Pardee Wiki - User contributions [en]

Worldwide Governance Indicators

2023-02-17T01:14:33Z

Fouad.Alkhouri: Removed dead link

The IFs system has collected information on governance quality from “Governance Matters VII: Governance Indicators for 1996–2007”, a World Bank Policy Research Working Paper published in 2008'', ''authored by D. Kaufmann A. Kraay, and M. Mastruzzi. The dataset provides estimates of six dimensions of governance covering 212 countries and territories for four time periods: 1996, 1998, 2000, 2002, and 2004-2006.

These indicators are estimated based on various individual variables measuring perceptions of governance. The governance indicators dataset collected information from 25 separate data sources constructed by 18 different organizations. The governance indicator dataset was initiated in 1998 and grouped in as six dimension of governance: Voice and Accountability, Political Stability and Absence of Violence, Government Effectiveness, Regulatory Quality, Rule of Law, Control of Corruption.

Series taken from WGI:

{| border="1" cellpadding="0" cellspacing="0" width="64"
|-
| height="20" width="64" | SeriesGovernanceEffect
|-
| height="20" | SeriesGovernanceRegQual
|-
| height="20" | SeriesGovWBGovMatCorrupt
|-
| height="20" | SeriesGovWBGovMatPolStab
|-
| height="20" | SeriesGovWBGovMatRuleLaw
|-
| height="20" | SeriesGovWBGovMatVoiceAcct
|}

= Instructions on importing data from Worldwide Governance Indicators =

'''Introduction-'''

This section explains the methodology that is to be followed when importing World Governance Indicators (WGI) data into Ifs. It also describes certain problems that a user might face in interpreting, understanding the data

'''Source'''- WGI data is published by the World Bank. The data is available at this website , [http://data.worldbank.org/data-catalog/worldwide-governance-indicators http://data.worldbank.org/data-catalog/worldwide-governance-indicators]

'''Types of Data-'''Following categories of data need to be imported into Ifs
<ol style="list-style-type:lower-alpha;">
<li>'''GovernanceEffect-'''Indicator of Governance effectiveness on a scale of -2.5 to 2.5 , where higher is better</li>
<li>'''GovernanceRegQual-'''Indicator of Government regulatory quality, on a scale of -2.5 to 2.5, where a higher score is better</li>
<li>'''GovWBGovMatCorrupt-'''Indicator of Control of corruption, on a scale of -2.5 to 2.5, where a higher score indicates better control over corruption.</li>
<li>'''GovWBGovMatPolStab-'''Indicator of Political Stability and absence of violence. , on a scale of -2.5 to 2.5 where a higher score indicates higher political stability.</li>
<li>'''GovWBGovMatRuleLaw-'''Indicator of quality of rule of law, on a scale of -2.5 to 2.5, where a higher score indicates better rule of law.</li>
<li>'''GovWBGovMatVoiceAcct-'''Indicator of Voice and accountability, on a scale of -2.5 to 2.5 where a higher score indicates better Voice for citizens and higher accountability.</li>
</ol>

'''Country List to be Used-'''World Bank countries

'''Issues with Data import for certain countries-''' In certain  cases, IFs does not populate data in relation to countries such as Cape Verde, Taiwan, Ivory Coast. It is recommended, that the importer verify the data in relation to said countries in order to ensure that there are no discrepancies.

World Population Prospects (WPP)

2023-02-17T01:11:53Z

Fouad.Alkhouri: Removed dead link

'''United Nations Population Division: World Population Prospects (The 2002 Revision Population Database)'''

The 2002 Revision Population Database, annually published by the United Nations Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, is a comprehensive data source for world population and official estimation of world population and projections. The 2002 Revision is the eighteenth round of global demographic estimates and projections undertaken by the Population Division since 1950. The database provides major demographic indicators like birth rate, death rate, mortality rate, fertility rate etc. for each member country of United Nations for 1950-2050 by age and sex of the population. This database provides demographic distribution for 20 age-cohorts by sex. In all datasets, figures for 1950-2000 are estimates and those for 2000-2050 are projections. In case of projection, the database has been organized in four sets of variants:  low, medium, high, and constant.  Thomas Buettner of the Population Division was kind enough to provide data from the 2002 revision to the IFs project.

{| border="1" bgcolor="#ffffff" cellspacing="0"
|+ '''DataDict 720'''
|-
! bgcolor="#c0c0c0" | Table
! bgcolor="#c0c0c0" | Group
! bgcolor="#c0c0c0" | Definition
! bgcolor="#c0c0c0" | Source
! bgcolor="#c0c0c0" | Last IFs Update
! bgcolor="#c0c0c0" | UsedInPreprocessor
! bgcolor="#c0c0c0" | Years
|- valign="TOP"
| SeriesForecastPopConstantFemaleUNPD
| Population
| Constant-fertility variant female population by country, annually for 2010-2100 (millions)
| United Nations, Department of Economic and Social Affairs, Population Division (2011). World Population Prospects: The 2010 Revision, CD-ROM Edition.
| 10/2012
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesForecastPopConstantMaleUNPD
| Population
| Constant-fertility variant male population by country, annually for 2010-2100 (millions)
| United Nations, Department of Economic and Social Affairs, Population Division (2011). World Population Prospects: The 2010 Revision, CD-ROM Edition.
| 10/2012
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesForecastPopConstantTotalUNPD
| Population
| Constant-fertility variant total population (both sexes combined) by country, annually for 2010-2100 (millions)
| United Nations, Department of Economic and Social Affairs, Population Division (2011). World Population Prospects: The 2010 Revision, CD-ROM Edition.
| 10/2012
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesForecastPopHighFemaleUNPD
| Population
| High-fertility variant female population by country, annually for 2010-2100 (millions)
| United Nations, Department of Economic and Social Affairs, Population Division (2011). World Population Prospects: The 2010 Revision, CD-ROM Edition.
| 2012/10
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesForecastPopHighMaleUNPD
| Population
| High-fertility variant male population by country, annually for 2010-2100 (millions)
| United Nations, Department of Economic and Social Affairs, Population Division (2011). World Population Prospects: The 2010 Revision, CD-ROM Edition.
| 2012/10
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesForecastPopHighTotalUNPD
| Population
| High-fertility variant total population (both sexes combined) by country, annually for 2010-2100 (millions)
| United Nations, Department of Economic and Social Affairs, Population Division (2011). World Population Prospects: The 2010 Revision, CD-ROM Edition.
| 2012/10
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesForecastPopLowFemaleUNPD
| Population
| Low-fertility variant female population by country, annually for 2010-2100 (millions)
| United Nations, Department of Economic and Social Affairs, Population Division (2011). World Population Prospects: The 2010 Revision, CD-ROM Edition.
| 2012/10
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesForecastPopLowMaleUNPD
| Population
| Low-fertility variant male population by country, annually for 2010-2100 (millions)
| United Nations, Department of Economic and Social Affairs, Population Division (2011). World Population Prospects: The 2010 Revision, CD-ROM Edition.
| 2012/10
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesForecastPopLowTotalUNPD
| Population
| Low-fertility variant total population (both sexes combined) by country, annually for 2010-2100 (millions)
| United Nations, Department of Economic and Social Affairs, Population Division (2011). World Population Prospects: The 2010 Revision, CD-ROM Edition.
| 2012/10
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesForecastPopMediumFemaleUNPDe
| Population
| Medium-fertility variant female population by country, annually for 2010-2100 (millions)
| United Nations, Department of Economic and Social Affairs, Population Division (2011). World Population Prospects: The 2010 Revision, CD-ROM Edition.
| 10/2012
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesForecastPopMediumMaleUNPD
| Population
| Medium-fertility variant male population by country, annually for 2010-2100 (millions)
| United Nations, Department of Economic and Social Affairs, Population Division (2011). World Population Prospects: The 2010 Revision, CD-ROM Edition.
| 10/2012
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesForecastPopMediumTotalUNPD
| Population
| Medium-fertility variant total population (both sexes combined) by country, annually for 2010-2100 (millions)
| United Nations, Department of Economic and Social Affairs, Population Division (2011). World Population Prospects: The 2010 Revision, CD-ROM Edition.
| 10/2012
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesForecastPopUrbanUNPD
| Population
| Urban Population by Country, 1950-2050
| United Nations, Department of Economic and Social Affairs, Population Division (2012). World Urbanization Prospects: The 2011 Revision, CD-ROM Edition.
| 10/2012
| align="RIGHT" | No
| 
|}

== Instructions on Annualizing WPP Population Data ==

Several of the WPP Population series are given in averages or sums every 5 years.

To provide values for the missing years, IFs has a built in “Annualize” feature under Extended Features>Manage Country Data>Historical Data File Processing.

There are two different functions:

#Annualize/Spread Period Data (Use this if the raw data values represent the sum of data for all years contained in the interval. The data will then be spread across the 5 years.)
#Annualize/Interpolate Interval Data (Use this if the raw data values represent the data for only one given year. In this case the missing years will be interpolated across the missing years.)

[[File:Annualize-screenshot-1.jpg|613x213px|Annualize-screenshot-1.jpg]]

In order use the Annualize functions in IFs, complete the following steps:

1. Format raw data in excel

2. Import Excel Data to an access file using IFs>Extended Features>Historical Data File Processing>Import Data

[[File:Annualize-screenshot-2.jpg|360x230px|Annualize-screenshot-2.jpg]]

3. Copy Series Table from access file Local Disk (C:)>Users>Public>IFs>DATA>IFsDataImport and paste in Local Disk (C:)>Users>Public>IFs>DATA>IFsHistSeries

4. Copy the corresponding row from the DataDict Table in access file “Local Disk (C:)>Users>Public>IFs>DATA>IFsDataImport” and paste in “Local Disk (C:)>Users>Public>IFs>DATA>DataDict”

5. Annualize data in IFs: Extended Features>Manage Country Data>Historical Data File Processing>Annualize/Interpolate Data OR Annualize/Spread Data

     a. Select source name that matches the table you pasted in IFsHistSeries

     b. Use a unique name for the Output Data table so you can differentiate the annualized version

*note that the Source series must be included in both IFsHistSeries and DataDict tables, otherwise it will not show up as an option for annualizing

[[File:Annualize-screenshot-3.jpg|358x145px|Annualize-screenshot-3.jpg]]

6. Compile all annualized tables in a new access file, include data dict table, and be sure series names match

World Energy Council (WEC)

2023-02-17T01:10:22Z

Fouad.Alkhouri: removed dead link

'''World Energy Council (WEC)'''

The World Energy Council (WEC) is the foremost multi-energy organization in the world.  WEC has Member Committees in nearly 100 countries, including most of the largest energy-producing and energy consuming countries. Established in 1923; the organization provides data on all types of energy, including coal, oil, natural gas, nuclear, hydro, and renewables. The World Energy Council is perhaps the only source of extensive analyses of ultimate resources for fossil fuels, which it provides by country in a publication called the ''Survey of Energy Resources''.   The publication details 16 key energy resources with data provided by 96 WEC Member Committees worldwide. 

{| border="1" bgcolor="#ffffff" cellspacing="0"
|+ '''DataDict 720'''
|-
! bgcolor="#c0c0c0" bordercolor="#000000" | Table
! bgcolor="#c0c0c0" bordercolor="#000000" | Group
! bgcolor="#c0c0c0" bordercolor="#000000" | Definition
! bgcolor="#c0c0c0" bordercolor="#000000" | Source
! bgcolor="#c0c0c0" bordercolor="#000000" | Last IFs Update
! bgcolor="#c0c0c0" bordercolor="#000000" | UsedInPreprocessor
! bgcolor="#c0c0c0" bordercolor="#000000" | Years
|- valign="TOP"
| bordercolor="#d0d7e5" | SeriesEnReserCoal
| bordercolor="#d0d7e5" | Energy, Infrastructure
| bordercolor="#d0d7e5" | Energy reserves, coal
| bordercolor="#d0d7e5" | WEC
| bordercolor="#d0d7e5" | 
| bordercolor="#d0d7e5" align="RIGHT" | No
| bordercolor="#d0d7e5" | 
|- valign="TOP"
| bordercolor="#d0d7e5" | SeriesEnReserGas
| bordercolor="#d0d7e5" | Energy, Infrastructure
| bordercolor="#d0d7e5" | Energy reserves, gas
| bordercolor="#d0d7e5" | WEC; Oil and Gas Journal; 1960 estimated
| bordercolor="#d0d7e5" | 02/25/2012
| bordercolor="#d0d7e5" align="RIGHT" | Yes
| bordercolor="#d0d7e5" | 
|- valign="TOP"
| bordercolor="#d0d7e5" | SeriesEnReserOil
| bordercolor="#d0d7e5" | Energy, Infrastructure
| bordercolor="#d0d7e5" | Energy reserve, oil, in billion barrels
| bordercolor="#d0d7e5" | WEC; Oil and Gas Journal; 1960 estimated
| bordercolor="#d0d7e5" | 02/25/2012
| bordercolor="#d0d7e5" align="RIGHT" | Yes
| bordercolor="#d0d7e5" | 
|- valign="TOP"
| bordercolor="#d0d7e5" | SeriesEnResorCoal
| bordercolor="#d0d7e5" | Energy, Infrastructure
| bordercolor="#d0d7e5" | Energy resources, coal
| bordercolor="#d0d7e5" | WEC
| bordercolor="#d0d7e5" | 
| bordercolor="#d0d7e5" align="RIGHT" | No
| bordercolor="#d0d7e5" | 
|- valign="TOP"
| bordercolor="#d0d7e5" | SeriesEnResorGas
| bordercolor="#d0d7e5" | Energy, Infrastructure
| bordercolor="#d0d7e5" | Energy resources, gas
| bordercolor="#d0d7e5" | WEC
| bordercolor="#d0d7e5" | 
| bordercolor="#d0d7e5" align="RIGHT" | No
| bordercolor="#d0d7e5" | 
|- valign="TOP"
| bordercolor="#d0d7e5" | SeriesEnResorOil
| bordercolor="#d0d7e5" | Energy, Infrastructure
| bordercolor="#d0d7e5" | Energy resources, oil
| bordercolor="#d0d7e5" | WEC
| bordercolor="#d0d7e5" | 
| bordercolor="#d0d7e5" align="RIGHT" | No
| bordercolor="#d0d7e5" | 
|- valign="TOP"
| bordercolor="#d0d7e5" | SeriesEnResorSynthetic
| bordercolor="#d0d7e5" | Energy, Infrastructure
| bordercolor="#d0d7e5" | Energy resources, synthetic fuels (oil shale, tar sands)
| bordercolor="#d0d7e5" | WEC
| bordercolor="#d0d7e5" | 
| bordercolor="#d0d7e5" align="RIGHT" | No
| bordercolor="#d0d7e5" | 
|}

World Development Indicators

2023-02-17T01:09:14Z

Fouad.Alkhouri: removed dead link

There are 380 series pulled from the World Development Indicators (WDI) in 2017. 72 of these are used in the preprocessor.

The most extensive and reliable source for socio-economic and development data is probably World Bank’s annually published World Development Indicators (WDI) database. The recent WDI, published in 2017, has approximately 1,500 indicators for 217 economies. Economies in WDI have been organized in 18 groups based on income, geography, and indebtedness criteria. Data in the WDI database are categorized in six major areas: overview, people, environment, economy, states and markets, and global links.

The major advantage of the WDI database is that it has longer time series coverage, for example, most of the indicators cover data since 1960. The World Bank collects data for particular series from the organizations who are pioneers in those areas. For example, WDI compiles external account data, like export, import, current account, debt etc. from the International Monetary Fund (IMF), and population data from United Nations Population Division. Major data sources for WDI are World Bank national accounts data, OECD National Accounts, Food and Agriculture Organization, United Nations Educational, Scientific, and Cultural Organization, World Health Organization, International Labor Organization, International Telecommunication Union etc. WDI database are available in three formats- online, published report in hard copy and CDROM version.

Some financial data series from the WDI database, used in IFs analysis, have been converted into billion US$ from current prices at US$ and some series related to population have been transformed into million figures. The IFs system also needed to calibrate some data series as percent of GDP in constructing social accounting matrices.

