Pardee Wiki - User contributions [en]

Core Series

2022-08-24T21:35:21Z

Audrey.Pirzadeh: Completed instructions for Population pull

== <big>'''GDP and GDPPC'''</big> ==
Quarterly, we update the GDP series in IFs by using a special process that involves pulling data from the WB and the IMF. It is special as the GDP values we have in IFs are based on constant 2011 prices, whereas World Bank is based on constant 2010 prices. The IMF updates its forecasts in its World Economic Outlook publication in October and January (Full Release) and in April and July (revisions).

If one country has a different WB historical value, all countries must be updated. Why? (1) We do not necessarily know which country’s historical series was revised. (2) If we have a method to update one or do not know which changed, we update all anyway.

GDP is a key economic series in IFs, so it’s '''ESSENTIAL''' the puller and vet person do this process carefully and have a full understanding of the process. Thus, we document.

''For this process, you will require three sources:''

* Primary Source: WB World Development Indicator (WDI)
** GDP in US Current $
** GDP Growth (Annual %) [Based on Constant Price]

* Secondary & Forecasting Source: IMF World Economic Outlook (WEO)
** GDP in US Current $
** GDP Growth [Based on Constant Price]

* 3rd Source: IFs
** GDP Growth Rates (Still Constant Price)

''Use of the Three Sources:''

* '''WB GDP in US Current $'''
** We use WB GDP values in current $ as the primary source to establish the 2011 constant $ GDP base. This means that we start by pulling the most up to date GDP value for the year 2011 for all countries. From here, we apply GDP growth rates to the 2011 GDP value both into the future and historically. If the WB does not have 2011 GDP data for certain countries, use IMF values, if there are no IMF values, we pull from IFs.

* '''WB GDP Growth (Annual %) Constant Price'''
** We use WB GDP growth as the primary source for calculating GDP values both into the future and historically. The WB has GDP growth data from 1960 to the current year -1 (EX: if it is 2021, the WB will have data from 1960-2020). In IFs, we also collect data from 1960. However, WB temporal coverage will be different depending on the country (i.e. not all countries will have data back to 1960). If a country does not have full coverage, use IMF GDP growth rates. Though, the IMF only has historical values back to 1980, therefore, we use IFs GDP growth rates to cover the rest of the missing historical values.
** NOTE: not all countries will have data back to 1960, this is because some countries did not exist until later in time. For these countries, we simply leave those historical values empty. Examples of such countries include (but are not limited to) Samoa, Timor-Leste, UAE, Kosovo.

* '''IMF GDP in US Current $'''
** As stated above, we use IMF GDP in US Current $ for those countries that are not covered or are missing from the WB. For example, the WB does not have data for Taiwan. In this case, you would use the 2011 Current GDP value from the IMF to establish the base.

* '''IMF GDP Growth (Annual %) Constant Price'''
** As stated above, we use IMF GDP growth rates as a secondary source when the WB does not have data both into the future and historically. We use IMF growth rates starting from the last available year from the WB (EX: if WB ends in 2018, start 2019 with IMF growth rate OR if WB ends in 1999, start in 1998 with IMF growth rate)
** The IMF also publishes forecasts of GDP growth rates 5 years into the future (EX: if it is 2021, they have GDP growth rates into 2026). We want to include these forecasts in our data. This is where it can get tricky. The IMF updates its forecasts for big economies twice a year (in April and July). These updates are only for big economies (usually around 30 countries) and only include revisions for the next 2 years. Going back to our example, if IMF releases forecasts for 2022-20226, the years 2022 and 2023 will be updated twice a year, but the following growth rates remain the same. Be careful when updating forecasts and remember to check for IMF revisions.
** If there are not IMF values (i.e., North Korea and Syria) then find a source with a value (i.e., CIA Factbook). We use that source to fill-in the missing data and extrapolate, assuming say, no growth is occurring in Syria at all. Reliable ones are hard to come across, for these, but we still want to have values at least in IFs.
'''''* BE CAREFUL, IMF and WB have different country concordances! *'''''

* '''IFs GDP and Growth Rates'''
** We use IFs GDP values when the WB and IMF do not have coverage for a country when establishing the 2011 base. This is very rare, the rule of thumb is that we trust the WB, then the IMF, and IFs as a last resort.
**We use IFs GDP growth rates when the WB and IMF do not have historical coverage (typically used for 1960-1980 because IMF data ends in 1980). IFs does not have a historical GDP growth variable from which we can pull data directly. However, we can calculate growth rates from the historical GDP values. To do this, you want to navigate to Flexible Displays and select Display Format form the menu then Use All Available Historical Data.
[[File:Image1.png|border|frameless|380x380px|center]]

* Next, choose the variable “GDP (MER), History and Forecast – Billion dollars”. This variable is based on seriesGDP2011. To check that you are pulling the proper data, select List Features then Explain List and make sure that the Historic Analog reads GDP2011.
[[File:Image2.png|border|center|frameless|660x660px]]
* We only want to use data, so make sure your horizon is not set into the future as you will not need this data (TIP: data in IFs appears in blue and forecasts are in black, so you will be able to tell when data ends and forecasts begin). Now, select all the countries and view the data in a Table. The table is now displaying GDP values. In order to get GDP growth, we need to know the percent change from year to year. IFs can calculate this for you, select Display Options then Percent. Now your dataset is complete. From here, you can save the data as a file or copy and paste from your clipboard into an excel.
[[File:Image3.png|border|center|frameless|454x454px]]
''Let us first consider an example to assess our understanding:''
[[File:Image4.png|border|center|frameless|637x637px]]
This is an example of Afghanistan. We see the GDP (current US$) for Afghanistan is $19.76 billion in 2015. The GDP Growth (annual %) for Afghanistan is 1.451% in 2015 and 2.260% in 2016. Given that the value for Afghanistan is $19.76 billion, we calculate the 2016 value as 20.206 billion and the 2014 value for 19.477 billion (as shown above).

You can check to make sure that you’ve done the calculation properly by calculating the growth rate. The growth rate that you calculate should equal the growth rate from whichever source it was pulled (WB, IMF, or IFs).

'''This is the approach we follow.''' We find GDP (current US$) in 2011 for each country (from WB current GDP). Then, use the GDP growth as annual % for 1960-2010 and 2011 to (Current Year-1), to calculate GDP (constant US$ based on 2011).

We use the IMF projections for our country GDP forecast values (typically 5 years into the future). Why can we do this? Regardless, of the base year, the growth rate should not change. Why is that true? Think of the base year, as a year (or index) that we want to compare changes to. The level of that index shifts to a different comparison year, the rates of growth do not change.

Therefore, this whole process of updating GDP in IFs will work even if we change the base year. For example, if we switch to GDP at 2015’s constant price instead of 2011’s constant price, the first step of taking WB values would switch to taking 2015’s GDP current price for all countries as opposed to 2011’s.

'''You think you get it?''' Try Afghanistan first. Verify. If you get it right, great work! Apply that method to all. The final deliverable should be a series with a GDP value for every country in Constant 2011 price.

'''How would someone vet?''' (1) Replicating the procedure for a handful of random countries. If these random countries are right, they probably are all right. However, you want to pay special close attention to the forecasts of the big economy countries that are updated by the IMF twice a year as these are the most complicated calculations. Follow typical vetting checks. (2) Vetting new series against old historical series in IFs.

The project supervisor then updates all the scenario files.

We '''repeat''' this logic for 2011 PCPP GDP. Do not fear, however, as it’s easier. For this one, we get the 2011 values, then the growth rates, and use that to calculate the GDP values (constant 2011). There are no forecasts, as PCPP is not commonly forecasted.
== '''POPULATION''' ==

Every two years, we update the population series in IFs by pulling data from the UN World Population Prospects. The UN updates this data biannually so we can expect to go through this process every two years.

As for all core series, every country must have a value for every year so it is important to check the data for any missing values before importing the series. Should you see missing data in any given year for any given country, you must find an alternative source to fill in the missing data. As of 2022, the UN has complete data for each country and year starting in 1950.

To find population data navigate to the [https://population.un.org/wpp/ UN World Population Prospects page]. Locate the Data tab, then select Data Portal. This will bring you to the UN Population Division Data Portal where you can search for various population-related variables. For the Indicator select "Total Population by Sex", for Location select "Alphabetical" then click "select all" to check all countries. For the Data Range, we want all data available so select the earliest possible data (1950) to the current year. Once you have chosen all your parameters, click "Get Started and Search". This should generate the specified data in a table (see image below). To download the data, select "Export" and "Excel 2007+ (.xlsx)".
[[File:Image5.png|border|center|frameless|751x751px]]
Once you have the data download you can go through the regular steps of importing a series. Start with a country concordance table and double check that all IFs countries are included in the dataset. '''ALL COUNTRIES MUST BE IN THE SERIES'''. If a country is missing, you will need to find an alternative data source. Next, you will need to check that there is data for every year. '''ALL YEARS MUST HAVE DATA'''. If there are years missing for any given country, you will need to find an alternative data source.

After checking that all countries and years are accounted for you may proceed importing the series via the normal process.
== '''LAND AREA''' ==

File:Image5.png

2022-08-24T21:29:48Z

Audrey.Pirzadeh:

Image5

Data

2022-08-24T20:15:24Z

Audrey.Pirzadeh: /* Data Sources */

International Futures (IFs) modeling application contains a large historical database that enables model users to explore patterns, understand relationships and estimate parameters. The database consists of a large collection of country level time series tables covering a large number of variables on IFs model issue areas like population, education, health, agriculture, energy, economics, environment, infrastructure, governance and international politics. The database is compiled, in most cases, from publicly available cross-country data series, e.g., World Development Indicators, UNESCO Institute for Education database on education, FAO database on agricultural production, Correlates of War data on conflicts etc. IFs data team add new series to the database and update the existing ones on a regular basis.

At present IFs data tables contain data for 186 countries. Temporal coverage, in most cases, start in 1960.

