with all dimensions of change, it might be more appropriate to conceptualize a syndrome or complex of developmental change than to portray an economically-driven process.<br />
<br />
For causal diagram see [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/index.html Socio-Political Flow Charts Overview].<br />
<br />
For equations see, for example, [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
=== Key dynamics are directly linked to the dominant relations: ===<br />
<br />
*The model computes some key social characteristics/life conditions, including life expectancy and fertility rates in the demographic model, but the user can affect them via multipliers (mortm, tfrm). Literacy rate is an endogenous function of education spending, which the user can influence (via gdsm).<br />
*The model computes value or cultural change on three dimensions: traditional versus secular-rational, survival versus self-expression, and modernism versus postmodernism, which the user can affect via additive factors (tradsrateadd, survseadd, matpostradd).<br />
*Freedom, democracy (the POLITY measure), autocracy, economic freedom, and the status of women are all computed endogenously but can all be shifted by the user via multipliers (freedomm, democm, autocm, econfreem, gemm)<br />
<br />
=== Domestic Socio-Political Change: Selected Added Value ===<br />
<br />
The larger socio-political model provides representation and control over government spending on education, health, the military, R&D, foreign aid, and a residual category. Military spending is linked to interstate politics, both as a driver of threat and as a result of action-and-reaction based arms spending. The sub-model provides aggregated indicators of the physical quality of life and the human development index.<br />
<br />
== Socio-political Flow Charts ==<br />
<br />
[[File:SP1.gif|frame|center]]<br />
<br />
The social and political module represents a complex of interacting structures and processes. These include:<br />
<br />
*The various social characteristics or life conditions of individuals<br />
*Human values, beliefs, and orientations’<br />
*Social and political structures, informal as well as formal<br />
*Social and political processes, both domestic and international<br />
<br />
Cultural foundations frame all of these components. And all of the components interact closely with human demographic and economic systems.<br />
<br />
The socio-political elements of IFs are among the most dynamically evolving aspects of the overall modeling system.&nbsp;Much, but not everything in the above figure has been fully represented yet within IFs; the figure indicates direction of development and shows implemented elements in italics.<br />
<br />
For more, please read the links below.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Social Characteristics: Life Conditions ==<br />
<br />
Individuals are the foundations of society. Many social indicators are actually aggregated indicators of their condition. The Human Development Index (HDI) is a widely-used summary measure of that life condition, based on life expectancy, educational attainment, and GDP per capita.<br />
<br />
[[File:Sp2.gif|frame|center|Sp2.gif]]<br />
<br />
== Physical Quality of Life (PQLI)[[File:Sp3.gif|frame|right|432x255px|Sp3.gif]] ==<br />
<br />
The Overseas Development Council (then under the leadership of Jim Grant) developed and publicized a measure of (physical) quality of life (the PQLI) many years ago. It combines literarcy rate, infant mortality rate, and life expectancy, using scales from the lowest to the highest values in the global system. It weights the three scales equally. The literacy rate is, in turn, a function of the per capita spending levels on education, estimated cross-sectionally. In many respects the PQLI was a predecessor of the [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/lifeconditions.html human development index (HDI)].<br />
<br />
Based on country/region-specific Physical Quality of Life, it is possible to compute world quality of life (WPQLI) and the North-South gap in quality of life (NSPQLI). Given country-specific literacy rates, it is also possible to compute world literacy (WLIT).<br />
<br />
== Income Distribution ==<br />
<br />
Income distribution is represented by the share of national income earned by the poorest 20 percent of the population. That share is obtained from data whenever possible, but is estimated from a cross-sectional relationship when necessary and changed over time by that relationship (the values tend, however, to be very stable both in the real world and in the model). Because initial conditions of variables affected by income share, such as fertility and mortality rates, already reflect existing income distributions, it is only the changes in that distribution relative to the expected value that the model uses in such relationships. A parameter (incshrm) is available to change income share and thus affect those variables influenced by it.</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Socio-Political&diff=2118Socio-Political2017-02-25T22:14:11Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete socio-political model documentation is available on Pardee'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.<br />
<br />
<span>A substantial portion of the socio-political model of IFs is scattered throughout the other models. There are "policy handles" or intervention points throughout those models. For instance, in the population model, multipliers on the total fertility rate can reflect policy decisions (although they can also reflect the model user's judgment concerning social changes in the country or region, independent of policy). Patterns of regulation, subsidy, tax incidence, and provision of state services are so diffuse and complicated that we resort to looking at their aggregate consequences through various "policy handles" rather than trying to represent them explicitly.</span><br />
<br />
For more information on this module, please use the links below or read more at [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
== Structure and Agent System: Socio-Political ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Socio-political</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social fabric</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Levels of human well-being and institutional development (human and social capital)</div><div>&nbsp;</div><div>Cultural structures</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social expenditures</div><div>&nbsp;</div><div>Value change</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Growth in literacy and human development;</div><div>&nbsp;</div><div>Democratic development, state failure</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Government efforts to develop human capital through spending on health, education, R&D</div><br />
|}<br />
<br />
Unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no standard organizing structure that is widely used for representing socio-political systems. In the context of the TERRA project, IFs developed a multi-component approach to structure that might be called the "social fabric" (a la Robert Pestel).<br />
<br />
Although representation of agent-class behavior would be of special interest in a socio-political module, most relationships in IFs remain at the level of aggregate specifications.<br />
<br />
== Dominant Relations: Socio-political ==<br />
<br />
=== Domestic Socio-Political Change: Dominant Relations ===<br />
<br />
Social and political change occurs on three dimensions (social characteristics or individual life conditions, values, socio-political institutions and process). Although GDP per capita is strongly correlated with all dimensions of change, it might be more appropriate to conceptualize a syndrome or complex of developmental change than to portray an economically-driven process.<br />
<br />
For causal diagram see [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/index.html Socio-Political Flow Charts Overview].<br />
<br />
For equations see, for example, [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
=== Key dynamics are directly linked to the dominant relations: ===<br />
<br />
*The model computes some key social characteristics/life conditions, including life expectancy and fertility rates in the demographic model, but the user can affect them via multipliers (mortm, tfrm). Literacy rate is an endogenous function of education spending, which the user can influence (via gdsm).<br />
*The model computes value or cultural change on three dimensions: traditional versus secular-rational, survival versus self-expression, and modernism versus postmodernism, which the user can affect via additive factors (tradsrateadd, survseadd, matpostradd).<br />
*Freedom, democracy (the POLITY measure), autocracy, economic freedom, and the status of women are all computed endogenously but can all be shifted by the user via multipliers (freedomm, democm, autocm, econfreem, gemm)<br />
<br />
=== Domestic Socio-Political Change: Selected Added Value ===<br />
<br />
The larger socio-political model provides representation and control over government spending on education, health, the military, R&D, foreign aid, and a residual category. Military spending is linked to interstate politics, both as a driver of threat and as a result of action-and-reaction based arms spending. The sub-model provides aggregated indicators of the physical quality of life and the human development index.<br />
<br />
== Socio-political Flow Charts ==<br />
<br />
<br />
<br/><br />
<br />
<br />
<br />
<br />
<br />
<br />
== Social Characteristics: Life Conditions ==<br />
<br />
Individuals are the foundations of society. Many social indicators are actually aggregated indicators of their condition. The Human Development Index (HDI) is a widely-used summary measure of that life condition, based on life expectancy, educational attainment, and GDP per capita.<br />
<br />
[[File:Sp2.gif|frame|center|Sp2.gif]]<br />
<br />
== Physical Quality of Life (PQLI)[[File:Sp3.gif|frame|right|432x255px|Sp3.gif]] ==<br />
<br />
The Overseas Development Council (then under the leadership of Jim Grant) developed and publicized a measure of (physical) quality of life (the PQLI) many years ago. It combines literarcy rate, infant mortality rate, and life expectancy, using scales from the lowest to the highest values in the global system. It weights the three scales equally. The literacy rate is, in turn, a function of the per capita spending levels on education, estimated cross-sectionally. In many respects the PQLI was a predecessor of the [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/lifeconditions.html human development index (HDI)].<br />
<br />
Based on country/region-specific Physical Quality of Life, it is possible to compute world quality of life (WPQLI) and the North-South gap in quality of life (NSPQLI). Given country-specific literacy rates, it is also possible to compute world literacy (WLIT).<br />
<br />
== Income Distribution ==<br />
<br />
Income distribution is represented by the share of national income earned by the poorest 20 percent of the population. That share is obtained from data whenever possible, but is estimated from a cross-sectional relationship when necessary and changed over time by that relationship (the values tend, however, to be very stable both in the real world and in the model). Because initial conditions of variables affected by income share, such as fertility and mortality rates, already reflect existing income distributions, it is only the changes in that distribution relative to the expected value that the model uses in such relationships. A parameter (incshrm) is available to change income share and thus affect those variables influenced by it.</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Socio-Political&diff=2117Socio-Political2017-02-25T21:48:02Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete socio-political model documentation is available on Pardee'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.<br />
<br />
<span>A substantial portion of the socio-political model of IFs is scattered throughout the other models. There are "policy handles" or intervention points throughout those models. For instance, in the population model, multipliers on the total fertility rate can reflect policy decisions (although they can also reflect the model user's judgment concerning social changes in the country or region, independent of policy). Patterns of regulation, subsidy, tax incidence, and provision of state services are so diffuse and complicated that we resort to looking at their aggregate consequences through various "policy handles" rather than trying to represent them explicitly.</span><br />
<br />
For more information on this module, please use the links below or read more at [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
== Structure and Agent System: Socio-Political ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Socio-political</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social fabric</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Levels of human well-being and institutional development (human and social capital)</div><div>&nbsp;</div><div>Cultural structures</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social expenditures</div><div>&nbsp;</div><div>Value change</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Growth in literacy and human development;</div><div>&nbsp;</div><div>Democratic development, state failure</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Government efforts to develop human capital through spending on health, education, R&D</div><br />
|}<br />
<br />
Unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no standard organizing structure that is widely used for representing socio-political systems. In the context of the TERRA project, IFs developed a multi-component approach to structure that might be called the "social fabric" (a la Robert Pestel).<br />
<br />
Although representation of agent-class behavior would be of special interest in a socio-political module, most relationships in IFs remain at the level of aggregate specifications.<br />
<br />
== Dominant Relations: Socio-political ==<br />
<br />
=== Domestic Socio-Political Change: Dominant Relations ===<br />
<br />
Social and political change occurs on three dimensions (social characteristics or individual life conditions, values, socio-political institutions and process). Although GDP per capita is strongly correlated with all dimensions of change, it might be more appropriate to conceptualize a syndrome or complex of developmental change than to portray an economically-driven process.<br />
<br />
For causal diagram see [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/index.html Socio-Political Flow Charts Overview].<br />
<br />
For equations see, for example, [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
=== Key dynamics are directly linked to the dominant relations: ===<br />
<br />
*The model computes some key social characteristics/life conditions, including life expectancy and fertility rates in the demographic model, but the user can affect them via multipliers (mortm, tfrm). Literacy rate is an endogenous function of education spending, which the user can influence (via gdsm).<br />
*The model computes value or cultural change on three dimensions: traditional versus secular-rational, survival versus self-expression, and modernism versus postmodernism, which the user can affect via additive factors (tradsrateadd, survseadd, matpostradd).<br />
*Freedom, democracy (the POLITY measure), autocracy, economic freedom, and the status of women are all computed endogenously but can all be shifted by the user via multipliers (freedomm, democm, autocm, econfreem, gemm)<br />
<br />
=== Domestic Socio-Political Change: Selected Added Value ===<br />
<br />
The larger socio-political model provides representation and control over government spending on education, health, the military, R&D, foreign aid, and a residual category. Military spending is linked to interstate politics, both as a driver of threat and as a result of action-and-reaction based arms spending. The sub-model provides aggregated indicators of the physical quality of life and the human development index.<br />
<br />
== Socio-political Flow Charts[[File:SP1.gif|frame|right|454x464px|SP1.gif]] ==<br />
<br />
=== Overview ===<br />
<br />
The social and political module represents a complex of interacting structures and processes. These include:<br />
<br />
*The various social characteristics or life conditions of individuals<br />
*Human values, beliefs, and orientations’<br />
*Social and political structures, informal as well as formal<br />
*Social and political processes, both domestic and international<br />
<br />
Cultural foundations frame all of these components. And all of the components interact closely with human demographic and economic systems.<br />
<br />
The socio-political elements of IFs are among the most dynamically evolving aspects of the overall modeling system.&nbsp;Much, but not everything in the above figure has been fully represented yet within IFs; the figure indicates direction of development and shows implemented elements in italics.<br />
<br />
For more, please read the links below.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Social Characteristics: Life Conditions ==<br />
<br />
Individuals are the foundations of society. Many social indicators are actually aggregated indicators of their condition. The Human Development Index (HDI) is a widely-used summary measure of that life condition, based on life expectancy, educational attainment, and GDP per capita.<br />
<br />
[[File:Sp2.gif|frame|center|Sp2.gif]]<br />
<br />
== Physical Quality of Life (PQLI)[[File:Sp3.gif|frame|right|432x255px|Sp3.gif]] ==<br />
<br />
The Overseas Development Council (then under the leadership of Jim Grant) developed and publicized a measure of (physical) quality of life (the PQLI) many years ago. It combines literarcy rate, infant mortality rate, and life expectancy, using scales from the lowest to the highest values in the global system. It weights the three scales equally. The literacy rate is, in turn, a function of the per capita spending levels on education, estimated cross-sectionally. In many respects the PQLI was a predecessor of the [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/lifeconditions.html human development index (HDI)].<br />
<br />
Based on country/region-specific Physical Quality of Life, it is possible to compute world quality of life (WPQLI) and the North-South gap in quality of life (NSPQLI). Given country-specific literacy rates, it is also possible to compute world literacy (WLIT). <br />
<br />
== Income Distribution ==<br />
<br />
Income distribution is represented by the share of national income earned by the poorest 20 percent of the population. That share is obtained from data whenever possible, but is estimated from a cross-sectional relationship when necessary and changed over time by that relationship (the values tend, however, to be very stable both in the real world and in the model). Because initial conditions of variables affected by income share, such as fertility and mortality rates, already reflect existing income distributions, it is only the changes in that distribution relative to the expected value that the model uses in such relationships. A parameter (incshrm) is available to change income share and thus affect those variables influenced by it.</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=File:Sp4.gif&diff=2116File:Sp4.gif2017-02-25T21:43:26Z<p>StellahKwasi: </p>
<hr />
<div></div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Socio-Political&diff=2115Socio-Political2017-02-25T21:42:21Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete socio-political model documentation is available on Pardee'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.<br />
<br />
<span>A substantial portion of the socio-political model of IFs is scattered throughout the other models. There are "policy handles" or intervention points throughout those models. For instance, in the population model, multipliers on the total fertility rate can reflect policy decisions (although they can also reflect the model user's judgment concerning social changes in the country or region, independent of policy). Patterns of regulation, subsidy, tax incidence, and provision of state services are so diffuse and complicated that we resort to looking at their aggregate consequences through various "policy handles" rather than trying to represent them explicitly.</span><br />
<br />
For more information on this module, please use the links below or read more at [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
== Structure and Agent System: Socio-Political ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Socio-political</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social fabric</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Levels of human well-being and institutional development (human and social capital)</div><div>&nbsp;</div><div>Cultural structures</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social expenditures</div><div>&nbsp;</div><div>Value change</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Growth in literacy and human development;</div><div>&nbsp;</div><div>Democratic development, state failure</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Government efforts to develop human capital through spending on health, education, R&D</div><br />
|}<br />
<br />
Unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no standard organizing structure that is widely used for representing socio-political systems. In the context of the TERRA project, IFs developed a multi-component approach to structure that might be called the "social fabric" (a la Robert Pestel).<br />
<br />
Although representation of agent-class behavior would be of special interest in a socio-political module, most relationships in IFs remain at the level of aggregate specifications.<br />
<br />
== Dominant Relations: Socio-political ==<br />
<br />
=== Domestic Socio-Political Change: Dominant Relations ===<br />
<br />
Social and political change occurs on three dimensions (social characteristics or individual life conditions, values, socio-political institutions and process). Although GDP per capita is strongly correlated with all dimensions of change, it might be more appropriate to conceptualize a syndrome or complex of developmental change than to portray an economically-driven process.<br />
<br />
For causal diagram see [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/index.html Socio-Political Flow Charts Overview].<br />
<br />
For equations see, for example, [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
=== Key dynamics are directly linked to the dominant relations: ===<br />
<br />
*The model computes some key social characteristics/life conditions, including life expectancy and fertility rates in the demographic model, but the user can affect them via multipliers (mortm, tfrm). Literacy rate is an endogenous function of education spending, which the user can influence (via gdsm).<br />
*The model computes value or cultural change on three dimensions: traditional versus secular-rational, survival versus self-expression, and modernism versus postmodernism, which the user can affect via additive factors (tradsrateadd, survseadd, matpostradd).<br />
*Freedom, democracy (the POLITY measure), autocracy, economic freedom, and the status of women are all computed endogenously but can all be shifted by the user via multipliers (freedomm, democm, autocm, econfreem, gemm)<br />
<br />
=== Domestic Socio-Political Change: Selected Added Value ===<br />
<br />
The larger socio-political model provides representation and control over government spending on education, health, the military, R&D, foreign aid, and a residual category. Military spending is linked to interstate politics, both as a driver of threat and as a result of action-and-reaction based arms spending. The sub-model provides aggregated indicators of the physical quality of life and the human development index.<br />
<br />
== Socio-political Flow Charts[[File:SP1.gif|frame|right|454x464px|SP1.gif]] ==<br />
<br />
=== Overview ===<br />
<br />
The social and political module represents a complex of interacting structures and processes. These include:<br />
<br />
*The various social characteristics or life conditions of individuals<br />
*Human values, beliefs, and orientations’<br />
*Social and political structures, informal as well as formal<br />
*Social and political processes, both domestic and international<br />
<br />
Cultural foundations frame all of these components. And all of the components interact closely with human demographic and economic systems.<br />
<br />
The socio-political elements of IFs are among the most dynamically evolving aspects of the overall modeling system.&nbsp;Much, but not everything in the above figure has been fully represented yet within IFs; the figure indicates direction of development and shows implemented elements in italics.<br />
<br />
For more, please read the links below.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Social Characteristics: Life Conditions ==<br />
<br />
Individuals are the foundations of society. Many social indicators are actually aggregated indicators of their condition. The Human Development Index (HDI) is a widely-used summary measure of that life condition, based on life expectancy, educational attainment, and GDP per capita.<br />
<br />
[[File:Sp2.gif|frame|center|Sp2.gif]] <br />
<br />
== Physical Quality of Life (PQLI)[[File:Sp3.gif|frame|right|432x255px]] ==<br />
<br />
The Overseas Development Council (then under the leadership of Jim Grant) developed and publicized a measure of (physical) quality of life (the PQLI) many years ago. It combines literarcy rate, infant mortality rate, and life expectancy, using scales from the lowest to the highest values in the global system. It weights the three scales equally. The literacy rate is, in turn, a function of the per capita spending levels on education, estimated cross-sectionally. In many respects the PQLI was a predecessor of the [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/lifeconditions.html human development index (HDI)]. <br />
<br />
Based on country/region-specific Physical Quality of Life, it is possible to compute world quality of life (WPQLI) and the North-South gap in quality of life (NSPQLI). Given country-specific literacy rates, it is also possible to compute world literacy (WLIT). <br />
<header><hgroup><br />