= Series pulled from 2017 WDI Batch Pull =

{| border="1" bgcolor="#ffffff" cellspacing="0"
|+ '''DataDict 720'''
|-
! bgcolor="#c0c0c0" | Table
! bgcolor="#c0c0c0" | Group
! bgcolor="#c0c0c0" | Definition
! bgcolor="#c0c0c0" | Source
! bgcolor="#c0c0c0" style="width: 61px;" | Last IFs Update
! bgcolor="#c0c0c0" style="width: 112px;" | UsedInPreprocessor
! bgcolor="#c0c0c0" | Years
|- valign="TOP"
| SeriesImportGoods
| Economic, Trade
| Imports of goods (current currency)
| WDI  BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesLandIr%
| Environment, Infrastructure, Water
| Land use, irrigated land (% of cropland)
| WDI  BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesLabor
| Economic
| Labor force size
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesLaborFemale%
| Economic
| Portion of labor force made up by women
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesLaborSecInd%Tot
| Economic
| Labor in industry as % of total
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesHouseCon%GDP
| Economic
| Household final consumption expenditure as percent of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesGovMilPers%Labor
| Government
| Military personnel as percent of labor force
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesGini
| SocioPolitical
| Gini index of income inequality (lower = more equal)
| WDI  BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesLaborSecSer%Tot
| Economic
| Labor in services as % of total
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesGovTaxGoodSer%Rev
| Government
| Taxes on goods and services % of revenue
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesVaddInd%
| Economic
| Value added in industry as percent of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesArmsImp%TotImp
| Economic, Trade
| Arms imports (SIPRI trend indicator values)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesOresMetsIm%MerchIm
| Economic
| Ores and Metals imports as % of merchandise imports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesGovTaxIncProCG%Rev
| Government
| Taxes on income, profits and capital gains % total revenue
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesGovTaxRev%GDP
| Government
| Tax revenue % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesGovTaxTrade%Rev
| Government
| Taxes on international trade (% of revenue)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesGovtCurRev%GDP
| Government
| Current government revenue as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesGovtDebt%GDP
| Government
| Central government debt as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesEnProdElec%Hydro
| Energy, Infrastructure
| Electricity production from hydroelectric sources (% of total)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesEnProdElec%Nuc
| Energy, Infrastructure
| Electricity production from nuclear sources (% of total)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPopAsylumSeekers
| Population
| Inflow of refugees in millions (country of asylum)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesVaddMan%
| Economic
| Value added in manufacturing as percent of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesGDPCurDol
| Economic
| Gross Domestic Product in Current US$
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesPopForeign%
| Population
| Foreign population as % of total population
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesICTCommCompIm%Ser
| Information Communication,Infrastructure, Trade
| Communications, computer, etc. imports as % of service imports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesIncBelow2Dollar%WDI2009
| SocioPolitical
| Population below poverty line of $2 per day PPP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesIncPovertyRural%Pop
| SocioPolitical
| Income, poverty headcount, rural (% of population)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesIncPovertyUrban%Pop
| SocioPolitical
| Income, poverty headcount, urban (% of population)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesInflation
| Economic
| Inflation of consumer prices, annual rate
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesIncBelow2Dollar%WDI2011
| SocioPolitical
| Population below poverty line of $2 per day PPP (2005)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesEnProdElec%Coal
| Energy, Infrastructure
| Electricity production from coal sources (% of total)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTaxOther%CurRev
| Government
| Other taxes as % of total govt revenue
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTaxProducts
| Government
| Taxes on products (net indirect taxes)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesExportHighTech%Man
| Economic, Infrastructure, Trade
| High technology exports, % of manufactured exports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTaxRev%GDP
| Government
| Tax revenue as a percent of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXNetCapitalAccount
| Economic
| Net capital account
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXNetCurTransfers
| Economic
| Net current transfers with ROW (of residents, no repayment provisions), billion US$
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXNetIncFrmAbr
| Economic
| Net income from abroad, billion current US$
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesExportsGoodsServices
| Economic, Trade
| Exports of goods and services (current currency)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesGovSubTrans%CurExp
| Government
| Government subsidies and transfers as % of current expenditures
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesImportsGoodsServices
| Economic, Trade
| Imports of goods and services (current currency)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesHealthSmokingFemales%
| Population, Health
| Smoking prevalence, age adjusted, females (% of adults)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesInterestRatesReal
| Economic
| Real interest rates (as measured by GDP deflator)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXDebtPPGTDS
| Economic
| External long-term (more than 1 year) debt: public or publically guaranteed (not private unguaranteed)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXDebtPNG%GDP
| Economic
| External debt, private non-guaranteed, as percentage of gross domestic product
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesXDebtPPG%GDP
| Economic
| External debt, public and publicly guaranteed, as percentage of gross domestic product
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesXFlowsIDA%GDP
| Economic
| Net flows from IDA as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesHealthMeaslesImmunRate
| Population, Health
| Immunization, measles (% of children ages 12-23 months)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXReservesMthImports
| Economic
| Total reserves in months of imports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPopulationWDIFilled
| Population
| Population
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesHealthDPTImmunRate
| Population, Health
| Children 12-23 months immunized DPT
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesHealthExpperCap
| Population, Health
| Health expenditures per capita
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesHealthPhysiciansper1000
| Population, Health
| Physicians per 1000 population
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesICTTelephoneMobileper1000
| Information Communication,Infrastructure
| Telephones, mobile per 1000 population
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesExportsGoodsServicesIncome
| Economic, Trade
| Exports of goods, services, and primary income (BoP, current currency)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesExportValueIndex
| Economic, Trade
| Export value index (2000=100)
| WDI 2015 September BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesExportsMerchandise
| Economic
| Exports of merchandise (current currency)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesGovWBDoingBusinessCustomsDays
| Government
| Number of days required to clear customs
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXNetTradeinGoods
| Economic
| Net trade in goods
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXNetTradeinGoodsServices
| Economic
| Net trade in goods and services
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXPortInvExclLCFAR
| Economic
| Portfolio investment excluding LCFAR
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesGovtHl%GDP
| Government, Health, Infrastructure
| Health expenditures as percent of GDP, public
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesHealthBrthsAttnSkilledPersWDI
| Population, Health
| Births attended by skilled health staff (% of total)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesHealthARITreatmentChil
| Population, Health
| ARI treatment (% of children under 5 taken to a health provider)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesHealthDiarrhORTChil%
| Population, Health
| Diarrhea treatment (% of children under 5 receiving oral rehydration and continued feeding)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesHealthExpOutofPocket%
| Population, Health
| Out-of-pocket health expenditure (% of private expenditure paid out of pocket)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesHealthHIVRateFemAdults
| Population, Health
| Female adults with HIV (% of population ages 15-49 with HIV)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesHealthHospBedsper1000
| Population, Health
| Hospital beds
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesHealthTBDOTSDetect%
| Population, Health
| Tuberculosis case detection rate (%, all forms)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesHealthTBTreatSuccess%
| Population, Health
| Tuberculosis treatment success rate (% of registered cases)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesGovExpend
| Government
| Government final consumption expenditures (current currency)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTourismArrivals
| Economic
| International tourism, number of arrivals
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXWorkerRemitPaid
| Economic, SocioPolitical
| Worker remittances by country where paid
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesTourismDepartures
| Economic
| International tourism, number of departures
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesICTTelecomRev%GDP
| Information Communication,Infrastructure, Government
| Telecommunications revenue as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXFlowsOthers
| Economic
| Net flows, others
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXFlowsIMFNonCon%GDP
| Economic
| Net nonconcessional flows from IMF as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesXIMFCredit%GDP
| Economic
| IMF credits as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesXIncPayments%GDP
| Economic
| Income payments as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesXPortBonds%GDP
| Economic
| Portfolio investment in bonds (PPG and PNG) as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesGasPricesWDI
| Energy, Infrastructure
| Pump price for gasoline
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesLifExpectFemaleWDI
| SocioPolitical
| Life expectancy at birth, female
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesLifExpectWDI
| SocioPolitical
| Life expectancy at birth
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXRealEffectExchRateInd
| Economic
| Real effective exchange rate index (2000 = 100)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXPortEquity%GDP
| Economic
| Portfolio investment in equity as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesHealthExpTotal%GDPWDI2009
| Population, Health
| Health Expenditure, Total (% of GDP)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPovertyGap$1c25perDay
| SocioPolitical
| Poverty gap at $1.25 per day (PPP), percent
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPovertyGap$2perDayWDI2009
| SocioPolitical
| Poverty gap at $2 per day (PPP), percent
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesWaterWithAgr%Fresh
| Agriculture, Infrastructure, Environment, Water
| Water withdrawals, agriculture, % of total freshwater withdrawals
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesWaterWithInd%Fresh
| Agriculture, Infrastructure, Environment, Water
| Water withdrawals, industry, % of total freshwater withdrawals
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesWomParlWDI%
| SocioPolitical
| Seats held by women in national parliament, percentage
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesLandArea
| Agriculture, Infrastructure, Environment
| Land Area
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesAgFertUse
| Agriculture
| Fertilizer consumption total
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXReserves%GDP
| Economic
| Gross international reserves as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesExportGoodSer%
| Economic, Trade
| Exports of goods and services as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesGasPricesDiesel
| Energy, Infrastructure
| Gasoline prices, diesel pump prices
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesExportServices
| Economic
| Exports of services (BoP, current currency)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesVaddTexCloth%Man
| Economic
| Textiles and clothing (% of value added in manufacturing)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXDebtPri%TotalXDEBT
| Economic
| External debt stocks, private nonguaranteed debt as % of external debt
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXFlowsRDBCon
| Economic
| Net concessional flows from Regional Development Banks
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXFlowsRDBNonCon%GDP
| Economic
| Net nonconcessional flows from Regional Development Banks as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXGoodSerBal%GDP
| Economic
| External balance on goods and services (% of GDP)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesGovEmplComp%Exp
| Government
| Compensation of employees, % total expenses
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTaxSocSec%CurRevNew
| Government
| Social security taxes as % of total govt revenue
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesEnElecTransLoss%
| Energy, Infrastructure, Trade
| Electric power transmission and distribution losses (% of output)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesXExchRateLCUtoDollar
| Economic
| Official exchange rate, local currency units per US dollar
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesAidRec%GNI
| Economic
| Official development assistance and official aid, net, % of GNI
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesInscFinIm%Ser
| Economic, Trade
| Insurance and financial imports as % of service imports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesR&DResearchersperMillion
| Science Technology, Infrastructure, Knowledge
| R&D researchers (FTE) per million inhabitants
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesExportQuantityIndex
| Economic, Trade
| Export quantum/quantity index (2000=100)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesHealthExtRes%Total
| Population, Health
| External resources for health as % of total resources
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesHealthSmokingMales%
| Population, Health
| Smoking prevalence, age adjusted, Males (% of adults) ales (% of adults)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesRailPassengerKM
| Transportation,Infrastructure
| Rail passenger-kilometers
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesVAddTot95
| Economic
| Total value added at factor cost
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesGovtEdPub%GDP
| Government
| Educational expenditures (public) as percent of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesGovWBDoingBusinessCostofStarting
| Governance
| Cost of registring a business as a percent of per capita income
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesPopulationUrban%
| Population
| Urban population as percent of total
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesAgFertUseperHectare
| Agriculture
| Fertilizer consumption (100 grams per hectare of arable land)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesAgFoodEx%MerchEx
| Agriculture, Trade
| Agricultural food exports as % of merchandise exports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesAgMachTracper100H
| Agriculture
| Agricultural machinery, tractors per 100 hectares of arable land
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesAidRecGrant%Total
| Economic
| Official development assistance and official aid, grants and other revenue, current LCU
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesCBR
| Population
| Crude birth rate
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesCDR
| Population
| Crude death rate
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesEnFuelEx%MerchEx
| Energy, Infrastructure, Trade
| Energy fuel exports as % of merchandise exports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesEnFuelIm%MerchIm
| Energy, Infrastructure, Trade
| Energy fuel imports as % of merchandise imports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesEnImportsNet%EnUse
| Energy, Infrastructure, Trade
| Energy imports (net) as a percentage of energy use
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesEnPricePumpDiesel
| Energy, Infrastructure
| Pump price for diesel fuel (US$ per liter)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesEnPricePumpSuper
| Energy, Infrastructure
| Pump price for gasoline (US$ per liter)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesEnProdElec%Gas
| Energy, Infrastructure
| Electricity production from natural gas sources (% of total)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXFlowsIDA
| Economic
| Net flows from IDA
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesGovtMil%GDPWDI
| Government, Conflict
| Military expenditures as percent of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesHealthExpTotal%GDP
| Population, Health
| Total expenditure on health as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesHealthTBIncper100000
| Population, Health
| Incidence of tuberculosis (per 100,000 people)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesICTBroadbandSubscribers
| Information Communication,Infrastructure
| Broadband subscribers
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesIncBelow1Dollar%WDI2006
| SocioPolitical
| Population below poverty line of $1 per day PPP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesEnProdElec%TransLoss
| Energy, Infrastructure
| Electricity power transmission and distribution losses % of total
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesExportImport%GDP
| Economic, Infrastructure, Trade
| Trade (exports and imports) as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesGasPricesSuper
| Energy, Infrastructure
| Gasoline prices, super pump prices
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesGovBudgBal%GDP
| Government
| Overall budget balance, including grants (% of GDP)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesGovCon%GDP
| Economic
| Government (general) final consumption as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesGovExpense%GDP
| Government
| Government expense as % of GDP (compare with govt expenditures)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesRiskPremLend%
| Economic
| Risk premium on lending
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXDebt%GNI
| Economic
| External debt as percentage of gross national income
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXDebtPNG
| Economic
| External debt, private non-guaranteed
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXDebtShort%TotalXDebt
| Economic
| Short-term debt as % of external debt
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPovertyGap$2perDay
| SocioPolitical
| Poverty gap at $2 per day (PPP), percent
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPovertyNatPovLineHC%
| SocioPolitical
| Poverty headcount ratio at national poverty line (% of population)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPovertyNatPovLineRurHC%
| SocioPolitical
| Poverty headcount ratio at national poverty rural line (% of population)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPovertyNatPovLineUrbHC%
| SocioPolitical
| Poverty headcount ratio at national poverty urban line (% of population)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPPPConvtoMER
| Economic
| PPP conversion factor to official exchange rate ratio
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesR&DTechniciansperMil
| Science Technology, Infrastructure, Knowledge
| R&D technicians
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesRailGoods
| Transportation,Infrastructure
| Rail freights million ton-kilometer
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesAidRecPerCap
| Economic
| Official development assistance and official aid per capita
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesEnProdElec%Oil
| Energy, Infrastructure
| Electricity production from oil sources (% of total)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPopulationYouthDep%
| Population
| Population of young people (
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesAgLifestockProdIndex
| Agriculture
| Livestock production index, 2004-2006=100
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesAidRec
| Economic
| Official development assistance and official aid, net, current dollars
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesAidRecGross%GDP
| Economic
| Official development assistance and official aid, gross, % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesEmissionsCO2WDI
| Environment
| CO2 emissions
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesICTCommCompEx%Ser
| Information Communication,Infrastructure, Trade
| Communications, computer, etc. exports as % of service exports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesICTInternetSecServsperMil
| Information Communication,Infrastructure
| Secure internet servers
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesICTInternetUsersper1000WDI
| Information Communication,Infrastructure
| Internet users. Internet users are people with access to the worldwide network
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesImportQuantityIndex
| Economic, Trade
| Import quantum/quantity index (2000=100)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesImports
| Economic, Trade
| Imports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesImportGoodSer%
| Economic
| Imports of goods and services as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesImportServices
| Economic, Trade
| Imports of services (current currency)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesImportsMerchandise
| Economic
| Imports of merchandise (current currency)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesLandCrop%
| Environment, Agriculture, Infrastructure
| Land use, permanent cropland (% of total area)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesLifExpectMaleWDI
| SocioPolitical
| Life expectancy at birth, Male
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesMalnChil%HeightBasedWDI2009
| SocioPolitical
| Malnutrition Prevalence, Height for Age (% of Children Under 5)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesMalnChil%WeightWDI2009
| SocioPolitical
| Malnutrition Prevalence, Weight for Age (% of Children Under 5)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesMalnPop%WDI2009
| SocioPolitical
| Prevalence of Undernourishment (% of Population)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesManEx%MerchEx
| Economic, Trade
| Manufactures exports as % of merchandise exports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesManIm%MerchIm
| Economic, Trade
| Manufactures imports as % of merchandise imports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesMerchExCurDol
| Economic, Trade
| Merchandise exports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesMalnPop%WB
| SocioPolitical, Agriculture
| Percentage of population malnourished
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesRailGoodsperMil$GDP
| Transportation,Infrastructure
| Rail goods-kilometer per million $ (PPP)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesRailPassengerKMperMil$GDP
| Transportation,Infrastructure
| Rail passenger-kilometers per million $ GDP (PPP)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesSafWaterRural%
| SocioPolitical,Infrastructure
| Percent of rural population with access to safe water
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesSafWaterUrban%
| SocioPolitical,Infrastructure
| Percent of urban population with access to safe water
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTermTrNetBarter
| Economic
| Terms of trade, net barter (2000 = 100)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTourismExpend%Imp
| Economic
| Tourism (international) expenditures as % of total imports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTourReceipt%Exp
| Economic
| Tourism (international) receipts as % of total exports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTransAircraftDeparts
| Economic, Infrastructure, Transportation
| Transportation, aircraft departures
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTransContainerTraf
| Economic, Infrastructure, Transportation
| Transportation, container port traffic of 20-foot equivalents
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTransEx%Ser
| Economic, Infrastructure, Transportation, Trade
| Transportation exports as % of service exports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTFR
| Population
| Total fertility rate: children per woman
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXFlowsRDBNonCon
| Economic
| Net nonconcessional flows from Regional Development Banks
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXIMFCreditUse
| Economic
| Use of IMF Credit
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXPortBonds
| Economic
| Portfolio investment in bonds (PPG and PNG)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXReserveswoGoldCurDols
| Economic
| Total reserves minus gold (current US$)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXRoyalLicPayments
| Economic
| Royalty and license fees, payments (BoP, current US$)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXRoyalLicReceipts
| Economic
| Royalty and license fees, receipts (BoP, current US$)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesWaterWithDom%Fresh
| Agriculture, Infrastructure, Environment, Water
| Water withdrawals, domestic, % of total freshwater withdrawals
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXFlowsRDBCon%GDP
| Economic
| Net concessional flows from Regional Development Banks as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesExportGoods
| Economic, Trade
| Exports of goods (BoP, current currency)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesGovGrantsRev%Rev
| Government
| Government grants from govts, Iorgs, assorted revenue, % of Rev
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesGovInt%Exp
| Government
| Interest payments on central govt debt as % of current expenditure
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesGovInt%Rev
| Government
| Interest payments on central govt debt as % of current revenue (dom and intl lenders)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesGovMil%GovExp
| Government
| Military expenditures as percent of central government expenditures
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesIncBelow2Dollar%WDI2006
| SocioPolitical
| Population below poverty line of $2 per day PPP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesInscFinEx%Ser
| Economic, Trade
| Insurance and financial exports as % of service exports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesInvestGrCapForm
| Economic
| Gross capital formation (Investment)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesEnElecConsPerCap
| Energy
| Electricity consumption per capita
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesInvestGrFixedCapForm%GDP
| Economic
| Gross domestic fixed capital formation as percent of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesIncBelow1D25c%WDI2009
| SocioPolitical
| Population below poverty line of $1.25 per day PPP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesInfMort
| SocioPolitical
| Infant mortality (per thousand births)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesInvestGrCapForm%GDP
| Economic
| Gross capital formation (Investment), percent of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesSafWaterWDI%
| SocioPolitical,Infrastructure
| Percent of population with access to safe water
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesSanitationAccess%
| SocioPolitical,Infrastructure
| Percent of population with access to improved sanitation
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesSanitationAccessRural%
| SocioPolitical,Infrastructure
| Percent of rural population with access to improved sanitation
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesSanitationAccessUrban%
| SocioPolitical,Infrastructure
| Percent of urban population with access to improved sanitation
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesSavings%GDP
| Economic
| Savings, gross, as percent of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesSavingsDomestic%GDP
| Economic
| Savings, gross, domestic, as percent of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesSavingsGenuine%
| Economic
| Savings (genuine; special measure) as percent of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesSavingsGross%GNI
| Economic
| Savings, gross, as percent of GNI
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesSciTechJournArticles
| Science Technology, Infrastructure, Knowledge
| Science and technology articles published
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTaxExportDuties%Exp
| Government
| Taxes (duties) on exports as % of exports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTravEx%Ser
| Economic, Transportation, Trade
| Travel exports as % of service exports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTravIm%Ser
| Economic, Transportation, Trade
| Travel imports as % of service imports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesVaddFoodBevTob%Man
| Economic
| Value added in food, beverages, and tobacco as % of value added in manufactures
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXChngNetReserves
| Economic
| Changes in net reserves
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXCurActBal
| Economic
| Current account balance
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXCurRevExcGr%GDP
| Economic
| Current revenue excluding grants (% of GDP)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXCurTransferRec
| Economic
| Current transfers with ROW, receipts (of residents, no repayment provisions), billion US$
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXDebt%Exports
| Economic
| Present value of debt as percentage of exports of goods, services
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXDebt%GDP
| Economic
| External debt as percentage of gross domestic product
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXFDINetFlows
| Economic
| Foreign direct investment, net inflows
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXFlowsIBRD
| Economic
| Net flows from IBRD
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXFlowsIBRDIDA
| Economic
| IBRD loans and IDA credits
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXFlowsIMFCon
| Economic
| Net concessional flows from IMF
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXFlowsIMFNonCon
| Economic
| Net nonconcessional flows from IMF
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXFlowsOthers%GDP
| Economic
| Net flows, others, as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXFDIInflows%GDP
| Economic
| Foreign direct investment net inflow as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesXFDIOutflows%GDP
| Economic
| Foreign direct investment net outflow as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesXWorkerRemit%GDP
| Economic
| Worker remittances by home country as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXWorkerRemitReceived
| Economic, SocioPolitical
| Worker remittances received by home country
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesAgRawEx%MerchEx
| Agriculture, Trade
| Agricultural raw material exports as % of merchandise exports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesGovPPGDebtService%Rev
| Government
| Public and publicly guaranteed debt service as % of current revenue
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesGovSocialContr%Rev
| Government
| Government total social conbtributions as percent of revenue
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesICTBroadbandSubsper100
| Information Communication,Infrastructure
| Broadband subscribers per 100 people
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesImportsGoodsServicesIncome
| Economic, Trade
| Imports of goods, services and income (current currency)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesImportValueIndex
| Economic, Trade
| Import value index (2000=100)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesIncShareL20%
| SocioPolitical
| Income share of poorest 20% of population
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesXIncPayments
| Economic
| Income payments
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesAgRawIm%MerchIm
| Agriculture, Trade
| Agricultural raw material imports as % of merchandise imports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTaxImportDuties%Imp
| Government
| Import duties as a percent of imports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesEnConComRenewWaste
| Energy, Infrastructure
| Energy from combustible renewable and waste (% of total)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesGovtExpend%GDP
| Government
| Government expenditures (central) as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesHealthExpPublic%GDPWDI2009
| Population, Health
| Health expenditures as percent of GDP, public
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesHlPriv%GDP
| Government
| Health expenditures as percent of GDP, private
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesHouseCon
| Economic
| Household consumption
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesMerchImCurDol
| Economic, Trade
| Merchandise imports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesMortChildUnder5
| SocioPolitical
| Mortality rate, under-5 (per 1,000)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesNationalExpendGross%GDP
| Economic
| National expenditure, gross, as percent of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTaxIncPrCG%CurRev
| Government
| Taxes on income, profits, cap gains as % of total govt revenue
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTaxIncPrCG%Tax
| Government
| Taxes on income, profits, cap gains as % of total taxes
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTaxTrade%CurRev
| Government
| Taxes on trade as percent of total govt revenue
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesOresMetsEx%MerchEx
| Economic
| Ores and Metals exports as % of merchandise exports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesPatentApplicationsNonRes
| Science Technology, Infrastructure, Knowledge
| Annual patent applications by nonresidents
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPatentApplicationsRes
| Science Technology, Infrastructure, Knowledge
| Annual patent applications by residents
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPopAgeDependRatio
| Population
| Percentage of Dependents to working-aged population
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPopBirthsSkilledAttend%
| SocioPolitical
| Percent of births attended by skilled health personnel
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPopContrUnmetNeed%
| Population
| Unmet need for contraception (% of married women ages 15-49)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPopForeignInflows
| Population, SocioPolitical
| Foreign population stock
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPopMigNetTest4BBH
| Population
| Migration data from WB test
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPopMigrationYearsWDI
| Population, SocioPolitical
| Annual net migration
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPopContrUse%WDI
| Population
| Prevalence of contraceptive use
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesPopulationUrban
| Population
| Urban population
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesTrademarksNonresidents
| Science Technology, Infrastructure
| Trademarks, nonresidents
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTrademarksResidents
| Science Technology, Infrastructure
| Trademarks, residents
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTransAirFreight
| Transportation,Infrastructure
| Air freight
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTransAirPassengers
| Transportation,Infrastructure
| Air transport, passengers carried
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesAgCropProdIndex
| Agriculture
| Crop production in terms of edible crops, 2004-2006=100
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesAgFoodIm%MerchIm
| Agriculture, Trade
| Agricultural food imports as % of merchandise imports
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesEnConElecPerCap
| Energy, Infrastructure
| Electric power consumption per capita
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPopulationDensity
| Population
| Population density, people per square kilometer
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPopulationDensityRural
| Population
| Population density rural, people per square kilometer of arable land
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPopulationLargestCity%Urb
| Population
| Population in the largest city (% of urban population)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPopulationRural
| Population
| Rural population
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPopulationRural%
| Population
| Rural population as percent of total
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesPopulationWDI2009
| Population
| Population, Total
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesMalnChil%WeightWB
| SocioPolitical, Agriculture
| Percentage of children under 5 malnourished based on weight; US benchmark
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesXDebtLongTerm
| Economic
| Long-term debt (DOD, current US$) (bill)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXDebtPPG
| Economic
| External long-term (more than 1 year) debt: public or publically guaranteed (not private unguaranteed)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXDebtServ%Exports
| Economic
| Total debt service as percentage of exports of goods and services
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXDebtServ%GNI
| Economic
| Total debt service as % of GNI
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXDebtServiceTot
| Economic
| External Debt Service, Total
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXDebtServMulti%PPGServ
| Economic
| Multilateral debt service, % of PPG debt serves
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXDebtServPPG%Exports
| Economic
| External PPG debt service as percentage of exports of goods and services
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXDebtServPPG%GNI
| Economic
| Total PPG debt service as % of GNI
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesTaxGoodSer%CurRev
| Government
| Taxes on goods and services as % of total govt revenue
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesTaxSocSec%CurRev
| Government
| Social security taxes as % of total govt revenue
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesXExtBalGoodsServ
| Economic
| External balance on goods and services
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXExtBalGoodsServ%GDP
| Economic
| External balance on goods and services as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXIncReceipts
| Economic
| Income receipts
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXReservesNetChange
| Economic
| Changes in net reserves
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesXReserveswGoldCurDols
| Economic
| Total reserves (includes gold, current US$)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesMalnChil%
| SocioPolitical, Agriculture
| Percentage of children under 5 malnourished by weight for age
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesICTInternet%Pop
| Information Communication,Infrastructure
| Percent of population on-line
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesLaborAgr%TotFemale
| Economic
| Employees, agriculture, female as % of female employment
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesWaterAnRenResourcesNew
| Environment, Infrastructure, Water
| Annually renewable water resources
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesWaterAnWithdrawalsNew
| Environment, Infrastructure, Water
| Annual water withdrawals/use (1990=70-99;2000=update, mostly 2000)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesVaddAg%
| Economic
| Value added in agriculture as percent of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesVaddSer%
| Economic
| Value added in services as percent of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesXFlowsIMFCon%GDP
| Economic
| Net concessional flows from IMF as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesXIncReceipts%GDP
| Economic
| Income receipts as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesLaborAgr%TotMale
| Economic
| Employees, agriculture, males as % of male employment
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesLiteracyAdultTotal%
| SocioPolitical, Knowledge, Education
| Literacy rate, percent of adults (15 or older)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesXCurActBal%GDP
| Economic
| Current account balance (% of GDP)
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesXDebt
| Economic
| External long-term (more than 1 year) debt: public, publically guaranteed and priv nonguaranteed
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesXFlowsIBRD%GDP
| Economic
| Net flows from IBRD as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesXWBLoans%GDP
| Economic
| IBRD loans and IDA credits as % of GDP
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | Yes
| 
|- valign="TOP"
| SeriesInflationCPIBase2010
| Economic
| Consumer Price Index
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|- valign="TOP"
| SeriesInflationWholesaleBase2010
| Economic
| Wholesale Price Index
| WDI BATCH PULL
| style="width: 61px;" | 2017/06/13
| align="RIGHT" style="width: 112px;" | No
| 
|}

= Discontinued WDI Series =

Since beginning to pull WDI data, there have been a number of series which have been discontinued in terms of collection by the WDI. We continue to keep this outdated data in IFs until proper replacements are found. Those data are:

'''Non-Preprocessor Series:'''

SeriesEmissionsOrgWatperWorkerDay

SeriesEnConElecWDIkwh

SeriesGovBudgeBal%GDP

SeriesICTDailyNewspapers

SeriesICTExpendperCap

SeriesICTInternetUsersper1000WDI

SeriesICTInternetUsersWDI

SeriesICTTelephoneLinesCellsper100

SeriesLaborParRateFemale%

SeriesLaborParRateMale%

SeriesLaborParRateTotal%

SeriesRoadDensity

SeriesRoadsPassengerKM

SeriesRoadsGoodsTrans

SeriesRoadsTotalNetworkClean

SeriesRoadsTotalNetworkOld

SeriesRoadsTotalNetworkWDI2010

SeriesTransCarsper1000

SeriesVehiclesperKilom

SeriesXFDIOutflows%GDPOld

SeriesXGrossPrivCapFlows%GDP

SeriesXPortEquity

'''Preprocessor Series:'''

SeriesEnConElec

SeriesEnConTotalWDI

SeriesEnElecShrEnDem

SeriesEnProdElec

SeriesEnvPM10

SeriesICTExpend%GDP

SeriesICTInternet%Pop

SeriesRoadsPaved%

SeriesRoadsTotalNetwork

SeriesVehiclesper1000

= Instructions on pulling WDI data =

'''Steps for pulling WDI Batch Data'''

#Access the World Development Indicators through the world bank at: [http://data.worldbank.org/data-catalog/world-development-indicators http://data.worldbank.org/data-catalog/world-development-indicators]
#Use batch import tool to import the WDI data. Import as user sees best fit (it's a good idea to start with a preprocessor import, a lot fewer series that generally allow user to see where errors might lie without combing through 400 series). Choose the World Bank Countries concordance table.
#During 2017 import issues arose around North Korea. In order to fix this the country name in World Bank Countries Concordance table was changed to "Korea, Dem. Rep." and was also changed to this in the WDI excel file. Will need to repeat this step in order to ensure data is imported. 
#Begin prevetting process. Blend data points for missing years and missing country data. Can generally expect that data will differ slightly from current consiering that WDI regularly updates old data points.
#Once all noticeable errors are fixed send to vetter for more intensive search of data for errors. 