*[[Database_files_in_IFs|Database files in IFs]]
*[[Importing_data_(general_instructions)|Importing data (general instructions)]]
*[[Vetting_data|Vetting data]]
*[[Consolidating_data|Consolidating data]]
*[[Sub-regional_data|Sub-Regional data]]
*[[IFs_Base_comparson|IFs Base comparison]]
[[File:World Bank.png|thumb|World Bank Logo (pulling data demo)]]

= Data Sources =
*[[(NEW) Master Sheet of Tables Series|(NEW) Master Sheet of Tables Series (A - GDP)]]
*[[(NEW) Master Sheet of Tables Series (GE-Z)|(NEW) Master Sheet of Tables Series (GEM - Z)]]
*[[Pulling Core Series]]
*[[Pulling Other Series]]
*[[(NEW) Country Concordance]]
*[[(NEW) Series Name]]
*[[(NEW) Tutorial Series Name]]

= Preprocessor =

*[[Agriculture_preprocessor|Agriculture preprocessor]]
*[[Economic_preprocessor|Economic_preprocessor]]
*[[Education_data|Education data]]
*[[Energy_data|Energy data]]
*[[Environmental_data|Environmental data]]
*[[Health_preprocessor|Health_preprocessor]]
*[[Infrastructure_data|Infrastructure data]]
*[[Population_data|Population data]]
*[[Socio-Political_data|Socio-Political data]]
*[[State_failure_data|State failure data]]
*[[Water_data|Water data]]
*[[World_Value_Survey_data|World Value Survey data]]

[[EconDash|EconDash]]

[[SDG_data|SDG_data]]

Data

2022-08-24T19:59:24Z

Audrey.Pirzadeh: /* Data Sources */

International Futures (IFs) modeling application contains a large historical database that enables model users to explore patterns, understand relationships and estimate parameters. The database consists of a large collection of country level time series tables covering a large number of variables on IFs model issue areas like population, education, health, agriculture, energy, economics, environment, infrastructure, governance and international politics. The database is compiled, in most cases, from publicly available cross-country data series, e.g., World Development Indicators, UNESCO Institute for Education database on education, FAO database on agricultural production, Correlates of War data on conflicts etc. IFs data team add new series to the database and update the existing ones on a regular basis.

At present IFs data tables contain data for 186 countries. Temporal coverage, in most cases, start in 1960.

*[[Database_files_in_IFs|Database files in IFs]]
*[[Importing_data_(general_instructions)|Importing data (general instructions)]]
*[[Vetting_data|Vetting data]]
*[[Consolidating_data|Consolidating data]]
*[[Sub-regional_data|Sub-Regional data]]
*[[IFs_Base_comparson|IFs Base comparison]]
[[File:World Bank.png|thumb|World Bank Logo (pulling data demo)]]

= Data Sources =
*[[(NEW) Master Sheet of Tables Series|(NEW) Master Sheet of Tables Series (A - GDP)]]
*[[(NEW) Master Sheet of Tables Series (GE-Z)|(NEW) Master Sheet of Tables Series (GEM - Z)]]
*[[Pulling Core Series]]
*[[Pulling Other Series]]
*[[(NEW) Country Concordance]]
*[[(NEW) Series Name]]
*[[(NEW) Tutorial Series Name]]
*[[World Value Survey]]

= Preprocessor =

*[[Agriculture_preprocessor|Agriculture preprocessor]]
*[[Economic_preprocessor|Economic_preprocessor]]
*[[Education_data|Education data]]
*[[Energy_data|Energy data]]
*[[Environmental_data|Environmental data]]
*[[Health_preprocessor|Health_preprocessor]]
*[[Infrastructure_data|Infrastructure data]]
*[[Population_data|Population data]]
*[[Socio-Political_data|Socio-Political data]]
*[[State_failure_data|State failure data]]
*[[Water_data|Water data]]
*[[World_Value_Survey_data|World Value Survey data]]

[[EconDash|EconDash]]

[[SDG_data|SDG_data]]

Pulling Other Series

2022-08-24T19:58:22Z

Audrey.Pirzadeh: added text

Most of the data for series in IFs come from open-source data queries found online. This page contains a list of the sources from which we pull data and how to navigate them.

* World Value Survey

Pulling Other Series

2022-08-24T19:54:04Z

Audrey.Pirzadeh: Created blank page

Data

2022-08-24T19:53:50Z

Audrey.Pirzadeh: /* Data Sources */

Data

2022-08-24T19:53:17Z

Audrey.Pirzadeh: /* Data Sources */

International Futures (IFs) modeling application contains a large historical database that enables model users to explore patterns, understand relationships and estimate parameters. The database consists of a large collection of country level time series tables covering a large number of variables on IFs model issue areas like population, education, health, agriculture, energy, economics, environment, infrastructure, governance and international politics. The database is compiled, in most cases, from publicly available cross-country data series, e.g., World Development Indicators, UNESCO Institute for Education database on education, FAO database on agricultural production, Correlates of War data on conflicts etc. IFs data team add new series to the database and update the existing ones on a regular basis.

At present IFs data tables contain data for 186 countries. Temporal coverage, in most cases, start in 1960.

*[[Database_files_in_IFs|Database files in IFs]]
*[[Importing_data_(general_instructions)|Importing data (general instructions)]]
*[[Vetting_data|Vetting data]]
*[[Consolidating_data|Consolidating data]]
*[[Sub-regional_data|Sub-Regional data]]
*[[IFs_Base_comparson|IFs Base comparison]]
[[File:World Bank.png|thumb|World Bank Logo (pulling data demo)]]

= Data Sources =
*[[(NEW) Master Sheet of Tables Series|(NEW) Master Sheet of Tables Series (A - GDP)]]
*[[(NEW) Master Sheet of Tables Series (GE-Z)|(NEW) Master Sheet of Tables Series (GEM - Z)]]
*[[Pulling Core Series]]
*[[(NEW) Country Concordance]]
*[[(NEW) Series Name]]
*[[(NEW) Tutorial Series Name]]

= Preprocessor =

*[[Agriculture_preprocessor|Agriculture preprocessor]]
*[[Economic_preprocessor|Economic_preprocessor]]
*[[Education_data|Education data]]
*[[Energy_data|Energy data]]
*[[Environmental_data|Environmental data]]
*[[Health_preprocessor|Health_preprocessor]]
*[[Infrastructure_data|Infrastructure data]]
*[[Population_data|Population data]]
*[[Socio-Political_data|Socio-Political data]]
*[[State_failure_data|State failure data]]
*[[Water_data|Water data]]
*[[World_Value_Survey_data|World Value Survey data]]

[[EconDash|EconDash]]

[[SDG_data|SDG_data]]

Core Series

2022-08-24T19:51:10Z

Audrey.Pirzadeh: fixed images

Core Series

2022-08-24T19:47:16Z

Audrey.Pirzadeh: Formatted GDP and GDPPC

== <big>'''GDP and GDPPC'''</big> ==
Quarterly, we update the GDP series in IFs by using a special process that involves pulling data from the WB and the IMF. It is special as the GDP values we have in IFs are based on constant 2011 prices, whereas World Bank is based on constant 2010 prices. The IMF updates its forecasts in its World Economic Outlook publication in October and January (Full Release) and in April and July (revisions).

If one country has a different WB historical value, all countries must be updated. Why? (1) We do not necessarily know which country’s historical series was revised. (2) If we have a method to update one or do not know which changed, we update all anyway.

GDP is a key economic series in IFs, so it’s '''ESSENTIAL''' the puller and vet person do this process carefully and have a full understanding of the process. Thus, we document.

''For this process, you will require three sources:''

* Primary Source: WB World Development Indicator (WDI)
** GDP in US Current $
** GDP Growth (Annual %) [Based on Constant Price]

* Secondary & Forecasting Source: IMF World Economic Outlook (WEO)
** GDP in US Current $
** GDP Growth [Based on Constant Price]

* 3rd Source: IFs
** GDP Growth Rates (Still Constant Price)

''Use of the Three Sources:''

* '''WB GDP in US Current $'''
** We use WB GDP values in current $ as the primary source to establish the 2011 constant $ GDP base. This means that we start by pulling the most up to date GDP value for the year 2011 for all countries. From here, we apply GDP growth rates to the 2011 GDP value both into the future and historically. If the WB does not have 2011 GDP data for certain countries, use IMF values, if there are no IMF values, we pull from IFs.

* '''WB GDP Growth (Annual %) Constant Price'''
** We use WB GDP growth as the primary source for calculating GDP values both into the future and historically. The WB has GDP growth data from 1960 to the current year -1 (EX: if it is 2021, the WB will have data from 1960-2020). In IFs, we also collect data from 1960. However, WB temporal coverage will be different depending on the country (i.e. not all countries will have data back to 1960). If a country does not have full coverage, use IMF GDP growth rates. Though, the IMF only has historical values back to 1980, therefore, we use IFs GDP growth rates to cover the rest of the missing historical values.
** NOTE: not all countries will have data back to 1960, this is because some countries did not exist until later in time. For these countries, we simply leave those historical values empty. Examples of such countries include (but are not limited to) Samoa, Timor-Leste, UAE, Kosovo.

* '''IMF GDP in US Current $'''
** As stated above, we use IMF GDP in US Current $ for those countries that are not covered or are missing from the WB. For example, the WB does not have data for Taiwan. In this case, you would use the 2011 Current GDP value from the IMF to establish the base.