== Income Distribution ==<br />
</hgroup></header><br />
Income distribution is represented by the share of national income earned by the poorest 20 percent of the population. That share is obtained from data whenever possible, but is estimated from a cross-sectional relationship when necessary and changed over time by that relationship (the values tend, however, to be very stable both in the real world and in the model). Because initial conditions of variables affected by income share, such as fertility and mortality rates, already reflect existing income distributions, it is only the changes in that distribution relative to the expected value that the model uses in such relationships. A parameter (incshrm) is available to change income share and thus affect those variables influenced by it.</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=File:Sp3.gif&diff=2114File:Sp3.gif2017-02-25T21:30:04Z<p>StellahKwasi: </p>
<hr />
<div></div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Socio-Political&diff=2113Socio-Political2017-02-25T21:29:25Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete socio-political model documentation is available on Pardee'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.<br />
<br />
<span>A substantial portion of the socio-political model of IFs is scattered throughout the other models. There are "policy handles" or intervention points throughout those models. For instance, in the population model, multipliers on the total fertility rate can reflect policy decisions (although they can also reflect the model user's judgment concerning social changes in the country or region, independent of policy). Patterns of regulation, subsidy, tax incidence, and provision of state services are so diffuse and complicated that we resort to looking at their aggregate consequences through various "policy handles" rather than trying to represent them explicitly.</span><br />
<br />
For more information on this module, please use the links below or read more at [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
== Structure and Agent System: Socio-Political ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Socio-political</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social fabric</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Levels of human well-being and institutional development (human and social capital)</div><div>&nbsp;</div><div>Cultural structures</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social expenditures</div><div>&nbsp;</div><div>Value change</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Growth in literacy and human development;</div><div>&nbsp;</div><div>Democratic development, state failure</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Government efforts to develop human capital through spending on health, education, R&D</div><br />
|}<br />
<br />
Unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no standard organizing structure that is widely used for representing socio-political systems. In the context of the TERRA project, IFs developed a multi-component approach to structure that might be called the "social fabric" (a la Robert Pestel).<br />
<br />
Although representation of agent-class behavior would be of special interest in a socio-political module, most relationships in IFs remain at the level of aggregate specifications.<br />
<br />
== Dominant Relations: Socio-political ==<br />
<br />
=== Domestic Socio-Political Change: Dominant Relations ===<br />
<br />
Social and political change occurs on three dimensions (social characteristics or individual life conditions, values, socio-political institutions and process). Although GDP per capita is strongly correlated with all dimensions of change, it might be more appropriate to conceptualize a syndrome or complex of developmental change than to portray an economically-driven process.<br />
<br />
For causal diagram see [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/index.html Socio-Political Flow Charts Overview].<br />
<br />
For equations see, for example, [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
=== Key dynamics are directly linked to the dominant relations: ===<br />
<br />
*The model computes some key social characteristics/life conditions, including life expectancy and fertility rates in the demographic model, but the user can affect them via multipliers (mortm, tfrm). Literacy rate is an endogenous function of education spending, which the user can influence (via gdsm).<br />
*The model computes value or cultural change on three dimensions: traditional versus secular-rational, survival versus self-expression, and modernism versus postmodernism, which the user can affect via additive factors (tradsrateadd, survseadd, matpostradd).<br />
*Freedom, democracy (the POLITY measure), autocracy, economic freedom, and the status of women are all computed endogenously but can all be shifted by the user via multipliers (freedomm, democm, autocm, econfreem, gemm)<br />
<br />
=== Domestic Socio-Political Change: Selected Added Value ===<br />
<br />
The larger socio-political model provides representation and control over government spending on education, health, the military, R&D, foreign aid, and a residual category. Military spending is linked to interstate politics, both as a driver of threat and as a result of action-and-reaction based arms spending. The sub-model provides aggregated indicators of the physical quality of life and the human development index. &lt;header&gt;&lt;hgroup&gt;<br />
<br />
== Socio-political Flow Charts[[File:SP1.gif|frame|right|454x464px|SP1.gif]] ==<br />
<br />
=== Overview ===<br />
<br />
The social and political module represents a complex of interacting structures and processes. These include:<br />
<br />
*The various social characteristics or life conditions of individuals<br />
*Human values, beliefs, and orientations’<br />
*Social and political structures, informal as well as formal<br />
*Social and political processes, both domestic and international<br />
<br />
Cultural foundations frame all of these components. And all of the components interact closely with human demographic and economic systems.<br />
<br />
The socio-political elements of IFs are among the most dynamically evolving aspects of the overall modeling system.&nbsp;Much, but not everything in the above figure has been fully represented yet within IFs; the figure indicates direction of development and shows implemented elements in italics.<br />
<br />
For more, please read the links below.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Social Characteristics: Life Conditions ==<br />
<br />
Individuals are the foundations of society. Many social indicators are actually aggregated indicators of their condition. The Human Development Index (HDI) is a widely-used summary measure of that life condition, based on life expectancy, educational attainment, and GDP per capita.<br />
<br />
[[File:Sp2.gif|frame|center]]<br />
<header><hgroup><br />
== Physical Quality of Life (PQLI) ==<br />
</hgroup></header><br />
The Overseas Development Council (then under the leadership of Jim Grant) developed and publicized a measure of (physical) quality of life (the PQLI) many years ago. It combines literarcy rate, infant mortality rate, and life expectancy, using scales from the lowest to the highest values in the global system. It weights the three scales equally. The literacy rate is, in turn, a function of the per capita spending levels on education, estimated cross-sectionally. In many respects the PQLI was a predecessor of the [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/lifeconditions.html human development index (HDI)].</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=File:Sp2.gif&diff=2112File:Sp2.gif2017-02-25T21:22:36Z<p>StellahKwasi: </p>
<hr />
<div></div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Socio-Political&diff=2111Socio-Political2017-02-25T21:19:11Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete socio-political model documentation is available on Pardee'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.<br />
<br />
<span>A substantial portion of the socio-political model of IFs is scattered throughout the other models. There are "policy handles" or intervention points throughout those models. For instance, in the population model, multipliers on the total fertility rate can reflect policy decisions (although they can also reflect the model user's judgment concerning social changes in the country or region, independent of policy). Patterns of regulation, subsidy, tax incidence, and provision of state services are so diffuse and complicated that we resort to looking at their aggregate consequences through various "policy handles" rather than trying to represent them explicitly.</span><br />
<br />
For more information on this module, please use the links below or read more at [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview]. <br />
<br />
== Structure and Agent System: Socio-Political ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Socio-political</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social fabric</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Levels of human well-being and institutional development (human and social capital)</div><div>&nbsp;</div><div>Cultural structures</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social expenditures</div><div>&nbsp;</div><div>Value change</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Growth in literacy and human development;</div><div>&nbsp;</div><div>Democratic development, state failure</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Government efforts to develop human capital through spending on health, education, R&D</div><br />
|}<br />
<br />
Unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no standard organizing structure that is widely used for representing socio-political systems. In the context of the TERRA project, IFs developed a multi-component approach to structure that might be called the "social fabric" (a la Robert Pestel).<br />
<br />
Although representation of agent-class behavior would be of special interest in a socio-political module, most relationships in IFs remain at the level of aggregate specifications. <br />
<br />
== Dominant Relations: Socio-political ==<br />
<br />
=== Domestic Socio-Political Change: Dominant Relations ===<br />
<br />
Social and political change occurs on three dimensions (social characteristics or individual life conditions, values, socio-political institutions and process). Although GDP per capita is strongly correlated with all dimensions of change, it might be more appropriate to conceptualize a syndrome or complex of developmental change than to portray an economically-driven process.<br />
<br />
For causal diagram see [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/index.html Socio-Political Flow Charts Overview].<br />
<br />
For equations see, for example, [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
=== Key dynamics are directly linked to the dominant relations: ===<br />
<br />
*The model computes some key social characteristics/life conditions, including life expectancy and fertility rates in the demographic model, but the user can affect them via multipliers (mortm, tfrm). Literacy rate is an endogenous function of education spending, which the user can influence (via gdsm).<br />
*The model computes value or cultural change on three dimensions: traditional versus secular-rational, survival versus self-expression, and modernism versus postmodernism, which the user can affect via additive factors (tradsrateadd, survseadd, matpostradd).<br />
*Freedom, democracy (the POLITY measure), autocracy, economic freedom, and the status of women are all computed endogenously but can all be shifted by the user via multipliers (freedomm, democm, autocm, econfreem, gemm)<br />
<br />
=== Domestic Socio-Political Change: Selected Added Value ===<br />
<br />
The larger socio-political model provides representation and control over government spending on education, health, the military, R&D, foreign aid, and a residual category. Military spending is linked to interstate politics, both as a driver of threat and as a result of action-and-reaction based arms spending. The sub-model provides aggregated indicators of the physical quality of life and the human development index.<br />
<header><hgroup><br />
== Socio-political Flow Charts[[File:SP1.gif|frame|right|454x464px]] ==<br />
</hgroup></header><br />
=== Overview ===<br />
<br />
The social and political module represents a complex of interacting structures and processes. These include:<br />
<br />
*The various social characteristics or life conditions of individuals<br />
*Human values, beliefs, and orientations’<br />
*Social and political structures, informal as well as formal<br />
*Social and political processes, both domestic and international<br />
<br />
Cultural foundations frame all of these components. And all of the components interact closely with human demographic and economic systems.<br />
<br />
The socio-political elements of IFs are among the most dynamically evolving aspects of the overall modeling system.&nbsp;Much, but not everything in the above figure has been fully represented yet within IFs; the figure indicates direction of development and shows implemented elements in italics.<br />
<br />
For more, please read the links below.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<header><hgroup><br />
== Social Characteristics: Life Conditions ==<br />
</hgroup></header><br />
Individuals are the foundations of society. Many social indicators are actually aggregated indicators of their condition. The Human Development Index (HDI) is a widely-used summary measure of that life condition, based on life expectancy, educational attainment, and GDP per capita.</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=File:SP1.gif&diff=2110File:SP1.gif2017-02-25T21:03:06Z<p>StellahKwasi: </p>
<hr />
<div></div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Socio-Political&diff=2109Socio-Political2017-02-25T20:59:43Z<p>StellahKwasi: Created page with "The most recent and complete socio-political model documentation is available on Pardee's [http://pardee.du.edu/ifs-governance-and-socio-cultural-model-documentation website]...."</p>
<hr />
<div>The most recent and complete socio-political model documentation is available on Pardee'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.<br />
<br />
<span>A substantial portion of the socio-political model of IFs is scattered throughout the other models. There are "policy handles" or intervention points throughout those models. For instance, in the population model, multipliers on the total fertility rate can reflect policy decisions (although they can also reflect the model user's judgment concerning social changes in the country or region, independent of policy). Patterns of regulation, subsidy, tax incidence, and provision of state services are so diffuse and complicated that we resort to looking at their aggregate consequences through various "policy handles" rather than trying to represent them explicitly.</span><br />
<br />
For more information on this module, please use the links below or read more at [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<header><hgroup><br />
== Structure and Agent System: Socio-Political ==<br />
</hgroup></header><br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Socio-political</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social fabric</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Levels of human well-being and institutional development (human and social capital)</div><div>&nbsp;</div><div>Cultural structures</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Social expenditures</div><div>&nbsp;</div><div>Value change</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Growth in literacy and human development;</div><div>&nbsp;</div><div>Democratic development, state failure</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Government efforts to develop human capital through spending on health, education, R&D</div><br />
|}<br />
<br />
Unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no standard organizing structure that is widely used for representing socio-political systems. In the context of the TERRA project, IFs developed a multi-component approach to structure that might be called the "social fabric" (a la Robert Pestel).<br />
<br />
Although representation of agent-class behavior would be of special interest in a socio-political module, most relationships in IFs remain at the level of aggregate specifications.<br />
<header><hgroup><br />
== Dominant Relations: Socio-political ==<br />
</hgroup></header><br />
=== Domestic Socio-Political Change: Dominant Relations ===<br />
<br />
Social and political change occurs on three dimensions (social characteristics or individual life conditions, values, socio-political institutions and process). Although GDP per capita is strongly correlated with all dimensions of change, it might be more appropriate to conceptualize a syndrome or complex of developmental change than to portray an economically-driven process.<br />
<br />
For causal diagram see [http://www.du.edu/ifs/help/understand/sociopolitical/flowcharts/index.html Socio-Political Flow Charts Overview].<br />
<br />
For equations see, for example, [http://www.du.edu/ifs/help/understand/sociopolitical/equations/index.html Socio-Political Equations Overview].<br />
<br />
=== Key dynamics are directly linked to the dominant relations: ===<br />
<br />
*The model computes some key social characteristics/life conditions, including life expectancy and fertility rates in the demographic model, but the user can affect them via multipliers (mortm, tfrm). Literacy rate is an endogenous function of education spending, which the user can influence (via gdsm).<br />
*The model computes value or cultural change on three dimensions: traditional versus secular-rational, survival versus self-expression, and modernism versus postmodernism, which the user can affect via additive factors (tradsrateadd, survseadd, matpostradd).<br />
*Freedom, democracy (the POLITY measure), autocracy, economic freedom, and the status of women are all computed endogenously but can all be shifted by the user via multipliers (freedomm, democm, autocm, econfreem, gemm)<br />
<br />
=== Domestic Socio-Political Change: Selected Added Value ===<br />
<br />
The larger socio-political model provides representation and control over government spending on education, health, the military, R&D, foreign aid, and a residual category. Military spending is linked to interstate politics, both as a driver of threat and as a result of action-and-reaction based arms spending. The sub-model provides aggregated indicators of the physical quality of life and the human development index.</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Sub-modules&diff=2108Sub-modules2017-02-25T20:21:05Z<p>StellahKwasi: </p>
<hr />
<div>[[Agriculture|Agriculture]]<br />
<br />
[[Population|Population]]<br />
<br />
[[Economics|Economics]]<br />
<br />
[[Education|Education]]<br />
<br />
[[Energy|Energy]]<br />
<br />
[[Environment|Environment]]<br />
<br />
[[Governance|Governance]]<br />
<br />
[[Health|Health]]<br />
<br />
[[Infrastructure|Infrastructure]]<br />
<br />
[[Interstate_Politics_(IP)|Interstate Politics (IP)]]<br />
<br />
[[Socio-Political]]</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Interstate_Politics_(IP)&diff=2107Interstate Politics (IP)2017-02-25T20:12:49Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete interstate politics model documentation is available on Pardee's [http://pardee.du.edu/ifs-interstate-politics-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.<br />
<br />
The interstate politics module traces changes in power balances across states and regions,&nbsp;allows exploration of changes in the level of interstate threat, and&nbsp;represents possible action-reaction processes and arms races with associated potential for conflict among countries. For more on how this data may used and analyzed within IFs, please read below.<br />
<br />
== Structure and Agent System: Interstate Interaction ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Interstate interaction</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Cooperation and conflict</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Power, threat levels</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Aid flows</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Changes in power, relative power, threat, action-reaction</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Spending on military, aid</div><div>&nbsp;</div><div>Alliances</div><div>&nbsp;</div><div>War</div><br />
|}<br />
<br />
As with the domestic socio-political environment, and unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no completely standard organizing structure that is widely used for representing interstate/international systems. Yet the representation of power-based interaction systems, including interactions related to relative power and to action-reaction dynamics, is common. IFs builds significantly on that conceptual and theoretical base.<br />
<br />
Among the most important understandings of students of interstate/international interaction is that conflict and cooperation are not really opposites in relationships. Although the balance of conflict and cooperation will vary within and across relationships, Intensity of interaction often brings both.<br />
<br />
== Dominant Relations: Interstate Politics ==<br />
<br />
=== Interstate Politics: Dominant Relations ===<br />
<br />
Threat of states towards each other is a function of many determinants. For instance, contiguity or physical proximity creates contact and therefore the potential for both threat and peaceful interaction. Cultural similarities and differences affect threat levels. Yet certain factors are more subject to rapid change over time than are contiguity or culture. Among factors that change, the relative power of states and of their level of democratization substantially affect threat levels.<br />
<br />
For a causal diagram see [http://www.du.edu/ifs/help/understand/interstate/flowcharts/power.html Process: Power]&nbsp;and&nbsp;[http://www.du.edu/ifs/help/understand/interstate/flowcharts/threatlevel.html Process: Threat Level].<br />
<br />
For equations see [http://www.du.edu/ifs/help/understand/interstate/equations/power.html IP Equations: Power] and [http://www.du.edu/ifs/help/understand/interstate/equations/overviewthreat.html IP Equations: Threat Formulation].<br />
<br />
=== Key dynamics are directly linked to the dominant relations: ===<br />
<br />
*Power is a function of population, GDP, technology, and conventional and nuclear military expenditures, in an aggregation with weights that the user can change (wpwghtpow).<br />
*Democratization is computed in the domestic socio-political model.<br />
<br />
=== Interstate Politics: Selected Added Value ===<br />
<br />
The larger interstate politics model provides representation and control over a changing index of the probability of war, based on threat levels. It is possible stochastically to introduce war based on that probability and to feed back the destruction of war to population levels and economic capital.<br />
<br />
== Interstate Politics Flow Charts ==<br />
<br />
== Power ==<br />
<br />
IFs computes a power indicator that shows each actor’s portion of global power. It does so by weighting (wpwghtpow) each actor’s share of global GDP (at exchange rates or purchasing power parity), population, a measure of technological sophistication (with GDP per capita as a proxy), government size, military spending, conventional power, and nuclear power. Weights of one "1" add the term to the power calculation, and weihts of 0 remove the term from power calculation.<br />
<br />
[[File:IP1.gif|border|center|IP1.gif]] Conventional and nuclear power have their own dynamics, dependent primarily on military spending. Parameters direct a share of that into nuclear power and determine the translation of spending into actual conventional power, with an assumption that Less Developed Countries can leverage spending into more power because of lower wages and other costs. Given country/regional conventional and nuclear power, it is possible to compute world conventional and nuclear power (CPOW, NPOW).<br />
<br />
== Threat Level ==<br />
<br />
The threat that any state poses to another (and which may lead to conflict, war, or nothing) is affected by many factors. IFs conceptualizes of threat in concrete terms, namely the probability of what is called a militarized international dispute (MID).<br />
<br />
IFs approaches calculation of that threat from two directions. The first is through the use of data on historic threat levels by dyad. The second is solely through the evolution of key factors that have been shown to give rise to disputes. The user can control whether the model should rely primarily upon historic patterns or primarily upon predicted ones with a convergence parameter (wpthrconv).<br />
<br />
Regardless of the basic approach (data-based or predictive), the evolution of threat over time depends upon a variety of underlying factors. IFs groups these roughly into two categories: power and nonpower terms, explained in detail elsewhere. It should be noted, however, that three factors seem to carry special weight in determining the threat of disputes: contiguity, whether or not countries are great powers, and the existence or absence of territorial disputes between countries.<br />
<br />
[[File:IP2.gif|border|center|IP2.gif]]<br />
<br />
== Threat Elaborated: Power Terms ==<br />
<br />
One important power term that affects interstate relations depends on relative power of the countries and is especially significant when two countries of relevance to each other enter a zone of power transition. Another important term is the concentration of power among the great powers themselves. It is increasingly uncertain whether the European Union should be treated as a single actor or as a group of individual states in such power concentration calculations, so IFs allows an option.<br />
<br />
A third factor is whether or not the two interacting states are great powers or not. It is important whether the dyad contains zero, one, or two such powers. Because the definition of great power is uncertain, IFs allows the user to specify a threshold level at which a state begins to meet that definition and a second level at which point it clearly does.<br />
<br />