'''Eelectricity Access data'''

'''Introduction-'''

This section explains the methodology that is to be followed when importing Electricity Access data into Ifs. It also describes certain problems that a user might face in interpreting, understanding the data.

'''Representation of Electricity access data in IFs'''

Electricity access data in Ifs is broken down into electricity access for Urban areas and Electricity access for rural areas. It is represented as a percentage i.e. percentage of urban population with electricity access and percentage of rural population with electricity access. This data is used in the following computations in Ifs,

# Desired level of electricity access
# Electricity use as a % of total energy use
# % of population that relies on solid fuels as the primary source of domestic energy 

This data is affected by the following drivers in IFs,

#The household size
#Population size
#Breakdown of population (Urban, Rural)
#Government effectiveness

Below is a simple diagrammatic representation of Electricity Access in Ifs,

[[File:Untitled Diagram (6).png|frame|center|upright|Untitled Diagram (6).png]]                                                                                                ''Source: International Futures 7.22''

'''Source for data'''- Electricity Access data is available from a variety of sources. The two main sources of data for the same are,

#WDI- the World Bank publishes data for electricity access data for 186 countries. A bifurcation is available between Rural Electrification and Urban Electrification. The data is available at  [http://data.worldbank.org/indicator/EG.ELC.ACCS.ZS http://data.worldbank.org/indicator/EG.ELC.ACCS.ZS]
#IEA- the International Energy Agency publishes this data as well, but only for developing countries. This data is published as a part of the world energy outlook. The data is available at [http://www.worldenergyoutlook.org/resources/energydevelopment/energyaccessdatabase/ http://www.worldenergyoutlook.org/resources/energydevelopment/energyaccessdatabase/]  

 

'''Series to be updated'''

#National Electricity Access - SeriesEnElecAccess%National
#Rural Electricity Access- SeriesEnElecAccess%Rural
#Urban Electricity Access- SeriesEnElecAccess%Urban

'''Comparison of WDI and IEA data'''

  When the WDI and IEA data were compared, in order to verify whether the datasets could be blended, we found that the year on year data was too dissimilar to blend. For Example, for some countries the 2013 value from IEA was lower than the 2012 value from WDI. (This is not logical, since electrification rates cannot go down). Also, for some other countries the 2013 IEA value was abnormally high considering the historical growth rates of electrification for the country or region in question. Blending these datasets would create transients in the IFs forecasts for future years. '''Hence, it is recommended that the user only use the WDI data when updating this series.'''

 

'''Country List to be used – '''World Bank countries

Water model documentation

2023-02-17T01:07:48Z

Fouad.Alkhouri:

<div>
Please cite as: Steve Hedden, Hughes, Barry B. 2018. ''"IFs Water Model Documentation."'' Pardee Center for International Futures, Josef Korbel School of International Studies, University of Denver, Denver, CO. Accessed DD Month YYYY

The water model of the International Futures (IFs) system forecasts water demand by sector (municipal, industrial, and agricultural) and supply by source (surface water, groundwater, non-renewable groundwater, desalinated water, and treated wastewater) for 186 countries to the year 2100. Water supply and demand are equilibrated using a shadow price index, which is determined using a proportional-integral-derivate (PID) controller. There are no “forward linkages” from the water model to the rest of the IFs system; however, user-controlled parameters allow water scarcity to impact agricultural yields.

= Introduction =
</div>
== Dominant Relations ==

We model water demand as the sum of the water demand of three sectors—municipal, industrial, and agriculture. The data we use from AQUASTAT differentiates between these three sectors and many other water models that use these same sectors. AQUASTAT refers to these data series as “water withdrawal.” For our purposes, we consider water withdrawal to be equivalent to water demand.

Water supply is defined as the sum of five components: surface water withdrawal, renewable groundwater withdrawal, non-renewable (fossil) water withdrawal, desalinated water, and direct use of treated wastewater.

The size of a country’s urban population and water use per capita for the urban population drive municipal water demand. Water use per capita for the urban population is driven by GDP per capita (at purchasing power parity), the portion of the population with access to piped water, and the portion of the population that lives in urban areas. Non-renewable electricity generation capacity and the overall size of a country’s manufacturing sector drive industrial water demand. The area of land under irrigation drives agricultural water demand. The shadow price index impacts all three water demand sectors.

Surface and (renewable) groundwater withdrawals are driven by the shadow price index and constrained by their country-specific exploitable limits. Estimated stocks constrain fossil water withdrawals. The price index and an initial growth rate (which can be specified by user) drive desalinated water. Direct use of treated wastewater is driven by the price index and the volume of wastewater which is treated.

Total water demand and total water supply are used to adjust the price index. The water price  index then impacts each sector of demand and most sources of supply the following year. This algorithmic logic keeps water demand and water supply in approximate equilibrium over time, even though it does not require exact equality in any time step.

== Structure and Agent System ==

{| border="1" cellspacing="0" cellpadding="0" width="501" style="width:501px;"
|-
! style="width:242px;" |
'''System/Subsystem'''

!
Environment (e.g. CO2, water)

|-
|
'''Organizing Structure'''

|
Systemic Accounting

|-
|
'''Stocks'''

|
Exploitable renewable water resources (surface and ground)

 

Total renewable water resources (surface and ground)

 

Non-renewable (fossil) water

 

Water price index

|-
|
'''Flows'''

|
Water demand (municipal, industrial, agriculture)

 

Wastewater (produced, treated, treated and reused)

 

Desalinated water

 

Non-renewable (fossil) water withdrawal

|-
|
'''Key Aggregate ''' '''Relationships '''

'''(illustrative, not comprehensive)'''

|
Water withdrawals as a portion of exploitable limit

 

Water price index

|-
|
'''Key Agent-Class Behavior ''' '''Relationships'''

'''(illustrative, not comprehensive)'''

|
Governments and environmental policies regarding exploitable limits

 

Water demand and supply responses to scarcity (elasticities on sectors)

|}

= Water Flow Charts =

== Water Overview ==

[[File:WaterOverview.jpg|frame|center|625x445px|Water supply and demand as equilibrated using a water price index.]]

 

== Water Demand Overview  ==

[[File:DemandOverview.jpg|frame|center|545x725px|Three components of water demand: municipal, industrial, and agriculture.]]

== Water Supply Overview ==

[[File:SuppleOverview.jpg|frame|center|545x725px|Five components of water supply and their driving variables.]]

== Forward linkages ==

[[File:ForwardLinkagesOverview.jpg|frame|center|725x445px|Conceptual framework for water constraints on the agriculture model]]

= Water Equations =

== Water Demand ==

Water demand is disaggregated into three sectors: agricultural, municipal, and industrial. Each of these three sectors have their own driving variables, mainly calculated in other sub-modules of the IFs model. Each of the equations for the three sectors of demand follow roughly the same structure – the first term in each of the equations is a level of water withdrawal intensity (water use per HA of irrigated land, municipal water use per capita, and water use per kWh of non-renewable electricity generation). This term is then multiplied by the overall size of the denominator in each of these intensity calculations (area of irrigated land, urban population, and non-renewable electricity generation).<ref>Industrial water demand is disaggregated into water use for electricity and the manufacturing sector. Manufacturing sector water demand follows a slightly different logic than the other sectors, see section below.</ref>

Each sector of water demand is also impacted by changes in the water price index using a -0.1 elasticity (see section on water price index for details on those calculations).

:<math>WATDEMAND_{r,municipal,t+1} = WATDEMAND_{r,municipal,t}* (1+(IncreaseInPrice_r * watdemandelas_{municipal}))</math>

:<math>WATDEMAND_{r,industrial,t+1} = WATDEMAND_{r,industrial,t}* (1+(IncreaseInPrice_r * watdemandelas_{industrial}))</math>

:<math>WATDEMAND_{r,agriculture,t+1} = WATDEMAND_{r,agriculture,t}* (1+(IncreaseInPrice_r * watdemandelas_{agricultural}))</math>

=== Agricultural water demand ===

Water use for irrigation can be estimated by the following equation:

:<math>WATDEMAND_{r,agriculture}=waterperha_{r}*\frac{LANDIRAREAACTUAL_{r}}{1000000}</math>

Where WATDEMAND is agricultural water demand, waterperha is the water requirements per hectare of irrigated land (in cubic meters per hectare), and LANDIRAREAACTUAL is the area of irrigated land (in thousand hectares).

Forecasting irrigation water demand thus requires forecasting these four components.

==== Irrigated land ====

Irrigated land is a subset of land equipped for irrigation, which is a subset of irrigable land, which is a subset of cropland (see figure below).

[[File:LandSubsets.jpg|frame|center|Representation of successive subsets of land areas.]]The area of irrigated land is estimated using the following equation:

:<math>LANDIRAREAACTUAL_{r}=slandirareaactualpercent_{r}*LANDIRAREAEQUIP_{r}*\mathbf{landirareaactualm_{r}}</math>

Where LANDIRAREAACTUAL is the area of land irrigated, slandirareaactualpercent is the portion of land equipped for irrigation that is irrigated (sometimes called the ‘utilization intensity’), LANDIRAREAEQUIP is the area of land equipped for irrigation, and landirareaactualm is a parameter that allows the user to exogenously impact the area of irrigated land.

While the area of irrigated land (LANDIRAREAACTUAL) is the variable that directly impacts crop yield, most of dynamics in the irrigation projection come from the formulation for the area of land equipped for irrigation (LANDIRAREAEQUIP). We assume the utilization intensity to remain constant in each country over time, unless otherwise specified by the user.<ref>Wada et al., 2015 identify four potential drivers of the utilization intensity: increased competitiveness (irrigated land shrinks faster than equipped); land may be equipped to reduce drought risk (as a safeguard in bad years); limited water supply; and under poor economic conditions, areas equipped may not be maintained and become unusable. These have not explicitly been included in this model, though some of these drivers are incorporated into the projection of land equipped for irrigation.</ref>

According to the literature, the area of land equipped for irrigation is driven by sub-national factors (mostly biophysical), and national factors (socio-political and governance related) (Neumann et al., 2011; Wada et al., 2016). Wada et al. identify nine potential drivers for the area of land equipped for irrigation: availability of land and water; reliability of water supply and access to water; irrigation impact (achievable yield increase and/or stabilization of yields and reduced variability); agricultural demand; availability of land with rain-fed potential; existing current yield gaps in rain-fed and/or irrigated land; cost; profitability, economic means available and support policies to invest in irrigation; state food security and self-reliance policies (Wada et al., 2015).

Neumann et al., (2011) modelled global irrigation patterns using a multilevel approach, using driving variables at the grid level as well as the national level. At the national level, they identified the following variables as significant: corruption, government effectiveness, GDP, political stability, autocracy, democracy. Many of these variables are correlated, so the authors of that paper used the variables, “government_performance” and “government_type” in their final analysis.<ref>The authors used, “factor analysis with principal component extraction and varimax rotation” to limit the number of variables while maintaining the maximum amount of information. Corruption, effectiveness, GDP, and political stability were found to be correlated with “government_performance” while autocracy and democracy were correlated with “government_type”.</ref> At the grid level, Neumann et al. found the following variables to be significant: slope discharge; humidity; evaporation; evapotranspiration; population density; and access to markets.

Because the IFs system uses countries (or provinces in some cases) as the unit of analysis, we are unable to incorporate indicators at the grid level. To account for these grid-level indicators, we have incorporated data on the area of potentially irrigable land into our model. “Irrigation potential” is defined by AQUASTAT as

Area of land which is potentially irrigable. Country/regional studies assess this value according to different methods. For example, some consider only land resources, others consider land resources plus water availability, others include economical aspects in their assessments (such as distance and/or difference in elevation between the suitable land and the available water) or environmental aspects, etc. If available, this information is given in the individual country profiles. The figure includes the area already under agricultural water management.<ref>AQUASTAT database <http://www.fao.org/nr/water/aquastat/data/popups/itemDefn.html?id=4307></ref>

Using this variable (irrigation potential) as the dependent variable, implicitly includes some of the constraining factors identified above in the analysis. For example, while we do not include land availability and the slope of the land as explicit driving variables, those factors limit the area of potentially irrigable land.

We project the area of land equipped for irrigation using three steps. First, we predict the portion of irrigable land which is irrigated using precipitation. Second, we use the historical growth rate of land equipped for irrigation to drive land equipped for irrigation. Third, we use food price and water price to adjust land equipped for irrigation. There is also an exogenous parameter that allows a user to directly change land equipped for irrigation (landirareaequipm).

First, we use precipitation to directly drive the portion of irrigable land which is irrigated:

:<math>Prediction_{r}=LandIrEquipPortionPotential_{r}*LANDIRAREASAT_{r}</math>

A residual is saved in the first year by dividing the area of land equipped for irrigation by the predicted value:

:<math>LandIrAreaEquipResid_{r}=\frac{slandirareaequip_{r}}{Prediction_{r}}</math>

This residual value is used in all future years to adjust the predicted value.

For the first 20 years of the projection, the historical growth rate of land equipped for irrigation drives land equipped for irrigation, but with a diminishing effect. This effect decreases to zero over 20 years. After 20 years, land equipped for irrigation is driven entirely by the predicted value (using precipitation), water price, and food price. The equation below describes how LANDIRAREAEQUIP is calculated as a weighted average of the predicted value based on the level of precipitation, and the extrapolated value based on the historical growth rate. In the first year of the forecast the value is driven entirely by the historical growth rate, and by year 20 of the forecast, the variable is driven entirely by the regression described above.

If Year < 21 then

:<math>LANDIRAREAEQUIP_{r}=</math>
:<math>\frac{((21-year)*(slandirareaequip_{r}*LandIrAreaEquipGR_{r}))+((year-1)*(Prediction_{r}*LandIrAreaEquipResid_{r}))}{20}</math>

Else

:<math>LANDIRAREAEQUIP_{r}=Prediction_{r}*LandIrAreaEquip_{r}</math>

In all years, the variable is then adjusted based on changes to water price and food price.

:<math>LANDIRAREAEQUIP_{r}=LANDIRAREAEQUIP_{r}</math>
:<math>*((1+\frac{WATPRICE_{r,t=1}-100}{100})*landirareaequipelas_{r})</math>
:<math>*((1+\frac{FPRI_{r,crop}-100}{100})*0.01)</math>

Where WATPRICE is the shadow water price index, FPRI is the shadow food price index for crops, and landirareaequipelas is the elasticity between water price and land equipped for irrigation, which is initialized at -0.15.

And it is then multiplied by a parameter (landirareaequipm, default value of 1).

==== Water requirement per unit of irrigated land (WRCI) ====

The water requirement per unit of irrigated land is the difference between total crop water requirements and the part supplied by precipitation. This is largely driven by the cropping intensity, or the ratio of harvested irrigated crop area over irrigated crop area<ref>Fischer, G., Tubiello, F. N., van Velthuizen, H., & Wiberg, D. A. (2007). Climate change impacts on irrigation water requirements: Effects of mitigation, 1990-2080. Technological Forecasting and Social Change, 74(7), 1083–1107. https://doi.org/10.1016/j.techfore.2006.05.021</ref><ref>Fischer, G., Tubiello, F. N., van Velthuizen, H., & Wiberg, D. A. (2007). Climate change impacts on irrigation water requirements: Effects of mitigation, 1990-2080. Technological Forecasting and Social Change, 74(7), 1083–1107. https://doi.org/10.1016/j.techfore.2006.05.021</ref>[[File:WaterRequirementsPerIrrigatedLand.png|frame|center|Drivers of water requirements per unit of irrigated land]]

Water use per unit of irrigated land in IFs is driven by the shadow water price:

:<math>waterperha_{r}=\frac{WATDEMAND_{r,agriculture}}{LANDIRAREAACTUAL_{r}}*(1+(percentincreaseinprice_{r}*watdemandelas_{r,agricultural}))</math>

Where waterperha is the water requirements per hectare of irrigated land, WATDEMAND is agricultural water demand, LANDIRAREAACTUAL is the area of irrigated land, percentincreaseinprice is the change in water price, and watdemandelas is the elasticity between water price and agricultural water demand, which is -0.1 unless otherwise specified by the user.

=== Municipal water demand ===

Municipal (domestic) water demand is modelled in IFs by multiplying sectoral water intensity (municipal water demand per capita) by the size of the urban population. This is a similar formulation to other global water models like WaterGAP<ref>Alcamo, J., DöLl, P., Henrichs, T., Kaspar, F., Lehner, B., RöSch, T., & Siebert, S. (2003). Development and testing of the WaterGAP 2 global model of water use and availability. Hydrological Sciences Journal, 48(3), 317–337. https://doi.org/10.1623/hysj.48.3.317.45290</ref>, H08<ref>Hanasaki, N., Kanae, S., Oki, T., Masuda, K., Motoya, K., Shirakawa, N., … Tanaka, K. (2008). An integrated model for the assessment of global water resources–Part 2: Applications and assessments. Hydrology and Earth System Sciences, 12(4), 1027–1037.</ref><ref>Shen, Y., Oki, T., Utsumi, N., Kanae, S., & Hanasaki, N. (2008). Projection of future world water resources under SRES scenarios: water withdrawal / Projection des ressources en eau mondiales futures selon les scénarios du RSSE: prélèvement d’eau. Hydrological Sciences Journal, 53(1), 11–33. https://doi.org/10.1623/hysj.53.1.11</ref> and PCR-GLOBWB.<ref>Wada, Y., Wisser, D., & Bierkens, M. F. P. (2014). Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources. Earth System Dynamics, 5(1), 15–40. https://doi.org/10.5194/esd-5-15-2014</ref>

Per capita municipal water use increases as household incomes increase. As incomes increase, people adopt a more water-intensive lifestyle, which eventually saturates.<ref>Alcamo, J., DöLl, P., Henrichs, T., Kaspar, F., Lehner, B., RöSch, T., & Siebert, S. (2003). Development and testing of the WaterGAP 2 global model of water use and availability. Hydrological Sciences Journal, 48(3), 317–337. https://doi.org/10.1623/hysj.48.3.317.45290</ref><ref>Alcamo, J., FlöRke, M., & MäRker, M. (2007). Future long-term changes in global water resources driven by socio-economic and climatic changes. Hydrological Sciences Journal, 52(2), 247–275. https://doi.org/10.1623/hysj.52.2.247</ref> The water sub-module of IFs uses GDP per capita and levels of access to piped water to forecast per capita municipal water use. To account for increased water use efficiency, the IFs model includes the percent of the population living in urban areas as an additional independent variable.

The dependent variable in the equation used to forecast municipal water demand is municipal water demand per capita:

:<math>\frac{WATDEMAND_{municipal,r}}{POPURBAN}</math>

We multiply this term by the size of the urban population (forecasted elsewhere) to get total municipal water demand.

:<math>WATDEMAND_{municipal,r} = \frac{WATDEMAND_{municipal,r}}{POPURBAN} * POPURBAN</math>

The independent variables for forecasting municipal water demand per capita are: GDP per capita (PPP); percent of the population living in urban areas; and the percentage of the population served with piped water.

Where municipal water demand per capita (WATDEMANDPC) is calculated using the following equation.

:<math>\frac{WATDEMAND_{municipal,r}}{POPURBAN} = x_1(ln(GDPPCP_r)) + x_2(ln(WATSAFE_{piped,r})) - x_3(ln(UrbanPercentSmooth_r))</math>

Where GDPPCP represents GDP per capita at purchasing power parity (PPP), WATSAFE  represents the portion of the population with access to piped water and UrbanPercentSmooth represents the portion of the population living in urban areas but decreased for countries which are rapidly urbanizing. That is why there is a ‘smoothing’ of that variable for this equation. The coefficients of the first two independent variables (“<math>x_1</math>” and “<math>x_2</math>”) are positive—as when GDP per capita and access to piped water increase, water use per capita will also increase. The third coefficient “<math>x_3</math>” is negative because higher levels of urbanization are negatively correlated with water use per capita. All three of these independent variables are statistically significant at the global level.