* '''IMF GDP Growth (Annual %) Constant Price'''
** As stated above, we use IMF GDP growth rates as a secondary source when the WB does not have data both into the future and historically. We use IMF growth rates starting from the last available year from the WB (EX: if WB ends in 2018, start 2019 with IMF growth rate OR if WB ends in 1999, start in 1998 with IMF growth rate)
** The IMF also publishes forecasts of GDP growth rates 5 years into the future (EX: if it is 2021, they have GDP growth rates into 2026). We want to include these forecasts in our data. This is where it can get tricky. The IMF updates its forecasts for big economies twice a year (in April and July). These updates are only for big economies (usually around 30 countries) and only include revisions for the next 2 years. Going back to our example, if IMF releases forecasts for 2022-20226, the years 2022 and 2023 will be updated twice a year, but the following growth rates remain the same. Be careful when updating forecasts and remember to check for IMF revisions.
** If there are not IMF values (i.e., North Korea and Syria) then find a source with a value (i.e., CIA Factbook). We use that source to fill-in the missing data and extrapolate, assuming say, no growth is occurring in Syria at all. Reliable ones are hard to come across, for these, but we still want to have values at least in IFs.
'''* BE CAREFUL, IMF and WB have different country concordances! *'''

* '''IFs GDP and Growth Rates'''
** We use IFs GDP values when the WB and IMF do not have coverage for a country when establishing the 2011 base. This is very rare, the rule of thumb is that we trust the WB, then the IMF, and IFs as a last resort.
**We use IFs GDP growth rates when the WB and IMF do not have historical coverage (typically used for 1960-1980 because IMF data ends in 1980). IFs does not have a historical GDP growth variable from which we can pull data directly. However, we can calculate growth rates from the historical GDP values. To do this, you want to navigate to Flexible Displays and select Display Format form the menu then Use All Available Historical Data.
[[File:Image1.png|border|frameless|380x380px|center]]

* Next, choose the variable “GDP (MER), History and Forecast – Billion dollars”. This variable is based on seriesGDP2011. To check that you are pulling the proper data, select List Features then Explain List and make sure that the Historic Analog reads GDP2011.
[[File:Image2.png|border|center|frameless|620x620px]]
* We only want to use data, so make sure your horizon is not set into the future as you will not need this data (TIP: data in IFs appears in blue and forecasts are in black, so you will be able to tell when data ends and forecasts begin). Now, select all the countries and view the data in a Table. The table is now displaying GDP values. In order to get GDP growth, we need to know the percent change from year to year. IFs can calculate this for you, select Display Options then Percent. Now your dataset is complete. From here, you can save the data as a file or copy and paste from your clipboard into an excel.
[[index.php?title=File:Image3.png|border|center|frameless|448x448px]]

''Let us first consider an example to assess our understanding:''
[[index.php?title=File:Image4.png|border|center|frameless|633x633px]]

This is an example of Afghanistan. We see the GDP (current US$) for Afghanistan is $19.76 billion in 2015. The GDP Growth (annual %) for Afghanistan is 1.451% in 2015 and 2.260% in 2016. Given that the value for Afghanistan is $19.76 billion, we calculate the 2016 value as 20.206 billion and the 2014 value for 19.477 billion (as shown above).

You can check to make sure that you’ve done the calculation properly by calculating the growth rate. The growth rate that you calculate should equal the growth rate from whichever source it was pulled (WB, IMF, or IFs).

'''This is the approach we follow.''' We find GDP (current US$) in 2011 for each country (from WB current GDP). Then, use the GDP growth as annual % for 1960-2010 and 2011 to (Current Year-1), to calculate GDP (constant US$ based on 2011).

We use the IMF projections for our country GDP forecast values (typically 5 years into the future). Why can we do this? Regardless, of the base year, the growth rate should not change. Why is that true? Think of the base year, as a year (or index) that we want to compare changes to. The level of that index shifts to a different comparison year, the rates of growth do not change.

Therefore, this whole process of updating GDP in IFs will work even if we change the base year. For example, if we switch to GDP at 2015’s constant price instead of 2011’s constant price, the first step of taking WB values would switch to taking 2015’s GDP current price for all countries as opposed to 2011’s.

'''You think you get it?''' Try Afghanistan first. Verify. If you get it right, great work! Apply that method to all. The final deliverable should be a series with a GDP value for every country in Constant 2011 price.

'''How would someone vet?''' (1) Replicating the procedure for a handful of random countries. If these random countries are right, they probably are all right. However, you want to pay special close attention to the forecasts of the big economy countries that are updated by the IMF twice a year as these are the most complicated calculations. Follow typical vetting checks. (2) Vetting new series against old historical series in IFs.

The project supervisor then updates all the scenario files.

We '''repeat''' this logic for 2011 PCPP GDP. Do not fear, however, as it’s easier. For this one, we get the 2011 values, then the growth rates, and use that to calculate the GDP values (constant 2011). There are no forecasts, as PCPP is not commonly forecasted.
== '''POPULATION''' ==

== '''LAND AREA''' ==

Core Series

2022-08-24T19:38:54Z

Audrey.Pirzadeh: I made a Core Series page

== <big>'''GDP and GDPPC'''</big> ==
Quarterly, we update the GDP series in IFs by using a special process that involves pulling data from the WB and the IMF. It is special as the GDP values we have in IFs are based on constant 2011 prices, whereas World Bank is based on constant 2010 prices. The IMF updates its forecasts in its World Economic Outlook publication in October and January (Full Release) and in April and July (revisions).

If one country has a different WB historical value, all countries must be updated. Why? (1) We do not necessarily know which country’s historical series was revised. (2) If we have a method to update one or do not know which changed, we update all anyway.

GDP is a key economic series in IFs, so it’s ESSENTIAL the puller and vet person do this process carefully and have a full understanding of the process. Thus, we document.

For this process, you will require three sources:

* Primary Source: WB World Development Indicator (WDI)
** GDP in US Current $
** GDP Growth (Annual %) [Based on Constant Price]

* Secondary & Forecasting Source: IMF World Economic Outlook (WEO)
** GDP in US Current $
** GDP Growth [Based on Constant Price]

* 3rd Source: IFs
** GDP Growth Rates (Still Constant Price)

Use of the Three Sources:

* WB GDP in US Current $

* We use WB GDP values in current $ as the primary source to establish the 2011 constant $ GDP base. This means that we start by pulling the most up to date GDP value for the year 2011 for all countries. From here, we apply GDP growth rates to the 2011 GDP value both into the future and historically. If the WB does not have 2011 GDP data for certain countries, use IMF values, if there are no IMF values, we pull from IFs.

* WB GDP Growth (Annual %) Constant Price

* We use WB GDP growth as the primary source for calculating GDP values both into the future and historically. The WB has GDP growth data from 1960 to the current year -1 (EX: if it is 2021, the WB will have data from 1960-2020). In IFs, we also collect data from 1960. However, WB temporal coverage will be different depending on the country (i.e. not all countries will have data back to 1960). If a country does not have full coverage, use IMF GDP growth rates. Though, the IMF only has historical values back to 1980, therefore, we use IFs GDP growth rates to cover the rest of the missing historical values.
* NOTE: not all countries will have data back to 1960, this is because some countries did not exist until later in time. For these countries, we simply leave those historical values empty. Examples of such countries include (but are not limited to) Samoa, Timor-Leste, UAE, Kosovo.

* IMF GDP in US Current $

* As stated above, we use IMF GDP in US Current $ for those countries that are not covered or are missing from the WB. For example, the WB does not have data for Taiwan. In this case, you would use the 2011 Current GDP value from the IMF to establish the base.

* IMF GDP Growth (Annual %) Constant Price

* As stated above, we use IMF GDP growth rates as a secondary source when the WB does not have data both into the future and historically. We use IMF growth rates starting from the last available year from the WB (EX: if WB ends in 2018, start 2019 with IMF growth rate OR if WB ends in 1999, start in 1998 with IMF growth rate)
* The IMF also publishes forecasts of GDP growth rates 5 years into the future (EX: if it is 2021, they have GDP growth rates into 2026). We want to include these forecasts in our data. This is where it can get tricky. The IMF updates its forecasts for big economies twice a year (in April and July). These updates are only for big economies (usually around 30 countries) and only include revisions for the next 2 years. Going back to our example, if IMF releases forecasts for 2022-20226, the years 2022 and 2023 will be updated twice a year, but the following growth rates remain the same. Be careful when updating forecasts and remember to check for IMF revisions.
* If there are not IMF values (i.e., North Korea and Syria) then find a source with a value (i.e., CIA Factbook). We use that source to fill-in the missing data and extrapolate, assuming say, no growth is occurring in Syria at all. Reliable ones are hard to come across, for these, but we still want to have values at least in IFs.

'''* BE CAREFUL, IMF and WB have different country concordances! *'''

* IFs GDP and Growth Rates

* We use IFs GDP values when the WB and IMF do not have coverage for a country when establishing the 2011 base. This is very rare, the rule of thumb is that we trust the WB, then the IMF, and IFs as a last resort.

* We use IFs GDP growth rates when the WB and IMF do not have historical coverage (typically used for 1960-1980 because IMF data ends in 1980). IFs does not have a historical GDP growth variable from which we can pull data directly. However, we can calculate growth rates from the historical GDP values. To do this, you want to navigate to Flexible Displays and select Display Format form the menu then Use All Available Historical Data.

[[File:Image1.png|border|left|frameless|380x380px]]
[[File:Image2.png|border|left|frameless|715x715px]]

* Next, choose the variable “GDP (MER), History and Forecast – Billion dollars”. This variable is based on seriesGDP2011. To check that you are pulling the proper data, select List Features then Explain List and make sure that the Historic Analog reads GDP2011.
* We only want to use data, so make sure your horizon is not set into the future as you will not need this data (TIP: data in IFs appears in blue and forecasts are in black, so you will be able to tell when data ends and forecasts begin). Now, select all the countries and view the data in a Table. The table is now displaying GDP values. In order to get GDP growth, we need to know the percent change from year to year. IFs can calculate this for you, select Display Options then Percent. Now your dataset is complete. From here, you can save the data as a file or copy and paste from your clipboard into an excel.

[[File:Image3.png|border|frameless|448x448px]]

Let us first consider an example to assess our understanding:

[[File:Image4.png|border|frameless|633x633px]]

This is an example of Afghanistan. We see the GDP (current US$) for Afghanistan is $19.76 billion in 2015. The GDP Growth (annual %) for Afghanistan is 1.451% in 2015 and 2.260% in 2016. Given that the value for Afghanistan is $19.76 billion, we calculate the 2016 value as 20.206 billion and the 2014 value for 19.477 billion (as shown above).

You can check to make sure that you’ve done the calculation properly by calculating the growth rate. The growth rate that you calculate should equal the growth rate from whichever source it was pulled (WB, IMF, or IFs).

'''This is the approach we follow.''' We find GDP (current US$) in 2011 for each country (from WB current GDP). Then, use the GDP growth as annual % for 1960-2010 and 2011 to (Current Year-1), to calculate GDP (constant US$ based on 2011).