A fourth factor in the figure below is less clearly a power term. Nonetheless, existence or absence of territorial disputes between states greatly affects the probability of a militarized dispute, quite possibly more than any other single factor.<br />
<br />
[[File:IP3.gif|border|center|IP3.gif]]<br />
<br />
== Threat Elaborated: Nonpower Terms ==<br />
<br />
Among the most important factors that determine the probability of disputes between countries that are not related to their power is their level of democracy. More specifically, threat depends on the level of democracy in actor and target countries and on the difference in level, sometimes called political distance.<br />
<br />
Studies find that trade relations between states also affect the probability of disputes between them, but results are ambiguous. Because IFs uses pooled trade, it cannot forecast the specific level of trade between any two states. The model has a term that links overall levels of global trade, as a portion of GDP, to dispute probability.<br />
<br />
Alliances generally reduce dispute probability. Another factor that may reduce the probability is that global community is growing. This is uncertain and the linkage is not now activated.<br />
<br />
One of the most basic factors affecting interstate relations is the closeness or contiguity of two states (small states on different continents pose little or no threat to each other).<br />
<br />
[[File:IP4.gif|border|center|IP4.gif]]<br />
<br />
== Probability of War ==<br />
<br />
The probability of conventional war depends most fundamentally on a basic conventional warfare probability per year (CWARBASE) that the user sets exogenously and that is set to zero for all country pairs (dyads) in the base case.<br />
<br />
That probability is potentially enhanced by threat from an actor to a target country and also by the threat perceived by a target from an actor.<br />
<br />
[[File:IP5.gif|border|center|IP5.gif]]<br />
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== War ==<br />
<br />
The outbreak of conventional war depends on the probability of war. The probability of war is converted into actual war randomly. The outbreak of nuclear war (only for countries with nuclear weapons) similarly depends probabilistically and randomly on the outbreak of conventional war. In order for a war to occur, the "waron" parameter must be turned on (set to 1). If it is turned on, wars may or may not occur; but a war can be forced if the waron parameter is set AND the war forcing switch (cwarf) is set to 100 or above).<br />
<br />
We calculate damage for both conventional and nuclear power capabilities, as well as for civilian society (population and GDP).<br />
<br />
[[File:IP6.gif|frame|center|IP6.gif]]<br />
<br />
== Interstate Politics Equations ==<br />
<br />
For help understanding the equations see [http://www.du.edu/ifs/help/understand/equations/notation.html Notation].<br />
<br />
== IP Equations: Power ==<br />
<br />
Foreign relations of states are sensitive to power calculations. There is a vast literature surrounding the measurement of power, with much debate among analysts around the components that should enter into calculations of power capabilities and how those components can best be aggregated into a single measure of power. Ray (1990) did a good job of reviewing that literature and has, himself, contributed to power calculation. Working with the Correlates of War project at the University of Michigan, he and others have frequently emphasized three primary components of power capabilities: economic, demographic, and military strength.<br />
<br />
The early representation of power in IFs used a formulation that aggregated these three components and that further differentiated between conventional and nuclear military strength. It allowed the user to provide weightings for the three. For instance, many analysts are loath to weight demographic size heavily for less economically developed countries like India.<br />
<br />
Over time, users of the model suggested that other components of power should also be considered. For instance, Evan Hillebrand suggested that economic-technological capability, as indicated by the product of GDP and GDP per capita, should be a core component of capabilities. There has also been a long tradition, dating at least to Ray Cline, suggesting that government capabilities (as perhaps indicated by government spending levels) should be an element. And there is uncertainty as to whether GDP is best measured for power purposes at purchasing power parity (PPP) or exchange rates. In response to these suggestions, it was decided to create a more general function for POWER in IFs that allows the user to create a flexibly weighted sum of 9 different components: population (POP), GDP at purchasing power (GDPP), GDP at market prices (GDP), economic-technological capability using GDP per capita at either purchasing power or exchange rates (GDPPCP, GDPPC), government size (GOVCON), military spending (GDS), conventional military power (CPOW) and nuclear power (NPOW). For each component, a global sum is created and country capabilities are computed as portions of the global total. Setting a weight to zero removes the component from the power calculation. In the base or default case, most or all weights (wpwghtpow) other than the ones on economic, demographic, technological, and military strength are set to zero.<br />
<br />
[http://www.du.edu/ifs/help/media/images/img00479.gif http://www.du.edu/ifs/help/media/images/img00479.gif]<br />
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Conventional power (CPOW) for each entity decreases with depreciation (drcpow) and increases with the non-nuclear portion (1-nmilf) of annual military spending. A conventional power factor variable (CPowF) converts new military spending into conventional capability. That factor is computed so that the spending by countries with GDP per capita below $10,000, because such countries can hire personnel at lower cost, has additional leverage in creating conventional power, as determined by a developing country conventional power factor (cpowldcf). The additional leverage is phased out as GDP per capita increases. The calculation of nuclear power (for those states that spend some portion of their military on nuclear capabilities) is analogous, but conversion of spending to power depends on a factor (npowf) that is invariant across countries.<br />
<br />
[http://www.du.edu/ifs/help/media/images/img00480.gif http://www.du.edu/ifs/help/media/images/img00480.gif]<br />
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[http://www.du.edu/ifs/help/media/images/img00481.gif http://www.du.edu/ifs/help/media/images/img00481.gif]<br />
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As indicators, it is also useful to calculate the world total of conventional (WCPOW) and nuclear power (WNPOW).<br />
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[http://www.du.edu/ifs/help/media/images/img00482.gif http://www.du.edu/ifs/help/media/images/img00482.gif]<br />
<br />
== IP Equations: Overview of Threat Formulation ==<br />
<br />
The threat that one country poses to others is a key concept in IFs. Unlike most variables in IFs, it is dyadic (actor country to target country). It is also different from most IFs variables in that it is a concept that has a probabilistic element in its implications for forward linkages. In fact, it is possible to think about threat as being the probability of military challenge or war, and that is the conceptualization in IFs. The database on Militarized Interstate Disputes (MIDS) was used in both conceptualization and initialization of threat in IFs.<br />
<br />
Because of its importance, a substantial sub-project, sponsored by the Strategic Assessments Group (SAG) of the CIA devoted time to specifying the drivers of threat and the formulation for creating forecasts based on those drivers. Although none of the participants in that subproject bear ultimate responsibility for the treatment of threat in IFs, the model owes a substantial debt to the sponsors and participants of that sub-project.<br />
<br />
Three key distinctions are important to understanding the overall threat formulation and its use in forecasting:<br />
<br />
#Using history to initialize threat levels versus using predictive formulations. The argument for using data to initialize dyadic threat levels is obvious: data tell us about historic relationships between countries like India and Pakistan, often carrying information that is not available in a predictive formulation calculated across many dyads and not picking up the historic path elements of a particular dyad. Yet the argument for not relying too heavily on such dyadic data in forecasting is also obvious: the U.S.-Russian relationship has fundamentally changed since the collapse of communism and the break-up of the Soviet Union, so that a forecast based heavily on historic data would now be questionable. The IFs formulation provides forecasts that are rooted in data, but it allows the user to relax the ties to historic data over time.<br />
#The complicated contribution of constant terms, switches, and variables. The single best predictor of conflict among countries historically may well be their physical proximity, with contiguous or geographically touching countries being much more conflict prone. But because contiguity is a constant, it is near useless in determining how the threat of overt conflict will change in the future. Somewhat similarly, territorial disputes are a near constant over time, but can be switched on or off. Quite differently, power levels and commitment to democracy fluctuate substantially over time. The different types of variables enter differently into the formulation.<br />
#The contribution of power-based drivers and other drivers. For purposes of clarity of conceptualization and presentation of it, there is value in distinguishing between drivers of threat that have their roots primarily in state power and those, like democracy level, that do not.<br />
<br />
Taking into account these important distinctions, the IFs formulation of threat has three key components. The first is a constant base term rooted in data and/or predictive theory. When it is rooted in predictive theory, the term draws on the constant and switch inputs to threat such as contiguity and territorial dispute. When it is rooted in data it represents recent history for the dyad as computed by the MIDs data. The second term is a delta or variable term rooted in power variables and the third term is a delta term rooted in non-power variables. The model user can use a parameter (wpthrconv) to determine whether the ultimate threat calculation (THREAT) should remain tied to the empirical initial condition (THREATIDATA), as modified by the delta terms, or should converge over time to a fully predicted threat formulation (THREATIPRED), again modified by delta terms.<br />
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[http://www.du.edu/ifs/help/media/images/img00483.gif http://www.du.edu/ifs/help/media/images/img00483.gif]<br />
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It is useful also to be able to see a summary measure of the average world threat level (WTHREAT) over time. The sum of all threat terms is normalized by the product of the number of regions (NR) times the number – 1 (there are no non-zero and meaningful self-threat terms).<br />
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[http://www.du.edu/ifs/help/media/images/img00484.gif http://www.du.edu/ifs/help/media/images/img00484.gif]<br />
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Detail on the component terms of this general formulation is available:<br />
<br />
*[http://www.du.edu/ifs/help/understand/interstate/equations/initialthreat.html Initial Threat Terms]<br />
*[http://www.du.edu/ifs/help/understand/interstate/equations/powerterm.html Delta Power Terms]<br />
*[http://www.du.edu/ifs/help/understand/interstate/equations/nonpowerterm.html Delta Non-Power Terms]<br />
<br />
== IP Equations: Threat Formulation Subproject ==<br />
<br />
The Strategic Assessment Group (SAG) of the Central Intelligence Agency sponsored a sub-project of IFs with the title of Threats and Opportunities Analysis (TAOS). Guided by Evan Hillebrand from SAG, a number of experts were drawn together to review and discuss enhancements to the IFs system, notably its representation of interstate politics. The participants were Stuart Bremer, Mark Crescenzi, Doug Lemke, Edward Mansfield, and Paul Senese.<br />
<br />
The project ultimately facilitated the incorporation into IFs of insights from these individuals as well as work recommended by them See Crescenzi and Enterline (2001), Huth (1996), Mansfield (1994), and Tammen, Kugler, Lemke, Stam, Alsharabati, Abdollahian, Efird, and Organski (2000). See, also, Bennett and Stam (2003), who were good enough to provide an early manuscript draft of their book.<br />
<br />
In addition, the project was able to draw on the database for militarized interstate disputes (MIDs) involving “the threat, display, or use of military force short of war” (Jones, Bremer and Singer 1996: 163). At the time of analysis for IFs the MIDs database did not extend beyond 1992, however, complicating estimation for the post-Cold War period.<br />
<br />
Some of the parameters for relationships in the IFs threat calculation were derived from Bennett and Stam. Senese estimated parameters for the democracy relationships from MIDs data. And Lemke put together a set of estimated and literature-based parameters that helps determine most of the base-case values in IFs. The parameter determination was guided by the desire to create a set of what economists sometimes call “stylized facts,” indicating the contribution of various factors to higher or lower probabilities of conflict in a dyad. For a detailed description of the work from that project and the formulation it produced, see Hughes, 2002.<br />
<br />
== IP Equations: Initial Threat Terms ==<br />
<br />
Threat in a dyad is calculated using either empirically based initial conditions (ThreatIData) or predicted initial conditions (ThreatIPred). Empirical values come from the Militarized International Dispute (MIDs) database. Mark Crescenzi provided empirical initial conditions from that database using a technique for representing "memory" of past events (more distant events are less memorable) that he and Andrew Enterline pioneered (Crescenzi and Enterline 2001). A significant problem was that the initial conditions for dyads involving Cold War adversaries were no longer credible after the end of the Cold War. Moreover, the MIDs database used for the estimations extended only to 1992. Therefore the IFs project relied on expert judgment to reset some of those value: essentially, conflict for important Cold-War dyads like the U.S.-Russia was put at zero after 1992 and the Crescennzi-Enterline technique was used through 2002 in order to erode the earlier Cold-War memory in the creation of initial conditions.<br />
<br />
Predicted initial conditions use a formulation that relies on some of the strongest empirical predictors of interstate conflict. The first three are constant or switch factors: great power status of the dyad members, contiguity, and existence of territorial dispute (all of which substantially increase the threat of conflict). Each term merits some comment:<br />
<br />
*Great power status is often (for instance, in the Correlates of War project) determined subjectively. Because IFs needs to forecast that status, not just to apply it without change over time, the model needed an objective definition of it. In general it may be safe to argue that all states with more than about 5% of total systemic power are great powers, and no states with less than 2% of systemic power are great powers. Yet even those cut-offs could be debated. Because of the range of uncertainty, IFs uses a variable representation of the status, designating any state below the lower level (carried by the parameter wpgreatthesh) as a non-great power, and any state above the upper level (wpgreatlev) as a great power, conferring partial status in between for selected computations (like that of systemic power concentration). The internal IFs variable GPowerTermI carries the resulting calculation of increased threat of conflict from great power status in the dyad, where wpgreat1 determines the contribution of full great power status and wpgreat2 determines the contribution of partial great power status.<br />
*Paul Diehl generously provided contiguity data for IFs (CONTIGUITY) and the parameter wpcontiguity translates the impact of contiguity into increased threat of conflict (ContiguityTerm).<br />
*The work of Paul Huth (1996) was tapped for territorial disputes (TerDispute). The parameter wpterdisp translates the existence of a dispute into increased threat of conflict (TerDisputeTerm).<br />
<br />
[http://www.du.edu/ifs/help/media/images/img00526.gif&nbsp http://www.du.edu/ifs/help/media/images/img00526.gif&nbsp];<br />
<br />
If any of these three factors are positive, the dyad members are "politically relevant" to each other, thereby increasing their sensitivity to other factors. In politically relevant dyads, IFs adds three other factors to the calculation of initial threat levels: a power transition term, an alliance term, and a two-term democracy representation. Again, each factor needs elaboration:<br />
<br />
*The initial power transition term (PowerTranTermI) begins with a computation of the ratio of power within the dyad (PowerRatio). If that ratio exceeds a threshold level (wppowtran1) then the increased threat of conflict in the power transition term is set equal to the difference between the power ratio and the threshold power transition level, multiplied by an impact parameter (wppowtran2).<br />
*The alliance term (AllyI) is simply the exogenously-specified existence (1) or absence (0) an alliance times a parameter (wpally) than translates alliance into conflict reduction.<br />
*Senese has found that the impact of democracy on the threat of conflict depends on both the lesser level of democracy in the states of the dyad and the difference between their levels. The lesser or minimum level is multiplied by a parameter (wpdemmin) to determine the minimum democracy term (DemocTermMin) and the distance in democracy is multiplied by a second term (wpdemdist) to determine the contribution of the distance term to changed threat of conflict (DemocTermDist).<br />
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[http://www.du.edu/ifs/help/media/images/img00527.gif http://www.du.edu/ifs/help/media/images/img00527.gif]<br />
<br />
== IP Equations: Power Term for Threat ==<br />
<br />
The threat calculation builds on an initial, constant term plus a delta power term (that is, a change in power term) and a delta non-power term. This topic explains the power term. It is a sum of four other terms: delta great power term (DeltaGPowerTerm), delta power transition term (DeltaPowerTranTerm), delta territorial dispute term (DeltaTerDisputeTerm), and delta power concentration term (DeltaConcenTerm). See the Initial Threat Topic for a discussion of the foundational elements of the first three of these. The delta term for each is the foundational term in a future year minus the initial value of the term. The only new term in future years is the delta power concentration term (DeltaConcenTerm), explained below.<br />
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[http://www.du.edu/ifs/help/media/images/img00528.gif http://www.du.edu/ifs/help/media/images/img00528.gif]<br />
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Power concentration is a concept focusing on the degree to which the power structure of the great powers (not all powers in the system) is heavily concentrated or not. The measure poses an alternative to the often less systematic estimation of whether a system is multipolar, bipolar, etc. (Singer, Bremer, and Stuckey 1972). IFs calculates systemic power concentration using the Ray and Singer (1973) calculation approach,<br />
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More specifically, IFs calculates four versions of power concentration. The first (PCONGREAT) is the most traditional, using a fixed cut-off for defining great powers (wpgreatlev) and normally setting that value at 5%. The measure also treats all European Union members as individual countries. The second variation (PCONGREATEU) treats the European Union as a single entity. The user has a parameter (eumembsw) to define membership in the EU over time. Although few would argue that it currently acts as a single great power, greater unity is possible in the future. Both of the first two measures are, however, somewhat erratic in forecasts, because they make a fixed distinction about great power status at the threshold level. Thus if Japan drops below five percent of systemic power (as it normally does relatively early in the base case), the number of powers considered changes and there is a transient in the calculation of power concentration. Because that seems rather arbitrary, a third measure (PCONGREATF) uses a more flexible measure of great power, phasing the status in or out above a threshold (wpgreatthresh) up to full status (wpgreatlev). The fourth measure does the same for the single EU variant (PCONGREATEUF). For completeness, IFs also calculates a global concentration measure (PCONSYS), not distinguishing between great and other powers.<br />
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Edward Mansfield (1994) has investigated the relationship between power concentration of great powers and propensity for war in the system, finding a non-linear pattern with war most likely at highest and lowest system concentration levels. Bennett and Stam (2003) investigated the relationship for MIDs rather than wars, and found parameters about half the magnitude that Mansfield reported for wars. IFs uses a Mansfield-type non-linear formulation, with reduced parameters more appropriate to disputes rather than wars (remember that the IFs approach is a fundamentally stylized one, bringing together insights from much research rather than relying on a single analysis).<br />
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The Mansfield formulation looks to change in power concentration (ConcenChange) and to the square of change in power concentration for impact on the threat of conflict, which is the reason that power concentration enters only the delta term, not the initial term. Parameters on the change in power concentration (wpcon) and the square of the change (wpconsq) determine the ultimate concentration term. Also in the above calculation, power concentration is bound at all time points to be between a minimum (wpconmin) and maximum (wpconmax) value. Mansfield suggests that the analysis is only valid for values between .202 and .417.<br />
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== IP Equations: Non Power Term for Threat ==<br />
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The threat calculation builds on an initial, constant term plus a delta power term (that is a change in power term) and a delta non-power term. This topic explains the non-power term (DeltaNonPowerTerm). It is a sum of five other terms: delta minimum democracy term (DeltaDemocTermMin), delta democracy distance term (DeltaDemocTermDist), delta alliance term (DeltaAllyTerm), delta trade term (DeltaTradeTerm), and delta GDP growth term (DeltaGDPGrowthTerm). See the Initial Threat Topic for a discussion of the foundational elements of the first three of these. The delta term for each is the foundational term in a future year minus the initial value of the term. The two new terms in future years are the delta trade term (DeltaTradeTerm) and delta GDP growth term (DeltaGDPGrowthTerm), both explained below.<br />
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The preponderance of empirical analysis appears to support the proposition that trade relationships reduce conflict, contributing with joint democracy to enhanced peace among states in the manner that Kant posited long ago. Most of the studies focus on trade specific to the dyad, generally using dyadic trade over GDP as a measure of trade dependence, and often focusing on the less dependent of the two trading partners (Oneal and Russett 1997). Bennett and Stam (2001) support the general tendency of these conclusions (although Barbieri 1997 challenges them). IFs does not represent dyadic trade, but Mansfield (1994) found that systemic trade over GDP is also inversely related to war, at least for the great powers. IFs has such an inverse relationship with change in world trade as a percent of world GDP (WTRADE), controlling it by a parameter (wptrade) that is set rather low in the base case. In fact, because the parameter used was derived from dyadic analysis, IFs arbitrarily divides it by 10 in order to dilute the affect when using a global trade representation. Even then, this is a rather powerful factor, because the base case of IFs normally exhibits a continuation of the historic increase in global trade as a percent of GDP.<br />
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Bennett and Stam (2003) also investigated the impact of global economic cycles and found that conflict propensity of all kind roughly doubles during upswings relative to downswings. IFs introduces a factor that compares global economic growth (WGDPR) with the long term pattern (LongTermGDPR), computed as a moving average. IFs translates the swings of growth into impact on threat with a parameter (wpsysgr), once again looking to Bennett and Stam for guidance on the magnitude of it. The Bennett and Stam estimate, however, was remarkably high, higher than any other driver of conflict potential other than the addition of a second great power to the dyad. Because this seemed theoretically implausible, the base case normally uses a value that was arbitrarily reduced by about a factor of 5. <br />
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== IP Equations: Action/Reaction ==<br />