While the portion of a country’s population living in urban areas is negatively correlated with water use per capita, the rate of urbanization is not necessarily negatively correlated with water use per capita. Countries with a large portion of their population living in urban areas might have water use efficiency infrastructure and policies in place, explaining the lower water use per capita. It takes time to put these water-conservation measures in place however.

For countries where the urbanization rate is greater than 1%:

:<math>UrbanPercentSmooth_r = 1.01 * \frac{(92 - UrbanPercent_r)^.75}{92}</math>

This ensures that rapid urbanization does not lead to rapid water conservation. This is of particular concern for China, where IFs forecasts rapid urbanization over the next decades.

Municipal water use per capita (urban) increases in all income groups[[File:MunDemandPerCapitaChart.jpg|frame|right|625x345px|Municipal water demand per capita for urban populations.]].

=== Industrial water demand ===

Industrial water demand is calculated as the sum of water demand for thermo-electric power generation (cooling) and water demand for the manufacturing sector.

==== Thermo-electric power generation ====

To forecast water consumption for thermo-electric power generation, we multiply total non-renewable electricity generation (in kWh) by a calculated value of water consumption per kWh. Water use per kWh is calculated as a function of both water scarcity within a country and the GDP per capita of the country. The more water-scarce a country is, the lower the desired water use per kWh. The actual water use per kWh is determined by this desired value together with the GDP per capita (PPP) of the country.

Water scarcity is calculated as the total water supply divided by the country’s population. This water-scarcity figure is then linearly mapped onto the range of [0.4 – 2.0]. This is a reasonable range for water use per kWh in a country (in terms of liters per kWh).<ref>National Renewable Energy Laboratory, NREL, Consumptive water use for U.S. power production, NREL/TP-550-33905. <http://www.nrel.gov/docs/fy04osti/33905.pdf></ref>

GDP per capita (PPP) for each country is linearly mapped onto the range [0-1] to calculate an ‘AbilityToAffordEfficiency’ variable. Actual water use per unit of power generation (liters/kWh) is then calculated as:

:<math>ActualWaterUsePerkWh_r = 2 - (AbilityToAffordElecEfficiency_r * (2-DesiredWaterUsePerkWh_r))</math>

This water use per kWh variable is then multiplied by non-renewable electricity generation to calculate total water consumption for electricity generation.

:<math>IndustrialWaterDemandForElectricity_r = ActualWaterUsePerkWh_r * NonRenewablePowerGeneration_r</math>

Where NonRenewablePowerGeneration is calculated as:

<math>INFRAELECPROD_r - ElecRenew</math>

Where ElecRenew’ is calculated as

:<math>(ENP_{renew,r} + ENP_{hydro,r})* 1699.41 * 1000</math>

Renewable energy production is forecast in terms of billion barrels oil equivalent so the model must convert to gigawatt hours and then to kWh before it is subtracted from INFRAELECPROD. Industrial water demand for electricity is thus a function of non-renewable electricity generation, water scarcity, and GDP per capita (PPP).

==== Manufacturing ====

Although we do not have data on water demand for the manufacturing sector, using the calculations above for industrial water demand for electricity generation, we can estimate the portion of industrial water demand that is required for the manufacturing sector:

:<math>IndustrialWaterDemand_{manufacturing,r} = WATDEMAND_{industrial,r} - IndustrialWaterDemand_{electricity,r}</math>

[[File:ManufacturingScatter.png|frame|right|425x345px|Manufacturing water withdrawal (km3) versus value added from the manufacturing sector (US$ billions).]]We use the size of a country’s manufacturing sector to drive industrial water demand for manufacturing. There is a correlation between this calculated industrial water demand for manufacturing and the size of the country’s manufacturing sector.

Industrial water demand for manufacturing is calculated by using the annual growth rate in the size of the manufacturing sector, adjusted by an elasticity of 0.45.

:<math>IndustrialWaterDemand_{manufacturing,r,t} = IndustrialWaterDemand_{manufacturing,r,t-1} * (\frac{VADD_{manufacturing,r,t}}{VADD_{manufacturing,r,t-1}})^{-.45}</math>

This elasticity figure (0.45) was calculated using country-specific elasticities between manufacturing value added growth rates and manufacturing water demand growth rates weighted by GDP:

:<math>\frac{\sum_{r=1}^{All Countries}{\frac{log(ManufacturingWaterWithdrawalGR_r)}{log(ManufacturingValueAddedGR_r)}*GDP_r}}{GlobalGDP} </math>

Where ManufacturingWaterWithdrawalGR is the compound annual growth rate for industrial water demand for the manufacturing sector and ManufacturingValueAddedGR is the compound annual growth rate for value added from the manufacturing sector. We log both growth rates because the elasticity is calculated as an exponent rather than a multiplicative factor.

This new formulation means that industrial water use per unit of value added from the manufacturing sector decreases for all income groups.

[[File:ManufacturingWaterDemandPerUnit.jpg|frame|right|525x295px|Industrial water demand per unit (US$ billions) of value added from the manufacturing sector.]]

Using the equations described above, we calculate an “expected” water demand for each sector for each country. Using data from the pre-processor we are able to calculate residuals—the difference between the expected water demand and the actual water demand. <math>Residual_{r,s} = ExpectedWaterDemand_{r,s} - WATDEMAND_{r,s}</math>

These residuals do not change over time. We include efficiency parameters ('''waterefficiencym''') on each of the water demand variables so that the user may decrease water demand in any sector.

Water demand is calculated the same way in all other years. We recalculate the expected level of water demand for each sector for each country using the driving variable, and then apply the residual. We also include the efficiency parameter multipliers in these calculations.

:<math>WATDEMAND_{r,w} = (ExpectedWatDemand_{r,w} - Residual_{r,w}) * \mathbf{waterefficiencym_{r,s}}</math>

== Water supply ==

To model water supply in IFs we use the sum of surface water withdrawals, renewable groundwater withdrawals, non-renewable (fossil) groundwater withdrawal, treated and directly reused wastewater, and desalinated water. Treated wastewater that is not directly reused is added to surface water yield. This constitutes secondary water.

=== Surface water and renewable groundwater ===

We use total renewable water resources as a constraint on exploitable renewable water resources (separated into surface water and groundwater) and exploitable renewable water resources as a constraint on water withdrawals. The diagram below shows the conceptual framework for forecasting surface water withdrawals. Withdrawals are driven by economic growth (GDP growth rate) and the [[Water_model_documentation#Water_price_index|water price index]]. Withdrawals are bound however, by the exploitable limit. Exploitable renewable surface water resources can increase due to “secondary water” – water that is treated but not directly reused. Exploitable surface water is bounded however, by the total renewable surface water.[[File:SurfaceAndGroundResources.jpg|frame|right|625x445px|Conceptual model for forecasting water withdrawals when withdrawals do not exceed exploitable limit.]]

We take our data for total renewable water resources, exploitable water resources, and water withdrawals from AQUASTAT. AQUASTAT defines total renewable water resources (TRWR) as, “The sum of internal renewable water resources (IRWR) and external renewable water resources (TRWR). It corresponds to the maximum theoretical yearly amount of water available for a country at a given moment”.<ref>AQUASTAT database glossary <http://www.fao.org/nr/water/aquastat/data/glossary/search.html></ref>

We use WATRESTOTALRENEW as a limit on the increase in WATRESEXPLOITRENEW. Exploitable water resources are usually a portion of total resources. This is because exploitable resources consider several restrictions on the use of water resources. The AQUASTAT data handbook defines these restrictions in terms of technical-economic criteria, environmental criteria, and geopolitical criteria.<ref>Margat, Jean. Et al. Key water resources statistics in AQUASTAT, 2005. <http://doc.abhatoo.net.ma/doc/IMG/pdf/key_water_resources_statistics_in_aquastat.pdf> pg. 11</ref>

AQUASTAT defines exploitable water resources as, “Exploitable water resources (also called manageable water resources or water development potential) are considered to be available for development, taking into consideration factors such as: the economic and environmental feasibility of storing floodwater behind dams, extracting groundwater, the physical possibility of storing water that naturally flows out to the sea, and minimum flow requirements (navigation, environmental services, aquatic life, etc). Methods to assess exploitable water resources vary from country to country”.<ref>AQUASTAT definition of 'irrigation potential' <http://www.fao.org/nr/water/aquastat/data/popups/itemDefn.html?id=4307></ref>

Exploitable water resources do however include secondary water so it can sometimes exceed total renewable water resources. This is rare but an important factor to keep in mind when forecasting. Thus, when we use total renewable resources as a limit on exploitable resources, we must remember to subtract secondary water from exploitable water resources first.

Since total renewable water resources does not account for the overlap between surface water and groundwater, we must recalculate total renewable water resources by subtracting this overlap. We then recalculate total renewable surface water and total renewable groundwater using this adjusted total.

:<math>WATRESTOTALRENEW_{r,w} = \frac{WATRESTOTALRENEW_{r,w}}{WATRESTOTALRENEW(Overlap)}*WATRESTOTALRENEW(No Overlap)</math>

It is not possible, however, for exploitable water resources (excluding secondary water) to reach the TRWR limit. While exploitable water resources can increase, we assume that it cannot grow by more than 20%. We also assume that exploitable water resources cannot grow by more than .5% per year and that only one fortieth of remaining resources can be extracted in any year. Thus, the equation for exploitable water resources is:

:<math>WATRESEXPLOITRENEW_{r,w}=</math> <math>Min(WATRESEXPLOITRENEW_{t-1,w}*1.005,((\frac{MaxExploitRenew_w-WATRESEXPLOITRENEW_{t-1,w}}{40}))+WATRESEXPLOITRENEW_{t-1,w}))</math>

Where

:<math>MaxExploitRenew_{w,r}=\mathbf{iwatresexploitrenew_{w,r}}*1.2</math>

iwatresexploitrenew is the exploitable renewable water resources in the first year. The subscript “w” represents that the variable is dimensioned in terms of surface and ground water. We also include a parameter, watresexploitrenewm on the total volume of exploitable water resources so that the user may increase or decrease this supply over time. After this growth rate is applied to exploitable surface water resources the secondary component is added to WATRESEXPLOITRENEW (surface). Secondary water is the total volume of treated wastewater that is not directly reused.

To forecast surface and groundwater withdrawals, we separate each country into one of two cases: countries where water withdrawals exceed the exploitable limit, and countries where water withdrawals do not exceed the exploitable limit. Surface water withdrawals and groundwater withdrawals are forecast separately. For example, a country can be overexploiting their surface water resources while groundwater withdrawals remain below the exploitable limit.

If surface water withdrawals are less than exploitable surface water resources, then surface water withdrawals are driven by a growth rate of 20 percent of the country’s latest GDP growth rate. This growth rate however cannot force surface water withdrawals to exceed one seventh of the remaining exploitable surface water (total exploitable surface water minus current surface water withdrawals).

The equation below describes the forecast equation for surface water withdrawals. It is calculated as the minimum of two terms: the previous year’s surface water withdrawals multiplied by 20 percent of the GDP growth rate of the country, and one seventh of the remaining exploitable potential plus the current level of withdrawal.

:<math>WATERWITHDRAWAL_{r,surface,t+1}=</math> <math>Min(WATERWITHDRAWAL_{r,surface,t}*1+0.2*IGDP_r),\frac{RemainingPotentialExploitable}{7}+WATERWITHDRAWAL_{r,surface,t})</math>

In the second case, where surface water withdrawals exceed the exploitable surface water limit, they are forced to converge to the exploitable limit over a 50-year time horizon. This time horizon is a parameter in the model (waterrenewconverge) that can be adjusted by the user.

:<math>WATERWITHDRAWAL_{r,surface,t+1}=</math> <math>WATERWITHDRAWAL_{r,surface,t}-(\frac{WATERWITHDRAWAL_{r,surface,t}-WATRESEXPLOITRENEW_{r,surface}}{\mathbf{waterrenewconverge}_{r,surface}})</math>

Where '''waterrenewconverge''' is the 50-year default convergence time.[[File:WaterwithdrawWhenWDExceedLimit.jpg|frame|right|645x425px|Conceptual model for forecasting water withdrawals when withdrawals exceed exploitable limit.]]

The exact same logic described above applies to groundwater withdrawals, and the parameter ('''waterrenewaconverge''') is dimensioned by surface and groundwater. In the case of groundwater however, secondary water does not increase the exploitable limit – we assume treated wastewater that is not directly reused is released back into the system in the form of surface water.

If groundwater withdrawals are less than the exploitable limit:

:<math>WATERWITHDRAWAL_{r,ground,t+1}=</math> <math>Min(WATERWITHDRAWAL_{r,ground,t}*(1+0.2*IGDP_r),\frac{RemainingPotentialExploitable}{7}+WATERWITHDRAWAL_{r,ground,t})</math>

If groundwater withdrawals exceed the exploitable limit:

:<math>WATERWITHDRAWAL_{r,ground,t+1}=</math> <math>WATERWITHDRAWAL_{r,ground,t}-\frac{WATERWITHDRAWAL_{r,ground,t}-WATRESEXPLOITRENEW_{r,ground}}{\mathbf{waterrenewconverge_{r,ground}}}</math>

Both surface and groundwater withdrawals are adjusted based on the change in the water price index. A 0.5 elasticity is applied to the change in the water price index, which is then applied to both surface water withdrawals and groundwater withdrawals. Again, however, neither is permitted to grow faster than one seventh of the remaining exploitable potential.

:<math>WATERWITHDRAWAL_{r,surface}=</math> <math>Min(WATERWITHDRAWAL_{r,surface}*(1+(IncreaseInPrice*0.05)),</math><math>{\frac{WATERWITHDRAWAL_{r,surface}-WATRESEXPLOITRENEW_{r,surface}}{7}})+WATERWITHDRAWAL_{r,surface}</math>

The same logic and elasticity is applied to groundwater:

:<math>WATERWITHDRAWAL_{r,ground}=</math> <math>Min(WATERWITHDRAWAL_{r,ground}*(1+(IncreaseInPrice*0.05)),</math><math>{\frac{WATERWITHDRAWAL_{r,ground}-WATRESEXPLOITRENEW_{r,ground}}{7}})+WATERWITHDRAWAL_{r,ground}</math>

=== Wastewater ===

WASTEWATERSUPPLY is a multidimensional variable in the IFs model. The three dimensions are Produced, Treated, and TreatedAndReused. Most of our data on wastewater comes from the Aquastat database so we use their conceptual framework to model and forecast wastewater.<ref>Aquastat Wastewater Conceptual Framework <http://www.fao.org/nr/water/aquastat/catalogues/Wastewater_Methodology_paper_20121130.pdf></ref> Total produced wastewater is forecast in IFs as a portion of total municipal water demand. This relies on the assumption that wastewater that is reused ''among'' sectors comes primarily from collected municipal wastewater. Wastewater that is reused within a sector (for example, reuse of industrial wastewater within the same industrial factory) does not increase the water supply but, rather, decreases water demand in that particular sector.

Since we take the most recent data for each of these series they often do not reconcile. For example, treated wastewater may exceed produced wastewater, which is impossible. If a country’s volume of treated wastewater exceeds their supply of produced wastewater we assume that they treat 95% of their produced wastewater.

If we do not have data for a country’s volume of produced wastewater, we estimate this value using their municipal water demand. If we do not have data on the volume of treated wastewater, we estimate this value using a portion of their produced wastewater. The portion of a country’s produced wastewater that is treated is driven by GDP per capita. If we do not have data on the portion of treated wastewater that is directly reused, we estimate this value using a global average of 66%. Parameters are in IFs on both the portion of wastewater that is treated and the portion of treated wastewater that is directly reused: '''wastewaterportiontreated''' and '''wastewaterportiontreatedreused.'''

A portion of treated wastewater is directly reused and a portion of treated wastewater is discharged directly back into the water system. This discharge is considered “secondary water” and is added to exploitable surface water resources. Likewise, a portion of non-treated wastewater is directly re-used, usually by the agricultural sector, and a portion is discharged. The portion of non-treated wastewater that is not directly re-used is not added to exploitable surface water resources however. We do not yet have forecasts in IFs for the amount of non-treated wastewater that is directly reused. This is something that needs to be added to the model as some countries, like Mexico, use a large volume of non-treated municipal wastewater to irrigate crops.

Wastewater that is treated but not directly reused is then added to exploitable surface water resources.

:<math>WATRESEXPLOITRENEW_{surface}=</math><math>WATRESEXPLOITRENEW_{surface}+WASTEWATERSUPPLY_{treated}-WASTEWATERSUPPLY_{TreatedAndReused}</math>

Wastewater supply is calculated for all years using the same regressions described above for all years. The residuals for each of the equations do not change. The only way to increase supply of wastewater is to adjust municipal demand (which will affect total wastewater produced) or to adjust the wastewaterportiontreated parameter. This parameter is initialized as 1 but can be adjusted by the user. If the user does not change this parameter, the portion of produced wastewater that is treated will be driven by GDP per capita. If the user changes this to a value between 0 and 1 then that is the portion of produced wastewater that is treated.

If '''wastewaterportiontreated''' != 1 Then

:<math>WASTEWATERSUPPLY_{treated,r}=WASTEWATER_{produced,r}*\mathbf{wastewaterportiontreated_r}</math>

Else

:<math>WASTEWATERSUPPLY_{treated,r}=WASTEWATERSUPPLY_{produced,r}*TF(GDPPCP_r)</math>

We use a log function of GDP per capita to forecast the portion of wastewater that is treated.

Treated wastewater is also affected by changes in the water price index, using a 0.5 elasticity. The equation below shows the water price index effect on treated wastewater. Treated wastewater is also bound by produced wastewater – a country cannot treat more wastewater than is produced.

:<math>WASTEWATERSUPPLY_{r,treated,t+1}=</math><math>Min(WASTEWATERSUPPLY_{r,produced},WASTEWATERSUPPLY_{r,treated,t}*(1+(IncreaseInPrice*0.05))</math>

==== 3.2.3 Non-renewable (fossil) water withdrawals ====

We take fossil water resources data from 3 sources: FAO, UNESCO, and a peer-reviewed journal article from IOPScience<ref>MacDonald, A. M. Quantitative maps of groundwater resources in Africa. Environmental Research Letters.</ref>. The IOP article however, does not differentiate between renewable groundwater resources and fossil groundwater resources so we subtract renewable groundwater resources from total fossil water resources.

For some countries, we have data on fossil water withdrawals but not fossil water resources. This creates problems in the forecast since future water withdrawals are based on remaining fossil water resources. For these countries, we estimate that their total fossil water resources are 10 times their current fossil water withdrawals.

:<math>WATERRESFOSSIL = 10*WATERWITHDRAWAL_{fossil}</math>

We model fossil water in IFs using a stock and flow dynamic. Total fossil water resources are used as a stock and fossil water withdrawal is the flow. Fossil water withdrawals increase at a one percent growth rate. This increase is bounded by the total supply of fossil water remaining. A country cannot extract more than one seventh of their remaining fossil water resources in any year. This number is taken from current fossil water extraction rates. The highest rate of extraction globally, occurring in the Nubian Sandstone Aquifer, is about one seventh of remaining resources.<ref>UNESCO Non-Renewable Groundwater Resources. 2006. < http://portalsostenibilidad.upc.edu/archivos/fichas/informes/aguassubterraneasnorenovables.guiagestion..pdf></ref>

:<math>WATERWITHDRAWAL_{t+1,f}=</math><math>Min(WATERWITHDRAWAL_{t,f}*1.01,\frac{WATERRESFOSSIL}{7})</math>

The subscript f represents fossil water.

==== 3.2.4 Desalinated water ====

Desalinated water grows at an 8% growth rate, but this growth rate diminished over time. Desalinated water cannot decrease faster than 2% per year. Desalinated water increases based on an 8 percent growth rate ('''waterdesalinatedgr''') which can be changed by the user. Growth in desalinated water is also adjusted based on the change in the water price index, using a 0.5 elasticity.

:<math>WATERDESALINATED_{r,t+1}=</math> <math>Min(WATERDESALINATED_{r,t}*(1+(IncreaseInPrice*0.05)),WASTERDESALINATED_{r,t}*(1+(\frac{\mathbf{waterdesalinated_r}}{100})))</math>

Total water supply (WATERTOTALSUPPLY) is then calculated as the sum of these 5 water sources i.e. reused water, desalinated water, surface water, ground water, and fossil water.

:<math>WATERTOTALSUPPLY=WASTEWATER_{TreatedAndReused}+DESALINATEDWATER+</math>
:<math>WATERWITHDRAWAL_{Surface}+WASTERWITHDRAWAL_{Ground}+WATERWITHDRAWAL_{FossilGround}</math>

Because of the way we define water supply, demand can exceed supply of water resources. This usually means that the country is over exploiting their resources. This means that the country is using more surface water then their available yield, or withdrawing groundwater faster than the recharge rate. There are currently 23 countries that are over exploiting their water resources on a national level. It should be noted however, that since we are only looking at the national scale, local over-exploitation is not captured. This is particularly relevant to China and India—countries where over-exploitation is known to occur but not captured in our analysis. The top 5 countries in terms of national water exploitation are: Turkmenistan, Singapore, Egypt, Uzbekistan, and Syria, respectively.<ref>Singapore imports water from Malaysia. Water imports are not yet captured in the model.</ref>

While we do not forecast water withdrawal by source (surface, ground, and fossil ground), we have the data in IFs. This will be useful to forecast in the future so that we can determine the type over-exploitation that is occurring in water scarce countries.

We pull in data on surface water withdrawal, groundwater withdrawal, and total water withdrawal from Aquastat. Since total water withdrawal is the sum of surface water withdrawal and groundwater withdrawal, we can estimate surface water withdrawal if we have total water withdrawal and groundwater withdrawal. Likewise, if we have total water withdrawal and groundwater withdrawal we can estimate surface water withdrawal. Since, however, Aquastat does not differentiate between renewable groundwater withdrawal and fossil groundwater withdrawal we must first subtract fossil water withdrawal from groundwater withdrawal using other data.

:<math>WATERWITHDRAWAL_{Ground}=</math> <math>WATERWITHDRAWAL_{Ground}-WATERWITHDRAWAL_{FossilGround}</math>

If we have data on total water withdrawals but not surface water withdrawals and groundwater withdrawals then we estimate using global averages. We assume that 67% of a country’s water withdrawals come from surface water and 33% come from groundwater. If we do not have data on total water withdrawals we assume that a country’s total water withdrawals are equivalent to their total water demand. We then use the same global averages to determine which portion of this demand is met by surface water and which portion is met by groundwater. We do not yet reconcile total water demand with total water withdrawals, though, theoretically, they should be equivalent.