We use the IMF projections for our country GDP forecast values (typically 5 years into the future). Why can we do this? Regardless, of the base year, the growth rate should not change. Why is that true? Think of the base year, as a year (or index) that we want to compare changes to. The level of that index shifts to a different comparison year, the rates of growth do not change.

Therefore, this whole process of updating GDP in IFs will work even if we change the base year. For example, if we switch to GDP at 2015’s constant price instead of 2011’s constant price, the first step of taking WB values would switch to taking 2015’s GDP current price for all countries as opposed to 2011’s.

'''You think you get it?''' Try Afghanistan first. Verify. If you get it right, great work! Apply that method to all. The final deliverable should be a series with a GDP value for every country in Constant 2011 price.

'''How would someone vet?''' (1) Replicating the procedure for a handful of random countries. If these random countries are right, they probably are all right. However, you want to pay special close attention to the forecasts of the big economy countries that are updated by the IMF twice a year as these are the most complicated calculations. Follow typical vetting checks. (2) Vetting new series against old historical series in IFs.

The project supervisor then updates all the scenario files.

We '''repeat''' this logic for 2011 PCPP GDP. Do not fear, however, as it’s easier. For this one, we get the 2011 values, then the growth rates, and use that to calculate the GDP values (constant 2011). There are no forecasts, as PCPP is not commonly forecasted.

== '''POPULATION''' ==

== '''LAND AREA''' ==

File:Image4.png

2022-08-24T19:34:39Z

Audrey.Pirzadeh:

Image4

File:Image3.png

2022-08-24T19:33:20Z

Audrey.Pirzadeh:

Image3

File:Image2.png

2022-08-24T19:30:16Z

Audrey.Pirzadeh:

Image2

File:Image1.png

2022-08-24T19:25:10Z

Audrey.Pirzadeh:

image1

Core Series

2022-08-24T19:18:30Z

Audrey.Pirzadeh: Created blank page

Data

2022-08-24T19:17:32Z

Audrey.Pirzadeh: /* Data Sources */

International Futures (IFs) modeling application contains a large historical database that enables model users to explore patterns, understand relationships and estimate parameters. The database consists of a large collection of country level time series tables covering a large number of variables on IFs model issue areas like population, education, health, agriculture, energy, economics, environment, infrastructure, governance and international politics. The database is compiled, in most cases, from publicly available cross-country data series, e.g., World Development Indicators, UNESCO Institute for Education database on education, FAO database on agricultural production, Correlates of War data on conflicts etc. IFs data team add new series to the database and update the existing ones on a regular basis.

At present IFs data tables contain data for 186 countries. Temporal coverage, in most cases, start in 1960.

*[[Database_files_in_IFs|Database files in IFs]]
*[[Importing_data_(general_instructions)|Importing data (general instructions)]]
*[[Vetting_data|Vetting data]]
*[[Consolidating_data|Consolidating data]]
*[[Sub-regional_data|Sub-Regional data]]
*[[IFs_Base_comparson|IFs Base comparison]]
[[File:World Bank.png|thumb|World Bank Logo (pulling data demo)]]

= Data Sources =
*[[(NEW) Master Sheet of Tables Series|(NEW) Master Sheet of Tables Series (A - GDP)]]
*[[(NEW) Master Sheet of Tables Series (GE-Z)|(NEW) Master Sheet of Tables Series (GEM-Z)]]
*[[Master Sheet]]
*[[New Page|Country Concordance]]
*[[Add New Page]]
*[[(NEW) Series Name]]
*[[(NEW) Tutorial Series Name]]
*[[Core Series]]

= Preprocessor =

*[[Agriculture_preprocessor|Agriculture preprocessor]]
*[[Economic_preprocessor|Economic_preprocessor]]
*[[Education_data|Education data]]
*[[Energy_data|Energy data]]
*[[Environmental_data|Environmental data]]
*[[Health_preprocessor|Health_preprocessor]]
*[[Infrastructure_data|Infrastructure data]]
*[[Population_data|Population data]]
*[[Socio-Political_data|Socio-Political data]]
*[[State_failure_data|State failure data]]
*[[Water_data|Water data]]
*[[World_Value_Survey_data|World Value Survey data]]

[[EconDash|EconDash]]

[[SDG_data|SDG_data]]