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The model links the [http://www.du.edu/ifs/help/understand/interstate/equations/overviewthreat.html threat indicator] to an action-reaction dynamic only if the model user leaves the action-reaction switch (ACTREAON) at "1." Setting it to 0.0 will turn off action-reaction. When that switch is thrown, threat affects military spending in a process that can set up a positive feedback loop to increase or decrease the spending of acting and reacting countries or regions. The model calculates a multiplier on military spending (GKMUL) based on the level of threat (THREAT) in comparison with the initial level. Dyads have different reactivities to each other; the United States is not as concerned by an increase in British defense spending as by an increase in Chinese spending – in fact the U.S. might welcome the British increase and feel able to shave its own rather than increase it. A parameter (reac) gives the user control over this reactivity differential. In the GLOBUS modeling project, Dale Smith developed a formulation to endogenize such reactivity coefficients for dyads, in response to trade and other factors. Because the dyadic threat formulation of IFs already incorporates a very large number of dyadic and global factors, it would be redundant to build them into the reactivity parameter, which serves, instead, to provide the user some dyadic specific control over arms spending action/reaction. The addition of the number 10 to numerator and denominator in the formulation has a fundamentally technical basis – if there is a very low level of initial threat in a dyad (say Belgium and Ecuador), small changes in the numerator should not be permitted to give rise to large changes in the ratio and therefore the multiplier on defense spending.<br />
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The multiplier shifts government expenditures into or away from the military and adjusts all other categories of expenditures proportionately to their size as calculated earlier, normalizing all expenditures (GDS) to the total level of government consumption (GOVCON).<br />
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The action-reaction dynamic thus works across the entire governmental expenditure and economic model. For instance, the normal "burden" term in the action-reaction dynamic is unnecessary because the economic model captures the burden of increased government spending on the military when it reduces spending on health and education. <br />
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== IP Equations: War ==<br />
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IFs conceptualizes the threat variable (THREAT) as a probability of conflict, and initial conditions and parameters for its calculation were estimated primarily from militarized interstate dispute data. Across all dyads only a small portion (wpthrwar) of militarized disputes result in actual war. Knowing that percentage, it is possible to translate THREAT into the actual probability of war (CWARB), a variable that ranges from 0 to 100 (100% being certainty), just as THREAT ranges from 0 to 100. To allow the user more complete control over the probability of war in any given dyad, a second parameter (cwarbase), with a default value of 0, can be set from 0 to 100. Obviously, many variables, including factors such as relative power, affect whether or not countries in a dyad go to war. Remember that the THREAT variable is calculated so as to be responsive to such factors.<br />
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Even after the calculation of a war probability, however, IFs does not automatically proceed to generate a war and compute its consequences. The main reason is that, in the process of translating a probability into an event using a random number generator, a randomness is introduced into any model doing so. The philosophy of IFs use is that, unless the user consciously adds a random element, any run with a given set of parameters should always generate the same results and thus be easily compared with the base case.<br />
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Thus proceeding from probabilities of war to actual war events requires action on the part of the model user. Specifically, turning on a war parameter (waron) by moving it from the default value of 0 to the on value of 1 will do engage the linkage from war probabilities to war events.<br />
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There are two additional parameters that give users further control over war, once the waron parameter is set to 1. First, the conventional war base parameter (cwarbase), as noted above, can be adjusted. A value of 100, when waron is set to 1, will force a war in a given dyad. Second, a conventional war parameter (cwarf), also normally set to 0, can be set to 100 or more to force wars in all dyads, a true global war. The purpose of doing so is to allow assessment of damage potential from war, not because it is expected that a truly all-world war would ever occur.<br />
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As indicated, a random number generation function (RNDF) controls the process of moving from probability of war to actual conventional war (CWAR=1) or lack of it (CWAR=0). When waron is set to 1, a dyad in which the probability of war were an unusually high 10 percent would experience a war approximately every 10 years. Still, random behavior can generate a war each year or no war over 100 years.<br />
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The length of any conventional war (LENWAR) is purely random and varies from 1 to 6 years.<br />
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When nuclear powers are involved in conventional war, there is always a possibility of the escalation to nuclear war (NWARPB). The probability depends on the existence of the conventional war, an exogenously specified severity of war (cwarsv), and an exogenously specified probability for escalation (nwarf). Again, a random number generator translates that probability into the existence (NWAR=1) or nonexistence (NWAR=0) of nuclear war.<br />
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When war occurs, there are at least two kinds of consequences. The first is a spread to allies of the members of a dyad. The second is damage to populations and economies.<br />
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With respect to the spread of war, IFs makes the simplifying assumption that all allies of each partner as specified exogenously in the alliance parameter (ally) become involved in the conflict. This is not always true, but the model user can easily control it through the alliance parameter.<br />
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With respect to the calculation of damage, IFs computes civilian damage multipliers (CIVDM) for all participants. The basic assumption is that damage to an active participant, the actor, is directly proportional through a severity factor (cwarsv) to the conventional power (CPOW) of the opposing participant, the target), and inversely proportional to the GDP of the actor. A further parameter, the civilian damage multiplier factor (cdmf) allows more complete control over the damage assessment. If there is a nuclear war involved as well as a conventional one, nuclear damage (NuclDam) from all "war partners" (targets) is added to the conventional damage.<br />
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The resultant civilian damage multiplier is used in population and economic models to reduce population and economic capital, as described in the documentation of those models.<br />
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War also weakens conventional power, through use and through destruction. The conventional damage multiplier (CPowDM) is fundamentally analogous to the civilian damage multiplier, except that it does not involve the inverse relationship to GDP size. The multiplier revises the earlier calculation of conventional power as it is carried forward to the next time period.<br />
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IFs assumes the reduction of nuclear power only in the case of nuclear war (an assumption, as Iraq can testify, that is not always valid). The reduction in each country's or region's nuclear power is proportional to their initial power level (in nuclear wars, almost all nuclear power would presumably be used).<br />
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[http://www.du.edu/ifs/help/media/images/img00492.gif http://www.du.edu/ifs/help/media/images/img00492.gif]</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Interstate_Politics_(IP)&diff=2106Interstate Politics (IP)2017-02-25T20:09:07Z<p>StellahKwasi: </p>
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<div>The most recent and complete interstate politics model documentation is available on Pardee's [http://pardee.du.edu/ifs-interstate-politics-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.<br />
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The interstate politics module traces changes in power balances across states and regions,&nbsp;allows exploration of changes in the level of interstate threat, and&nbsp;represents possible action-reaction processes and arms races with associated potential for conflict among countries. For more on how this data may used and analyzed within IFs, please read below.<br />
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== Structure and Agent System: Interstate Interaction ==<br />
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{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Interstate interaction</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Cooperation and conflict</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Power, threat levels</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Aid flows</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Changes in power, relative power, threat, action-reaction</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Spending on military, aid</div><div>&nbsp;</div><div>Alliances</div><div>&nbsp;</div><div>War</div><br />
|}<br />
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As with the domestic socio-political environment, and unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no completely standard organizing structure that is widely used for representing interstate/international systems. Yet the representation of power-based interaction systems, including interactions related to relative power and to action-reaction dynamics, is common. IFs builds significantly on that conceptual and theoretical base.<br />
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Among the most important understandings of students of interstate/international interaction is that conflict and cooperation are not really opposites in relationships. Although the balance of conflict and cooperation will vary within and across relationships, Intensity of interaction often brings both.<br />
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== Dominant Relations: Interstate Politics ==<br />
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=== Interstate Politics: Dominant Relations ===<br />
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Threat of states towards each other is a function of many determinants. For instance, contiguity or physical proximity creates contact and therefore the potential for both threat and peaceful interaction. Cultural similarities and differences affect threat levels. Yet certain factors are more subject to rapid change over time than are contiguity or culture. Among factors that change, the relative power of states and of their level of democratization substantially affect threat levels.<br />
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For a causal diagram see [http://www.du.edu/ifs/help/understand/interstate/flowcharts/power.html Process: Power]&nbsp;and&nbsp;[http://www.du.edu/ifs/help/understand/interstate/flowcharts/threatlevel.html Process: Threat Level].<br />
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For equations see [http://www.du.edu/ifs/help/understand/interstate/equations/power.html IP Equations: Power] and [http://www.du.edu/ifs/help/understand/interstate/equations/overviewthreat.html IP Equations: Threat Formulation].<br />
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=== Key dynamics are directly linked to the dominant relations: ===<br />
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*Power is a function of population, GDP, technology, and conventional and nuclear military expenditures, in an aggregation with weights that the user can change (wpwghtpow).<br />
*Democratization is computed in the domestic socio-political model.<br />
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=== Interstate Politics: Selected Added Value ===<br />
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The larger interstate politics model provides representation and control over a changing index of the probability of war, based on threat levels. It is possible stochastically to introduce war based on that probability and to feed back the destruction of war to population levels and economic capital.<br />
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== Interstate Politics Flow Charts ==<br />
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== Power ==<br />
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IFs computes a power indicator that shows each actor’s portion of global power. It does so by weighting (wpwghtpow) each actor’s share of global GDP (at exchange rates or purchasing power parity), population, a measure of technological sophistication (with GDP per capita as a proxy), government size, military spending, conventional power, and nuclear power. Weights of one "1" add the term to the power calculation, and weihts of 0 remove the term from power calculation.<br />
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[[File:IP1.gif|border|center|IP1.gif]] Conventional and nuclear power have their own dynamics, dependent primarily on military spending. Parameters direct a share of that into nuclear power and determine the translation of spending into actual conventional power, with an assumption that Less Developed Countries can leverage spending into more power because of lower wages and other costs. Given country/regional conventional and nuclear power, it is possible to compute world conventional and nuclear power (CPOW, NPOW).<br />
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== Threat Level ==<br />
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The threat that any state poses to another (and which may lead to conflict, war, or nothing) is affected by many factors. IFs conceptualizes of threat in concrete terms, namely the probability of what is called a militarized international dispute (MID).<br />
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IFs approaches calculation of that threat from two directions. The first is through the use of data on historic threat levels by dyad. The second is solely through the evolution of key factors that have been shown to give rise to disputes. The user can control whether the model should rely primarily upon historic patterns or primarily upon predicted ones with a convergence parameter (wpthrconv).<br />
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Regardless of the basic approach (data-based or predictive), the evolution of threat over time depends upon a variety of underlying factors. IFs groups these roughly into two categories: power and nonpower terms, explained in detail elsewhere. It should be noted, however, that three factors seem to carry special weight in determining the threat of disputes: contiguity, whether or not countries are great powers, and the existence or absence of territorial disputes between countries.<br />
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[[File:IP2.gif|border|center|IP2.gif]]<br />
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== Threat Elaborated: Power Terms ==<br />
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One important power term that affects interstate relations depends on relative power of the countries and is especially significant when two countries of relevance to each other enter a zone of power transition. Another important term is the concentration of power among the great powers themselves. It is increasingly uncertain whether the European Union should be treated as a single actor or as a group of individual states in such power concentration calculations, so IFs allows an option.<br />
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A third factor is whether or not the two interacting states are great powers or not. It is important whether the dyad contains zero, one, or two such powers. Because the definition of great power is uncertain, IFs allows the user to specify a threshold level at which a state begins to meet that definition and a second level at which point it clearly does.<br />
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A fourth factor in the figure below is less clearly a power term. Nonetheless, existence or absence of territorial disputes between states greatly affects the probability of a militarized dispute, quite possibly more than any other single factor.<br />
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[[File:IP3.gif|border|center|IP3.gif]]<br />
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== Threat Elaborated: Nonpower Terms ==<br />
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Among the most important factors that determine the probability of disputes between countries that are not related to their power is their level of democracy. More specifically, threat depends on the level of democracy in actor and target countries and on the difference in level, sometimes called political distance.<br />
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Studies find that trade relations between states also affect the probability of disputes between them, but results are ambiguous. Because IFs uses pooled trade, it cannot forecast the specific level of trade between any two states. The model has a term that links overall levels of global trade, as a portion of GDP, to dispute probability.<br />
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Alliances generally reduce dispute probability. Another factor that may reduce the probability is that global community is growing. This is uncertain and the linkage is not now activated.<br />
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One of the most basic factors affecting interstate relations is the closeness or contiguity of two states (small states on different continents pose little or no threat to each other).<br />
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[[File:IP4.gif|border|center|IP4.gif]]<br />
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== Probability of War ==<br />
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The probability of conventional war depends most fundamentally on a basic conventional warfare probability per year (CWARBASE) that the user sets exogenously and that is set to zero for all country pairs (dyads) in the base case.<br />
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That probability is potentially enhanced by threat from an actor to a target country and also by the threat perceived by a target from an actor.<br />
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[[File:IP5.gif|border|center|IP5.gif]]<br />
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== War ==<br />
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The outbreak of conventional war depends on the probability of war. The probability of war is converted into actual war randomly. The outbreak of nuclear war (only for countries with nuclear weapons) similarly depends probabilistically and randomly on the outbreak of conventional war. In order for a war to occur, the "waron" parameter must be turned on (set to 1). If it is turned on, wars may or may not occur; but a war can be forced if the waron parameter is set AND the war forcing switch (cwarf) is set to 100 or above).<br />
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We calculate damage for both conventional and nuclear power capabilities, as well as for civilian society (population and GDP).<br />
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[[File:IP6.gif|frame|center|IP6.gif]]<br />
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== Interstate Politics Equations ==<br />
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For help understanding the equations see [http://www.du.edu/ifs/help/understand/equations/notation.html Notation].<br />
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== IP Equations: Power ==<br />
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Foreign relations of states are sensitive to power calculations. There is a vast literature surrounding the measurement of power, with much debate among analysts around the components that should enter into calculations of power capabilities and how those components can best be aggregated into a single measure of power. Ray (1990) did a good job of reviewing that literature and has, himself, contributed to power calculation. Working with the Correlates of War project at the University of Michigan, he and others have frequently emphasized three primary components of power capabilities: economic, demographic, and military strength.<br />
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The early representation of power in IFs used a formulation that aggregated these three components and that further differentiated between conventional and nuclear military strength. It allowed the user to provide weightings for the three. For instance, many analysts are loath to weight demographic size heavily for less economically developed countries like India.<br />
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Over time, users of the model suggested that other components of power should also be considered. For instance, Evan Hillebrand suggested that economic-technological capability, as indicated by the product of GDP and GDP per capita, should be a core component of capabilities. There has also been a long tradition, dating at least to Ray Cline, suggesting that government capabilities (as perhaps indicated by government spending levels) should be an element. And there is uncertainty as to whether GDP is best measured for power purposes at purchasing power parity (PPP) or exchange rates. In response to these suggestions, it was decided to create a more general function for POWER in IFs that allows the user to create a flexibly weighted sum of 9 different components: population (POP), GDP at purchasing power (GDPP), GDP at market prices (GDP), economic-technological capability using GDP per capita at either purchasing power or exchange rates (GDPPCP, GDPPC), government size (GOVCON), military spending (GDS), conventional military power (CPOW) and nuclear power (NPOW). For each component, a global sum is created and country capabilities are computed as portions of the global total. Setting a weight to zero removes the component from the power calculation. In the base or default case, most or all weights (wpwghtpow) other than the ones on economic, demographic, technological, and military strength are set to zero.<br />
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Conventional power (CPOW) for each entity decreases with depreciation (drcpow) and increases with the non-nuclear portion (1-nmilf) of annual military spending. A conventional power factor variable (CPowF) converts new military spending into conventional capability. That factor is computed so that the spending by countries with GDP per capita below $10,000, because such countries can hire personnel at lower cost, has additional leverage in creating conventional power, as determined by a developing country conventional power factor (cpowldcf). The additional leverage is phased out as GDP per capita increases. The calculation of nuclear power (for those states that spend some portion of their military on nuclear capabilities) is analogous, but conversion of spending to power depends on a factor (npowf) that is invariant across countries.<br />
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As indicators, it is also useful to calculate the world total of conventional (WCPOW) and nuclear power (WNPOW).<br />
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== IP Equations: Overview of Threat Formulation ==<br />
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The threat that one country poses to others is a key concept in IFs. Unlike most variables in IFs, it is dyadic (actor country to target country). It is also different from most IFs variables in that it is a concept that has a probabilistic element in its implications for forward linkages. In fact, it is possible to think about threat as being the probability of military challenge or war, and that is the conceptualization in IFs. The database on Militarized Interstate Disputes (MIDS) was used in both conceptualization and initialization of threat in IFs.<br />
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Because of its importance, a substantial sub-project, sponsored by the Strategic Assessments Group (SAG) of the CIA devoted time to specifying the drivers of threat and the formulation for creating forecasts based on those drivers. Although none of the participants in that subproject bear ultimate responsibility for the treatment of threat in IFs, the model owes a substantial debt to the sponsors and participants of that sub-project.<br />
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Three key distinctions are important to understanding the overall threat formulation and its use in forecasting:<br />
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#Using history to initialize threat levels versus using predictive formulations. The argument for using data to initialize dyadic threat levels is obvious: data tell us about historic relationships between countries like India and Pakistan, often carrying information that is not available in a predictive formulation calculated across many dyads and not picking up the historic path elements of a particular dyad. Yet the argument for not relying too heavily on such dyadic data in forecasting is also obvious: the U.S.-Russian relationship has fundamentally changed since the collapse of communism and the break-up of the Soviet Union, so that a forecast based heavily on historic data would now be questionable. The IFs formulation provides forecasts that are rooted in data, but it allows the user to relax the ties to historic data over time.<br />
#The complicated contribution of constant terms, switches, and variables. The single best predictor of conflict among countries historically may well be their physical proximity, with contiguous or geographically touching countries being much more conflict prone. But because contiguity is a constant, it is near useless in determining how the threat of overt conflict will change in the future. Somewhat similarly, territorial disputes are a near constant over time, but can be switched on or off. Quite differently, power levels and commitment to democracy fluctuate substantially over time. The different types of variables enter differently into the formulation.<br />