== Water price index ==

The water price index is initialized at 100 in the first year of the model and changes year-to-year as a function of differences between supply and demand, using a proportional-integral-derivative controller (PID controller). The PID controller uses two terms to adjust the water price index: the integral or absolute distance of the system from the target, and the derivative, the change in value of the system from the previous year. In this case, the integral term is the absolute difference between the demand to supply ratio and 1 (the demand supply ratio in the first year) and the derivative term is the change in the demand to supply ratio from the previous year.

The following equation describes the four terms that are used by the PID controller to calculate the price index adjustment: the first term is the absolute change in the demand-supply ratio (the integral), the second term is the change in the demand-supply ratio from the previous year (the derivative), the third term is the impact that the integral has on the price index (0.3 but adjustable by user), and the fourth term is the impact that the derivative has on the price index (0.6 but adjustable by user).

<math>PriceAdjustment_r=PID(DemandSupplyRatio_{r,t}-1,(DemandSupplyRatio_{r,t}-DemandSupplyRatio_{r,t-1}),</math> <math>ImpactofIntegralOnPrice,ImpactOfDerivativeonPrice)</math>

The change in the price index is then used to adjust water demand for each sector and water supply from each source.

== Forward linkages ==

The water model directly affects the agricultural model through irrigation – irrigated land is more productive in terms of yield than non-irrigated land. We calculate the effect of irrigation on yield in three steps. First, we calculate the area of irrigated land and the area of non-irrigated land in the first year. Second, we use the current year’s yield, the areas of land irrigated and not irrigated in the first year, and the assumption that irrigated land is 2.35 times more productive than non-irrigated land, to calculate irrigated yield and non-irrigated yield (Evans & Sadler, 2008)<ref>Evans, R. G., & Sadler, E. J. (2008). Methods and technologies to improve efficiency of water use. Water Resources Research, 44(7). https://doi.org/10.1029/2007WR006200</ref>. Third, we use the current year’s area of irrigated land, and the two yields calculated in step 2, to calculate the current year’s yield.

The area of irrigated land and non-irrigated land is calculated in the first year using the following equation:

:<math>IrrigatedCropland_{r,t=1}=LANDIRAREAACTUAL_{r,t=1}</math>
:<math>NonIrrigatedCropland_{r,t=1} = (LD_{r,t=1} * 1000) - IrrigatedCropland_{r,t=1}</math>

We then calculate the yield of irrigated land and non-irrigated land. Yield is a function of many other variables from the agricultural model (see figure 4). The effect that irrigation has on yield described below occurs after all of the other yield calculations.

To calculate the yield of non-irrigated land we use the following equation. This equation relies on the assumption that irrigated land is 2.35 times more productive than non-irrigated land.

:<math>NonIrrigatedYield_{r}=\frac{Ildc_{r}*1000*YL_{r}}{NonIrrigatedCropland_{r}+(2.35*IrrigatedCropland_{r})}</math>

Where NonIrrigatedYield is the yield (tons per hectare) of non-irrigated land, Ildc is total cropland, and YL is total yield. We then calculate irrigated yield as:

:<math>IrrigatedYield_{r}=NonIrrigatedYield_{r}*2.35</math>

Now that we have both irrigated yield and non-irrigated yield, we can calculate yield as the weighted average of irrigated yield and non-irrigated yield:

:<math>YL_{r}=\frac{((slandirareaactual_{r}*IrrigatedYield_{r})+(NonIrrigatedCropland_{r}*NonIrrigatedYield_{r}))}{Ildc_{r}*1000}</math>

=== Legacy forward linkage ===

There is a switch (watertoyieldswitch) that can be set to 1, which allows water availability to directly impact agricultural yields.

:<math>YL_{r}*(1+(\frac{(adjustedWaterPerHA-waterPerHA)}{waterPerHA}))^{waterToYieldElasticity}</math>

Where YL<ref>We actually use syl here, which is the variable YL from the previous year. This is because YL is not yet calculated and cannot be used at this point in the code.</ref> is agricultural yield, adjustedWaterPerHA is the agricultural water availability per HA of irrigated land after adjusting for the change in the price index, and waterPerHA is the agricultural water availability per HA of irrigated land before adjusting for the price index.

waterToYieldElasticity is set to 1.

This linkage was built before the connection from irrigation to yield was in place, but it is no longer necessary as water scarcity now affects yield through irrigation.

= Next steps =

*Incorporate changes in crop land in the forecast for irrigated land and non-irrigated land.
*Include direct use of non-treated wastewater.
*More explicitly differentiate between “consumption” and “withdrawals”. Most other water models differentiate between the two.<ref>Wada, Y., Flörke, M., Hanasaki, N., Eisner, S., Fischer, G., Tramberend, S., … Wiberg, D. (2016). Modeling global water use for the 21st century: the Water Futures and Solutions (WFaS) initiative and its approaches. Geoscientific Model Development, 9(1), 175–222. https://doi.org/10.5194/gmd-9-175-2016</ref>
*Include “livestock” as a sub-sector of agricultural water demand.
*Refine elasticities on demand and supply from price.
*Refine elasticity between water price and irrigation.

= References =

<references />

Joint Monitoring Programme, WHO/UNICEF

2023-02-17T01:06:08Z

Fouad.Alkhouri:

=== '''WHO/UNICEF Joint Monitoring Program: Water Supply and Sanitation''' ===

The water and sanitation series is developed using data from the WHO/UNICEF Joint Monitoring Program for water supply and sanitation (JMP). The JMP reports country, regional and global estimates of progress on drinking water, sanitation and hygiene (WASH) from 2000 to 2015. 

The WSS JMP dataset provides substantial information on water, sanitation and hygiene, broken into national, urban and rural subgroups. Within these subgroups, global estimates are provided that show the proportion of a country's population using improved and unimproved access to water, sanitation and hygiene. For the water dataset, IFs draws primarily on...... For the sanitation dataset, IFs uses the sanitation estimates to break the data down into the proportion of population (%) and the amount of people within a population. To construct these data series, IFs draws primarily on the population served with improved sanitation, unimproved sanitation, other unimproved sanitation, shared sanitation, and open defication sanitation.

== '''Instructions on Importing Water Data into IFs''' ==

This section explains the methodology to be followed when importing WSS JMP  water supply data into IFs. It also describes certain problems that a user might face in interpreting and understanding the data.

'''Source: '''Water supply data is published by the WHO/UNICEF JMP.

'''Country List to be Used:''' WSS JMP

'''Types of Data: '''The following categories of data need to be imported into IFs:

'''Issues in the data and their resolution:'''

== '''Instructions on Importing Sanitation Data into IFs''' ==

This section explains the methodology to be followed when importing WSS JMP sanitation data into IFs. It also describes certain problems that a user might face in interpreting and understanding the data.

'''Source: '''Sanitation data is published by the WHO/UNICEF JMP. The data is available at the website [https://washdata.org/data.&nbsp https://washdata.org/data]

'''Country List to be Used:''' WSS JMP

'''Types of Data: '''The following categories of data need to be imported into IFs:

#'''WSSJMPSanitationTotal%Improved'''- Describes the proportion of the total population served with improved sanitation (%). Sum of National Latrines, Septic Tanks, Sewer Connections.
#'''WSSJMPSanitationTotal%Unimproved'''- Describes the proportion of the total population served with Unimproved Sanitation (%). Total Unimproved Sanitation is the sum of Open Defecation, Other Unimproved, and Shared sanitation access.
#'''WSSJMPSanitationTotal%OtherUnimproved'''- Describes the proportion of the total population with Other Unimproved Sanitation (%). National Unimproved
#'''WSSJMPSanitationTotal%Shared-''' Describes the proportion of the total population served with Shared Sanitation (%). National Limited (shared)
#'''WSSJMPSanitationTotal%OpenDefecation'''- Describes the proportion of the total population served with Open Defecation Sanitation (%). National Open Defecation
#'''WSSJMPSanitationUrban%Improved-''' Describes the proportion of the urban population served with Improved Sanitation (%). Sum of National Latrines, Septic Tanks, Sewer Connections.
#'''WSSJMPSanitationUrban%Unimproved-''' Describes the proportion of the urban population served with Unimproved Sanitation (%). Urban Unimproved Sanitation is the sum of Open Defecation, Other Unimproved, and Shared sanitation access.
#'''WSSJMPSanitationUrban%OtherUnimproved-''' Describes the proportion of the urban population with Other Unimproved Sanitation (%). National Unimproved
#'''WSSJMPSanitationUrban%Shared-''' Describes the proportion of the urban population served with Shared Sanitation (%). National Limited (shared)
#'''WSSJMPSanitationUrban%OpenDefecation-''' Describes the proportion of the urban population served with Open Defecation Sanitation (%). National Open Defecation
#'''WSSJMPSanitationRural%Improved-''' Describes the proportion of the rural population served with Improved Sanitation (%). Sum of National Latrines, Septic Tanks, Sewer Connections.
#'''WSSJMPSanitationRural%Unimproved-''' Describes the proportion of the rural population served with Unimproved Sanitation (%). Rural Unimproved Sanitation is the sum of Open Defecation, Other Unimproved, and Shared sanitation access.
#'''WSSJMPSanitationRural%OtherUnimproved-''' Describes the proportion of the rural population with Other Unimproved Sanitation (%). National Unimproved
#'''WSSJMPSanitationRural%Shared-''' Describes the proportion of the rural population served with Shared Sanitation (%). National Limited (shared)
#'''WSSJMPSanitationRural%OpenDefecation-''' Describes the proportion of the rural population served with Open Defecation Sanitation (%). National Open Defecation
#'''WSSJMPSanitationTotalPopImproved-''' Describes the total population served with Improved Sanitation. Calculated by the population*(Sum of Latrines, Septic Tanks, Sewer Connections/100).
#'''WSSJMPSanitationTotalPopUnimproved-''' Describes the total population served with Unimproved Sanitation. Total Unimproved Sanitation is the sum of Open Defecation, Other Unimproved, and Shared sanitation access.
#'''WSSJMPSanitationTotalPopOtherUnimproved-''' Describes the total population with Other Unimproved Sanitation. Calculated by the population*(National Unimproved/100).
#'''WSSJMPSanitationTotalPopShared-''' Describes the total population served with Shared Sanitation. Calculated by the population*(National Limited (shared)/100).
#'''WSSJMPSanitationTotalPopOpenDefecation-''' Describes the total population served with Open Defecation Sanitation. Calculated by the population*(National Open Defecation/100)
#'''WSSJMPSanitationUrbanPopImproved-''' Describes the urban population served with Improved Sanitation. Calculated by the population*(Urban Pop/100)*(Sum of Latrines, Septic Tanks, Sewer Connections/100)
#'''WSSJMPSanitationUrbanPopUnimproved-''' Describes the urban population served with Unimproved Sanitation. Urban Unimproved Sanitation is the sum of Open Defecation, Other Unimproved, and Shared sanitation access.
#'''WSSJMPSanitationUrbanPopOtherUnimproved-''' Describes the urban population with Other Unimproved Sanitation. Calculated by the population*(Urban Pop/100)*(Urban Unimproved).
#'''WSSJMPSanitationUrbanPopShared-''' Describes the urban population served with Shared Sanitation. Calculated by the population*(Urban Pop/100)*(Urban Limited (shared)/100).
#'''WSSJMPSanitationUrbanPopOpenDefecation-''' Describes the urban population served with Open Defecation Sanitation. Calculated by the population*(Urban Pop/100)*(Urban Open Defecation/100).
#'''WSSJMPSanitationRuralPopImproved-''' Describes the rural population served with Improved Sanitation. Calculated by the population*(100-Urban Pop)*(Sum of Latrines, Septic Tanks, Sewer Connections / 100).
#'''WSSJMPSanitationRuralPopUnimproved-''' Describes the rural population served with Unimproved Sanitation. Rural Unimproved Sanitation is the sum of Open Defecation, Other Unimproved, and Shared sanitation access.
#'''WSSJMPSanitationRuralPopOtherUnimproved-''' Describes the rural population with Other Unimproved Sanitation, Calculated by the population*(100-Urban Pop/100)*(Rural Unimproved/100).
#'''WSSJMPSanitationRuralPopShared-''' Describes the rural population served with Shared Sanitation. Calculated by the population*(100-Urban Pop/100)*(Rural Limited (shared)/100).
#'''WSSJMPSanitationRuralPopOpenDefecation-''' Describes the rural population served with Open Defecation Sanitation. Calculated by the population*(100-Urban Pop/100)*(Rural Open Defecation/100)

'''Issues in the data and their resolution:'''

WHO Tobacco Free Initiative (TFI)

2023-02-17T01:04:29Z

Fouad.Alkhouri: removed dead links

The Tobacco Free Initiative of the World Health Organization is part of the Non-communicable Diseases and Mental Health cluster at WHO headquarters in Geneva, Switzerland. TFI is divided into three separate units:

*National Capacity Building, which helps countries to enhance their ability to resist the epidemic of tobacco and implement the WHO Framework Convention on Tobacco Control
*Comprehensive Information Systems for Tobacco Control, whose responsibilities encompass surveillance of the global tobacco epidemic
*Tobacco Control Economics, which advises countries on taxation as an instrument of tobacco control policy and other economic policies to control tobacco

'''TFI’s activities are based around four key products:'''

*All World No Tobacco Days
*WHO report on the global tobacco epidemic
*WHO FCTC Health Warnings Database
*Tobacco control country profiles

The International Futures System utilizes data from WHO report on the global tobacco epidemic and the WHO FCTC Health Warnings Database.

'''WHO Framework Convention on Tobacco Control'''

To address the global burden of tobacco, the World Health Assembly in 2003 unanimously adopted the World Health Organization Framework Convention on Tobacco Control (WHO FCTC) with the objective of protecting present and future generations from the devastating health, social, environmental and economic consequences of tobacco consumption and exposure. Ratified by 180 Parties as at March 2015, the WHO FCTC currently covers about 90% of the world's population. It is a legally binding treaty which commits Parties to the Convention to develop and implements a series of evidence-based tobacco control measures to regulate tobacco industry marketing activities and sales reach, reduce the demand for tobacco and provide agricultural alternatives for those involved in growing and producing tobacco. To assist countries to fulfill their WHO FCTC obligations, in 2008 WHO introduced a package of six evidence-based tobacco control demand reduction measures that are proven to reduce tobacco use. These measures known as the MPOWER package measures reflect one or more provisions of the WHO FCTC. MPOWER refers to M: Monitoring tobacco use and prevention policies; P: Protecting people from tobacco smoke; O: Offering help to quit tobacco use; W: Warning about the dangers of tobacco; E: Enforcing bans on tobacco advertising, promotion, and sponsorship, and R: Raising taxes on tobacco.

WHO report on the global tobacco epidemic

Since 2008, 5 reports on the global pandemic have been published under the WHO TFI program. This report aggregates all the key product of the TFI. The report provides a snapshot of the MPOWER measures, with all country-specific data updated and aggregated to currently available data. In addition, the report provides a special focus on tobacco taxation and in-depth analyses of tobacco taxes in all WHO Member States, allowing for a more detailed understanding of progress and future challenges in this area.

= '''Updates to Ifs''' =

5 series were up for updates and only two were updated after extensive for data. The two series SI seem to have gone through a process that we are not aware of and the Tobacco control Score is not readily available. An email has been sent to WHO to enquire about this series. In the datadict, it is noted that it was supplied by Cecilia.

{| cellpadding="0" cellspacing="0" border="0" style="width: 312px;" width="312"
|-
| style="width: 312px; height: 21px;" nowrap="nowrap" |
SeriesHealthSmokingFemales%SI

|-
| style="width: 312px; height: 21px;" nowrap="nowrap" |
SeriesHealthSmokingMales%SI

|-
| style="width: 312px; height: 21px;" nowrap="nowrap" |
'''SeriesHealthSmokingPrevalenceWHOFemales%'''

|-
| style="width: 312px; height: 21px;" nowrap="nowrap" |
'''SeriesHealthSmokingPrevalenceWHOMales%'''

|-
| style="width: 312px; height: 21px;" nowrap="nowrap" |
SeriesHealthSmokingTCS

|}

 '''Sources and definition for the smoking prevalence update:'''

Definition of series: For all the updated series the definition of the series is Age-adjusted statistics of tobacco smoking (any form of tobacco including cigarettes and cigars) among adults (both current and daily) for people 15 years and older.

Step 1: Searching for sources of data and data from the WHO TFI database (<nowiki>http://www.who.int/tobacco/</nowiki>) and finding the following data sources: WHO reports on the global tobacco epidemic which currently covers the years 2008, 2009,2011,2013 and 2015. Only 3 of the sources (2009,2013 and 2015)were relevant for the update and the 2008 and 2011 served to confirm that these reports were the sources of the historical data in IFS.

Source 1: <nowiki>http://www.who.int/tobacco/global_report/2015/en/</nowiki>, 2013table_10_1_comparable_prevalence_estimates_for_tobacco_smoking_2013.xls. data are taken from the '''Current Tobacco Smoking''' tab according to the definition of smoking all tobacco products.

Source 2: <nowiki>http://www.who.int/tobacco/global_report/2013/full_dataset/en/</nowiki> : appendix_x_table_10_1_xls. under '''Global '''tab.

Source 3: <nowiki>http://www.who.int/tobacco/mpower/2009/en/</nowiki>: 2006 data was found in a . Appendix VII - Age-standardized prevalence estimates for smoking, 2006 under the''' Global7.1.0 tab'''.

Step 2The data were then compiled from these appendices. The sources allow for the download of CSV documents.

Step 3:The data were the blended with IFS data which contained 2008 and 2009(2008 and 2013 WHO reports)data for both series. 2006 data did not previously appear in IFs and was discovered in the 2009 report: WHO REPORT ON THE GLOBAL TOBACCO EPIDEMIC, 2009: Implementing smoke-free environments bringing the year coverage of both series from 2 to 5 adding 2006,2011 and 2013.

Country Concordance: TFI is a part of the WHO and the update utilized the WHO Countries concordance. 

Coverage: Data available in the series cover 161 countries. Due to the inconsistency in the self-reported survey methods, the updated for 2006, 2011 and 2013 are not equal in coverage: 124 countries were covered in 2013

'''Outstanding'''

Two series out of five were updated and efforts to contact WHO are underway for the remaining three series. Various sources were investigated for Tobacco control scoring and a decision will be made in the future.  Jose has advised us on two methods for the updating of the remaining series.

Possible sources of Smoking TCS: www.europeancancerleagues.org/.../'''TobaccoControl'''/TCS_2013_in_Europe_13-03-1.

Global Health Observatory (GHO)

2023-02-17T01:02:37Z

Fouad.Alkhouri: Removed dead links

The World Health Organization (WHO)'s Statistical Interactive Database (WHOSIS) is now called the Global Health Observatory (GHO). There are 53 series that come from the WHO Statistical Information System (WHOSIS). Most have not been updated since 2007, some are even older. 20 of these series are being read into the preprocessor.

The WHO Statistical Information System (WHOSIS) was an interactive database bringing together core health statistics, but has now been absorbed into the Global Health Obserbatory (GHO). The GHO provides over 1000 health indicators for the 194 WHO member states and can be accessed online: [http://apps.who.int/gho/data/node.home http://apps.who.int/gho/data/node.home]

The IFs system uses various measures from WHOSIS including diabetes relative risk, mortality and burden of disease, obesity, tobacco smoking prevalence, and life expectancy. As the series are updated, data will come from the GHO instead of WHOSIS.