(NEW) Master Sheet of Tables

2022-08-10T18:14:12Z

Audrey.Pirzadeh: edit

Audrey's edit

Example Table
{| class="wikitable"
|
|Series Name
|Source
|Last IFs Update
|Pulled By (Initials)
|Description
|-
|1
|Example Series
|[https://pardeewiki.du.edu//index.php?title=FAOSTAT FAOSTAT]
|2022/07/27
|TZ
|The Example Series is pulled from [https://pardeewiki.du.edu//index.php?title=FAOSTAT FAOSTAT] and is typically used to do stuff
|-
|2
|Example 2 Series
|[https://pardeewiki.du.edu//index.php?title=World_Bank World Bank]
|2022/07/27
|TZ
|Link to [https://data.worldbank.org/ external source] in Master Sheet
|-
|3
|Example Formula
|N/A
|N/A
|N/A
|This is showcasing a formula insert
|}
DataDict Tables
{| class="wikitable"
|Table
|Source (LINK)
|Last IFs Update
|Pulled By (Initials)
|-
|AbortJustifPercent
|World Value Survey
|2010/08/01
|
|-
|AuthorbyEduc
|World Value Survey
|
|
|-
|AutonPercent
|World Value Survey
|2010/08/01
|
|-
|DemocBest
|World Value Survey
|
|
|-
|DiploIGOWeightMaxStandardized
|Pardee Center Diplometrics
|2013/04/01
|JDM
|-
|GodImprtPercent
|World Value Survey
|2010/08/01
|
|-
|HappybyEduc
|World Value Survey
|2010/08/01
|
|-
|HappyPercent
|World Value Survey
|2010/08/01
|
|-
|HomoJustifPercent
|World Value Survey
|2010/08/01
|
|-
|MatPMTop
|World Value Survey
|2010/08/01
|
|-
|NationProudPercent
|
|
|
|-
|PMMinMatPercent
|World Value Survey
|
|
|-
|RespectAuthPercent
|World Value Survey
|2010/08/01
|
|-
|Series%boys(15-19)marriedorunion
|UNICEF
|2022/03/03
|KG
|-
|Series%girls(15-19)marriedorunion
|UNICEF
|2022/03/03
|KG
|-
|Series%men(20-24)marriedorunionbefore18
|UNICEF
|2022/03/03
|KG
|-
|Series%women(20-24)marriedorunionbefore15
|UNICEF
|2022/03/03
|KG
|-
|Series%women(20-24)marriedorunionbefore18
|UNICEF
|2022/03/03
|KG
|-
|SeriesAdditivePolyarchyIndexVDEM
|Varieties of Democracy
|2019/08/06
|AW
|-
|SeriesAgBovineMeatProductionFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgCerealsEx
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgCerealsIm
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgCerealSupply
|FAO BATCH PULL
|2017/05/28
|KN,HF,LW
|-
|SeriesAgCerealsYieldperHec
|FAO BATCH PULL
|2018/02/12
|HF,EB
|-
|SeriesAgCerealWaste
|FAO BATCH PULL
|2017/05/28
|KN,HF,LW
|-
|SeriesAgConMeat
|WRI Earthtrends <nowiki>http://earthtrends.wri.org/</nowiki>
|2008/05/01
|Converted to million metric tons
|-
|SeriesAGCropCalPerCapPerDayFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGCropDomesticSupplyFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgCropExportQuantityFAOTrade
|FAOSTAT, Trade Domain
|2018/02/22
|HF,EB
|-
|SeriesAGCropExportsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgCropExportValueFAOTrade
|FAOSTAT, Trade Domain
|2018/02/22
|HF,EB
|-
|SeriesAGCropFatPerCapPerDayFAO
|FAO Food Balance Sheets
|2017/05/09
|KN,AJM,HF, Aggregation rules on Wiki for each series
|-
|SeriesAGCropFoodSupplyPerCapPerDayFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgCropImportQuantityFAOTrade
|FAOSTAT, Trade Domain
|2018/02/22
|HF,EB
|-
|SeriesAGCropImportsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgCropImportValueFAOTrade
|FAOSTAT, Trade Domain
|2018/02/22
|HF,EB
|-
|SeriesAgCropProdIndex
|WDI BATCH PULL
|2019/02/01
|KBN,HF.MD
|-
|SeriesAGCropProductionFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGCropProteinPerCapPerDayFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGCropStockVarFAO
|FAO BATCH PULL
|2021/12/29
|KS, GE
|-
|SeriesAGCroptoFeedFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGCroptoFoodFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGCroptoFoodManuFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGCroptoOtherUtilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGCroptoSeedFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgCropTotEx
|Computed
|
|
|-
|SeriesAgCropTotIm
|Computed Sum
|
|
|-
|SeriesAGCroptoWasteFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFertUse
|WDI BATCH PULL
|2019/02/01
|KBN,HF.MD
|-
|SeriesAgFertUseperHectare
|WDI BATCH PULL
|2019/02/01
|KBN,HF.MD
|-
|SeriesAgFish%Protein
|WRI Earthtrends <nowiki>http://earthtrends.wri.org/</nowiki>
|2005/04/01
|
|-
|SeriesAgFishAquaCatchTot
|WRI Earthtrends <nowiki>http://earthtrends.wri.org/</nowiki>
|2009/07/25
|
|-
|SeriesAgFishAquaInland
|WRI Earthtrends <nowiki>http://earthtrends.wri.org/</nowiki>
|2010/11/23
|EWF
|-
|SeriesAgFishAquaMarine
|WRI Earthtrends <nowiki>http://earthtrends.wri.org/</nowiki>
|2010/11/23
|EWF
|-
|SeriesAgFishAquaOther
|FAOSTAT
|2012/03/04
|EWF;CN
|-
|SeriesAgFishAquaProdAqAnimalsFSJ
|FAO FishstatJ software, Global Aquaculture Production Quantity data
|2015/08/20
|N.S
|-
|SeriesAgFishAquaProdAqPlantsFSJ
|FAO FishstatJ software, Global Aquaculture Production Quantity data
|2017/07/14
|ALN, MKH
|-
|SeriesAgFishAquaProdCephalopodsFSJ
|FAO FishstatJ software, Global Aquaculture Production Quantity data
|2017/07/14
|ALN, MKH
|-
|SeriesAgFishAquaProdCrustaceansFSJ
|FAO FishstatJ software, Global Aquaculture Production Quantity data
|2017/07/13
|ALN, MKH
|-
|SeriesAgFishAquaProdDemersalFSJ
|FAO FishstatJ software, Global Aquaculture Production Quantity data
|2017/07/13
|ALN, MKH
|-
|SeriesAgFishAquaProdFreshwaterFSJ
|FAO FishstatJ software, Global Aquaculture Production Quantity data
|2017/07/14
|ALN, MKH
|-
|SeriesAgFishAquaProdMarineFSJ
|FAO FishstatJ software, Global Aquaculture Production Quantity data
|2017/07/14
|ALN, MKH
|-
|SeriesAgFishAquaProdMolluscsFSJ
|FAO FishstatJ software, Global Aquaculture Production Quantity data
|2017/07/13
|ALN, MKH
|-
|SeriesAgFishAquaProdOthersFSJ
|FAO FishstatJ software, Global Aquaculture Production Quantity data
|2015/08/20
|N.S
|-
|SeriesAgFishAquaProdPelagicFSJ
|FAO FishstatJ software, Global Aquaculture Production Quantity data
|2017/07/14
|ALN, MKH
|-
|SeriesAgFishAquaTotal
|WRI Earthtrends <nowiki>http://earthtrends.wri.org/</nowiki>
|2009/07/25
|Converted to million metric tons
|-
|SeriesAgFishCalPerCapPerDayAqAnimalsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishCalPerCapPerDayAqMammalsFAO
|FAOSTAT Food Balance Sheets
|2015/08/19
|N.S
|-
|SeriesAgFishCalPerCapPerDayAqPlantsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishCalPerCapPerDayBodyOilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishCalPerCapPerDayCephalopodsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishCalPerCapPerDayCrustaceansFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishCalPerCapPerDayDemersalFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGFishCalPerCapPerDayFAO
|FAO Food Balance Sheets
|2017/05/09
|KN,AJM,HF, Aggregation rules on Wiki for each series
|-
|SeriesAgFishCalPerCapPerDayFreshwaterFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishCalPerCapPerDayLiverOilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishCalPerCapPerDayMarineFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishCalPerCapPerDayMolluscsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishCalPerCapPerDayPelagicFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishCatchProdAqAnimalsFSJ
|FAO FishstatJ software, Global Catch Production Quantity data
|2017/07/13
|ALN, MKH
|-
|SeriesAgFishCatchProdAqMammalsFSJ
|FAO FishstatJ software, Global Catch Production Quantity data
|2017/07/13
|ALN, MKH
|-
|SeriesAgFishCatchProdAqPlantsFSJ
|FAO FishstatJ software, Global Catch Production Quantity data
|2017/07/13
|ALN, MKH
|-
|SeriesAgFishCatchProdCephalopodsFSJ
|FAO FishstatJ software, Global Catch Production Quantity data
|2017/07/13
|ALN, MKH
|-
|SeriesAgFishCatchProdCrustaceansFSJ
|FAO FishstatJ software, Global Catch Production Quantity data
|2017/07/13
|ALN, MKH
|-
|SeriesAgFishCatchProdDemersalFSJ
|FAO FishstatJ software, Global Catch Production Quantity data
|2017/07/13
|ALN, MKH
|-
|SeriesAgFishCatchProdFreshwaterFSJ
|FAO FishstatJ software, Global Catch Production Quantity data
|2017/07/13
|ALN, MKH
|-
|SeriesAgFishCatchProdMarineFSJ
|FAO FishstatJ software, Global Catch Production Quantity data
|2017/07/13
|ALN, MKH
|-
|SeriesAgFishCatchProdMolluscsFSJ
|FAO FishstatJ software, Global Catch Production Quantity data
|2017/07/13
|ALN, MKH
|-
|SeriesAgFishCatchProdOthersFSJ
|FAO FishstatJ software, Global Catch Production Quantity data
|2015/08/20
|N.S
|-
|SeriesAgFishCatchProdPelagicFSJ
|FAO FishstatJ software, Global Catch Production Quantity Data
|2017/07/13
|ALN, MKH
|-
|SeriesAgFishDomesticSupplyAqAnimalsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishDomesticSupplyAqMammalsFAO
|FAOSTAT Food Balance Sheets
|2015/08/19
|N.S
|-
|SeriesAgFishDomesticSupplyAqPlantsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishDomesticSupplyBodyOilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishDomesticSupplyCephalopodsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishDomesticSupplyCrustaceansFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishDomesticSupplyDemersalFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGFishDomesticSupplyFAO
|FAO Food Balance Sheets
|2017/05/09
|KN,AJM,HF, Aggregation rules on Wiki for each series
|-
|SeriesAgFishDomesticSupplyFreshwaterFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishDomesticSupplyLiverOilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishDomesticSupplyMarineFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishDomesticSupplyMealFAO
|FAOSTAT
|2015/08/20
|N.S
|-
|SeriesAgFishDomesticSupplyMolluscsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishDomesticSupplyPelagicFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishExportQuantityFAOTrade
|FAO, FishstatJ
|2015/07/02
|SDT
|-
|SeriesAgFishExportsAqAnimalsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishExportsAqMammalsFAO
|FAOSTAT Food Balance Sheets