#The contribution of power-based drivers and other drivers. For purposes of clarity of conceptualization and presentation of it, there is value in distinguishing between drivers of threat that have their roots primarily in state power and those, like democracy level, that do not.<br />
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Taking into account these important distinctions, the IFs formulation of threat has three key components. The first is a constant base term rooted in data and/or predictive theory. When it is rooted in predictive theory, the term draws on the constant and switch inputs to threat such as contiguity and territorial dispute. When it is rooted in data it represents recent history for the dyad as computed by the MIDs data. The second term is a delta or variable term rooted in power variables and the third term is a delta term rooted in non-power variables. The model user can use a parameter (wpthrconv) to determine whether the ultimate threat calculation (THREAT) should remain tied to the empirical initial condition (THREATIDATA), as modified by the delta terms, or should converge over time to a fully predicted threat formulation (THREATIPRED), again modified by delta terms.<br />
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It is useful also to be able to see a summary measure of the average world threat level (WTHREAT) over time. The sum of all threat terms is normalized by the product of the number of regions (NR) times the number – 1 (there are no non-zero and meaningful self-threat terms).<br />
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Detail on the component terms of this general formulation is available:<br />
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*[http://www.du.edu/ifs/help/understand/interstate/equations/initialthreat.html Initial Threat Terms]<br />
*[http://www.du.edu/ifs/help/understand/interstate/equations/powerterm.html Delta Power Terms]<br />
*[http://www.du.edu/ifs/help/understand/interstate/equations/nonpowerterm.html Delta Non-Power Terms]<br />
<br />
== IP Equations: Threat Formulation Subproject ==<br />
<br />
The Strategic Assessment Group (SAG) of the Central Intelligence Agency sponsored a sub-project of IFs with the title of Threats and Opportunities Analysis (TAOS). Guided by Evan Hillebrand from SAG, a number of experts were drawn together to review and discuss enhancements to the IFs system, notably its representation of interstate politics. The participants were Stuart Bremer, Mark Crescenzi, Doug Lemke, Edward Mansfield, and Paul Senese.<br />
<br />
The project ultimately facilitated the incorporation into IFs of insights from these individuals as well as work recommended by them See Crescenzi and Enterline (2001), Huth (1996), Mansfield (1994), and Tammen, Kugler, Lemke, Stam, Alsharabati, Abdollahian, Efird, and Organski (2000). See, also, Bennett and Stam (2003), who were good enough to provide an early manuscript draft of their book.<br />
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In addition, the project was able to draw on the database for militarized interstate disputes (MIDs) involving “the threat, display, or use of military force short of war” (Jones, Bremer and Singer 1996: 163). At the time of analysis for IFs the MIDs database did not extend beyond 1992, however, complicating estimation for the post-Cold War period.<br />
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Some of the parameters for relationships in the IFs threat calculation were derived from Bennett and Stam. Senese estimated parameters for the democracy relationships from MIDs data. And Lemke put together a set of estimated and literature-based parameters that helps determine most of the base-case values in IFs. The parameter determination was guided by the desire to create a set of what economists sometimes call “stylized facts,” indicating the contribution of various factors to higher or lower probabilities of conflict in a dyad. For a detailed description of the work from that project and the formulation it produced, see Hughes, 2002.<br />
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== IP Equations: Initial Threat Terms ==<br />
<br />
Threat in a dyad is calculated using either empirically based initial conditions (ThreatIData) or predicted initial conditions (ThreatIPred). Empirical values come from the Militarized International Dispute (MIDs) database. Mark Crescenzi provided empirical initial conditions from that database using a technique for representing "memory" of past events (more distant events are less memorable) that he and Andrew Enterline pioneered (Crescenzi and Enterline 2001). A significant problem was that the initial conditions for dyads involving Cold War adversaries were no longer credible after the end of the Cold War. Moreover, the MIDs database used for the estimations extended only to 1992. Therefore the IFs project relied on expert judgment to reset some of those value: essentially, conflict for important Cold-War dyads like the U.S.-Russia was put at zero after 1992 and the Crescennzi-Enterline technique was used through 2002 in order to erode the earlier Cold-War memory in the creation of initial conditions.<br />
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Predicted initial conditions use a formulation that relies on some of the strongest empirical predictors of interstate conflict. The first three are constant or switch factors: great power status of the dyad members, contiguity, and existence of territorial dispute (all of which substantially increase the threat of conflict). Each term merits some comment:<br />
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*Great power status is often (for instance, in the Correlates of War project) determined subjectively. Because IFs needs to forecast that status, not just to apply it without change over time, the model needed an objective definition of it. In general it may be safe to argue that all states with more than about 5% of total systemic power are great powers, and no states with less than 2% of systemic power are great powers. Yet even those cut-offs could be debated. Because of the range of uncertainty, IFs uses a variable representation of the status, designating any state below the lower level (carried by the parameter wpgreatthesh) as a non-great power, and any state above the upper level (wpgreatlev) as a great power, conferring partial status in between for selected computations (like that of systemic power concentration). The internal IFs variable GPowerTermI carries the resulting calculation of increased threat of conflict from great power status in the dyad, where wpgreat1 determines the contribution of full great power status and wpgreat2 determines the contribution of partial great power status.<br />
*Paul Diehl generously provided contiguity data for IFs (CONTIGUITY) and the parameter wpcontiguity translates the impact of contiguity into increased threat of conflict (ContiguityTerm).<br />
*The work of Paul Huth (1996) was tapped for territorial disputes (TerDispute). The parameter wpterdisp translates the existence of a dispute into increased threat of conflict (TerDisputeTerm).<br />
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[http://www.du.edu/ifs/help/media/images/img00526.gif&nbsp http://www.du.edu/ifs/help/media/images/img00526.gif&nbsp];<br />
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If any of these three factors are positive, the dyad members are "politically relevant" to each other, thereby increasing their sensitivity to other factors. In politically relevant dyads, IFs adds three other factors to the calculation of initial threat levels: a power transition term, an alliance term, and a two-term democracy representation. Again, each factor needs elaboration:<br />
<br />
*The initial power transition term (PowerTranTermI) begins with a computation of the ratio of power within the dyad (PowerRatio). If that ratio exceeds a threshold level (wppowtran1) then the increased threat of conflict in the power transition term is set equal to the difference between the power ratio and the threshold power transition level, multiplied by an impact parameter (wppowtran2).<br />
*The alliance term (AllyI) is simply the exogenously-specified existence (1) or absence (0) an alliance times a parameter (wpally) than translates alliance into conflict reduction.<br />
*Senese has found that the impact of democracy on the threat of conflict depends on both the lesser level of democracy in the states of the dyad and the difference between their levels. The lesser or minimum level is multiplied by a parameter (wpdemmin) to determine the minimum democracy term (DemocTermMin) and the distance in democracy is multiplied by a second term (wpdemdist) to determine the contribution of the distance term to changed threat of conflict (DemocTermDist).<br />
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== IP Equations: Power Term for Threat ==<br />
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The threat calculation builds on an initial, constant term plus a delta power term (that is, a change in power term) and a delta non-power term. This topic explains the power term. It is a sum of four other terms: delta great power term (DeltaGPowerTerm), delta power transition term (DeltaPowerTranTerm), delta territorial dispute term (DeltaTerDisputeTerm), and delta power concentration term (DeltaConcenTerm). See the Initial Threat Topic for a discussion of the foundational elements of the first three of these. The delta term for each is the foundational term in a future year minus the initial value of the term. The only new term in future years is the delta power concentration term (DeltaConcenTerm), explained below.<br />
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Power concentration is a concept focusing on the degree to which the power structure of the great powers (not all powers in the system) is heavily concentrated or not. The measure poses an alternative to the often less systematic estimation of whether a system is multipolar, bipolar, etc. (Singer, Bremer, and Stuckey 1972). IFs calculates systemic power concentration using the Ray and Singer (1973) calculation approach,<br />
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More specifically, IFs calculates four versions of power concentration. The first (PCONGREAT) is the most traditional, using a fixed cut-off for defining great powers (wpgreatlev) and normally setting that value at 5%. The measure also treats all European Union members as individual countries. The second variation (PCONGREATEU) treats the European Union as a single entity. The user has a parameter (eumembsw) to define membership in the EU over time. Although few would argue that it currently acts as a single great power, greater unity is possible in the future. Both of the first two measures are, however, somewhat erratic in forecasts, because they make a fixed distinction about great power status at the threshold level. Thus if Japan drops below five percent of systemic power (as it normally does relatively early in the base case), the number of powers considered changes and there is a transient in the calculation of power concentration. Because that seems rather arbitrary, a third measure (PCONGREATF) uses a more flexible measure of great power, phasing the status in or out above a threshold (wpgreatthresh) up to full status (wpgreatlev). The fourth measure does the same for the single EU variant (PCONGREATEUF). For completeness, IFs also calculates a global concentration measure (PCONSYS), not distinguishing between great and other powers.<br />
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Edward Mansfield (1994) has investigated the relationship between power concentration of great powers and propensity for war in the system, finding a non-linear pattern with war most likely at highest and lowest system concentration levels. Bennett and Stam (2003) investigated the relationship for MIDs rather than wars, and found parameters about half the magnitude that Mansfield reported for wars. IFs uses a Mansfield-type non-linear formulation, with reduced parameters more appropriate to disputes rather than wars (remember that the IFs approach is a fundamentally stylized one, bringing together insights from much research rather than relying on a single analysis).<br />
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The Mansfield formulation looks to change in power concentration (ConcenChange) and to the square of change in power concentration for impact on the threat of conflict, which is the reason that power concentration enters only the delta term, not the initial term. Parameters on the change in power concentration (wpcon) and the square of the change (wpconsq) determine the ultimate concentration term. Also in the above calculation, power concentration is bound at all time points to be between a minimum (wpconmin) and maximum (wpconmax) value. Mansfield suggests that the analysis is only valid for values between .202 and .417.<br />
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== IP Equations: Non Power Term for Threat ==<br />
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The threat calculation builds on an initial, constant term plus a delta power term (that is a change in power term) and a delta non-power term. This topic explains the non-power term (DeltaNonPowerTerm). It is a sum of five other terms: delta minimum democracy term (DeltaDemocTermMin), delta democracy distance term (DeltaDemocTermDist), delta alliance term (DeltaAllyTerm), delta trade term (DeltaTradeTerm), and delta GDP growth term (DeltaGDPGrowthTerm). See the Initial Threat Topic for a discussion of the foundational elements of the first three of these. The delta term for each is the foundational term in a future year minus the initial value of the term. The two new terms in future years are the delta trade term (DeltaTradeTerm) and delta GDP growth term (DeltaGDPGrowthTerm), both explained below.<br />
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The preponderance of empirical analysis appears to support the proposition that trade relationships reduce conflict, contributing with joint democracy to enhanced peace among states in the manner that Kant posited long ago. Most of the studies focus on trade specific to the dyad, generally using dyadic trade over GDP as a measure of trade dependence, and often focusing on the less dependent of the two trading partners (Oneal and Russett 1997). Bennett and Stam (2001) support the general tendency of these conclusions (although Barbieri 1997 challenges them). IFs does not represent dyadic trade, but Mansfield (1994) found that systemic trade over GDP is also inversely related to war, at least for the great powers. IFs has such an inverse relationship with change in world trade as a percent of world GDP (WTRADE), controlling it by a parameter (wptrade) that is set rather low in the base case. In fact, because the parameter used was derived from dyadic analysis, IFs arbitrarily divides it by 10 in order to dilute the affect when using a global trade representation. Even then, this is a rather powerful factor, because the base case of IFs normally exhibits a continuation of the historic increase in global trade as a percent of GDP.<br />
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Bennett and Stam (2003) also investigated the impact of global economic cycles and found that conflict propensity of all kind roughly doubles during upswings relative to downswings. IFs introduces a factor that compares global economic growth (WGDPR) with the long term pattern (LongTermGDPR), computed as a moving average. IFs translates the swings of growth into impact on threat with a parameter (wpsysgr), once again looking to Bennett and Stam for guidance on the magnitude of it. The Bennett and Stam estimate, however, was remarkably high, higher than any other driver of conflict potential other than the addition of a second great power to the dyad. Because this seemed theoretically implausible, the base case normally uses a value that was arbitrarily reduced by about a factor of 5.<br />
<header><hgroup><br />
== IP Equations: Action/Reaction ==<br />
</hgroup></header><br />
The model links the [http://www.du.edu/ifs/help/understand/interstate/equations/overviewthreat.html threat indicator] to an action-reaction dynamic only if the model user leaves the action-reaction switch (ACTREAON) at "1." Setting it to 0.0 will turn off action-reaction. When that switch is thrown, threat affects military spending in a process that can set up a positive feedback loop to increase or decrease the spending of acting and reacting countries or regions. The model calculates a multiplier on military spending (GKMUL) based on the level of threat (THREAT) in comparison with the initial level. Dyads have different reactivities to each other; the United States is not as concerned by an increase in British defense spending as by an increase in Chinese spending – in fact the U.S. might welcome the British increase and feel able to shave its own rather than increase it. A parameter (reac) gives the user control over this reactivity differential. In the GLOBUS modeling project, Dale Smith developed a formulation to endogenize such reactivity coefficients for dyads, in response to trade and other factors. Because the dyadic threat formulation of IFs already incorporates a very large number of dyadic and global factors, it would be redundant to build them into the reactivity parameter, which serves, instead, to provide the user some dyadic specific control over arms spending action/reaction. The addition of the number 10 to numerator and denominator in the formulation has a fundamentally technical basis – if there is a very low level of initial threat in a dyad (say Belgium and Ecuador), small changes in the numerator should not be permitted to give rise to large changes in the ratio and therefore the multiplier on defense spending.<br />
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The multiplier shifts government expenditures into or away from the military and adjusts all other categories of expenditures proportionately to their size as calculated earlier, normalizing all expenditures (GDS) to the total level of government consumption (GOVCON).<br />
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The action-reaction dynamic thus works across the entire governmental expenditure and economic model. For instance, the normal "burden" term in the action-reaction dynamic is unnecessary because the economic model captures the burden of increased government spending on the military when it reduces spending on health and education.<br />
<header><hgroup><br />
== IP Equations: War ==<br />
</hgroup></header><br />
IFs conceptualizes the threat variable (THREAT) as a probability of conflict, and initial conditions and parameters for its calculation were estimated primarily from militarized interstate dispute data. Across all dyads only a small portion (wpthrwar) of militarized disputes result in actual war. Knowing that percentage, it is possible to translate THREAT into the actual probability of war (CWARB), a variable that ranges from 0 to 100 (100% being certainty), just as THREAT ranges from 0 to 100. To allow the user more complete control over the probability of war in any given dyad, a second parameter (cwarbase), with a default value of 0, can be set from 0 to 100. Obviously, many variables, including factors such as relative power, affect whether or not countries in a dyad go to war. Remember that the THREAT variable is calculated so as to be responsive to such factors.<br />
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Even after the calculation of a war probability, however, IFs does not automatically proceed to generate a war and compute its consequences. The main reason is that, in the process of translating a probability into an event using a random number generator, a randomness is introduced into any model doing so. The philosophy of IFs use is that, unless the user consciously adds a random element, any run with a given set of parameters should always generate the same results and thus be easily compared with the base case.<br />
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Thus proceeding from probabilities of war to actual war events requires action on the part of the model user. Specifically, turning on a war parameter (waron) by moving it from the default value of 0 to the on value of 1 will do engage the linkage from war probabilities to war events.<br />
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There are two additional parameters that give users further control over war, once the waron parameter is set to 1. First, the conventional war base parameter (cwarbase), as noted above, can be adjusted. A value of 100, when waron is set to 1, will force a war in a given dyad. Second, a conventional war parameter (cwarf), also normally set to 0, can be set to 100 or more to force wars in all dyads, a true global war. The purpose of doing so is to allow assessment of damage potential from war, not because it is expected that a truly all-world war would ever occur.<br />
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As indicated, a random number generation function (RNDF) controls the process of moving from probability of war to actual conventional war (CWAR=1) or lack of it (CWAR=0). When waron is set to 1, a dyad in which the probability of war were an unusually high 10 percent would experience a war approximately every 10 years. Still, random behavior can generate a war each year or no war over 100 years.<br />
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The length of any conventional war (LENWAR) is purely random and varies from 1 to 6 years.<br />
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When nuclear powers are involved in conventional war, there is always a possibility of the escalation to nuclear war (NWARPB). The probability depends on the existence of the conventional war, an exogenously specified severity of war (cwarsv), and an exogenously specified probability for escalation (nwarf). Again, a random number generator translates that probability into the existence (NWAR=1) or nonexistence (NWAR=0) of nuclear war.<br />
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When war occurs, there are at least two kinds of consequences. The first is a spread to allies of the members of a dyad. The second is damage to populations and economies.<br />
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With respect to the spread of war, IFs makes the simplifying assumption that all allies of each partner as specified exogenously in the alliance parameter (ally) become involved in the conflict. This is not always true, but the model user can easily control it through the alliance parameter.<br />
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With respect to the calculation of damage, IFs computes civilian damage multipliers (CIVDM) for all participants. The basic assumption is that damage to an active participant, the actor, is directly proportional through a severity factor (cwarsv) to the conventional power (CPOW) of the opposing participant, the target), and inversely proportional to the GDP of the actor. A further parameter, the civilian damage multiplier factor (cdmf) allows more complete control over the damage assessment. If there is a nuclear war involved as well as a conventional one, nuclear damage (NuclDam) from all "war partners" (targets) is added to the conventional damage.<br />
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The resultant civilian damage multiplier is used in population and economic models to reduce population and economic capital, as described in the documentation of those models.<br />
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War also weakens conventional power, through use and through destruction. The conventional damage multiplier (CPowDM) is fundamentally analogous to the civilian damage multiplier, except that it does not involve the inverse relationship to GDP size. The multiplier revises the earlier calculation of conventional power as it is carried forward to the next time period.<br />
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IFs assumes the reduction of nuclear power only in the case of nuclear war (an assumption, as Iraq can testify, that is not always valid). The reduction in each country's or region's nuclear power is proportional to their initial power level (in nuclear wars, almost all nuclear power would presumably be used).<br />
<br />
http://www.du.edu/ifs/help/media/images/img00492.gif</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Interstate_Politics_(IP)&diff=2105Interstate Politics (IP)2017-02-25T19:44:23Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete interstate politics model documentation is available on Pardee's [http://pardee.du.edu/ifs-interstate-politics-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.<br />
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The interstate politics module traces changes in power balances across states and regions,&nbsp;allows exploration of changes in the level of interstate threat, and&nbsp;represents possible action-reaction processes and arms races with associated potential for conflict among countries. For more on how this data may used and analyzed within IFs, please read below.<br />
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== Structure and Agent System: Interstate Interaction ==<br />
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{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Interstate interaction</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Cooperation and conflict</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Power, threat levels</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Aid flows</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Changes in power, relative power, threat, action-reaction</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Spending on military, aid</div><div>&nbsp;</div><div>Alliances</div><div>&nbsp;</div><div>War</div><br />
|}<br />
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As with the domestic socio-political environment, and unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no completely standard organizing structure that is widely used for representing interstate/international systems. Yet the representation of power-based interaction systems, including interactions related to relative power and to action-reaction dynamics, is common. IFs builds significantly on that conceptual and theoretical base.<br />