= Series pulled from WHOSIS =

{| bgcolor="#ffffff" border="1" cellspacing="0"
|+ '''DataDict 720'''
|-
! bgcolor="#c0c0c0" | Table
! bgcolor="#c0c0c0" | Group
! bgcolor="#c0c0c0" | Definition
! bgcolor="#c0c0c0" | Source
! bgcolor="#c0c0c0" | Last IFs Update
! bgcolor="#c0c0c0" | UsedInPreprocessor
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| SeriesHealthCommDisFemales0to4
| Population, Health
| Deaths by Communicable Diseases among female children under 5
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | No
| 
|- valign="TOP"
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| Population, Health
| Deaths by Communicable Diseases among male children under 5
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthDiabetesRRFemales15to29
| Population, Health
| Diabetes Relative Risk among females 15 to 29
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | Yes
| 
|- valign="TOP"
| SeriesHealthDiabetesRRFemales30to44
| Population, Health
| Diabetes Relative Risk among females 30 to 44
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | Yes
| 
|- valign="TOP"
| SeriesHealthDiabetesRRFemales45to59
| Population, Health
| Diabetes Relative Risk among females 45 to 59
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | Yes
| 
|- valign="TOP"
| SeriesHealthDiabetesRRFemales60to69
| Population, Health
| Diabetes Relative Risk among females 60 to 69
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | Yes
| 
|- valign="TOP"
| SeriesHealthDiabetesRRFemales70to79
| Population, Health
| Diabetes Relative Risk among females 70 to 79
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | Yes
| 
|- valign="TOP"
| SeriesHealthDiabetesRRFemales80to100
| Population, Health
| Diabetes Relative Risk among females 80 plus
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | Yes
| 
|- valign="TOP"
| SeriesHealthDiabetesRRMales15to29
| Population, Health
| Diabetes Relative Risk among Males 15 to 29
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | Yes
| 
|- valign="TOP"
| SeriesHealthDiabetesRRMales30to44
| Population, Health
| Diabetes Relative Risk among Males 30 to 44
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | Yes
| 
|- valign="TOP"
| SeriesHealthDiabetesRRMales45to59
| Population, Health
| Diabetes Relative Risk among Males 45 to 59
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | Yes
| 
|- valign="TOP"
| SeriesHealthDiabetesRRMales60to69
| Population, Health
| Diabetes Relative Risk among Males 60 to 69
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | Yes
| 
|- valign="TOP"
| SeriesHealthDiabetesRRMales70to79
| Population, Health
| Diabetes Relative Risk among males 70 to 79
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | Yes
| 
|- valign="TOP"
| SeriesHealthDiabetesRRMales80to100
| Population, Health
| Diabetes Relative Risk among Males 80 plus
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | Yes
| 
|- valign="TOP"
| SeriesHealthMortChilDiarrh%
| Population, Health
| Deaths among children under 5 due to diarrhoeal diseases
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthMortChilHIV%
| Population, Health
| Deaths among children under 5 due to HIV/AIDS
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthMortChilInjuries%
| Population, Health
| Deaths among children under 5 due to injuries
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthMortChilMalaria%
| Population, Health
| Deaths among children under 5 due to malaria
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthMortChilMeasles%
| Population, Health
| Deaths among children under 5 due to measles
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthMortChilNeoNatal%
| Population, Health
| Deaths among children under 5 due to neonatal causes
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthMortChilOther%
| Population, Health
| Deaths among children under 5 due to other causes
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthMortChilPneu%
| Population, Health
| Deaths among children under 5 due to pneumonia
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthMortNeoNatAsphyxia%
| Population, Health
| Neonatal deaths due to asphyxia
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthMortNeoNatCongen%
| Population, Health
| Neonatal deaths due to congenital causes
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthMortNeoNatDiarrh%
| Population, Health
| Neonatal deaths due to diarrhoea
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthMortNeoNatInfect%
| Population, Health
| Neonatal deaths due to infection
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthMortNeoNatOther%
| Population, Health
| Neonatal deaths due to other causes
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthMortNeoNatPreTerm%
| Population, Health
| Neonatal deaths due to other preterm
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthMortNeoNatRate
| Population, Health
| Neonatal mortality rate per 1000 live births
| WHO Statistical Information System (WHOSIS)
| 2008/07/11
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthMortNeoNatTetanus%
| Population, Health
| Neonatal deaths due to tetanus
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthMortRateCancer
| Population, Health
| Age-standardized mortality rate for cancer
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthMortRateCardVasc
| Population, Health
| Age-standardized mortality rate for cardiovascular disease
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthMortRateInjuries
| Population, Health
| Age-standardized mortality rate for injuries
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthMortRateNonComDis
| Population, Health
| Age-standardized mortality rate for non-communicable diseases
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthObesityChildren%
| Population, Health
| Children under 5 overweight for age
| WHO Statistical Information System (WHOSIS)
| 2006/12
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthObesityFemales%
| Population, Health
| Obesity among females 15 and older
| WHO Statistical Information System (WHOSIS)
| 2006/12
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthObesityMales%
| Population, Health
| Obesity among males 15 and older
| WHO Statistical Information System (WHOSIS)
| 2006/12
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthPolioCasesConf
| Population, Health
| Number of confirmed poliomyelitis cases
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthSmokingAdolescents%
| Population, Health
| Tobacco smoking among adolescents 13-15
| WHO Statistical Information System (WHOSIS)
| 2006/12
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthSmokingImpactFemales30to44
| Population, Health
| Health impact for females 30 to 44
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | Yes
| 
|- valign="TOP"
| SeriesHealthSmokingImpactFemales45to59
| Population, Health
| Health impact for females 45 to 59
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | Yes
| 
|- valign="TOP"
| SeriesHealthSmokingImpactFemales60to69
| Population, Health
| Health impact for females 60 to 69
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | Yes
| 
|- valign="TOP"
| SeriesHealthSmokingImpactFemales70to100
| Population, Health
| Health impact for females 70 to 100
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | Yes
| 
|- valign="TOP"
| SeriesHealthSmokingImpactMales30to44
| Population, Health
| Health impact for males 30 to 44
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | Yes
| 
|- valign="TOP"
| SeriesHealthSmokingImpactMales45to59
| Population, Health
| Health impact for males 45 to 59
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | Yes
| 
|- valign="TOP"
| SeriesHealthSmokingImpactMales60to69
| Population, Health
| Health impact for males 60 to 69
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | Yes
| 
|- valign="TOP"
| SeriesHealthSmokingImpactMales70to100
| Population, Health
| Health impact for males 70 and older
| WHO Statistical Information System (WHOSIS)
| 2007/06
| align="RIGHT" | Yes
| 
|- valign="TOP"
| SeriesHealthyLifeExpFemales
| Population, Health
| Healthy life expectancy (HALE) at birth, females
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthyLifeExpMales
| Population, Health
| Healthy life expectancy (HALE) at birth, males
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthYLLComDis%
| Population, Health
| Years of life lost to communicable diseases
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthYLLInjuries%
| Population, Health
| Years of life lost to injuries
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesHealthYLLNonComDis%
| Population, Health
| Years of life lost to non-communicable diseases
| WHO Statistical Information System (WHOSIS)
| 2007/01
| align="RIGHT" | No
| 
|- valign="TOP"
| SeriesMalnChil%WHOWeight 
| SocioPolitical, Agriculture
| Children under five years of age underweight for age
| WHO Statistical Information System (WHOSIS)
| 2006/12
| align="RIGHT" | No
| 
|}

= Instructions on pulling GHO data =

1. Go to the GHO page for the data being pulled.

   A link should be provided in the description above. If it is not, search for it here: [http://apps.who.int/gho/data/node.resources?search= http://apps.who.int/gho/data/node.resources?search=]

2. Underneath the title, there will be two tabs, "Data" and "Downloads". Click on "Downloads".

3. Download the data in the Excel List format.

4. The data will be found in the "data-text" sheet in the excel file.

5. If the data being pulled is anything other than the child or neonatal mortality % (e.g., HealthMortChilMalaria%, HealthMortNeoNatCongen%), save the file and import into IFs. The country concordance that should be used is WHO 2012. If the data being pulled is child or neonatal mortality %, continue on to step 6.

6. The data for child and neonatal mortality includes both in the same column of the spreadsheet, so it needs to be separated into two distinct columns to be imported into IFs. First, create a new sheet in the excel file. Then, make sure the years in the "data-text" are in order (sort A to Z). Sort the countries next (A to Z). This will make sure that the years and countries will line up when you move both the neonatal mortality and the under 5 mortality. Next, select the "Age Group" column and select only 0-27 days. Move the columns for year, country, age, and distribution of death (numeric) to the new sheet.

Go back to "data-text" and select only 0-4 years in the "Age Group" column. Move the columns for age and distribution of death (numeric) to the new sheet. In the new sheet there should be 6 columns: year, country, age group (0-27 days), distribution of deaths (numerical) (0-27 days), age group (0-4 years), and distribution of deaths (numerical) (0-4 years).

Save this file and save a copy. Import the first copy of the file into IFs for one of the age groups, then import the second age group using the second copy of the excel file. The country concordance that should be used is WHO 2012.

Institute for Statistics, UNESCO (UIS)

2023-02-17T00:43:25Z

Fouad.Alkhouri: Removed dead link

'''United Nations Statistics Division: UNESCO Institute for Statistics (UIS) Education Database'''

The United Nations Statistics Division, UIS Education Database is the most comprehensive education databse in the world, containing data from more than 200 countries and territories. The UIS has the mandate to monitor progress towards Education for All and education-related Millennium Development Goals. The statistics, which are updated twice a year, are used by diverse partners, including governments, donor agencies and other UN organizations. In particular, the UIS is the primary education data source for the Education for All Global Monitoring Report, the World Development Indicators and the Human Development Report Data is collected through the UIS annual education survey, which covers all education levels and a range of issues such as enrolment rate, gender parity, teachers, and financing.

The IFs system uses data from the UIS Education Database on a wide variety of topics relating to education. The following sections provide instructions for pulling data from the UIS Education Database for IFs pre-processor series in the Education submodule.

= Pulling Data on Enrollment in Vocational Programmes: =

The pre-processor series in IFs relating to enrollment in vocational programmes are:

#EdSecUpperVoc%AllMale
#EdSecUpperVoc%AllFemale
#EdSecUpperVoc%AllTotal
#EdSecLowerVoc%AllMale
#EdSecLowerVoc%Allfemale
#EdSecLowerVoc%AllTotal

Data for these series was obtained from the UIS Education Database in the following drop down menu sequence: Education>Participation>Distribution of Enrollment>Distribution of Enrollment by Programme Orientation.

Because these particular series contain confusing wording, the full names in UIS of each fo the six series are provided below.

#'''Percentage of male students in lower secondary education enrolled in vocational programmes, male (%)'''
#'''Percentage of female students in lower secondary education enrolled in vocational programmes, female (%)'''
#'''Percentage of students in lower secondary education enrolled in vocational programmes, both sexes (%)'''
#'''Percentage of male students in upper secondary education enrolled in vocational programmes, male (%)'''
#'''Percentage of female students in upper secondary education enrolled in vocational programmes, female (%)'''
#'''Percentage of students in upper secondary education enrolled in vocational programmes, both sexes (%)'''

= Pulling Data on Educational Expenditure: =

The pre-processor series in IFs relating to educational expenditure are:

#EdExpPri%GDPPC
#EdExpSec%GDPPC
#EdExpSecLowr%GDPPC
#EdExpSecUppr%GDPPC
#EdExpTer%GDPP

Data for these series was obtained from the UNESCO Institute for Statistics ([http://data.uis.unesco.org/ http://data.uis.unesco.org/]). Specifically, they are found in the following drop down menu sequence: Education>Financial Resources>Government Expenditure per Student>Government Expenditure per Student as Percentage of GDP per capita. 

Once here, the following 5 sheets were used: '''Government expenditure per (primary/secondary/lower seconday/upper seconday/tertiary) student as percentage of GDP per capita. '''

= Pulling Data on Graduation Rates: =

The pre-processor series in IFs relating to graduation rates are:

#EdTerGradRate1stDegreeFemale% 
#EdterGradRate1stDegreeMale%
#EdTerGradRate1stDegreeTotal%
#EdTerGrad%SciEngg
#EdPriCompletionFemale%
#EdPriCompletionMale%
#EdPriCompletionTotal%

Data for these series was obtained from the UNESCO Institute for Statistics ([http://data.uis.unesco.org/ http://data.uis.unesco.org/]). Specifically, they are found in the following drop down menu sequence: Education>Completion and Graduation Ratios>Gross Graduation Ratio.

Once here, the following 4 sheets were used: '''Gross Graduation Ratio from First Degree Programmes (ISCED 6 & 7) in Tertiary Education, (Female (%)/Male (%)/Both Sexes (%)).'''

For 5-7 above, the following 3sheets were used:'''(Gross Graduation Ratio from Primary Education, (Female(%)/Male(%)/Both Sexes (%)).'''

= Pulling Data on Completion Rates: =

The pre-processor series in IFs relating to graduation rates are:

#EdSecLowrCompletionFemale
#EdSecLowrCompletionMale
#EdSecLowrCompletionTotal
#EdSecUpprCompletionFemale
#EdSecUpprCompletionMale
#EdSecUpprCompletionTotal

Data for these series was obtained from the UNESCO Institute for Statistics ([http://data.uis.unesco.org/ http://data.uis.unesco.org/]). Specifically, they are found in the following dropdown menu sequence: Education>Education>Completion>Completion and graduation ratios. The specific location for each series is as follows.

*EdPriCompletionFemale: Education>Education>Completion>Completion and graduation ratios>Completion rate for primary education (household survey data), select female (%) from dropdown
*EdPriCompletionMale: Education>Education>Completion>Completion and graduation ratios>Completion rate for primary education (household survey data), select male (%) from dropdown
*EdPriCompletionTotal: Education>Education>Completion>Completion and graduation ratios>Completion rate for primary education (household survey data), Both Sexes (%)
*EdSecLowrCompletionFemale: Education>Education>Completion>Completion and graduation ratios>Completion rate for lower secondary education (household survey data), select female (%) from dropdown
*EdSecLowrCompletionMale: Education>Education>Completion>Completion and graduation ratios>Completion rate for lower secondary education (household survey data), select male (%) from dropdown
*EdSecLowrCompletionTotal: Education>Education>Completion>Completion and graduation ratios>Completion rate for lower secondary education (household survey data), select both sexes (%) from dropdown
*EdSecUpprCompletionFemale: Education>Education>Completion>Completion and graduation ratios>Completion rate for upper secondary education (household survey data), select female (%) from dropdown
*EdSecUpprCompletionMale: Education>Education>Completion>Completion and graduation ratios>Completion rate for upper secondary education (household survey data), select male (%) from dropdown
*EdSecUpprCompletionTotal: Education>Education>Completion>Completion and graduation ratios>Completion rate for upper secondary education (household survey data), select both sexes (%) from dropdown

= Pulling Data on Intake Rates: =

The pre-processor series in IFs relating to apparent (gross) intake rates are:

#EdPriAIRFemale%
#EdPriAIRMale%
#EdPriAIRParity
#EdPriAIRTotal%

Data for these series was obtained from the UNESCO Institute for Statistics ([http://data.uis.unesco.org/ http://data.uis.unesco.org/]). Specifically, they are found in the following drop down menu sequence: Education>Entry>Gross (Apparent) Intake Ratio to Primary and Lower Secondary Education.

Once here, the following three sheets were used: '''Gross Intake Rate to Grade 1 of Primary Education, (Female%/Male%/Total/GenderParityIndex (GPI)).'''

The pre-processor series in IFs relating to net (adjusted) intake rates are:

#EdPriNIRFemale%
#EdPriNIRMale%
#EdPriNIRTotal%

Data for these series was obtained from the UNESCO Institute for Statistics ([http://data.uis.unesco.org/ http://data.uis.unesco.org/]). Specifically, they are found in the following drop down menu sequence: Education>Entry>Gross (Apparent) Intake Ratio to Primary and Lower Secondary Education.

Once here, the following three sheets were used: Adjusted Net Intake Rate to Grade 1 of Primary Education, '''(Female/Male/Total).'''

'''Net Intake Rate to Grade1 of Primary Education'''

EDPRINIRENTRNFemale

EDPRINIRENTRNMale

EDPRINIRENTRNTotal

[http://data.uis.unesco.org/ http://data.uis.unesco.org/]

*education>entry>net intake rate to primary education> Net intake rate to Grade 1 of primary education, female (%)
*education>entry>net intake rate to primary education> Net Intake Rate to Grade1 of Primary Education male (%)
*education>entry>net intake rate to primary education> Net Intake Rate to Grade1 of Primary Education both sexes (%)

 

'''Net Intake Rate to Grade1 of Primary Education (over-age by +1 year)'''

EDPRINIRENTRNP1Male

EDPRINIRENTRNP1Female

EDPRINIRENTRNP1Total

[http://data.uis.unesco.org/ http://data.uis.unesco.org/]

*education>entry>net intake rate to primary education> Net intake rate to Grade 1 of primary education by over-age entrants (+1 year), male (%)
*education>entry>net intake rate to primary education> Net intake rate to Grade 1 of primary education by over-age entrants (+1 year), female (%)
*education>entry>net intake rate to primary education> Net intake rate to Grade 1 of primary education by over-age entrants (+1 year), both sexes (%)

'''Net intake rate to Grade 1 of primary education by under-age entrants (-1 year), male (%)'''

EDPRINIRENTRNM1Male

EDPRINIRENTRNM1Female

EDPRINIRENTRNM1Total

[http://data.uis.unesco.org/ http://data.uis.unesco.org/]

*education>entry>net intake rate to primary education> Net intake rate to Grade 1 of primary education by under-age entrants (-1 year), male (%)
*education>entry>net intake rate to primary education> Net intake rate to Grade 1 of primary education by under-age entrants (-1 year), female (%)
*education>entry>net intake rate to primary education> Net intake rate to Grade 1 of primary education by under-age entrants (-1 year), both sexes (%)

'''Adjusted Net Intake Rate to Grade1 of Primary Education'''

EDPRINIRADJFemale

EDPRINIRADJMale

EDPRINIRADJTotal

[http://data.uis.unesco.org/ http://data.uis.unesco.org/]

*education>entry>net intake rate to primary education> Adjusted net intake rate to Grade 1 of primary education, female (%)
*education>entry>net intake rate to primary education> Adjusted net intake rate to Grade 1 of primary education, male (%)
*education>entry>net intake rate to primary education> Adjusted net intake rate to Grade 1 of primary education, both sexes (%)

= Pulling Data on Survival Rates: =

The pre-processor series in IFs relating to survival rates are:

#EdPriSurvivalFemale%
#EdPriSurvivalMale%
#EdPriSurvivalTotal%

Data for these series was obtained from the UNESCO Institute for Statistics ([http://data.uis.unesco.org/ http://data.uis.unesco.org/]). Specifically, they are found in the following drop down menu sequence: Education>Progression>Survival Rates>Survival Rate to Last Grade of Primary Education.

Once here, the following three sheets were used: '''Survival Rate to the Last Grade of Primary Education, (Female%/Male%/Total%).'''

= Pulling Data on Duration: =

The pre-processor series in IFs relating to survival rates are:

#EdPriDuration
#EdSecLowerDuration
#EdSecUpperDuration

Data for these series was obtained from the UNESCO Institute for Statistics ([http://data.uis.unesco.org/ http://data.uis.unesco.org/]). Specifically, they are found in the following drop down menu sequence: Education>System>Duration>Duration by Level of Education.

Once here, the following three sheets were used: '''Theoretical Duration of (Primary/Lower Seconday/Upper Secondary) Education.'''

= Pulling Data on Transition Rates: =

The pre-processor series in IFs relating to transition rates are:

#EdPriTransition2Sec%Female
#EdPriTransition2Sec%Male
#EdPriTransition2SecTotal%

Data for these series was obtained from the UNESCO Institute for Statistics ([http://data.uis.unesco.org/ http://data.uis.unesco.org/]). Specifically, they are found in the following drop down menu sequence: Education>Progression>Transition Rates.

Once here, the following three sheets were used: '''Effective Transition Rate from Primary to Lower Secondary General Education, (Female%/Male%/Total%).'''

= Items of Note =

#As of May 2016, there was no data for 2015 for the LowerVoc series, only 1999-2014. However, there was 2015 data  for the UpperVoc series.
#Country Concordance: UNESCO countries. 
##As of March 2017, UIS utilizes "Cabo Verde" instead of "Cape Verde." The UNESCO Country Concordance utilizes "Cape Verde." '''You thus must replace "Cabo Verde" with "Cape Verde" in UIS data prior to importing into IFs.'''

UNDP HDR

2023-02-17T00:42:18Z

Fouad.Alkhouri: Most links dead. removed

'''United Nations Development Program (UNDP):  Human Development Report'''

United Nations Development Program publishes a widely circulated annual report, namely the ''Human Development Report'', which contains indicator tables for 191 UN member countries along with Hong Kong, SAR (China) and Occupied Palestinian Territories. These countries and areas are classified in four ways: by human development level, by income, in major world aggregates, and by region. All countries included in the HDI are classified into three clusters by achievement in human development: high human development (with an HDI of 0.800 or above), medium human development (0.500–0.799) and low human development (less than 0.500). It is an annual publication that is normally launched in June or July and also available online at [http://www.undp.org/ www.undp.org].

The HDI (human development index) is a composite index that measures a country's average achievements in three basic areas of human development: longevity, knowledge, and a decent standard of living. Longevity is measured by life expectancy at birth; knowledge is measured by a combination of the adult literacy rate and the combined primary, secondary, and tertiary gross enrolment ratio; and standard of living by GDP per capita (PPP US$).

= Instructions for importing data from the HDR =

This document explains the process undertaken to update four series in the Human Development series.The UNHDR reports are done annually but 2012 data is found in 2013 reports. The HDI series data came from one report (2015 report) and the rest were put together from various reports as explained below.

'''Series investigated for updates'''

'''Gender empowerment measures of the UNDP-SeriesGEM''': A composite index measuring loss in achievements in three dimensions of human development—reproductive health, empowerment, and labour market, due to inequality between genders. For details on how the index is calculated, see UN HDR Technical note 4.  The Gender empowerment measure of the UNDP: The GEM does not have current data in post-2010 reports. This suggests a discontinuation of the series as some of its effects are captured in the gender inequality index and the inequality-adjusted index of the HDI. 

'''The Gender Development Index (GDI'''): The index measures gender inequalities in achievement in three basic dimensions of human development: health, measured by female and male life expectancy at birth; education, measured by female and male expected years of schooling for children and female and male mean years of schooling for adults ages 25 and older; and command over economic resources, measured by female and male estimated earned income.      

'''Gender Inequality Index -SeriesGenIneqInd'''-The Gender Inequality Index (GII) reflects gender-based disadvantage in three dimensions—reproductive health, empowerment, and the labour market—for as many countries as data of reasonable quality allow. It shows the loss in potential human development due to inequality between female and male achievements in these dimensions. It ranges between 0, where women and men fare equally, and 1, where one gender fares as poorly as possible in all measured dimensions. The GII is computed using the association-sensitive inequality measure suggested by Seth (2009), which implies that the index is based on the general mean of general means of different orders—the first aggregation is by a geometric mean across dimensions; these means, calculated separately for women and men, are then aggregated using a harmonic mean across genders  

'''Gini index of income inequality (lower = more equal)-SeriesGiniExtended''': This series is updated by WDI batch pull.                    

'''Human Development Index'''-SeriesHDI: Human Development Index and its components, ranks countries by 2014 HDI value and details the values of the three HDI components: longevity, education (with two indicators) and income. The Human Development Index (HDI) is a summary measure of achievements in key dimensions of human development: a long and healthy life, access to knowledge and a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.                               

''''IHDI, Inequality Adjusted''''''''''-seriesHDIIneqAd'''j : '''The Inequality-adjusted Human Development Index (IHDI) adjusts the Human Development Index (HDI) for inequality in the distribution of each dimension across the population. It is based on a distribution-sensitive class of composite indices proposed by Foster, Lopez-Calva and Szekely (2005), which draws on the Atkinson (1970) family of inequality measures. It is computed as a geometric mean of inequality-adjusted dimension indices. The IHDI accounts for inequalities in HDI dimensions by ‘discounting’ each dimension’s average value according to its level of inequality. The IHDI equals the HDI when there is no inequality across people but falls below the HDI as inequality rises. In this sense, the IHDI measures the level of human development when inequality is accounted for.  

'''Human poverty index (1)''' of the UNDP; higher is worse  SeriesHPI1                         

'''Poverty multi-dimensional poverty index(MPI)-SeriesPovMulDim:''' The Multidimensional Poverty Index (MPI) identifies multiple deprivations at the household level in education, health, and standard of living. It uses micro data from household surveys, and—unlike the Inequality-adjusted Human Development Index—all the indicators needed to construct the measure must come from the same survey. More details about the general methodology can be found in Alkire and Santos (2010). More details about changes in the methodology and the treatment of missing responses and nonapplicable households are given in Klasen and Dotter (2013) and Kovacevic and Calderon (2014)   

'''Life expectancy-''': '''SeriesLifeExpectancyUNHDRO:'''    

'''Average year of education, 25 and above-''','''SeriesEdYearsAge25UNHDRO: ''''''  '''

This document explains the process undertaken to update four series in the Human Development series.

Data obtained from [http://hdr.undp.org/en http://hdr.undp.org/en]. The UNHDR reports have been done annually since 1990 except for  2012 for which 2012 data is found in the 2013 report.