|2015/08/19
|N.S
|-
|SeriesAgFishExportsAqPlantsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishExportsBodyOilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishExportsCephalopodsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishExportsCrustaceansFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishExportsDemersalFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGFishExportsFAO
|FAO Food Balance Sheets
|2017/05/09
|KN,AJM,HF, Aggregation rules on Wiki for each series
|-
|SeriesAgFishExportsFreshwaterFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishExportsLiverOilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishExportsMarineFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishExportsMealFAO
|FAOSTAT
|2015/08/20
|N.S
|-
|SeriesAgFishExportsMolluscsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishExportsPelagicFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishExportVal
|FAO FishstatJ Software
|2017/07/18
|ALN, MKH
|-
|SeriesAgFishExportValueFAOTrade
|FAO, FishstatJ
|2015/07/02
|SDT
|-
|SeriesAgFishExpt
|WRI Earthtrends <nowiki>http://earthtrends.wri.org/</nowiki>
|2009/07/25
|
|-
|SeriesAgFishFatPerCapPerDayAqAnimalsFAO
|FAO BATCH PULL
|2017/05/10
|KN;JM
|-
|SeriesAgFishFatPerCapPerDayAqMammalsFAO
|FAOSTAT Food Balance Sheets
|2015/08/19
|N.S
|-
|SeriesAgFishFatPerCapPerDayAqPlantsFAO
|FAO BATCH PULL
|2017/05/10
|KN;JM
|-
|SeriesAgFishFatPerCapPerDayBodyOilFAO
|FAO BATCH PULL
|2017/05/10
|KN;JM
|-
|SeriesAgFishFatPerCapPerDayCephalopodsFAO
|FAO BATCH PULL
|2017/05/10
|KN;JM
|-
|SeriesAgFishFatPerCapPerDayCrustaceansFAO
|FAO BATCH PULL
|2017/05/10
|KN;JM
|-
|SeriesAgFishFatPerCapPerDayDemersalFAO
|FAO BATCH PULL
|2017/05/10
|KN;JM
|-
|SeriesAGFishFatPerCapPerDayFAO
|FAO Food Balance Sheets
|2017/05/09
|KN,AJM,HF, Aggregation rules on Wiki for each series
|-
|SeriesAgFishFatPerCapPerDayFreshwaterFAO
|FAO BATCH PULL
|2017/05/10
|KN;JM
|-
|SeriesAgFishFatPerCapPerDayLiverOilFAO
|FAO BATCH PULL
|2017/05/10
|KN;JM
|-
|SeriesAgFishFatPerCapPerDayMarineFAO
|FAO BATCH PULL
|2017/05/10
|KN;JM
|-
|SeriesAgFishFatPerCapPerDayMolluscsFAO
|FAO BATCH PULL
|2017/05/10
|KN;JM
|-
|SeriesAgFishFatPerCapPerDayPelagicFAO
|FAO BATCH PULL
|2017/05/10
|KN;JM
|-
|SeriesAgFishFoodSupplyPerCapPerDayAqAnimalsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishFoodSupplyPerCapPerDayAqMammalsFAO
|FAOSTAT Food Balance Sheets
|2015/08/19
|N.S
|-
|SeriesAgFishFoodSupplyPerCapPerDayAqPlantsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishFoodSupplyPerCapPerDayBodyOilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishFoodSupplyPerCapPerDayCephalopodsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishFoodSupplyPerCapPerDayCrustaceansFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishFoodSupplyPerCapPerDayDemersalFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGFishFoodSupplyPerCapPerDayFAO
|FAO Food Balance Sheets
|2017/05/09
|KN,AJM,HF, Aggregation rules on Wiki for each series
|-
|SeriesAgFishFoodSupplyPerCapPerDayFreshwaterFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishFoodSupplyPerCapPerDayLiverOilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishFoodSupplyPerCapPerDayMarineFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishFoodSupplyPerCapPerDayMolluscsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishFoodSupplyPerCapPerDayPelagicFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishFreshwaterCatch
|<nowiki>http://earthtrends.wri.org/text/coastal-marine/variables.html</nowiki>
|2011/12/01
|CN
|-
|SeriesAgFishImportQuantityFAOTrade
|FAO, FishstatJ
|2015/07/02
|SDT
|-
|SeriesAgFishImportsAqAnimalsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishImportsAqPlantsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishImportsBodyOilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishImportsCephalopodsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishImportsCrustaceansFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishImportsDemersalFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGFishImportsFAO
|FAO Food Balance Sheets
|2017/05/09
|KN,AJM,HF, Aggregation rules on Wiki for each series
|-
|SeriesAgFishImportsFreshwaterFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishImportsLiverOilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishImportsMarineFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishImportsMealFAO
|FAOSTAT
|2015/08/20
|N.S
|-
|SeriesAgFishImportsMolluscsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishImportsPelagicFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishImportVal
|FAO FishstatJ Software
|2017/07/18
|ALN, MKH
|-
|SeriesAgFishImportValueFAOTrade
|FAO, FishstatJ
|2015/07/02
|SDT
|-
|SeriesAgFishImpt
|WRI Earthtrends <nowiki>http://earthtrends.wri.org/</nowiki>
|2012/03/04
|EWF;CN
|-
|SeriesAgFishInlandProd
|WRI Earthtrends <nowiki>http://earthtrends.wri.org/</nowiki>
|2009/07/25
|
|-
|SeriesAgFishMarineCatch
|<nowiki>http://earthtrends.wri.org/text/coastal-marine/variables.html</nowiki>
|2011/12/01
|CN
|-
|SeriesAgFishProdAqAnimalsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishProdAqMammalsFAO
|FAOSTAT Food Balance Sheets
|2015/08/19
|N.S
|-
|SeriesAgFishProdAqPlantsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishProdAquaInland
|FAO FishstatJ software, Global Aquaculture Production Quantity data
|2017/07/13
|ALN, MKH
|-
|SeriesAgFishProdAquaMarine
|FAO FishstatJ software, Global Aquaculture Production Quantity data
|2017/07/13
|ALN, MKH
|-
|SeriesAgFishProdBodyOilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishProdCatchInland
|FAO FishstatJ software, Global Catch Production Quantity Data
|2017/07/14
|ALN, MKH
|-
|SeriesAgFishProdCatchMarine
|FAO FishstatJ software, Global Catch Production Quantity Data
|2017/07/14
|ALN, MKH
|-
|SeriesAgFishProdCephalopodsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishProdCrustaceansFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishProdDemersalFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishProdFreshwaterFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishProdLiverOilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishProdMarineFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishProdMealFAO
|FAOSTAT
|2015/08/20
|N.S
|-
|SeriesAgFishProdMolluscsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishProdPelagicFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGFishProductionFAO
|FAO Food Balance Sheets
|2017/05/09
|KN,AJM,HF, Aggregation rules on Wiki for each series
|-
|SeriesAgFishProteinPerCapPerDayAqAnimalsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishProteinPerCapPerDayAqMammalsFAO
|FAOSTAT Food Balance Sheets
|2015/08/19
|N.S
|-
|SeriesAgFishProteinPerCapPerDayAqPlantsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishProteinPerCapPerDayBodyOilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishProteinPerCapPerDayCephalopodsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishProteinPerCapPerDayCrustaceansFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishProteinPerCapPerDayDemersalFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGFishProteinPerCapPerDayFAO
|FAO Food Balance Sheets
|2017/05/09
|KN,AJM,HF, Aggregation rules on Wiki for each series
|-
|SeriesAgFishProteinPerCapPerDayFreshwaterFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishProteinPerCapPerDayLiverOilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishProteinPerCapPerDayMarineFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishProteinPerCapPerDayMolluscsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishProteinPerCapPerDayPelagicFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishStockVarAqAnimalsFAO
|FAO BATCH PULL
|2021/12/29
|KS, GE
|-
|SeriesAgFishStockVarAqPlantsFAO
|FAO BATCH PULL
|2021/12/29
|KS, GE
|-
|SeriesAgFishStockVarBodyOilFAO
|FAO BATCH PULL
|2021/12/29
|KS, GE
|-
|SeriesAgFishStockVarCephalopodsFAO
|FAO BATCH PULL
|2021/12/29
|KS, GE
|-
|SeriesAgFishStockVarCrustaceansFAO
|FAO BATCH PULL
|2021/12/29
|KS, GE
|-
|SeriesAgFishStockVarDemersalFAO
|FAO BATCH PULL
|2021/12/29
|KS, GE
|-
|SeriesAGFishStockVarFAO
|FAO BATCH PULL
|2021/12/29
|KS, GE
|-
|SeriesAgFishStockVarFreshwaterFAO
|FAO BATCH PULL
|2021/12/29
|KS, GE
|-
|SeriesAgFishStockVarLiverOilFAO
|FAO BATCH PULL
|2021/12/29
|KS, GE
|-
|SeriesAgFishStockVarMarineFAO
|FAO BATCH PULL
|2021/12/29
|KS, GE
|-
|SeriesAgFishStockVarMealFAO
|FAOSTAT
|2015/08/20
|N.S
|-
|SeriesAgFishStockVarMolluscsFAO
|FAO BATCH PULL
|2021/12/29
|KS, GE
|-
|SeriesAgFishStockVarPelagicFAO
|FAO BATCH PULL
|2021/12/29
|KS, GE
|-
|SeriesAgFishtoFeedAqPlantsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoFeedBodyOilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoFeedCephalopodsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoFeedCrustaceansFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoFeedDemersalFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGFishtoFeedFAO
|FAO Food Balance Sheets
|2017/05/09
|KN,AJM,HF, Aggregation rules on Wiki for each series
|-
|SeriesAgFishtoFeedFreshwaterFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoFeedLiverOilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoFeedMarineFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoFeedMealFAO
|FAOSTAT
|2015/08/20
|N.S
|-
|SeriesAgFishtoFeedMolluscsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoFeedPelagicFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoFoodAqAnimalsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoFoodAqMammalsFAO
|FAOSTAT Food Balance Sheets
|2015/08/19
|N.S
|-
|SeriesAgFishtoFoodAqPlantsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoFoodBodyOilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoFoodCephalopodsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoFoodCrustaceansFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoFoodDemersalFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGFishtoFoodFAO
|FAO Food Balance Sheets
|2017/05/09
|KN,AJM,HF, Aggregation rules on Wiki for each series
|-
|SeriesAgFishtoFoodFreshwaterFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoFoodLiverOilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoFoodMarineFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoFoodMolluscsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoFoodPelagicFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoOtherUtilAqAnimalsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoOtherUtilAqMammalsFAO
|FAOSTAT Food Balance Sheets
|2015/08/19
|N.S
|-
|SeriesAgFishtoOtherUtilAqPlantsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoOtherUtilBodyOilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoOtherUtilCephalopodsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoOtherUtilCrustaceansFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoOtherUtilDemersalFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGFishtoOtherUtilFAO
|FAO Food Balance Sheets
|2017/05/09
|KN,AJM,HF, Aggregation rules on Wiki for each series
|-
|SeriesAgFishtoOtherUtilFreshwaterFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoOtherUtilLiverOilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoOtherUtilMarineFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoOtherUtilMealFAO
|FAOSTAT
|2015/08/20
|N.S
|-
|SeriesAgFishtoOtherUtilMolluscsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoOtherUtilPelagicFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoSeedAqAnimalsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoSeedCephalopodsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoSeedCrustaceansFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoSeedDemersalFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGFishtoSeedFAO
|FAO Food Balance Sheets
|2017/05/09
|KN,AJM,HF, Aggregation rules on Wiki for each series
|-
|SeriesAgFishtoSeedFreshwaterFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoSeedMarineFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoSeedMolluscsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFishtoSeedPelagicFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFoodEx%MerchEx
|WDI BATCH Update 2018
|2018/05/05
|KBN,HF.MD
|-
|SeriesAgFoodIm%MerchIm
|WDI BATCH Update 2018
|2018/05/05
|KBN,HF.MD
|-
|SeriesAgFoodPriceIndex
|WDI CD 05
|2005/06/01
|
|-
|SeriesAgFoodProductIndex
|World Bank World Development Indicators 2008
|2008/08/28
|Food production index covers food crops that are considered edible and that contain nutrients. Coffee and tea are excluded because, although edible, they have no nutritive value
|-
|SeriesAgFruitEx
|FAO BATCH PULL
|2017/05/28
|KN,HF,LW
|-
|SeriesAgFruitIm
|FAO BATCH PULL
|2017/05/28
|KN,HF,LW
|-
|SeriesAgFruitSupply
|FAO BATCH PULL
|2017/05/28
|KN,HF,LW
|-
|SeriesAgFruitWaste
|FAO BATCH PULL
|2017/05/28
|KN,HF,LW
|-
|SeriesAgFruVegEx
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgFruVegIm
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAggBlanks
|JRS
|2012/01/23
|Created by JRS 2012/01/23
|-
|SeriesAgGrainLiv%GrainCon
|WRI online 2012
|2011/12/01
|Blended with latest data. The country list for the online data ends with Serbia and Montenegro; CN
|-
|SeriesAgLifestockProdIndex
|WDI BATCH PULL
|2019/02/01
|KBN,HF.MD
|-
|SeriesAgMachTracper100H
|WDI BATCH PULL
|2019/02/01
|KBN,HF.MD
|-
|SeriesAGMeatCalPerCapPerDayFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGMeatDomesticSupplyFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgMeatEx
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgMeatExportQuantityFAOTrade
|FAOSTAT, Trade Domain
|2018/02/22
|HF,EB
|-
|SeriesAGMeatExportsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgMeatExportValueFAOTrade
|FAOSTAT, Trade Domain
|2018/02/22
|HF,EB
|-
|SeriesAGMeatFatPerCapPerDayFAO
|FAO Food Balance Sheets
|2017/05/09
|KN,AJM,HF, Aggregation rules on Wiki for each series
|-
|SeriesAGMeatFoodSupplyPerCapPerDayFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgMeatIm
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgMeatImportQuantityFAOTrade
|FAOSTAT, Trade Domain
|2018/02/22
|HF,EB
|-
|SeriesAGMeatImportsFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgMeatImportValueFAOTrade
|FAOSTAT, Trade Domain
|2018/02/22
|HF,EB
|-
|SeriesAgMeatOtherProductionFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGMeatProductionFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGMeatProteinPerCapPerDayFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGMeatStockVarFAO
|FAO BATCH PULL
|2021/12/29
|KS, GE
|-
|SeriesAGMeattoFeedFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGMeattoFoodFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGMeattoFoodManuFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGMeattoOtherUtilFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGMeattoSeedFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAGMeattoWasteFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgMuttonandGoatMeatProductionFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgPigMeatProductionFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgPoultryMeatProductionFAO
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgProdCereals
|FAO BATCH PULL
|2018/02/12
|HF,EB
|-
|SeriesAgProdCocoaBeans
|FAO BATCH PULL
|2018/02/12
|HF,EB
|-
|SeriesAgProdCoffeeGreen
|FAO BATCH PULL
|2018/02/12
|HF,EB
|-
|SeriesAgProdEggs
|FAO BATCH PULL
|2018/02/12
|HF,EB
|-
|SeriesAgProdFiberCrops
|FAO BATCH PULL
|2018/02/12
|HF,EB
|-
|SeriesAgProdFruitsExclMelons
|FAO BATCH PULL
|2018/02/12
|HF,EB
|-
|SeriesAgProdMeat
|FAO FAOstat; <nowiki>http://faostat.fao.org/site/569/default.aspx#ancor</nowiki>
|2018/02/22
|HF,EB
|-
|SeriesAgProdMilk
|FAO BATCH PULL
|2018/02/12
|HF,EB
|-
|SeriesAgProdOilCrops
|FAO BATCH PULL
|2018/02/12
|HF,EB
|-
|SeriesAgProdPulses
|FAO BATCH PULL
|2018/02/12
|HF,EB
|-
|SeriesAgProdRootsTub
|FAO BATCH PULL
|2018/02/12
|HF,EB
|-
|SeriesAgProdSugarCane
|FAO BATCH PULL
|2018/02/12
|HF,EB
|-
|SeriesAgProdTreenuts
|FAO BATCH PULL
|2018/02/12
|HF,EB
|-
|SeriesAgProdVegMel
|FAO BATCH PULL
|2018/02/12
|HF,EB
|-
|SeriesAgPulsesEx
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgPulsesIm
|FAO BATCH PULL
|2022/01/14
|KS
|-
|SeriesAgRawEx%MerchEx
|WDI BATCH Update 2018
|2018/05/05
|KBN,HF.MD
|-
|SeriesAgRawIm%MerchIm
|WDI BATCH Update 2018
|2018/05/05
|KBN,HF.MD
|-
|SeriesAgVegetableSupply
|FAO BATCH PULL
|2017/05/28
|KN,HF,LW
|-
|SeriesAgVegetableWaste
|FAO BATCH PULL
|2017/05/28
|KN,HF,LW
|-
|SeriesAgVegEx
|FAO BATCH PULL
|2017/05/28
|KN,HF,LW
|-
|SeriesAgVegIm
|FAO BATCH PULL
|2017/05/28
|KN,HF,LW
|-
|SeriesAid%Untied
|Millennium Indicators Database, UN <nowiki>http://unstats.un.org/unsd/mi/mi_goals.asp</nowiki>
|2004/10/01
|
|-
|SeriesAidCerealDon
|WRI Earthtrends <nowiki>http://earthtrends.wri.org/</nowiki>
|2012/03/04
|EWF;CN
|-
|SeriesAidCerealRec
|WRI Earthtrends <nowiki>http://earthtrends.wri.org/</nowiki>
|2012/03/04
|EWF;CN
|-
|SeriesAidDACEd%GDP
|OECD; <nowiki>http://stats.oecd.org/</nowiki>
|2016/03/22
|BV; JM; data converted from current dollar to % of GDP; had to leave out data points due to unavailabiity of GDP data
|-
|SeriesAidDon%GNI
|United Nations Statistics Division available at: <nowiki>http://mdgs.un.org/unsd/mdg/SeriesDetail.aspx?srid=568&crid=</nowiki>
|2017/02/17
|AN;JM
|-
|SeriesAidDonEd%GDP
|OECD
|2015/12/21
|JM, BV, Percent of GDP computed in IFs Project
|-
|SeriesAidDonEdScholar
|UN SDG Indicators Global Database
|2019/06/28
|JD
|-
|SeriesAidDonLDCs%GNI
|United Nations Statistics Division available at <nowiki>http://mdgs.un.org/unsd/mdg/</nowiki>
|2017/02/17
|AN;JM
|-
|SeriesAidDonSocServ%Total
|Millennium Indicators Database, United Nation's Statistics Division; Available at <nowiki>http://unstats.un.org/unsd/mi/mi_goals.asp</nowiki>
|2005/01/01
|
|-
|SeriesAidEnergyInfrastructure
|OECD
|2011/04/01
|MJE; MJE?JM asked for exact source
|-
|SeriesAidforTradeCommitDonor
|UN SDG Indicators Global Database
|2019/06/28
|JD
|-
|SeriesAidforTradeCommitRecipient
|UN SDG Indicators Global Database
|2017/08/04
|MH, RG, CW, official indicator
|-
|SeriesAidforTradeDisburseDonor
|UN SDG Indicators Global Database
|2017/08/04
|MH, RG, CW, official indicator
|-
|SeriesAidforTradeDisburseRecipient
|UN SDG Indicators Global Database
|2017/08/04
|MH, RG, CW, official indicator
|-
|SeriesAidHealthcareGross
|UN SDG Indicators Global Database
|2019/06/28
|JD
|-
|SeriesAidHealthcareNet
|UN SDG Indicators Global Database
|2019/06/28
|JD
|-
|SeriesAidICTInfrastructure
|OECD
|2011/04/01
|MJE; MJE?JM asked for exact source
|-
|SeriesAidInfrastructure%TotalAid
|OECD
|2011/04/01
|MJE; MJE?JM asked for exact source
|-
|SeriesAidRec
|WDI BATCH PULL
|2019/02/01
|KBN,HF.MD
|-
|SeriesAidRec%GNI
|WDI BATCH PULL
|2020/08/05
|RG,KBN,KM
|-
|SeriesAidRec%GNIOECD
|OECDStat and World Bank and OECD GNI estimates (current US)
|2022/06/06
|YX
|-
|SeriesAidRecGrant%Total
|WDI BATCH PULL
|2018/12/13
|KBN,AZ
|-
|SeriesAidRecGrant%TotRev
|WDI BATCH PULL 2020
|2020/08/07
|YX
|-
|SeriesAidRecGrants%GNI
|OECDStat and World Bank and OECD GNI estimates (current US)
|2022/06/06
|YX
|-
|SeriesAidRecGross%GDP
|WDI BATCH Update 2018
|2018/05/05
|KBN,HF.MD
|-
|SeriesAidRecLoanGross%GNI
|OECDStat and World Bank and OECD GNI estimates (current US)
|2022/06/06
|YX
|-
|SeriesAidRecLoanNet%GNI
|OECDStat and World Bank and OECD GNI estimates (current US)
|2022/06/06
|YX
|-
|SeriesAidRecLoanRepay%GNI
|OECDStat and World Bank and OECD GNI estimates (current US)