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Among the most important understandings of students of interstate/international interaction is that conflict and cooperation are not really opposites in relationships. Although the balance of conflict and cooperation will vary within and across relationships, Intensity of interaction often brings both.<br />
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== Dominant Relations: Interstate Politics ==<br />
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=== Interstate Politics: Dominant Relations ===<br />
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Threat of states towards each other is a function of many determinants. For instance, contiguity or physical proximity creates contact and therefore the potential for both threat and peaceful interaction. Cultural similarities and differences affect threat levels. Yet certain factors are more subject to rapid change over time than are contiguity or culture. Among factors that change, the relative power of states and of their level of democratization substantially affect threat levels.<br />
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For a causal diagram see [http://www.du.edu/ifs/help/understand/interstate/flowcharts/power.html Process: Power]&nbsp;and&nbsp;[http://www.du.edu/ifs/help/understand/interstate/flowcharts/threatlevel.html Process: Threat Level].<br />
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For equations see [http://www.du.edu/ifs/help/understand/interstate/equations/power.html IP Equations: Power] and [http://www.du.edu/ifs/help/understand/interstate/equations/overviewthreat.html IP Equations: Threat Formulation].<br />
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=== Key dynamics are directly linked to the dominant relations: ===<br />
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*Power is a function of population, GDP, technology, and conventional and nuclear military expenditures, in an aggregation with weights that the user can change (wpwghtpow).<br />
*Democratization is computed in the domestic socio-political model.<br />
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=== Interstate Politics: Selected Added Value ===<br />
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The larger interstate politics model provides representation and control over a changing index of the probability of war, based on threat levels. It is possible stochastically to introduce war based on that probability and to feed back the destruction of war to population levels and economic capital.<br />
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== Interstate Politics Flow Charts ==<br />
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== Power ==<br />
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IFs computes a power indicator that shows each actor’s portion of global power. It does so by weighting (wpwghtpow) each actor’s share of global GDP (at exchange rates or purchasing power parity), population, a measure of technological sophistication (with GDP per capita as a proxy), government size, military spending, conventional power, and nuclear power. Weights of one "1" add the term to the power calculation, and weihts of 0 remove the term from power calculation.<br />
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[[File:IP1.gif|border|center|IP1.gif]] Conventional and nuclear power have their own dynamics, dependent primarily on military spending. Parameters direct a share of that into nuclear power and determine the translation of spending into actual conventional power, with an assumption that Less Developed Countries can leverage spending into more power because of lower wages and other costs. Given country/regional conventional and nuclear power, it is possible to compute world conventional and nuclear power (CPOW, NPOW).<br />
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== Threat Level ==<br />
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The threat that any state poses to another (and which may lead to conflict, war, or nothing) is affected by many factors. IFs conceptualizes of threat in concrete terms, namely the probability of what is called a militarized international dispute (MID).<br />
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IFs approaches calculation of that threat from two directions. The first is through the use of data on historic threat levels by dyad. The second is solely through the evolution of key factors that have been shown to give rise to disputes. The user can control whether the model should rely primarily upon historic patterns or primarily upon predicted ones with a convergence parameter (wpthrconv).<br />
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Regardless of the basic approach (data-based or predictive), the evolution of threat over time depends upon a variety of underlying factors. IFs groups these roughly into two categories: power and nonpower terms, explained in detail elsewhere. It should be noted, however, that three factors seem to carry special weight in determining the threat of disputes: contiguity, whether or not countries are great powers, and the existence or absence of territorial disputes between countries.<br />
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[[File:IP2.gif|border|center|IP2.gif]]<br />
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== Threat Elaborated: Power Terms ==<br />
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One important power term that affects interstate relations depends on relative power of the countries and is especially significant when two countries of relevance to each other enter a zone of power transition. Another important term is the concentration of power among the great powers themselves. It is increasingly uncertain whether the European Union should be treated as a single actor or as a group of individual states in such power concentration calculations, so IFs allows an option.<br />
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A third factor is whether or not the two interacting states are great powers or not. It is important whether the dyad contains zero, one, or two such powers. Because the definition of great power is uncertain, IFs allows the user to specify a threshold level at which a state begins to meet that definition and a second level at which point it clearly does.<br />
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A fourth factor in the figure below is less clearly a power term. Nonetheless, existence or absence of territorial disputes between states greatly affects the probability of a militarized dispute, quite possibly more than any other single factor.<br />
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[[File:IP3.gif|border|center|IP3.gif]]<br />
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== Threat Elaborated: Nonpower Terms ==<br />
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Among the most important factors that determine the probability of disputes between countries that are not related to their power is their level of democracy. More specifically, threat depends on the level of democracy in actor and target countries and on the difference in level, sometimes called political distance.<br />
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Studies find that trade relations between states also affect the probability of disputes between them, but results are ambiguous. Because IFs uses pooled trade, it cannot forecast the specific level of trade between any two states. The model has a term that links overall levels of global trade, as a portion of GDP, to dispute probability.<br />
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Alliances generally reduce dispute probability. Another factor that may reduce the probability is that global community is growing. This is uncertain and the linkage is not now activated.<br />
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One of the most basic factors affecting interstate relations is the closeness or contiguity of two states (small states on different continents pose little or no threat to each other).<br />
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[[File:IP4.gif|border|center|IP4.gif]]<br />
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== Probability of War ==<br />
<br />
The probability of conventional war depends most fundamentally on a basic conventional warfare probability per year (CWARBASE) that the user sets exogenously and that is set to zero for all country pairs (dyads) in the base case.<br />
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That probability is potentially enhanced by threat from an actor to a target country and also by the threat perceived by a target from an actor.<br />
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[[File:IP5.gif|border|center|IP5.gif]]<br />
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== War ==<br />
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The outbreak of conventional war depends on the probability of war. The probability of war is converted into actual war randomly. The outbreak of nuclear war (only for countries with nuclear weapons) similarly depends probabilistically and randomly on the outbreak of conventional war. In order for a war to occur, the "waron" parameter must be turned on (set to 1). If it is turned on, wars may or may not occur; but a war can be forced if the waron parameter is set AND the war forcing switch (cwarf) is set to 100 or above).<br />
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We calculate damage for both conventional and nuclear power capabilities, as well as for civilian society (population and GDP).<br />
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[[File:IP6.gif|frame|center|IP6.gif]] <br />
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== Interstate Politics Equations ==<br />
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For help understanding the equations see [http://www.du.edu/ifs/help/understand/equations/notation.html Notation]. <br />
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== IP Equations: Power ==<br />
<br />
Foreign relations of states are sensitive to power calculations. There is a vast literature surrounding the measurement of power, with much debate among analysts around the components that should enter into calculations of power capabilities and how those components can best be aggregated into a single measure of power. Ray (1990) did a good job of reviewing that literature and has, himself, contributed to power calculation. Working with the Correlates of War project at the University of Michigan, he and others have frequently emphasized three primary components of power capabilities: economic, demographic, and military strength.<br />
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The early representation of power in IFs used a formulation that aggregated these three components and that further differentiated between conventional and nuclear military strength. It allowed the user to provide weightings for the three. For instance, many analysts are loath to weight demographic size heavily for less economically developed countries like India.<br />
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Over time, users of the model suggested that other components of power should also be considered. For instance, Evan Hillebrand suggested that economic-technological capability, as indicated by the product of GDP and GDP per capita, should be a core component of capabilities. There has also been a long tradition, dating at least to Ray Cline, suggesting that government capabilities (as perhaps indicated by government spending levels) should be an element. And there is uncertainty as to whether GDP is best measured for power purposes at purchasing power parity (PPP) or exchange rates. In response to these suggestions, it was decided to create a more general function for POWER in IFs that allows the user to create a flexibly weighted sum of 9 different components: population (POP), GDP at purchasing power (GDPP), GDP at market prices (GDP), economic-technological capability using GDP per capita at either purchasing power or exchange rates (GDPPCP, GDPPC), government size (GOVCON), military spending (GDS), conventional military power (CPOW) and nuclear power (NPOW). For each component, a global sum is created and country capabilities are computed as portions of the global total. Setting a weight to zero removes the component from the power calculation. In the base or default case, most or all weights (wpwghtpow) other than the ones on economic, demographic, technological, and military strength are set to zero.<br />
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[http://www.du.edu/ifs/help/media/images/img00479.gif http://www.du.edu/ifs/help/media/images/img00479.gif]<br />
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Conventional power (CPOW) for each entity decreases with depreciation (drcpow) and increases with the non-nuclear portion (1-nmilf) of annual military spending. A conventional power factor variable (CPowF) converts new military spending into conventional capability. That factor is computed so that the spending by countries with GDP per capita below $10,000, because such countries can hire personnel at lower cost, has additional leverage in creating conventional power, as determined by a developing country conventional power factor (cpowldcf). The additional leverage is phased out as GDP per capita increases. The calculation of nuclear power (for those states that spend some portion of their military on nuclear capabilities) is analogous, but conversion of spending to power depends on a factor (npowf) that is invariant across countries.<br />
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[http://www.du.edu/ifs/help/media/images/img00480.gif http://www.du.edu/ifs/help/media/images/img00480.gif]<br />
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[http://www.du.edu/ifs/help/media/images/img00481.gif http://www.du.edu/ifs/help/media/images/img00481.gif]<br />
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As indicators, it is also useful to calculate the world total of conventional (WCPOW) and nuclear power (WNPOW).<br />
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[http://www.du.edu/ifs/help/media/images/img00482.gif http://www.du.edu/ifs/help/media/images/img00482.gif] <br />
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== IP Equations: Overview of Threat Formulation ==<br />
<br />
The threat that one country poses to others is a key concept in IFs. Unlike most variables in IFs, it is dyadic (actor country to target country). It is also different from most IFs variables in that it is a concept that has a probabilistic element in its implications for forward linkages. In fact, it is possible to think about threat as being the probability of military challenge or war, and that is the conceptualization in IFs. The database on Militarized Interstate Disputes (MIDS) was used in both conceptualization and initialization of threat in IFs.<br />
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Because of its importance, a substantial sub-project, sponsored by the Strategic Assessments Group (SAG) of the CIA devoted time to specifying the drivers of threat and the formulation for creating forecasts based on those drivers. Although none of the participants in that subproject bear ultimate responsibility for the treatment of threat in IFs, the model owes a substantial debt to the sponsors and participants of that sub-project.<br />
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Three key distinctions are important to understanding the overall threat formulation and its use in forecasting:<br />
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#Using history to initialize threat levels versus using predictive formulations. The argument for using data to initialize dyadic threat levels is obvious: data tell us about historic relationships between countries like India and Pakistan, often carrying information that is not available in a predictive formulation calculated across many dyads and not picking up the historic path elements of a particular dyad. Yet the argument for not relying too heavily on such dyadic data in forecasting is also obvious: the U.S.-Russian relationship has fundamentally changed since the collapse of communism and the break-up of the Soviet Union, so that a forecast based heavily on historic data would now be questionable. The IFs formulation provides forecasts that are rooted in data, but it allows the user to relax the ties to historic data over time.<br />
#The complicated contribution of constant terms, switches, and variables. The single best predictor of conflict among countries historically may well be their physical proximity, with contiguous or geographically touching countries being much more conflict prone. But because contiguity is a constant, it is near useless in determining how the threat of overt conflict will change in the future. Somewhat similarly, territorial disputes are a near constant over time, but can be switched on or off. Quite differently, power levels and commitment to democracy fluctuate substantially over time. The different types of variables enter differently into the formulation.<br />
#The contribution of power-based drivers and other drivers. For purposes of clarity of conceptualization and presentation of it, there is value in distinguishing between drivers of threat that have their roots primarily in state power and those, like democracy level, that do not.<br />
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Taking into account these important distinctions, the IFs formulation of threat has three key components. The first is a constant base term rooted in data and/or predictive theory. When it is rooted in predictive theory, the term draws on the constant and switch inputs to threat such as contiguity and territorial dispute. When it is rooted in data it represents recent history for the dyad as computed by the MIDs data. The second term is a delta or variable term rooted in power variables and the third term is a delta term rooted in non-power variables. The model user can use a parameter (wpthrconv) to determine whether the ultimate threat calculation (THREAT) should remain tied to the empirical initial condition (THREATIDATA), as modified by the delta terms, or should converge over time to a fully predicted threat formulation (THREATIPRED), again modified by delta terms.<br />
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[http://www.du.edu/ifs/help/media/images/img00483.gif http://www.du.edu/ifs/help/media/images/img00483.gif]<br />
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It is useful also to be able to see a summary measure of the average world threat level (WTHREAT) over time. The sum of all threat terms is normalized by the product of the number of regions (NR) times the number – 1 (there are no non-zero and meaningful self-threat terms).<br />
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[http://www.du.edu/ifs/help/media/images/img00484.gif http://www.du.edu/ifs/help/media/images/img00484.gif]<br />
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Detail on the component terms of this general formulation is available:<br />
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*[http://www.du.edu/ifs/help/understand/interstate/equations/initialthreat.html Initial Threat Terms]<br />
*[http://www.du.edu/ifs/help/understand/interstate/equations/powerterm.html Delta Power Terms]<br />
*[http://www.du.edu/ifs/help/understand/interstate/equations/nonpowerterm.html Delta Non-Power Terms]<br />
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== IP Equations: Threat Formulation Subproject ==<br />
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The Strategic Assessment Group (SAG) of the Central Intelligence Agency sponsored a sub-project of IFs with the title of Threats and Opportunities Analysis (TAOS). Guided by Evan Hillebrand from SAG, a number of experts were drawn together to review and discuss enhancements to the IFs system, notably its representation of interstate politics. The participants were Stuart Bremer, Mark Crescenzi, Doug Lemke, Edward Mansfield, and Paul Senese.<br />
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The project ultimately facilitated the incorporation into IFs of insights from these individuals as well as work recommended by them See Crescenzi and Enterline (2001), Huth (1996), Mansfield (1994), and Tammen, Kugler, Lemke, Stam, Alsharabati, Abdollahian, Efird, and Organski (2000). See, also, Bennett and Stam (2003), who were good enough to provide an early manuscript draft of their book.<br />
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In addition, the project was able to draw on the database for militarized interstate disputes (MIDs) involving “the threat, display, or use of military force short of war” (Jones, Bremer and Singer 1996: 163). At the time of analysis for IFs the MIDs database did not extend beyond 1992, however, complicating estimation for the post-Cold War period.<br />
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Some of the parameters for relationships in the IFs threat calculation were derived from Bennett and Stam. Senese estimated parameters for the democracy relationships from MIDs data. And Lemke put together a set of estimated and literature-based parameters that helps determine most of the base-case values in IFs. The parameter determination was guided by the desire to create a set of what economists sometimes call “stylized facts,” indicating the contribution of various factors to higher or lower probabilities of conflict in a dyad. For a detailed description of the work from that project and the formulation it produced, see Hughes, 2002. <br />
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== IP Equations: Initial Threat Terms ==<br />
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Threat in a dyad is calculated using either empirically based initial conditions (ThreatIData) or predicted initial conditions (ThreatIPred). Empirical values come from the Militarized International Dispute (MIDs) database. Mark Crescenzi provided empirical initial conditions from that database using a technique for representing "memory" of past events (more distant events are less memorable) that he and Andrew Enterline pioneered (Crescenzi and Enterline 2001). A significant problem was that the initial conditions for dyads involving Cold War adversaries were no longer credible after the end of the Cold War. Moreover, the MIDs database used for the estimations extended only to 1992. Therefore the IFs project relied on expert judgment to reset some of those value: essentially, conflict for important Cold-War dyads like the U.S.-Russia was put at zero after 1992 and the Crescennzi-Enterline technique was used through 2002 in order to erode the earlier Cold-War memory in the creation of initial conditions.<br />
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Predicted initial conditions use a formulation that relies on some of the strongest empirical predictors of interstate conflict. The first three are constant or switch factors: great power status of the dyad members, contiguity, and existence of territorial dispute (all of which substantially increase the threat of conflict). Each term merits some comment:<br />
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*Great power status is often (for instance, in the Correlates of War project) determined subjectively. Because IFs needs to forecast that status, not just to apply it without change over time, the model needed an objective definition of it. In general it may be safe to argue that all states with more than about 5% of total systemic power are great powers, and no states with less than 2% of systemic power are great powers. Yet even those cut-offs could be debated. Because of the range of uncertainty, IFs uses a variable representation of the status, designating any state below the lower level (carried by the parameter wpgreatthesh) as a non-great power, and any state above the upper level (wpgreatlev) as a great power, conferring partial status in between for selected computations (like that of systemic power concentration). The internal IFs variable GPowerTermI carries the resulting calculation of increased threat of conflict from great power status in the dyad, where wpgreat1 determines the contribution of full great power status and wpgreat2 determines the contribution of partial great power status.<br />
*Paul Diehl generously provided contiguity data for IFs (CONTIGUITY) and the parameter wpcontiguity translates the impact of contiguity into increased threat of conflict (ContiguityTerm).<br />
*The work of Paul Huth (1996) was tapped for territorial disputes (TerDispute). The parameter wpterdisp translates the existence of a dispute into increased threat of conflict (TerDisputeTerm).<br />
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[http://www.du.edu/ifs/help/media/images/img00526.gif&nbsp http://www.du.edu/ifs/help/media/images/img00526.gif&nbsp];<br />
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If any of these three factors are positive, the dyad members are "politically relevant" to each other, thereby increasing their sensitivity to other factors. In politically relevant dyads, IFs adds three other factors to the calculation of initial threat levels: a power transition term, an alliance term, and a two-term democracy representation. Again, each factor needs elaboration:<br />
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*The initial power transition term (PowerTranTermI) begins with a computation of the ratio of power within the dyad (PowerRatio). If that ratio exceeds a threshold level (wppowtran1) then the increased threat of conflict in the power transition term is set equal to the difference between the power ratio and the threshold power transition level, multiplied by an impact parameter (wppowtran2).<br />
*The alliance term (AllyI) is simply the exogenously-specified existence (1) or absence (0) an alliance times a parameter (wpally) than translates alliance into conflict reduction.<br />
*Senese has found that the impact of democracy on the threat of conflict depends on both the lesser level of democracy in the states of the dyad and the difference between their levels. The lesser or minimum level is multiplied by a parameter (wpdemmin) to determine the minimum democracy term (DemocTermMin) and the distance in democracy is multiplied by a second term (wpdemdist) to determine the contribution of the distance term to changed threat of conflict (DemocTermDist).<br />
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[http://www.du.edu/ifs/help/media/images/img00527.gif http://www.du.edu/ifs/help/media/images/img00527.gif] <br />