'''Operations carried out:'''

'''Step 1: Go to ''' [http://hdr.undp.org/en http://hdr.undp.org/en] and click on "Download the 2015 Human development Report" under the "Other Resources' tab. Consolidated data can be accessed at <nowiki>http://hdr.undp.org/sites/default/files/hdr_2015_statistical_annex.pdf</nowiki>

'''Step 2:''' Then access the statistical annexes for the respective series.

'''Step 3''': Sort the data and align withy the UNDP country concordance.

'''Gender Inequality Index (GII):''' The series was updated from 2013 and 2014 reports.GII was updated up to 2014(HDR 2014 Statistical Tables: Table 4 and 2013 report)

'''Multidimensional Poverty Index''': IFs had 2008 data points, a search of the multiple reports yielded scattered information on the series. Four tables were used containing data from 2005-2014. The data was combined and blended with the 2008 data within the IFs historical database. The most recent data points for 2008 were adopted during the blending process. The tables used were as listed below:

Table 6 2015 statistical tables and 2015 table 7

6 A 2014 Statistical tables and 2014 Table 6

Data was gathered from four tables then sorted (sorting sheet). A merged table was then created (Importing file).

'''Human Development Index''' : The data came for the current updates came from one report (2015 report) and the rest were put together from various reports as explained below.

Due to the changes in calculations that occurred in 2010, the new series contains data from 2015 HDR report (2015 Statistical Tables Annex 2).

'''Inequality-adjusted Human Development Index'''

Data was sorted and aligned with the UNDp country concordance. There were minute data variation within successive years and the most recent data points were added.

Data came from 2015 Statistical Tables: Table 3, 2014(Table 3 for 2013 data). Data also came from HDR report of 2013 in the annex tables for 2012 data.

The following  section explains the process undertaken to update the UNDP education Series and the addition of two new series.

'''The d'''ata was obtained from <nowiki>http://hdr.undp.org/en/data-theme/education</nowiki>. The original source: Barro and Lee (2013), UNESCO Institute for Statistics (2013b) and HDRO estimates based on data on educational attainment from UNESCO Institute for Statistics (2013b) and on methodology from Barro and Lee (2013). The education year’s data that we collect from Barro and Lee are quinquennial. For their HDR, UNDP calculates annual values (starting from 2010).

'''New series name: EDYearsMaleAge25UNHDRO '''

Data obtained at <nowiki>http://hdr.undp.org/en/content/mean-years-schooling-males-aged-25-years-and-above-years</nowiki>

Mean years of schooling (males aged 25 years and above) (years): the average number of years of education received by people ages 25 and older, converted from educational attainment levels using official durations of each level. An Excel sheet was downloaded from the site and a simple import carried out(1980-2013).

'''New series name: EDYearsFemaleAge25UNDRO'''

Data obtained at <nowiki>http://hdr.undp.org/en/content/mean-years-schooling-females-aged-25-years-and-above-years</nowiki>

This is a new series defined as mean years of schooling (females aged 25 years and above) (years):the average number of years of education received by people ages 25 and older, converted from educational attainment levels using official durations of each level. An Excel sheet was downloaded from the site and a simple import carried out(1980-2013).

'''Series name: EDYearsAge25UNHDRO'''

Data obtained <nowiki>http://hdr.undp.org/en/content/mean-years-schooling-adults-years</nowiki>

Current IFs table goes up to 2011 and the series has been updated to 2013. An Excel sheet was downloaded from the site and a simp,e updated was done.

 

'''         '''                

{| border="0" cellpadding="0" cellspacing="0" width="545" style="width: 1011px;"
|-
|
|}

Stockholm International Peace Research Institute (SIPRI)

2023-02-17T00:36:18Z

Fouad.Alkhouri: Removed dead online database link

'''Stockholm International Peace Research Institute (SIPRI)'''

The SIPRI database provides data on military expenditure for more than 160 countries. The SIPRI provides data for the period of 1949-2002 and contains three basic formats: military expenditure in local currency, at current prices; military expenditure in US dollars, at constant (2000) prices and exchange rates; and military expenditure as a share (%) of gross domestic product (GDP). The online database is available at <nowiki>http://projects.sipri.org/milex/mex_database1.html</nowiki>, but this database is missing data from before 1988. To acquire updated pre-1988 data, you need to use the contact us link on the linked database to request access. 

The sources for military expenditure data are, in order of priority: (a) primary sources, that is, official data provided by national governments, either in their official publications or in response to questionnaires; (b) secondary sources which quote primary data; and (c) other secondary sources. The second category includes international statistics, such as those of NATO and the International Monetary Fund (IMF).

The database is revised when new and better sources are available. Therefore, there is sometimes considerable variation between data sets for individual countries in different yearbooks.

{| border="1" bgcolor="#ffffff" cellspacing="0"
|+ '''DataDict 720'''
|-
! bgcolor="#c0c0c0" | Table
! bgcolor="#c0c0c0" | Group
! bgcolor="#c0c0c0" | Definition
! bgcolor="#c0c0c0" | Source
! bgcolor="#c0c0c0" | Last IFs Update
! bgcolor="#c0c0c0" | UsedInPreprocessor
! bgcolor="#c0c0c0" | Years
|- valign="TOP"
| SeriesGovtMil%GDPBlendACDASIPRI
| SocioPolitical, Conflict
| Military spending % GDP blend of ACDA and SIPRI WITHOUT Estimates
| Arms Control and Disarmament Agency (ACDA) and Stockholm International Peace Research Institute (SIPRI)
| 2016/06/29
| align="RIGHT" | No
| 1963-2015
|- valign="TOP"
| SeriesGovtMil%GDPBlendACDASIPRIwEstimates
| SocioPolitical, Conflict
| Military spending % GDP blend of ACDA and SIPRI WITH Estimates
| Arms Control and Disarmament Agency (ACDA) and Stockholm International Peace Research Institute (SIPRI)
| 2016/06/29
| align="RIGHT" | No
| 1960-2015
|- valign="TOP"
| SeriesGovtMil%GDPSIPRI
| Government, Conflict
| Military expenditures as percent of GDP (SIPRI)
| SIPRI, Stockholm International Peace Research Institute
| 2016/06/29
| align="RIGHT" | Yes
| 1949-2015
|- valign="TOP"
| SeriesGovtMil2008SIPRI
| Government, Conflict
| Military expenditures in constant 2008 US$ (SIPRI)
| Stockholm International Peace Research Institute, SIPRI
| 2010/10/06
| align="RIGHT" | No
| 
|}

State failure preprocesor

2023-02-17T00:34:52Z

Fouad.Alkhouri: Removed DIPLO link (empty landing page)

Sources: DIPLO, [[State_failure_project|State failure project]]

= Series read into state failure preprocessor =

{| border="1" cellpadding="0" cellspacing="0" width="320"
|- height="20"
| height="20" width="64" | Table
| width="64" | Definition
| width="64" | Source
| width="64" | Notes
| width="64" | Last IFs Update
|- height="20"
| height="20" | SeriesSFPITFConsolidatedEvProb
| Consolidated event probabilty (with continuing events)
| Computed from State Failure Project
|  
|  
|- height="20"
| height="20" | SeriesSFPITFConsolidatedMagAvg
| Consolidated event, average magnitude
| Computed from State Failure Project
|  
|  
|- height="20"
| height="20" | SeriesSFPITFConsolidatedY1Prob
| Consolidated event probabilty (initial events)
| Computed from State Failure Project
|  
|  
|- height="20"
| height="20" | SeriesSFPITFDomInstabilityEvProb
| Domestic instability event probabilty (with continuing events)
| Computed from State Failure Project
|  
|  
|- height="20"
| height="20" | SeriesSFPITFDomInstabilityMagAvg
| Domestic instability event, average magnitude
| Computed from State Failure Project
|  
|  
|- height="20"
| height="20" | SeriesSFPITFDomInstabilityY1Prob
| Domestic instability event probabilty (initial events)
| Computed from State Failure Project
|  
|  
|- height="20"
| height="20" | SeriesSFPITFInternalWarEvProb
| Internal war event probabilty (with continuing events)
| Computed from State Failure Project
|  
|  
|- height="20"
| height="20" | SeriesSFPITFInternalWarMagAvg
| Internal war event probabilty, average magnitude
| Computed from State Failure Project
|  
|  
|- height="20"
| height="20" | SeriesSFPITFInternalWarY1Prob
| Internal war event probabilty (initial events)
| Computed from State Failure Project
|  
|  
|- height="20"
| height="20" | SeriesSFPITFConsolidatedEv
| Consolidated event occurrence (1 = yes, 0 = no)
| State Failure Project
| BBH new code to compute; copy manually back from Runfiles to Data after rebuild base
| 2011/12/16 BBH
|- height="20"
| height="20" | SeriesSFPITFConsolidatedMag
| Consolidated event, maximum magnitude
| State Failure Project
| BBH new code to compute; copy manually back from Runfiles to Data after rebuild base
| 2011/12/16 BBH
|- height="20"
| height="20" | SeriesSFPITFInternalWarEv
| Revolution, ethnic, genocide/politicide  eventoccurrence (1 = yes, 0 = no)
| State Failure Project
| BBH new code to compute; copy manually back from Runfiles to Data after rebuild base
| 2011/12/16 BBH
|- height="20"
| height="20" | SeriesDiploEmbassyStandCountry
| Standarized Number of Embassies that a country has in other countries in a given year
|  
|  
| 2012/10/29
|- height="20"
| height="20" | SeriesDiploIGOWeightedStandCountry
| Standarized Weighted Number of Intergovernmental Organizations that a country belongs in a given year
|  
|  
| 2012/10/29
|- height="20"
| height="20" | SeriesDiploTreatyWeightedSign1Rat2StandCountry
| Standarized Weighted Number of Treaties that a country has signed (1) or ratified (2) in a given year
|  
|  
| 2012/10/29
|- height="20"
| height="20" | SeriesDiploDistanceToEurope
| Distance to Europe (Belgium)
|  
|  
| 2012/11/06
|- height="20"
| height="20" | SeriesDiploINGOAdvocacy
| The number of advocacy INGOs operating in, on or about a country.
| Diplometrics
| AER: Cells are left null if the country didn't exist yet; JMc
| 2016/01/11
|- height="20"
| height="20" | SeriesDiploINGOProgrammatic
| The number of programmatic INGOs operating in, on or about a country.
| Diplometrics
| AER: Cells are left null if the country didn't exist yet; JMc
| 2016/01/11
|- height="20"
| height="20" | SeriesSFPITFEthnicWarEv
| Ethnic war occurrence (1 = yes, 0 = no)
| State Failure Project (PITF); http://www.systemicpeace.org/inscr/inscr.htm
| AER; KN- Sudan North was treated as Sudan, and yugoslavia was split between serbia and kosovo depending on the conflict description
| 2016/03/11
|- height="20"
| height="20" | SeriesSFPITFEthnicWarMag
| Ethnic war occurrence and severity
| tate Failure Project (PITF); http://www.systemicpeace.org/inscr/inscr.htm
| AER; KN- If multiple events in one year, the higher magnitude was chosen
| 2016/03/11
|- height="20"
| height="20" | SeriesSFPITFGenocideEv
| Genocide occurrence  (1 = yes, 0 = no)
| State Failure Project; http://www.systemicpeace.org/inscr/inscr.htm
| AER; KN-The conflict in Yugoslavia in 1998 is considered Kosovo
| 2016/03/11
|- height="20"
| height="20" | SeriesSFPITFGenocideMag
| Genocide occurrence and severity
| State Failure Project (PITF); http://www.systemicpeace.org/inscr/inscr.htm
| AER; KN-The conflict in Yugoslavia in 1998 is considered Kosovo,If multiple events in one year, the higher magnitude was chosen
| 2016/03/11
|- height="20"
| height="20" | SeriesSFPITFRegTranEv
| Abnormal regime transition occurrence  (1 = yes, 0 = no)
| State Failure Project (PITF); http://www.systemicpeace.org/inscr/inscr.htm
| AER;KN- Yugoslavia was treated as Serbia
| 2016/03/11
|- height="20"
| height="20" | SeriesSFPITFRegTranMag
| Abnormal regime transition occurrence and severity
| State Failure Project (PITF); http://www.systemicpeace.org/inscr/inscr.htm
| AER;KN- Yugoslavia was treated as Serbia
| 2016/03/11
|- height="20"
| height="20" | SeriesSFPITFRevolWarEv
| Revolutionary war occurrence  (1 = yes, 0 = no)
| State Failure Project (PITF); http://www.systemicpeace.org/inscr/inscr.htm
| AER;KN-Yemen North and Yemen South were considered as Yemen
| 2016/03/11
|- height="20"
| height="20" | SeriesSFPITFRevolWarMag
| Revolutionary war occurrence and severity
| State Failure Project (PITF); http://www.systemicpeace.org/inscr/inscr.htm
| AER;KN-Yemen North and Yemen South were considered as Yemen
| 2016/03/11
|}

 

Scenario Analysis

2023-02-17T00:27:07Z

Fouad.Alkhouri: Removed dead link for guide to scenario analysis

= Scenario Description =

A scenario is a story or story outline. Thinking about the future normally involves creating alternative scenarios, or stories, about the possible interactive evolution of variables. Some such scenarios are exploratory and consider the possible unfolding of different futures around key uncertainties, such as the rate of some aspect of technological advance or the fragility of some element in the global environment. Other scenarios are normative and develop stories about preferred futures, such as a global transformation to sustainability.

Scenarios in a computer model typically are built from multiple interventions that collectively help create a coherent story about the future. Often, but somewhat imprecisely, the word scenario is used more loosely to refer to any intervention (such as the change of a fertility rate for a country or an alternative assumption about oil resources).

Scenarios or interventions with respect to what? When IFs or other computer simulations are "run", without making any changes to parameters or initial conditions specified as the default values, they generate a forecast that is typically called the Base Case. The IFs Base Case, always available when a model session is initiated, is itself a scenario. Sometimes the Base Case is incorrectly referred to as a trend extrapolation or a "business as usual" scenario. More accurately, however, the IFs Base Case is a computation that involves the full dynamics of the model and therefore has very nonlinear behavior, often quite different from trends. It is a good starting point for scenario analysis for two reasons. First, it is built from initial conditions of all variables and on parameters that have been given reasonable values from data or other analysis. These initial conditions and parameters make up the package of interventions that constitute the Base Case scenario. Second, the Base Case is periodically analyzed relative to the forecasts of many other projects across the range of issue areas covered by IFs and is to a degree "tuned" for internal coherence and consistency with insights of respected forecasters.

There are two file types involved in IFs scenario creation: scenario files (or Scenario-Load-Files) and run files (or Run-Result-Files). Scenario files, the first type, are saved with an extension of .sce. Very small in size, .sce files contain information that the IFs model uses to create alternative scenarios; i.e., .sce files contain a list of parameter values that diverge from the Base Case. It is important to note that scenario files do not contain any forecasts. Forecasts are generated and saved only in the second type of file, run files with the .run extension. Because they contain forecasts of all IFs variables and parameters, .run files are much larger than scenario files. Although the IFs standalone model software allows users to save both types of files, web users are only able to save .sce files to retrieve their parameters and regenerate their scenarios.

In addition to the Base Case, most versions of IFs will include a number of other previously-run scenarios (see [[Lesson_1:_Display|How Do I?/Lesson 0]] for additional important terminology), typically those for the Global Environmental Outlook (GEO) of the United Nations Environmental Program (UNEP). If you look, for instance, at the Flexible Displays form, you will see a list of previously-run scenarios in the box at the bottom of the screen. Because those have already been run, based on a set of interventions constituting their foundations, the user can immediately display their results.

= Quick Scenario Analysis with Tree Overview =

== Overview ==

=== What is a scenario? ===

This section of the help menu will guide you through a brief description of a scenario as well as the Base Case used in IFs and will discuss the difference between Scenario-Load-Files (.sce files) and Run-Result-Files (.run files).

After you have finished with [[#Scenario_Description|this associated topic]], you should be able to answer/do the following:

*What is a scenario?
*What is the Base Case of IFs?
*What is the difference between a Scenario-Load-File and a Run-Result-File?

=== Introduction to the Scenario Tree ===

The Quick Scenario Analysis with Tree allows you to call up or to mix and match an extensive number of your own interventions and/or a set of stored scenario intervention files. This feature of IFs allows you to change any parameter and selected initial conditions used in the model, thus shaping forecasts. Use the Quick Scenario Analysis with Tree to create Scenario-Load-Files and/or run scenario files through IFs in order to create Run-Result-Files that you can use throughout IFs.

Below is the main menu of the Quick Scenario Analysis with Tree: [[File:Quicktree.png|frame|center|Quick Scenario Analysis with Tree Main Menu]]

=== Loading Previously-Structured Scenarios ===

The Quick Scenario Analysis with Tree allows you to load the previously-structured scenarios and user-saved scenarios. From the Scenario Files or the Adding Scenario Component menu options of the Quick Analysis with Tree, you can load a wide range of pre-packaged scenario intervention files and see what interventions were made in the files.

After you have finished with [[#Loading_Previously-Structured_Scenarios_2|this topic]], you should be able to do/answer the following:

*How does a previously-structured scenario differ from a previously-run scenario?
*What previously-structured scenarios came installed in your version of IFs?
*How does one search through the scenarios until you find one that deals with environmental change (or some other specific topic of interest)?
*What do you have to click on to understand exactly what is being changed by different previously-structured scenarios?

=== Finding the Intervention You Want ===

In order to tailor your scenario file to your needs, you must be able to quickly find the parameter you are looking for.

After you have finished with [[#Finding_Parameters_and_Variables|this topic]], you should be able to do/answer the following:

*What is the organizational logic of the Scenario Tree?
*What is the difference between the Selected Initial Conditions, Relationship Parameters and the other five main categories used in the Scenario Tree?
*How would you search for a specific parameter that might help shape the scenario intervention of interest to you?

=== Exploring and Changing Parameters ===

Once you have found the parameter you are looking for, for instance, the total fertility rate multiplier (Households/Individuals, Demographic/Population, tfrm), a number of new options become available.

After you have finished with [[#Exploring_and_Changing_Parameters_2|this topic]], you should be able to do/answer the following:

*How do you select a parameter to change?
*What do the Select, Drivers, Explain, View Equations and Define pop-up options (when you click on a parameter name from the tree) all allow you to do?
*How do you clear parameter changes from the Scenario Tree?
*How do you create a Run-Result-File?
*How do you save the results?
*How can you display the results of your change in IFs?

=== Activate Pre-Run Scenario(s) for Display ===

Although not accessed through the scenario tree, it is important to know that hundreds of scenarios (the parameters for which are accessible in the tree) have been pre-run with the full forecasting results stored on the IFs server. It is not necessary for you to load the parameters and re-run these scenarios unless you want to create variations of them with new parameters or new parameter values. Instead you can go to the [[Activate_Pre-Run_Scenario_for_Display|Display/Activate Pre-Run Scenario for Display]] sub-option of the Main Menu, choose the pre-run scenario, and then display results from it using any IFs display option.

== Loading Previously-Structured Scenarios ==

Many packages of scenarios in IFs come pre-built. These previously-structured scenarios (that is, .sce files) are helpful components for building alternative futures in the model. They contain changes made in parameters but not yet in computed variables. They can be run to generate the forecasting results for all IFs variables (but if you are making no changes it will normally be easier simply to [[Activate_Pre-Run_Scenario_for_Display|activate the results]] of running the .sce or scenario file). More often on the web-based version of IFs you will load an .sce file in order to make changes to it or to combine multiple .sce files.

There are two different ways of loading .sce files: the Add Scenario Component option (usually the easier of the two options) and the Open/Other sub-option under Scenario Files. Although the format of the two is different, the functionality is the same. From the main menu of Quick Scenario Analysis with Tree (reached with the Scenario Analysis option from the Main Menu of IFs), select Add Scenario Component and you will be presented with a drop-down list with the names of categories in which you can find different previously-structured scenarios, as shown below (the ultimate scenario names are often in sub-categories or even sub-sub categories because there are a large number of previously structured scenarios in IFs for your use). These scenarios can also be reached by clicking on the Scenario Files option (sub-option Open).

[[File:Useifs36.png|frame|center|Previously-structured scenarios in main menu]]

Any of the previously-structured scenarios can be loaded into the Scenario Tree of IFs. You can also create a larger scenario in the tree by adding several smaller scenarios together. Once you have loaded a scenario into the tree (experiment by doing so), if you would like to know more about the parameter changes from the Base Case that make up the scenario, click on Explain Scenario located on the menu.

Besides these previously-structured scenarios, hundreds of .sce files have been pre-run with the results saved as .run files (so called previously-run scenarios) on the web. Because previously-run files have already been run (as the name indicates) users can immediately display the results through different display options such as the Flexible Display and the Self-Managed Display. The menu that provides access to previously-run scenarios is the [[Activate_Pre-Run_Scenario_for_Display|activate the results]] sub-option under Display from the Main Menu of IFs. By simply clicking the scenario name that the user would like to load, the activate feature would call it up. After activation, the user can see it in the scenario lists of the Flexible Display, for example. It is possible to activate multiple different scenarios by selecting them individually. 

== Parameter Types ==

Parameters are numbers that determine relationships among variables in the equations of IFs. You often set parameters to a single value across time and they therefore do not always "vary" as do "real" variables. Many parameters are "policy handles." More generally, parameters can actually be thought of as a special type of variable, the value of which you set in order to determine the behavior of the model. See the IFs project document called Guide to Scenario Analysis for much more information on parameter types and especially on the important parameters of IFs, organized by the models of the IFs system.

'''Multipliers:''' They have a normal value of 1, and to increase whatever they multiply (say agricultural yield) by 50 percent you increase the parameter to 1.5. To decrease it by 25 percent you would decrease the multiplier parameter to 0.75. You will almost always spread such changes out over time, keeping the multiplier's value at 1 in the base year and gradually increasing or decreasing it over a period of years. You should almost never change a multiplier in the initial year because the model is set up to provide accurate results for that year and will compensate for and thereby offset your change. For instance, if you set a multiplier on agricultural production equal to 1.5 for the first year and all years thereafter, you might find that the results were no different than in the base case. You must instead gradually introduce your change, preserving the multiplier value of "1" in the initial year. Examples of multipliers include: agdemm, enpm, freedomm, mortm, protecm, qem, rdm, resorm, tfrm, and ylm. Note that multipliers typically end with the letter "m".