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== IP Equations: Power Term for Threat ==<br />
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The threat calculation builds on an initial, constant term plus a delta power term (that is, a change in power term) and a delta non-power term. This topic explains the power term. It is a sum of four other terms: delta great power term (DeltaGPowerTerm), delta power transition term (DeltaPowerTranTerm), delta territorial dispute term (DeltaTerDisputeTerm), and delta power concentration term (DeltaConcenTerm). See the Initial Threat Topic for a discussion of the foundational elements of the first three of these. The delta term for each is the foundational term in a future year minus the initial value of the term. The only new term in future years is the delta power concentration term (DeltaConcenTerm), explained below.<br />
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[http://www.du.edu/ifs/help/media/images/img00528.gif http://www.du.edu/ifs/help/media/images/img00528.gif]<br />
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Power concentration is a concept focusing on the degree to which the power structure of the great powers (not all powers in the system) is heavily concentrated or not. The measure poses an alternative to the often less systematic estimation of whether a system is multipolar, bipolar, etc. (Singer, Bremer, and Stuckey 1972). IFs calculates systemic power concentration using the Ray and Singer (1973) calculation approach,<br />
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[http://www.du.edu/ifs/help/media/images/img00529.gif http://www.du.edu/ifs/help/media/images/img00529.gif]<br />
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More specifically, IFs calculates four versions of power concentration. The first (PCONGREAT) is the most traditional, using a fixed cut-off for defining great powers (wpgreatlev) and normally setting that value at 5%. The measure also treats all European Union members as individual countries. The second variation (PCONGREATEU) treats the European Union as a single entity. The user has a parameter (eumembsw) to define membership in the EU over time. Although few would argue that it currently acts as a single great power, greater unity is possible in the future. Both of the first two measures are, however, somewhat erratic in forecasts, because they make a fixed distinction about great power status at the threshold level. Thus if Japan drops below five percent of systemic power (as it normally does relatively early in the base case), the number of powers considered changes and there is a transient in the calculation of power concentration. Because that seems rather arbitrary, a third measure (PCONGREATF) uses a more flexible measure of great power, phasing the status in or out above a threshold (wpgreatthresh) up to full status (wpgreatlev). The fourth measure does the same for the single EU variant (PCONGREATEUF). For completeness, IFs also calculates a global concentration measure (PCONSYS), not distinguishing between great and other powers.<br />
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Edward Mansfield (1994) has investigated the relationship between power concentration of great powers and propensity for war in the system, finding a non-linear pattern with war most likely at highest and lowest system concentration levels. Bennett and Stam (2003) investigated the relationship for MIDs rather than wars, and found parameters about half the magnitude that Mansfield reported for wars. IFs uses a Mansfield-type non-linear formulation, with reduced parameters more appropriate to disputes rather than wars (remember that the IFs approach is a fundamentally stylized one, bringing together insights from much research rather than relying on a single analysis).<br />
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The Mansfield formulation looks to change in power concentration (ConcenChange) and to the square of change in power concentration for impact on the threat of conflict, which is the reason that power concentration enters only the delta term, not the initial term. Parameters on the change in power concentration (wpcon) and the square of the change (wpconsq) determine the ultimate concentration term. Also in the above calculation, power concentration is bound at all time points to be between a minimum (wpconmin) and maximum (wpconmax) value. Mansfield suggests that the analysis is only valid for values between .202 and .417. <br />
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== IP Equations: Non Power Term for Threat ==<br />
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The threat calculation builds on an initial, constant term plus a delta power term (that is a change in power term) and a delta non-power term. This topic explains the non-power term (DeltaNonPowerTerm). It is a sum of five other terms: delta minimum democracy term (DeltaDemocTermMin), delta democracy distance term (DeltaDemocTermDist), delta alliance term (DeltaAllyTerm), delta trade term (DeltaTradeTerm), and delta GDP growth term (DeltaGDPGrowthTerm). See the Initial Threat Topic for a discussion of the foundational elements of the first three of these. The delta term for each is the foundational term in a future year minus the initial value of the term. The two new terms in future years are the delta trade term (DeltaTradeTerm) and delta GDP growth term (DeltaGDPGrowthTerm), both explained below.<br />
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[http://www.du.edu/ifs/help/media/images/img00530.gif http://www.du.edu/ifs/help/media/images/img00530.gif]<br />
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The preponderance of empirical analysis appears to support the proposition that trade relationships reduce conflict, contributing with joint democracy to enhanced peace among states in the manner that Kant posited long ago. Most of the studies focus on trade specific to the dyad, generally using dyadic trade over GDP as a measure of trade dependence, and often focusing on the less dependent of the two trading partners (Oneal and Russett 1997). Bennett and Stam (2001) support the general tendency of these conclusions (although Barbieri 1997 challenges them). IFs does not represent dyadic trade, but Mansfield (1994) found that systemic trade over GDP is also inversely related to war, at least for the great powers. IFs has such an inverse relationship with change in world trade as a percent of world GDP (WTRADE), controlling it by a parameter (wptrade) that is set rather low in the base case. In fact, because the parameter used was derived from dyadic analysis, IFs arbitrarily divides it by 10 in order to dilute the affect when using a global trade representation. Even then, this is a rather powerful factor, because the base case of IFs normally exhibits a continuation of the historic increase in global trade as a percent of GDP.<br />
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Bennett and Stam (2003) also investigated the impact of global economic cycles and found that conflict propensity of all kind roughly doubles during upswings relative to downswings. IFs introduces a factor that compares global economic growth (WGDPR) with the long term pattern (LongTermGDPR), computed as a moving average. IFs translates the swings of growth into impact on threat with a parameter (wpsysgr), once again looking to Bennett and Stam for guidance on the magnitude of it. The Bennett and Stam estimate, however, was remarkably high, higher than any other driver of conflict potential other than the addition of a second great power to the dyad. Because this seemed theoretically implausible, the base case normally uses a value that was arbitrarily reduced by about a factor of 5.</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Interstate_Politics_(IP)&diff=2104Interstate Politics (IP)2017-02-25T19:25:02Z<p>StellahKwasi: </p>
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<div>The most recent and complete interstate politics model documentation is available on Pardee's [http://pardee.du.edu/ifs-interstate-politics-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.<br />
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The interstate politics module traces changes in power balances across states and regions,&nbsp;allows exploration of changes in the level of interstate threat, and&nbsp;represents possible action-reaction processes and arms races with associated potential for conflict among countries. For more on how this data may used and analyzed within IFs, please read below.<br />
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== Structure and Agent System: Interstate Interaction ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Interstate interaction</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Cooperation and conflict</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Power, threat levels</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Aid flows</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Changes in power, relative power, threat, action-reaction</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Spending on military, aid</div><div>&nbsp;</div><div>Alliances</div><div>&nbsp;</div><div>War</div><br />
|}<br />
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As with the domestic socio-political environment, and unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no completely standard organizing structure that is widely used for representing interstate/international systems. Yet the representation of power-based interaction systems, including interactions related to relative power and to action-reaction dynamics, is common. IFs builds significantly on that conceptual and theoretical base.<br />
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Among the most important understandings of students of interstate/international interaction is that conflict and cooperation are not really opposites in relationships. Although the balance of conflict and cooperation will vary within and across relationships, Intensity of interaction often brings both.<br />
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== Dominant Relations: Interstate Politics ==<br />
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=== Interstate Politics: Dominant Relations ===<br />
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Threat of states towards each other is a function of many determinants. For instance, contiguity or physical proximity creates contact and therefore the potential for both threat and peaceful interaction. Cultural similarities and differences affect threat levels. Yet certain factors are more subject to rapid change over time than are contiguity or culture. Among factors that change, the relative power of states and of their level of democratization substantially affect threat levels.<br />
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For a causal diagram see [http://www.du.edu/ifs/help/understand/interstate/flowcharts/power.html Process: Power]&nbsp;and&nbsp;[http://www.du.edu/ifs/help/understand/interstate/flowcharts/threatlevel.html Process: Threat Level].<br />
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For equations see [http://www.du.edu/ifs/help/understand/interstate/equations/power.html IP Equations: Power] and [http://www.du.edu/ifs/help/understand/interstate/equations/overviewthreat.html IP Equations: Threat Formulation].<br />
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=== Key dynamics are directly linked to the dominant relations: ===<br />
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*Power is a function of population, GDP, technology, and conventional and nuclear military expenditures, in an aggregation with weights that the user can change (wpwghtpow).<br />
*Democratization is computed in the domestic socio-political model.<br />
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=== Interstate Politics: Selected Added Value ===<br />
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The larger interstate politics model provides representation and control over a changing index of the probability of war, based on threat levels. It is possible stochastically to introduce war based on that probability and to feed back the destruction of war to population levels and economic capital.<br />
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== Interstate Politics Flow Charts ==<br />
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== Power ==<br />
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IFs computes a power indicator that shows each actor’s portion of global power. It does so by weighting (wpwghtpow) each actor’s share of global GDP (at exchange rates or purchasing power parity), population, a measure of technological sophistication (with GDP per capita as a proxy), government size, military spending, conventional power, and nuclear power. Weights of one "1" add the term to the power calculation, and weihts of 0 remove the term from power calculation.<br />
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[[File:IP1.gif|border|center|IP1.gif]] Conventional and nuclear power have their own dynamics, dependent primarily on military spending. Parameters direct a share of that into nuclear power and determine the translation of spending into actual conventional power, with an assumption that Less Developed Countries can leverage spending into more power because of lower wages and other costs. Given country/regional conventional and nuclear power, it is possible to compute world conventional and nuclear power (CPOW, NPOW).<br />
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== Threat Level ==<br />
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The threat that any state poses to another (and which may lead to conflict, war, or nothing) is affected by many factors. IFs conceptualizes of threat in concrete terms, namely the probability of what is called a militarized international dispute (MID).<br />
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IFs approaches calculation of that threat from two directions. The first is through the use of data on historic threat levels by dyad. The second is solely through the evolution of key factors that have been shown to give rise to disputes. The user can control whether the model should rely primarily upon historic patterns or primarily upon predicted ones with a convergence parameter (wpthrconv).<br />
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Regardless of the basic approach (data-based or predictive), the evolution of threat over time depends upon a variety of underlying factors. IFs groups these roughly into two categories: power and nonpower terms, explained in detail elsewhere. It should be noted, however, that three factors seem to carry special weight in determining the threat of disputes: contiguity, whether or not countries are great powers, and the existence or absence of territorial disputes between countries.<br />
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[[File:IP2.gif|border|center|IP2.gif]]<br />
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== Threat Elaborated: Power Terms ==<br />
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One important power term that affects interstate relations depends on relative power of the countries and is especially significant when two countries of relevance to each other enter a zone of power transition. Another important term is the concentration of power among the great powers themselves. It is increasingly uncertain whether the European Union should be treated as a single actor or as a group of individual states in such power concentration calculations, so IFs allows an option.<br />
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A third factor is whether or not the two interacting states are great powers or not. It is important whether the dyad contains zero, one, or two such powers. Because the definition of great power is uncertain, IFs allows the user to specify a threshold level at which a state begins to meet that definition and a second level at which point it clearly does.<br />
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A fourth factor in the figure below is less clearly a power term. Nonetheless, existence or absence of territorial disputes between states greatly affects the probability of a militarized dispute, quite possibly more than any other single factor.<br />
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[[File:IP3.gif|border|center|IP3.gif]]<br />
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== Threat Elaborated: Nonpower Terms ==<br />
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Among the most important factors that determine the probability of disputes between countries that are not related to their power is their level of democracy. More specifically, threat depends on the level of democracy in actor and target countries and on the difference in level, sometimes called political distance.<br />
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Studies find that trade relations between states also affect the probability of disputes between them, but results are ambiguous. Because IFs uses pooled trade, it cannot forecast the specific level of trade between any two states. The model has a term that links overall levels of global trade, as a portion of GDP, to dispute probability.<br />
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Alliances generally reduce dispute probability. Another factor that may reduce the probability is that global community is growing. This is uncertain and the linkage is not now activated.<br />
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One of the most basic factors affecting interstate relations is the closeness or contiguity of two states (small states on different continents pose little or no threat to each other).<br />
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[[File:IP4.gif|border|center|IP4.gif]]<br />
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== Probability of War ==<br />
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The probability of conventional war depends most fundamentally on a basic conventional warfare probability per year (CWARBASE) that the user sets exogenously and that is set to zero for all country pairs (dyads) in the base case.<br />
<br />
That probability is potentially enhanced by threat from an actor to a target country and also by the threat perceived by a target from an actor.<br />
<br />
[[File:IP5.gif|border|center|IP5.gif]]<br />
<br />
== War ==<br />
<br />
The outbreak of conventional war depends on the probability of war. The probability of war is converted into actual war randomly. The outbreak of nuclear war (only for countries with nuclear weapons) similarly depends probabilistically and randomly on the outbreak of conventional war. In order for a war to occur, the "waron" parameter must be turned on (set to 1). If it is turned on, wars may or may not occur; but a war can be forced if the waron parameter is set AND the war forcing switch (cwarf) is set to 100 or above).<br />
<br />
We calculate damage for both conventional and nuclear power capabilities, as well as for civilian society (population and GDP).<br />
<br />
[[File:IP6.gif|frame|center|IP6.gif]]<br />
<header><hgroup><br />
== Interstate Politics Equations ==<br />
</hgroup></header><br />
For help understanding the equations see [http://www.du.edu/ifs/help/understand/equations/notation.html Notation].<br />
<header><hgroup><br />
== IP Equations: Power ==<br />
</hgroup></header><br />
Foreign relations of states are sensitive to power calculations. There is a vast literature surrounding the measurement of power, with much debate among analysts around the components that should enter into calculations of power capabilities and how those components can best be aggregated into a single measure of power. Ray (1990) did a good job of reviewing that literature and has, himself, contributed to power calculation. Working with the Correlates of War project at the University of Michigan, he and others have frequently emphasized three primary components of power capabilities: economic, demographic, and military strength.<br />
<br />
The early representation of power in IFs used a formulation that aggregated these three components and that further differentiated between conventional and nuclear military strength. It allowed the user to provide weightings for the three. For instance, many analysts are loath to weight demographic size heavily for less economically developed countries like India.<br />
<br />
Over time, users of the model suggested that other components of power should also be considered. For instance, Evan Hillebrand suggested that economic-technological capability, as indicated by the product of GDP and GDP per capita, should be a core component of capabilities. There has also been a long tradition, dating at least to Ray Cline, suggesting that government capabilities (as perhaps indicated by government spending levels) should be an element. And there is uncertainty as to whether GDP is best measured for power purposes at purchasing power parity (PPP) or exchange rates. In response to these suggestions, it was decided to create a more general function for POWER in IFs that allows the user to create a flexibly weighted sum of 9 different components: population (POP), GDP at purchasing power (GDPP), GDP at market prices (GDP), economic-technological capability using GDP per capita at either purchasing power or exchange rates (GDPPCP, GDPPC), government size (GOVCON), military spending (GDS), conventional military power (CPOW) and nuclear power (NPOW). For each component, a global sum is created and country capabilities are computed as portions of the global total. Setting a weight to zero removes the component from the power calculation. In the base or default case, most or all weights (wpwghtpow) other than the ones on economic, demographic, technological, and military strength are set to zero.<br />
<br />
http://www.du.edu/ifs/help/media/images/img00479.gif<br />
<br />
Conventional power (CPOW) for each entity decreases with depreciation (drcpow) and increases with the non-nuclear portion (1-nmilf) of annual military spending. A conventional power factor variable (CPowF) converts new military spending into conventional capability. That factor is computed so that the spending by countries with GDP per capita below $10,000, because such countries can hire personnel at lower cost, has additional leverage in creating conventional power, as determined by a developing country conventional power factor (cpowldcf). The additional leverage is phased out as GDP per capita increases. The calculation of nuclear power (for those states that spend some portion of their military on nuclear capabilities) is analogous, but conversion of spending to power depends on a factor (npowf) that is invariant across countries.<br />
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<br />
http://www.du.edu/ifs/help/media/images/img00481.gif<br />
<br />
As indicators, it is also useful to calculate the world total of conventional (WCPOW) and nuclear power (WNPOW).<br />
<br />
http://www.du.edu/ifs/help/media/images/img00482.gif<br />
<header><hgroup><br />
== IP Equations: Overview of Threat Formulation ==<br />
</hgroup></header><br />
The threat that one country poses to others is a key concept in IFs. Unlike most variables in IFs, it is dyadic (actor country to target country). It is also different from most IFs variables in that it is a concept that has a probabilistic element in its implications for forward linkages. In fact, it is possible to think about threat as being the probability of military challenge or war, and that is the conceptualization in IFs. The database on Militarized Interstate Disputes (MIDS) was used in both conceptualization and initialization of threat in IFs.<br />
<br />
Because of its importance, a substantial sub-project, sponsored by the Strategic Assessments Group (SAG) of the CIA devoted time to specifying the drivers of threat and the formulation for creating forecasts based on those drivers. Although none of the participants in that subproject bear ultimate responsibility for the treatment of threat in IFs, the model owes a substantial debt to the sponsors and participants of that sub-project.<br />
<br />
Three key distinctions are important to understanding the overall threat formulation and its use in forecasting:<br />
<br />
#Using history to initialize threat levels versus using predictive formulations. The argument for using data to initialize dyadic threat levels is obvious: data tell us about historic relationships between countries like India and Pakistan, often carrying information that is not available in a predictive formulation calculated across many dyads and not picking up the historic path elements of a particular dyad. Yet the argument for not relying too heavily on such dyadic data in forecasting is also obvious: the U.S.-Russian relationship has fundamentally changed since the collapse of communism and the break-up of the Soviet Union, so that a forecast based heavily on historic data would now be questionable. The IFs formulation provides forecasts that are rooted in data, but it allows the user to relax the ties to historic data over time.<br />
#The complicated contribution of constant terms, switches, and variables. The single best predictor of conflict among countries historically may well be their physical proximity, with contiguous or geographically touching countries being much more conflict prone. But because contiguity is a constant, it is near useless in determining how the threat of overt conflict will change in the future. Somewhat similarly, territorial disputes are a near constant over time, but can be switched on or off. Quite differently, power levels and commitment to democracy fluctuate substantially over time. The different types of variables enter differently into the formulation.<br />
#The contribution of power-based drivers and other drivers. For purposes of clarity of conceptualization and presentation of it, there is value in distinguishing between drivers of threat that have their roots primarily in state power and those, like democracy level, that do not.<br />
<br />
Taking into account these important distinctions, the IFs formulation of threat has three key components. The first is a constant base term rooted in data and/or predictive theory. When it is rooted in predictive theory, the term draws on the constant and switch inputs to threat such as contiguity and territorial dispute. When it is rooted in data it represents recent history for the dyad as computed by the MIDs data. The second term is a delta or variable term rooted in power variables and the third term is a delta term rooted in non-power variables. The model user can use a parameter (wpthrconv) to determine whether the ultimate threat calculation (THREAT) should remain tied to the empirical initial condition (THREATIDATA), as modified by the delta terms, or should converge over time to a fully predicted threat formulation (THREATIPRED), again modified by delta terms.<br />