'''Additive Factors:''' Most have a normal value of 0, thereby leaving that to which you add them unchanged. How much you would add to achieve a 50 percent increase might depend on the amount to which you added it to. Some additive parameters are, however, applied multiplicatively to the quantity they modify (that is, 1 plus the parameter is multiplied times the quantity), thereby scaling the parameter. In that case, the base or normal value of the parameter will be zero, but one can achieve a 50 percent increase in the quantity modified with a value of 0.5 and a 50 percent decrease with -0.5. You will very seldom want to change the base year value of additive parameters because it will either incorrectly change model results in the base year or, more likely, will result in model compensation to protect initial model results. An example of an additive parameter is: xshift (export shift as result of promotion of exports). Although early versions of IFs used additive factors and multipliers with comparable frequency, most additive factors have been replaced by multipliers to standardize most parameter changes.

'''Exponents:''' For instance, many "elasticities" raise something to a power. For these parameters the "normal value" will vary greatly, but they will most often fall between -2 and 2, with many clustering around 0. In most cases it will make sense to change these parameters for all years including the first - generally the model will not use them in the first year and they will affect results only in subsequent years. Elasticities in IFs include: elass and engel.

'''Reactivities:''' These are factors that relate growth in one process to growth in another. Although many will range between -2 and 2 (with 0 eliminating linkage of the processes), some have very large values. They are very much like elasticities, but the formulations that use them do not have exponential form. Reactivities include: cdmf, cpowdf, cwarf, nwarf, and reac.

'''Growth Rates:''' It is possible to force some processes to grow at specified rates. More commonly, the specified rates serve as targets and the dynamics of the model often shift actual growth rates somewhat, necessitating experimentation with targets to achieve a desired growth. Examples include: eprodr and tgrld.

'''Allocating Coefficients:''' Coefficients are often used in multiplicative relationships with other variables, but many such coefficients are not what were earlier called multipliers (with a base value of 1). Instead they can serve an allocating role. For instance, you can use parameters to allocate governmental spending to health, education, and the military. Allocating coefficients frequently have values between 0 and 1. Again, you should generally not change these parameters in the initial year because the model will often compensate for changed values in the first year. Instead, change them by series over time. Allocating coefficients in IFs include: aidlp, carabr, drcpow, drnpow, nmilf, and rfssh.

'''Transforming Coefficients:''' Some coefficients transform units of variables or link variables in other ways. Examples in IFs are: carfuel1, carfuel2, and carfuel3.

'''Switches:''' These parameters turn something on or off. They generally take on values of 1 (on) or 0 (off), but can have additional settings. For instance, some switches not only turn on some process, but set a key value within it (like the level of energy exports). Switches are most often on or off for the entire run, but it sometimes makes sense to "throw a switch" in the middle of a run. Switches allow you to fundamentally alter the structure of a model. Switches include: actreaon, agon, ally, enon, enprix, and squeez.

'''Variables:''' This category should technically not be called parameters at all. They could and would be computed endogenously, if the model included the appropriate theoretical structure. They generally do not determine the interaction of other variables. Such variables include: AQUACUL and EDPRIPTR.

'''Initial Conditions:''' Again, these are not strictly parameters, but rather first-year values for variables subsequently computed by the model. Although many initial conditions, like the population (POP) of the U.S., are sufficiently well-known that they should not be changed by model users, others, like the ultimate availability of oil and gas resources are only reasonable guesses. Thus users should feel free to change some initial conditions based on new data or even simply to test the implications. This category includes a great many variables, such as: AIDSDTHS and RESER.

The focus here is on exogenous parameters only - on those elements of the model that you can change. Many computed variables are used in the computation of other variables in the same way that parameters are, as multipliers, additive factors, coefficients, and so on. You can display those, but unlike true parameters, you cannot change them.

== Finding Parameters and Variables ==

The main menu of Quick Scenario Analysis with Tree (found under Scenario Analysis from the Main Menu of IFs) provides you with a "tree" on the left hand side of the screen. This tree (Scenario Tree) represents all of the parameters and many of the initial conditions for variables used in IFs.

One way to find the parameter or variable initial condition you would like to change is to understand the general groupings of these categories (the main and secondary branches of the tree). The first two options or large branches on the tree, Technological Change and Environmental Uncertainties, lead you to primary parameters that allow you to shape the speed of technological change and the intensity of various environmental trends or constraints. The third, fourth and fifth main options on the tree, Households/Individuals, Governments/Social-Political Systems and Firms/Businesses, allow you to find and shape the primary parameters for the three main global actor categories. The Selected Initial Conditions option leads you to some of those for key IFs variables. Finally, the Relationship Parameters branch or option allows you to change all other model parameters in the model that are not identified in the branches above. Whereas most of the parameters accessed in the other branches are primary for scenario analysis, most of those opened by this branch more technically control model specifications and are unlikely to be altered by most users.[[File:Useifs37.png|frame|right|Parameter search window]]

Another way for you to find the parameters you would like to change is by selecting the Parameter Search option near the right end of the form's menu. Clicking on this will bring up a window with a search box, as shown below. For example, if you are looking to work with the malnourished children parameters, type in "malnourished" and hit enter. This will bring up any parameter or variable initial condition with the word "malnourished" in its name or definition. You can also search for the parameter or variable by its name in IFs (like MALNCHP).  If you know the parameter or initial condition you want to change (or its likely general characterization), it is often easier to search for it with this feature rather than by digging down into the branches of the tree. 

From this window you can accomplish the following:

*'''Type Variable Name:''' In the top box, type in a variable name or a key word and click '''Search'''. This will bring up any variable/parameter that has your search term in its name/definition.
*'''Load:''' After you have located and clicked on a variable/parameter name you would like to manipulate in the tree, click Load.  That will take you back to the [[#Quick_Scenario_Analysis_with_Tree_Overview|Quick Scenario Analysis with Tree]] form and allow you to change values of it for scenario analysis.
*'''Continue:''' Click here if you would like to return to the scenario tree and its menu.

Clicking on and selecting a parameter or variable name for initial condition will bring up a small pop-up menu with the following three options to get more information about it: [[Repeated_Features#Define,_Drivers,_Explain,_Code_and_Delete|Define, Block Diagram and Equations]].

== Exploring and Changing Parameters ==

One of the key features of Quick Scenario Analysis with Tree is the ability to change specific variables and parameters.

The image below shows you what becomes available to you if you click on a branch on the tree on the left side of the form, thereby generating the box of parameters accessible to you from that branch (see the box in the lower-right of the figure), and then click on the name of a specific parameter in the box (as with tfrm in the example). You are presented with five options in the pop-up: 1) Select the parameter in order to change it; 2) click on Drivers in order to see what variables are affecting what (may be de-activated); 3) click on Explain to see a causal diagram and an explanation of what this parameter affects; 4) click on View Equations to see the mathematical equations that link this parameter to variables; 5) click on Define to see a brief definition of what the parameter is and what it does. 

[[File:Useifs38.png|frame|center|Parameter selection window]]

In order to effectively use the Scenario Tree to change values, it is very useful to understand the different parameter types in the IFs system.  See the [[#Parameter_Types|Parameter Types topic]].  In the case of the ''tfrm'' parameter, it is a multiplier with a value of 1.0 in the Base Case. Because you selected a multiplier, the base-case value is always "1"—the model uses multipliers to easily raise or lower base-case values of many variables, and the multipliers are always "1" in the Base Case, so that values are unchanged by them. A multiplier can be distinguished from a variable because the ending of most multiplier abbreviations is the letter "m" such as tfrm.

You could use the [[#Customization_of_Parameters|Fully Customize option]] to create any pattern of intervention over time you desire, and at some point you should explore its use. But for now select the High option above the graph and note the change in the figure. This option will phase in a higher fertility (a 20% increase over the next ten years) for the country you select. Another type of simple change (short of full customization) is facilitated by the slider bar to the right of the graphic. Moreover, you can change the speed in which your increase or decrease in tfrm takes place with the drop-down list labeled "Shift Years:" above the graph. Again, if you would like more control over your parameters, you can click on the Fully Customize button located below the graph and you will be presented with a new form.

If you have mistakenly changed a parameter and you would like to remove your alteration, simply click on the parameter change you would like to remove and then click on the Delete Selection option (sub-option Delete) from the menu. If you would like to clear all the changes made to the Scenario Tree, simply click on the Clear Tree sub-option located under Scenario Files.

If you have changed a parameter, it will affect nothing else until you run the model and recalculate all of the variables in it for all of the countries. That is, if you have created a Scenario-Load-File (.sce) in order to analyze your scenario, you need a Run-Result-File (.run). Click on the Run Scenario option then the [[#Running_Working_Scenario|Run sub-option]] from the menu. Your parameter will first be loaded into the working file. You will then be shown the Running Working Scenario form. You can change the end year or leave it as is. Click on ''Start Run'' for IFs to create a new run-file based on your specifications.

After IFs has recomputed the model with your new parameter(s) and temporarily saved the results as a .run file, you will be presented with a screen that says, "Run Successful – Click to Continue." The resulting Working File (temporarily saved .run file) with new forecast computations will now be available for your analysis by itself or in comparison with the Base Case or other scenarios. Look, for instance, at Gross Domestic Product (GDP) and GDP per capita (GDPPC) of your country before and after the intervention by selecting both 0-Working File and 1-IFSBASE.run (IFs Base Case) in the Flexible Displays. If the country or grouping for which you changed one or more parameters was fairly large, you will see changes in many variables elsewhere around the world. Note that when you exit from IFs, the revised Working File will be lost. In order to get it again, you will need to go to the Scenario Tree, retrieve your scenario file, and run it again.

In order to hold onto parameter changes you make and a scenario you may want to explore again or develop further, return to Quick Scenario Analysis with Tree and select the ''Scenario Files'' option, Name and Save sub-option. Give your scenario file and folder names. The parameter intervention in the form of a .sce file (but not the full results after your run) can be saved in the system for one month and can be retrieved under Add Scenario Component/User Defined Scenarios/your folder, or under Scenario Files/Open/Other/User Defined Scenarios/your folder. If you would like to keep your intervention permanently, click on the Export Working File sub-option under Scenario Files. You will be asked to choose where you would like to save your scenario and under what name. This will allow you to save your scenario file (.sce file) on your hard drive. Although you will not be able to bring this file saved in your machine back to the web-version of IFs, it is possible to use the .sce file with the stand-alone version of IFs;  it is best if your file is stored in the Scenario folder under IFs on your hard drive (C:/Users/Public/IFs/Scenario).    

== Customization of Parameters ==

The [[#Exploring_and_Changing_Parameters_2|Exploring and Changing Parameters Topic]] explains the general use of the [[#Quick_Scenario_Analysis_with_Tree_Overview|Scenario Tree]] to identify and begin to change parameters. The Fully Customize option calls up a specialized form that allows greater control over changes than the High/Low/Base radio buttons or the slider bar on the scenario tree form itself.

Alternative Ways to Use the Change Values Form:

:'''Specify a Desired Value for One or More Years.''' You specify a desired numerical value, indicate the number of years you wish to repeat that value (one or more) and then touch the Change/Repeat button.

:'''Interpolate to a Desired Value over Several Years.''' You specify a desired value, indicate the number of years over which you wish to interpolate to that value, and then touch the Interpolate button.

:'''Cancel All Changes (only becomes active after you have made changes).''' ''' '''You touch the Cancel All Changes button to revert to the parameter values before you began making changes.

:'''Move Forward or Backward Across Years.''' You touch the Previous Year or the Next Year buttons to move across time without changing parameter values.

The graphic in the upper right of the form will show you the changes made.  You can also use the slider bar below that graphic to make changes.

When changes are complete, Exit to Scenario Tree will take you back to the tree.

= Change Selected Functions =

Variables are forecast based on mathematic relationships that are represented by functions within IFs. These functions can be changed by users based on different understandings of relationships between variables. Users of IFs can change relationships between  two variables  or  multiple variables . Follow the links below to learn more about how to change these relationships.

== Bivariate Functions ==

Begin at the Main Menu of IFs. Choose the Change Selected Functions option under Scenario Analysis. Then choose the Bivariate Functions sub-option. That will give you the Change Bi-Variate Functions window, below.

[[File:Useifs39.png|frame|right|Change Bi-Variate Functions window]]

To see the full list of functions or relationships that you can change in IFs, check Extend List. Click on any of the function names (relationships) in the Functions list box in order to see the relationship already in IFs. When you click a graphic of the function will be displayed in the lower right of the window. Many of the functions will also display the r-squared and standard error (SE) that indicate the strength of the relationship that we found when creating it via [[Data_Analysis#Analyze_Across_Countries_.28Cross-Sectional_Analysis.29|cross-sectional analysis]] of our database.

Our creation of the functions was sometimes to use in computations of the model run from the base year, sometimes to be used in the historical run, and sometimes in the model's data preprocessor to fill holes and clean up data (again, for either historical forecasts or those looking forward). The options in the upper right are all checked by default, but if you wanted to see only those functions used in the model run, you could turn off all of the other checks. This window actually gives you the opportunity to revise the functions used in the model run.

There are two ways to specify (and therefore also to change) relationships in IFs. The first is called a "table function." Table functions allow you to specify two or more points in a relationship and let IFs connect those points with lines to create the relationship. This is a very simple process because you need not understand equations; but it also allows the creation of complex functions. The second is via "analytic functions" or equations. Look at each in turn.

Option A for Specifying Relationships: '''Table Functions'''. Click on the "A Function to Play With" relationship so that you can do just that. In the Table Function Points frame, you see boxes for specifying new or changing old X-axis and Y-axis values. Let's start by adding a point to the five that already make up the table function. Specify 7 as the X-axis value and 8 as the Y-axis value. Touch the Add button and the point will be added, creating a relationship between X and Y that increases to a point and then starts down. Try specifying 1 as the X-axis value and 8 as the Y-axis value, then touching the Alter button. Now you have a relationship that starts downward, climbs, and then drops. Table functions give you nearly unlimited control over the form of a relationship. You can always touch Reverse Changes to return to the original form. As it says, play around.

Option B for Specifying Relationships: '''Analytic Functions (Equations)'''. Click again on the "A Function to Play With" relationship. This time, however, touch the Analytic Function button in the lower left to call up the following screen.

[[File:Useifs40.PNG|frame|right|Specify Analytic Function window]]

If you do not understand equations, you may want to skip this part of the lesson – table functions can serve you well. But you can create a simple analytical function by specifying the constant (a) as 20 and the logarithmic parameter (b4) as 3. Leave the other parameters at 0. Note that by selectively specifying various parameters you can create a wide variety of analytic functions (including most of those that Excel will create when you fit lines to scatter plots). You may also specify the lower and upper range over which you will allow the independent variable to vary. For instance, you may not want the independent variable to be negative.

Save and Continue returns you to the Change Functions window and displays the analytic form you have specified. Note that the function you have created looks much like the one that Excel fit to the relationship between GDP per capita and life expectancy in Lesson 4 . You now have the capability of discovering relationships and good analytic representations of them using the cross-section analysis capability of IFs or Excel with the IFs database or Excel and then taking those relationships into IFs itself.

When you Exit from the Change Functions window, IFs gives you a very important informational warning. If you have made any changes to functions, those will remain active only until you exit from IFs. Starting IFs again resets all functions to standard values. Moreover, although runs of the model that you make with altered functions will reflect your changes, no information about the changed functions is saved with the .RUN files. You must keep track of the changes you make in functions.

After you have changed one or more functions, you can run the model and create a new working file that reflects the changed functions and their impact on all computations in IFs. You can compare that working file (or a saved version of it) with the Base Case or with other scenarios.

== Multivariate Functions ==

Begin at the Main Menu of IFs. Choose the Change Selected Functions option under Scenario Analysis. Then choose the Multivariate Functions sub-option. That will give you the Change Multi-Variate Functions window, below.

[[File:Useifs41.PNG|frame|right|Change Multi-Variate Functions window]]

Our creation of the functions was most often to use in computations of the model run from the base year, sometimes to be used in the historical run, and sometimes in the model's data preprocessor to fill holes and clean up data (again, for either historical forecasts or those looking forward). The options in the upper right are all checked by default, but if you wanted to see only those functions used in the model run, you could turn off all of the other checks. This form actually gives you the opportunity to revise the functions used in the model run.

The form has two grids. The first grid on the default page shows a list of all multivariate functions in the model. When you select one of the functions in the first grid, the second grid will show up, which shows the independent variables and parameters in the particular function you have chosen.

*'''Intercepts''': Change the intercept value for the function by left-clicking on the value in the intercept cell of the function of interest (the intercept cell is just to the right of the name of the dependent variable).
*'''Other Parameters''': Change parameters associated with specific independent variables for a selected function by clicking on the appropriate cell and changing values as desired. The general function at the top of the form explains the meaning of each possible parameter for independent variables. For instance, b1 is the parameter and would be used to identify a linear relationship of the independent and dependent variables. Normally, of course, most parameters will be zero (or null which means 0).

Changing functions gives you a powerful tool for using IFs to investigate possible futures. To an extent, it allows you to change the model itself.

= Running Working Scenario =

The Run option can be found by selecting Scenario Analysis from the Main Menu.[[File:Useifs42.PNG|frame|right|Running Working Scenario Window]]

Use this IFs option if you would like to take a Working File to which you have made scenario changes (see the topic on [[#Quick_Scenario_Analysis_with_Tree_Overview|Quick Scenario Analysis with Tree]]) and run it so that all of the variables of IFs are recomputed. It is important to remember that scenario changes of parameters or initial conditions by themselves do not affect the forecasted variables of IFs. It requires a run of the model to do those computations. When the run is completed after parameter changes, the forecast variables of the Working File will have been completely changed.

Entering this form directly from the Main Menu is actually not the most common path. Typically, users will change parameters in the Quick Scenario Tree and use the [[#Exploring_and_Changing_Parameters_2|Run]] option from that form. It will also take them to this form for Running (the) Working Scenario.

On this form you can specify for how many years IFs should run. Choose the year in which you would like the run to stop and then click Start Run.

= Reload the Base =

When you have used the scenario tree (see Quick Analysis with Scenario Tree) to build a scenario via parameter changes or to load a pre-existing set of parameter changes (an .sce file) and then run the scenario to generate a run or results file, the Working File will hold the contents of that model run. It will therefore be different from the Base Case, to which it is automatically set every time you start IFs again. Should you wish to reset the Working File to the Base Case in a session of work with IFs, simply use the Reload the Base sub-option under the Scenario Analysis option of the Main Menu.

Result replication instructions for The Impact of COVID-19 on Long-term Projections of Poverty

2023-02-16T23:07:13Z

Fouad.Alkhouri: removed dead links

International Futures (IFs) is a tool for thinking about long-term country-specific, regional, and global futures. IFs integrates forecasts across different sub-models, including: population, economy, agriculture, education, energy, sociopolitical, international political, environment, technology, infrastructure, and health. The interconnection of these sub-models allows IFs to simulate how changes in one system lead to changes across all other systems.

These instructions will help you download and run scenarios that reflect work done in this study. If you would like additional information on specific result replication please direct requests to [mailto:pardee.center@du.edu. pardee.center@du.edu].

= General instructions =

#Download model and all supplementary files (see below)
#Install IFs
#Add supplementary files to their specified locations (see below)
#Run the scenarios (see below)

= Necessary files =

'''IFs Model: '''[ftp://ifs.du.edu/Replication%20Files/COVID-19%20and%20Poverty/IFs%20with%20Pardee%207_52%20May%2012%202020.zip Link to download IFs] (version 7.52)

'''Scenario files: '''[ftp://ifs.du.edu/Replication%20Files/COVID-19%20and%20Poverty/7_52_Scenarios.zip Link to download SCE files]

= Specific instruction =

== How to install IFs ==

Requirements for installing and running IFs on your computer can be found on our website [https://pardee.du.edu/access-ifs here]. Requirements and instructions on installing IFs on a Mac can be found here. 

After downloading IFs (as a zipped folder), you will need to extract all the files from the zipped folder. Then double-click 'IFsSetupStarter". This should initiate the installation. After installation, go to 'Start' and select 'IFs' from the list of programs to run IFs.

== How to add scenario files ==

Scenario files can be manually added to IFs once they have been downloaded. To manually add scenario files (files with the .sce extension) to IFs, you will need to copy the downloaded .sce file to the Scenario folder within IFs. The scenario folder can be accessed using the following path:

''C:/Users/Public/IFs/Scenario''

Create a new folder and copy over all scenario files that are necessary for the reproduction of desired results. For more information on scenario analysis in IFs please click here.

== How to batch run scenarios ==

The batch run feature in IFs runs multiple scenarios in succession and saves each resulting run files. To do this, select the ‘Run’ sub-option from ‘Scenario Analysis’ in the main menu bar and then select the option to perform a ‘Batch Run’. From this point all folders contained within the ‘IFs/Scenario’ folder should be visible. Navigate to the desired folder, choose a run horizon, and then press ‘Select.’ IFs will then run each scenario contained within the selected folder save them as a sperate run file.

== Visualizing results ==

There are many ways to visualize Base Case and scenario results in IFs. The Flexible Displays allows a user to display a wide variety of projected variables (sometimes in combination with historical data series), create graphs and view tables of these displays. This display also allows the user to compare results across countries, groups, and scenarios. Not all of the variables available in IFs can be found in Flexible Displays; its purpose is to provide users with access to commonly used variables. If you wish to access the full range of variables and parameters, you can do so in Self-Managed Displays. For more information on using Flexible Displays, click here and for more information on self managed displays, please click here.

== Sample results ==

The following instructions will produce a table of extreme poverty projections accross the seven scenarios described in this study. After completeing the above steps, go to "Displays" followed by "Flex Display" in the main menu. In the subsequent window's menu bar, choose "Geography Options" and toggle "Using Groups." Navigate to "Poverty" under the display category menu, then select the display "Poverty Headcount <$1.90 per Day, Log Normal - Millions." Next, under the geography menu, scroll down and select "World." Choose a horizon of 2050, and then select "IFsBase" and all six scenarios from the scenario menu. Finally, click on "Table" at the bottom left of the window. IFs should now display the desired output table.

= Relevant Documentation =

'''IFs training manual:''' [https://pardee.du.edu/international-futures-ifs-training-manual Link to IFs Training Manual]

'''IFs scenario guide:''' [https://pardee.du.edu/guide-scenario-analysis-international-futures-ifs Link to IFs Scenario Guide]

'''Model documentation:''' [https://pardee.du.edu/node/484 Link to all documentation]

'''Relevant code:''' [ftp://ifs.du.edu/Replication%20Files/COVID-19%20and%20Poverty/IFs_7_52_Code.zip Link to all code]

2023-01-21T21:18:41Z

Fouad.Alkhouri:

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2023-01-21T21:12:56Z

Fouad.Alkhouri:

Step91