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http://www.du.edu/ifs/help/media/images/img00483.gif<br />
<br />
It is useful also to be able to see a summary measure of the average world threat level (WTHREAT) over time. The sum of all threat terms is normalized by the product of the number of regions (NR) times the number – 1 (there are no non-zero and meaningful self-threat terms).<br />
<br />
http://www.du.edu/ifs/help/media/images/img00484.gif<br />
<br />
Detail on the component terms of this general formulation is available:<br />
<br />
*[http://www.du.edu/ifs/help/understand/interstate/equations/initialthreat.html Initial Threat Terms]<br />
*[http://www.du.edu/ifs/help/understand/interstate/equations/powerterm.html Delta Power Terms]<br />
*[http://www.du.edu/ifs/help/understand/interstate/equations/nonpowerterm.html Delta Non-Power Terms]<br />
<header><hgroup><br />
== IP Equations: Threat Formulation Subproject ==<br />
</hgroup></header><br />
The Strategic Assessment Group (SAG) of the Central Intelligence Agency sponsored a sub-project of IFs with the title of Threats and Opportunities Analysis (TAOS). Guided by Evan Hillebrand from SAG, a number of experts were drawn together to review and discuss enhancements to the IFs system, notably its representation of interstate politics. The participants were Stuart Bremer, Mark Crescenzi, Doug Lemke, Edward Mansfield, and Paul Senese.<br />
<br />
The project ultimately facilitated the incorporation into IFs of insights from these individuals as well as work recommended by them See Crescenzi and Enterline (2001), Huth (1996), Mansfield (1994), and Tammen, Kugler, Lemke, Stam, Alsharabati, Abdollahian, Efird, and Organski (2000). See, also, Bennett and Stam (2003), who were good enough to provide an early manuscript draft of their book.<br />
<br />
In addition, the project was able to draw on the database for militarized interstate disputes (MIDs) involving “the threat, display, or use of military force short of war” (Jones, Bremer and Singer 1996: 163). At the time of analysis for IFs the MIDs database did not extend beyond 1992, however, complicating estimation for the post-Cold War period.<br />
<br />
Some of the parameters for relationships in the IFs threat calculation were derived from Bennett and Stam. Senese estimated parameters for the democracy relationships from MIDs data. And Lemke put together a set of estimated and literature-based parameters that helps determine most of the base-case values in IFs. The parameter determination was guided by the desire to create a set of what economists sometimes call “stylized facts,” indicating the contribution of various factors to higher or lower probabilities of conflict in a dyad. For a detailed description of the work from that project and the formulation it produced, see Hughes, 2002.<br />
<header><hgroup><br />
== IP Equations: Initial Threat Terms ==<br />
</hgroup></header><br />
Threat in a dyad is calculated using either empirically based initial conditions (ThreatIData) or predicted initial conditions (ThreatIPred). Empirical values come from the Militarized International Dispute (MIDs) database. Mark Crescenzi provided empirical initial conditions from that database using a technique for representing "memory" of past events (more distant events are less memorable) that he and Andrew Enterline pioneered (Crescenzi and Enterline 2001). A significant problem was that the initial conditions for dyads involving Cold War adversaries were no longer credible after the end of the Cold War. Moreover, the MIDs database used for the estimations extended only to 1992. Therefore the IFs project relied on expert judgment to reset some of those value: essentially, conflict for important Cold-War dyads like the U.S.-Russia was put at zero after 1992 and the Crescennzi-Enterline technique was used through 2002 in order to erode the earlier Cold-War memory in the creation of initial conditions.<br />
<br />
Predicted initial conditions use a formulation that relies on some of the strongest empirical predictors of interstate conflict. The first three are constant or switch factors: great power status of the dyad members, contiguity, and existence of territorial dispute (all of which substantially increase the threat of conflict). Each term merits some comment:<br />
<br />
*Great power status is often (for instance, in the Correlates of War project) determined subjectively. Because IFs needs to forecast that status, not just to apply it without change over time, the model needed an objective definition of it. In general it may be safe to argue that all states with more than about 5% of total systemic power are great powers, and no states with less than 2% of systemic power are great powers. Yet even those cut-offs could be debated. Because of the range of uncertainty, IFs uses a variable representation of the status, designating any state below the lower level (carried by the parameter wpgreatthesh) as a non-great power, and any state above the upper level (wpgreatlev) as a great power, conferring partial status in between for selected computations (like that of systemic power concentration). The internal IFs variable GPowerTermI carries the resulting calculation of increased threat of conflict from great power status in the dyad, where wpgreat1 determines the contribution of full great power status and wpgreat2 determines the contribution of partial great power status.<br />
*Paul Diehl generously provided contiguity data for IFs (CONTIGUITY) and the parameter wpcontiguity translates the impact of contiguity into increased threat of conflict (ContiguityTerm).<br />
*The work of Paul Huth (1996) was tapped for territorial disputes (TerDispute). The parameter wpterdisp translates the existence of a dispute into increased threat of conflict (TerDisputeTerm).<br />
<br />
http://www.du.edu/ifs/help/media/images/img00526.gif&nbsp;<br />
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If any of these three factors are positive, the dyad members are "politically relevant" to each other, thereby increasing their sensitivity to other factors. In politically relevant dyads, IFs adds three other factors to the calculation of initial threat levels: a power transition term, an alliance term, and a two-term democracy representation. Again, each factor needs elaboration:<br />
<br />
*The initial power transition term (PowerTranTermI) begins with a computation of the ratio of power within the dyad (PowerRatio). If that ratio exceeds a threshold level (wppowtran1) then the increased threat of conflict in the power transition term is set equal to the difference between the power ratio and the threshold power transition level, multiplied by an impact parameter (wppowtran2).<br />
*The alliance term (AllyI) is simply the exogenously-specified existence (1) or absence (0) an alliance times a parameter (wpally) than translates alliance into conflict reduction.<br />
*Senese has found that the impact of democracy on the threat of conflict depends on both the lesser level of democracy in the states of the dyad and the difference between their levels. The lesser or minimum level is multiplied by a parameter (wpdemmin) to determine the minimum democracy term (DemocTermMin) and the distance in democracy is multiplied by a second term (wpdemdist) to determine the contribution of the distance term to changed threat of conflict (DemocTermDist).<br />
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http://www.du.edu/ifs/help/media/images/img00527.gif<br />
<header><hgroup><br />
== IP Equations: Power Term for Threat ==<br />
</hgroup></header><br />
The threat calculation builds on an initial, constant term plus a delta power term (that is, a change in power term) and a delta non-power term. This topic explains the power term. It is a sum of four other terms: delta great power term (DeltaGPowerTerm), delta power transition term (DeltaPowerTranTerm), delta territorial dispute term (DeltaTerDisputeTerm), and delta power concentration term (DeltaConcenTerm). See the Initial Threat Topic for a discussion of the foundational elements of the first three of these. The delta term for each is the foundational term in a future year minus the initial value of the term. The only new term in future years is the delta power concentration term (DeltaConcenTerm), explained below.<br />
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http://www.du.edu/ifs/help/media/images/img00528.gif<br />
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Power concentration is a concept focusing on the degree to which the power structure of the great powers (not all powers in the system) is heavily concentrated or not. The measure poses an alternative to the often less systematic estimation of whether a system is multipolar, bipolar, etc. (Singer, Bremer, and Stuckey 1972). IFs calculates systemic power concentration using the Ray and Singer (1973) calculation approach,<br />
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http://www.du.edu/ifs/help/media/images/img00529.gif<br />
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More specifically, IFs calculates four versions of power concentration. The first (PCONGREAT) is the most traditional, using a fixed cut-off for defining great powers (wpgreatlev) and normally setting that value at 5%. The measure also treats all European Union members as individual countries. The second variation (PCONGREATEU) treats the European Union as a single entity. The user has a parameter (eumembsw) to define membership in the EU over time. Although few would argue that it currently acts as a single great power, greater unity is possible in the future. Both of the first two measures are, however, somewhat erratic in forecasts, because they make a fixed distinction about great power status at the threshold level. Thus if Japan drops below five percent of systemic power (as it normally does relatively early in the base case), the number of powers considered changes and there is a transient in the calculation of power concentration. Because that seems rather arbitrary, a third measure (PCONGREATF) uses a more flexible measure of great power, phasing the status in or out above a threshold (wpgreatthresh) up to full status (wpgreatlev). The fourth measure does the same for the single EU variant (PCONGREATEUF). For completeness, IFs also calculates a global concentration measure (PCONSYS), not distinguishing between great and other powers.<br />
<br />
Edward Mansfield (1994) has investigated the relationship between power concentration of great powers and propensity for war in the system, finding a non-linear pattern with war most likely at highest and lowest system concentration levels. Bennett and Stam (2003) investigated the relationship for MIDs rather than wars, and found parameters about half the magnitude that Mansfield reported for wars. IFs uses a Mansfield-type non-linear formulation, with reduced parameters more appropriate to disputes rather than wars (remember that the IFs approach is a fundamentally stylized one, bringing together insights from much research rather than relying on a single analysis).<br />
<br />
The Mansfield formulation looks to change in power concentration (ConcenChange) and to the square of change in power concentration for impact on the threat of conflict, which is the reason that power concentration enters only the delta term, not the initial term. Parameters on the change in power concentration (wpcon) and the square of the change (wpconsq) determine the ultimate concentration term. Also in the above calculation, power concentration is bound at all time points to be between a minimum (wpconmin) and maximum (wpconmax) value. Mansfield suggests that the analysis is only valid for values between .202 and .417.<br />
<header><hgroup><br />
== IP Equations: Non Power Term for Threat ==<br />
</hgroup></header><br />
The threat calculation builds on an initial, constant term plus a delta power term (that is a change in power term) and a delta non-power term. This topic explains the non-power term (DeltaNonPowerTerm). It is a sum of five other terms: delta minimum democracy term (DeltaDemocTermMin), delta democracy distance term (DeltaDemocTermDist), delta alliance term (DeltaAllyTerm), delta trade term (DeltaTradeTerm), and delta GDP growth term (DeltaGDPGrowthTerm). See the Initial Threat Topic for a discussion of the foundational elements of the first three of these. The delta term for each is the foundational term in a future year minus the initial value of the term. The two new terms in future years are the delta trade term (DeltaTradeTerm) and delta GDP growth term (DeltaGDPGrowthTerm), both explained below.<br />
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http://www.du.edu/ifs/help/media/images/img00530.gif<br />
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The preponderance of empirical analysis appears to support the proposition that trade relationships reduce conflict, contributing with joint democracy to enhanced peace among states in the manner that Kant posited long ago. Most of the studies focus on trade specific to the dyad, generally using dyadic trade over GDP as a measure of trade dependence, and often focusing on the less dependent of the two trading partners (Oneal and Russett 1997). Bennett and Stam (2001) support the general tendency of these conclusions (although Barbieri 1997 challenges them). IFs does not represent dyadic trade, but Mansfield (1994) found that systemic trade over GDP is also inversely related to war, at least for the great powers. IFs has such an inverse relationship with change in world trade as a percent of world GDP (WTRADE), controlling it by a parameter (wptrade) that is set rather low in the base case. In fact, because the parameter used was derived from dyadic analysis, IFs arbitrarily divides it by 10 in order to dilute the affect when using a global trade representation. Even then, this is a rather powerful factor, because the base case of IFs normally exhibits a continuation of the historic increase in global trade as a percent of GDP.<br />
<br />
Bennett and Stam (2003) also investigated the impact of global economic cycles and found that conflict propensity of all kind roughly doubles during upswings relative to downswings. IFs introduces a factor that compares global economic growth (WGDPR) with the long term pattern (LongTermGDPR), computed as a moving average. IFs translates the swings of growth into impact on threat with a parameter (wpsysgr), once again looking to Bennett and Stam for guidance on the magnitude of it. The Bennett and Stam estimate, however, was remarkably high, higher than any other driver of conflict potential other than the addition of a second great power to the dyad. Because this seemed theoretically implausible, the base case normally uses a value that was arbitrarily reduced by about a factor of 5.</div>StellahKwasihttps://pardeewiki.du.edu//index.php?title=Interstate_Politics_(IP)&diff=2103Interstate Politics (IP)2017-02-25T18:27:12Z<p>StellahKwasi: </p>
<hr />
<div>The most recent and complete interstate politics model documentation is available on Pardee's [http://pardee.du.edu/ifs-interstate-politics-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.<br />
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The interstate politics module traces changes in power balances across states and regions,&nbsp;allows exploration of changes in the level of interstate threat, and&nbsp;represents possible action-reaction processes and arms races with associated potential for conflict among countries. For more on how this data may used and analyzed within IFs, please read below.<br />
<br />
== Structure and Agent System: Interstate Interaction ==<br />
<br />
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"<br />
|-<br />
| style="width: 50%" | <div>'''System/Subsystem'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Interstate interaction</div><br />
|-<br />
| style="text-align: left" | <div>'''Organizing Structure'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Cooperation and conflict</div><br />
|-<br />
| style="text-align: left" | <div>'''Stocks'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Power, threat levels</div><br />
|-<br />
| style="text-align: left" valign="center" | <div>'''Flows'''</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Aid flows</div><br />
|-<br />
| style="text-align: left" | <div>'''Key Aggregate&nbsp;''' '''Relationships&nbsp;'''</div><div>(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Changes in power, relative power, threat, action-reaction</div><br />
|-<br />
| style="text-align: left" valign="center" | <div style="text-align: left">'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div><br />
| style="text-align: left; padding-left: 10px" align="center" | <div>Spending on military, aid</div><div>&nbsp;</div><div>Alliances</div><div>&nbsp;</div><div>War</div><br />
|}<br />
<br />
As with the domestic socio-political environment, and unlike the use of cohort-component structures in demographics and of markets and social accounting matrices for economics, there is no completely standard organizing structure that is widely used for representing interstate/international systems. Yet the representation of power-based interaction systems, including interactions related to relative power and to action-reaction dynamics, is common. IFs builds significantly on that conceptual and theoretical base.<br />
<br />
Among the most important understandings of students of interstate/international interaction is that conflict and cooperation are not really opposites in relationships. Although the balance of conflict and cooperation will vary within and across relationships, Intensity of interaction often brings both.<br />
<br />
== Dominant Relations: Interstate Politics ==<br />
<br />
=== Interstate Politics: Dominant Relations ===<br />
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Threat of states towards each other is a function of many determinants. For instance, contiguity or physical proximity creates contact and therefore the potential for both threat and peaceful interaction. Cultural similarities and differences affect threat levels. Yet certain factors are more subject to rapid change over time than are contiguity or culture. Among factors that change, the relative power of states and of their level of democratization substantially affect threat levels.<br />
<br />
For a causal diagram see [http://www.du.edu/ifs/help/understand/interstate/flowcharts/power.html Process: Power]&nbsp;and&nbsp;[http://www.du.edu/ifs/help/understand/interstate/flowcharts/threatlevel.html Process: Threat Level].<br />
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For equations see [http://www.du.edu/ifs/help/understand/interstate/equations/power.html IP Equations: Power] and [http://www.du.edu/ifs/help/understand/interstate/equations/overviewthreat.html IP Equations: Threat Formulation].<br />
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=== Key dynamics are directly linked to the dominant relations: ===<br />
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*Power is a function of population, GDP, technology, and conventional and nuclear military expenditures, in an aggregation with weights that the user can change (wpwghtpow).<br />
*Democratization is computed in the domestic socio-political model.<br />
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=== Interstate Politics: Selected Added Value ===<br />
<br />
The larger interstate politics model provides representation and control over a changing index of the probability of war, based on threat levels. It is possible stochastically to introduce war based on that probability and to feed back the destruction of war to population levels and economic capital.<br />
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== Interstate Politics Flow Charts ==<br />
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== Power ==<br />
<br />
IFs computes a power indicator that shows each actor’s portion of global power. It does so by weighting (wpwghtpow) each actor’s share of global GDP (at exchange rates or purchasing power parity), population, a measure of technological sophistication (with GDP per capita as a proxy), government size, military spending, conventional power, and nuclear power. Weights of one "1" add the term to the power calculation, and weihts of 0 remove the term from power calculation.<br />
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[[File:IP1.gif|border|center|IP1.gif]] Conventional and nuclear power have their own dynamics, dependent primarily on military spending. Parameters direct a share of that into nuclear power and determine the translation of spending into actual conventional power, with an assumption that Less Developed Countries can leverage spending into more power because of lower wages and other costs. Given country/regional conventional and nuclear power, it is possible to compute world conventional and nuclear power (CPOW, NPOW).<br />
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== Threat Level ==<br />
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The threat that any state poses to another (and which may lead to conflict, war, or nothing) is affected by many factors. IFs conceptualizes of threat in concrete terms, namely the probability of what is called a militarized international dispute (MID).<br />
<br />
IFs approaches calculation of that threat from two directions. The first is through the use of data on historic threat levels by dyad. The second is solely through the evolution of key factors that have been shown to give rise to disputes. The user can control whether the model should rely primarily upon historic patterns or primarily upon predicted ones with a convergence parameter (wpthrconv).<br />
<br />
Regardless of the basic approach (data-based or predictive), the evolution of threat over time depends upon a variety of underlying factors. IFs groups these roughly into two categories: power and nonpower terms, explained in detail elsewhere. It should be noted, however, that three factors seem to carry special weight in determining the threat of disputes: contiguity, whether or not countries are great powers, and the existence or absence of territorial disputes between countries.<br />
<br />
[[File:IP2.gif|border|center|IP2.gif]]<br />
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== Threat Elaborated: Power Terms ==<br />
<br />
One important power term that affects interstate relations depends on relative power of the countries and is especially significant when two countries of relevance to each other enter a zone of power transition. Another important term is the concentration of power among the great powers themselves. It is increasingly uncertain whether the European Union should be treated as a single actor or as a group of individual states in such power concentration calculations, so IFs allows an option.<br />
<br />
A third factor is whether or not the two interacting states are great powers or not. It is important whether the dyad contains zero, one, or two such powers. Because the definition of great power is uncertain, IFs allows the user to specify a threshold level at which a state begins to meet that definition and a second level at which point it clearly does.<br />
<br />
A fourth factor in the figure below is less clearly a power term. Nonetheless, existence or absence of territorial disputes between states greatly affects the probability of a militarized dispute, quite possibly more than any other single factor.<br />
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[[File:IP3.gif|border|center|IP3.gif]]<br />
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== Threat Elaborated: Nonpower Terms ==<br />
<br />
Among the most important factors that determine the probability of disputes between countries that are not related to their power is their level of democracy. More specifically, threat depends on the level of democracy in actor and target countries and on the difference in level, sometimes called political distance.<br />
<br />
Studies find that trade relations between states also affect the probability of disputes between them, but results are ambiguous. Because IFs uses pooled trade, it cannot forecast the specific level of trade between any two states. The model has a term that links overall levels of global trade, as a portion of GDP, to dispute probability.<br />
<br />
Alliances generally reduce dispute probability. Another factor that may reduce the probability is that global community is growing. This is uncertain and the linkage is not now activated.<br />
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One of the most basic factors affecting interstate relations is the closeness or contiguity of two states (small states on different continents pose little or no threat to each other).<br />
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[[File:IP4.gif|border|center|IP4.gif]]<br />
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== Probability of War ==<br />
<br />
The probability of conventional war depends most fundamentally on a basic conventional warfare probability per year (CWARBASE) that the user sets exogenously and that is set to zero for all country pairs (dyads) in the base case.<br />
<br />
That probability is potentially enhanced by threat from an actor to a target country and also by the threat perceived by a target from an actor.<br />
<br />
[[File:IP5.gif|border|center|IP5.gif]] <br />
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== War ==<br />
<br />
The outbreak of conventional war depends on the probability of war. The probability of war is converted into actual war randomly. The outbreak of nuclear war (only for countries with nuclear weapons) similarly depends probabilistically and randomly on the outbreak of conventional war. In order for a war to occur, the "waron" parameter must be turned on (set to 1). If it is turned on, wars may or may not occur; but a war can be forced if the waron parameter is set AND the war forcing switch (cwarf) is set to 100 or above).<br />
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We calculate damage for both conventional and nuclear power capabilities, as well as for civilian society (population and GDP).<br />
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