|2022/06/06
|YX
|-
|SeriesAidRecPerCap
|WDI BATCH PULL
|2019/02/01
|KBN,HF.MD
|-
|SeriesAidRecRecover%GNI
|OECDStat and World Bank and OECD GNI estimates (current US)
|2022/06/06
|YX
|-
|SeriesAidRoadsInfrastructure
|OECD
|2011/04/01
|MJE; MJE?JM asked for exact source
|-
|SeriesAIDSDths
|WRI Earthtrends <nowiki>http://earthtrends.wri.org/</nowiki>
|2010/11/23
|EWF
|-
|SeriesAidtoDevelopingCountriesUSD
|UN SDG Indicators Global Database
|2019/06/28
|JD
|-
|SeriesAidTotalInfrastructure
|UN SDG Indicators Global Database
|2019/06/28
|JD
|-
|SeriesAidTotalOECD
|OECD
|2011/04/01
|MJE; MJE?JM asked for exact source
|-
|SeriesAidTotFlowsAgSector
|UN SDG Indicators Global Database
|2019/06/28
|JD
|-
|SeriesAidWaterSanitation
|UN SDG Indicators Global Database
|2019/06/28
|JD
|-
|SeriesAidWaterSanitationInfrastructure
|OECD
|2011/04/01
|MJE; MJE?JM asked for exact source
|-
|SeriesAlternativeSourcesofInformationIndexVDEM
|Varieties of Democracy
|2019/08/06
|AW
|-
|SeriesArmsExp%TotExp
|WDI BATCH PULL
|2019/01/18
|KBN,HF.MD
|-
|SeriesArmsExports
|WDI BATCH PULL
|2019/01/18
|KBN,HF.MD
|-
|SeriesArmsExportsMonadic%GDP
|WB; SIPRI directly for Palestine, Montenegro, and Taiwan; Pardee calculations
|2022/06/01
|CLP; Unit fix
|-
|SeriesArmsImp%TotImp
|WDI BATCH PULL
|2019/01/18
|KBN,EM
|-
|SeriesArmsImports
|WDI BATCH PULL
|2019/01/18
|KBN,HF.MD
|-
|SeriesArmsImportsMonadic%GDP
|WB; SIPRI directly for Palestine, Montenegro, and Taiwan; Pardee calculations
|2022/06/01
|CLP; Unit fix
|-
|SeriesAutomobileSales
|GM IEMA
|
|
|-
|SeriesAvgAgeAtFirstMarriageMen-UN
|UN World Marriage Data 2019
|2022/04/01
|AP
|-
|SeriesAvgAgeAtFirstMarriageMen-WB
|World Bank
|2022/04/01
|AP
|-
|SeriesAvgAgeAtFirstMarriageWomen-UN
|UN World Marriage Data 2019
|2022/04/01
|AP
|-
|SeriesAvgAgeAtFirstMarriageWomen-UNOECD
|Our World in Data
|2022/04/01
|AP
|-
|SeriesAvgAgeAtFirstMarriageWomen-WB
|World Bank
|2022/04/01
|AP
|-
|SeriesAvgLifeExpectancyIHMEForecasts
|IHME
|2019/01/06
|KBN
|-
|SeriesBankActAdult%
|UN SDG Indicators Global Database
|2019/06/28
|JD
|-
|SeriesBankATMTotl
|UN SDG Indicators Global Database
|2019/06/28
|JD
|-
|SeriesBankBranchTotl
|UN SDG Indicators Global Database
|2017/08/04
|MH, RG, CW, official indicator
|-
|SeriesBirthsRegisteredUnder5%
|UN SDG Indicators Global Database
|2019/06/28
|JD
|-
|SeriesCalPCap
|FAO BATCH PULL
|2018/02/12
|HF,EB
|-
|SeriesCalPCapAnimal
|WRI Earthtrends <nowiki>http://earthtrends.wri.org/</nowiki>
|2005/06/01
|
|-
|SeriesCarsHigh
|
|
|
|-
|SeriesCarsIntermediate
|
|
|
|-
|SeriesCarsLarge
|
|
|
|-
|SeriesCarsLargeFamilyWagon
|
|
|
|-
|SeriesCarsLowMedium
|
|
|
|-
|SeriesCarsMini
|
|
|
|-
|SeriesCarsSmall
|
|
|
|-
|SeriesCarsSmallFamilyWagon
|
|
|
|-
|SeriesCarsSportHigh
|
|
|
|-
|SeriesCarsSportLow
|
|
|
|-
|SeriesCarsTotal
|
|
|
|-
|SeriesCarsTrucksTotal
|
|
|
|-
|SeriesCarsUpperMedium
|
|
|
|-
|SeriesCBCapitalCont
|Computed for Productivity Project with Data from Conference Board
|2018/10/29
|KBN
|-
|SeriesCBCapitalCont10YrMovavg
|Computed for Productivity Project with Data from Conference Board
|2018/11/25
|KBN
|-
|SeriesCBGDPgr
|Computed for Productivity Project with Data from Conference Board
|2018/11/25
|KBN
|-
|SeriesCBGDPgr10Yravg
|Computed for Productivity Project with Data from Conference Board
|2018/11/25
|KBN
|-
|SeriesCBLabQualCont
|Computed for Productivity Project with Data from Conference Board
|2018/10/29
|KBN
|-
|SeriesCBLabQuantCont
|Computed for Productivity Project with Data from Conference Board
|2018/10/29
|KBN
|-
|SeriesCBLabQuantCont10YrAvg
|Computed for Productivity Project with Data from Conference Board
|2018/11/25
|KBN
|-
|SeriesCBR
|World Development Indicators
|2022/03/21
|YX, JD
|-
|SeriesCBTFPTot
|Computed for Productivity Project with Data from Conference Board
|2018/10/29
|KBN
|-
|SeriesCDR
|World Development Indicators
|2022/03/21
|YX, JD
|-
|SeriesChiefExecutiveNolongerElectedVDEM
|Varieties of Democracy
|2019/08/06
|CW,MM,AW
|-
|SeriesChildStuntingPercentWDI
|World Development Indicators
|2022/03/21
|YX; JS
|-
|SeriesCivilLibertiesindexVDEM
|Varieties of Democracy
|2019/08/06
|AW
|-
|SeriesCivilSocietyParticipationIndexVDEM
|Varieties of Democracy
|2019/08/08
|BG
|-
|SeriesCleanElectionsIndexVDEM
|Varieties of Democracy
|2019/08/08
|BG
|-
|SeriesCompanyValue%GDP
|WDI BATCH PULL
|2019/02/01
|KBN,HF.MD
|-
|SeriesCompetitivenessMicroEcon
|World Economic Forum. <nowiki>http://www.weforum.org/site/homepublic.nsf/Content/Global+Competitiveness+Programme%5CReports%5CGlobal+Competitiveness+Report+2002-2003</nowiki>
|
|
|-
|SeriesCompetitivenessRank
|World Economic Forum. <nowiki>http://www.weforum.org/site/homepublic.nsf/Content/Global+Competitiveness+Programme%5CReports%5CGlobal+Competitiveness+Report+2002-2003</nowiki>
|2005/02/01
|
|-
|SeriesCompetitivenessScore
|World Economic Forum. <nowiki>http://www.weforum.org/en/initiatives/gcp/Global%20Competitiveness%20Report/index.htm</nowiki>
|2010/04/01
|MJS
|-
|SeriesComplianceParisPrinc
|UN SDG Indicators Global Database
|
|CW, Official Indicator, Number of countries with National Human Rights Institutions with (1) no status; (2) no application for accreditation; (3) not fully compliant; (4) in compliance with the Paris Principles
|-
|SeriesConflictandTerrorsimDeathRate
|IHME GBD
|2018/03/19
|KN
|-
|SeriesConflictandTerrorsimpPrevRate
|IHME GBD
|2018/03/19
|KN
|-
|SeriesConflictAreaAffectedUCDP
|World Bank
|2019/08/16
|JD
|-
|SeriesConflictDeathsUCDP
|World Bank
|2019/08/16
|JD
|-
|SeriesConflictOngoingUCDP
|World Bank
|2019/08/16
|JD
|-
|SeriesConflictPeaceYearsUCDP
|World Bank
|2019/08/16
|JD
|-
|SeriesConflictPopulationAffectedUCDP
|World Bank
|2019/08/16
|JD
|-
|SeriesConflictPopulationShareAffectedUCDP
|World Bank
|2019/08/16
|JD
|-
|SeriesConflictProbHegreSSP1
|Taken from paper by Havard Hegre (2016)
|2021/07/30
|VY;
|-
|SeriesConflictProbHegreSSP2
|Taken from paper by Havard Hegre (2016)
|2021/07/30
|VY;
|-
|SeriesConflictProbHegreSSP3
|Taken from paper by Havard Hegre (2016)
|2021/07/30
|VY;
|-
|SeriesConflictProbHegreSSP4
|Taken from paper by Havard Hegre (2016)
|2021/07/30
|VY;
|-
|SeriesConflictProbHegreSSP5
|Taken from paper by Havard Hegre (2016)
|2021/07/30
|VY;
|-
|SeriesConflictStateDeathsUCDP
|World Bank
|2019/08/16
|JD
|-
|SeriesConsumerPriceIndex
|WDI BATCH PULL
|2018/12/13
|KBN,HF.MD
|-
|SeriesConsumFinal%GDP
|WDI BATCH PULL
|2019/01/18
|KBN,HF.MD
|-
|SeriesConsumGenGovt%GDP
|World Development Indicators
|2022/04/25
|GE,
|-
|SeriesConsumHHFinal%GDP
|World Development Indicators
|2022/04/25
|GE,
|-
|SeriesConsumptionperCapitaDM
|Pulled from IFs
|2018/02/15
|KN
|-
|SeriesConsumTotal%GDP
|World Development Indicators
|2022/04/25
|GE,
|-
|SeriesCoreCivilSocietyIndexVDEM
|Varieties of Democracy
|2019/08/06
|AW
|-
|SeriesCorpTax%GDPimf
|IMF WoRLD
|2017/03/30
|HF;JM Coutry concordance created for this series IMF WoRLD. Data unable to be pulled as batch.
|-
|SeriesCorruption
|Transparency International www.transparency.org/documents/index.html. Various years
|2012/02/01
|AS;CN
|-
|SeriesCorruptionCPINew
|Transparency International
|2022/04/19
|KG
|-
|SeriesCrimesAssault
|UNODC United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS 12)
|2012/01/10
|Until 2002 this data was a part of the Human Development Report, which obtained the information from the ICVS, defunct since 2002;CN
|-
|SeriesCrimesBribery
|Human Development Report 2002
|2011/08/12
|AS; split from SeriesCrimes
|-
|SeriesCrimesDrugper100000
|UNODC United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS 12)
|2012/01/10
|CN
|-
|SeriesCrimesHomicper100000
|UNODC United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS 12)
|2012/01/10
|CN
|-
|SeriesCrimesKidnapping
|UNODC United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS 12)
|2012/01/10
|AT;CN
|-
|SeriesCrimesPrisonersper100000
|UNODC United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS 12)
|2012/01/10
|CN
|-
|SeriesCrimesProperty
|UNODC United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS 12)
|2012/01/10
|Until 2002 this data was a part of the Human Development Report, which obtained the information from the ICVS, defunct since 2002; CN
|-
|SeriesCrimesRobbery
|UNODC United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS 12)
|2012/01/10
|Until 2002 this data was a part of the Human Development Report, which obtained the information from the ICVS, defunct since 2002; CN
|-
|SeriesCrimesSexualAssault
|UNODC United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS 12)
|2012/01/10
|Until 2002 this data was a part of the Human Development Report, which obtained the information from the ICVS, defunct since 2002;CN
|-
|SeriesCrimesTotal
|Human Development Report 2002
|2011/08/12
|AS; split from SeriesCrimes
|-
|SeriesCulAfricaDum
|World Value Survey
|
|
|-
|SeriesCulBuddhistDum
|World Value Survey
|
|
|-
|SeriesCulCathDum
|World Value Survey
|
|
|-
|SeriesCulConfucDum
|World Value Survey
|
|
|-
|SeriesCulEngSpeakDum
|World Value Survey
|
|
|-
|SeriesCulExComDum
|World Value Survey
|
|
|-
|SeriesCulHinduDum
|World Value Survey
|
|
|-
|SeriesCulIslamicDum
|World Value Survey
|
|
|-
|SeriesCulLatAmerDum
|World Value Survey
|
|
|-
|SeriesCulOrthDum
|World Value Survey
|
|
|-
|SeriesCulProtDum
|World Value Survey
|
|
|-
|SeriesDeathsper1000IHMEForecasts
|IHME
|2019/01/06
|KBN
|-
|SeriesDeliberativeComponentIndexVDEM
|Varieties of Democracy
|2019/08/06
|AW
|-
|SeriesDeliberativeDemocracyIndexVDEM
|Varieties of Democracy
|2019/08/06
|AW
|-
|SeriesDesalinatedWater
|AQU (AQUASTAT) BATCH PULL
|2019/09/08
|BG
|-
|SeriesDipExIn
|Correlates of War
|2012/01/25
|TL;CN
|-
|SeriesDipExOut
|Correlates of War
|2012/01/25
|TL;CN
|-
|SeriesDiploDiplomacyRelativePercent
|Pardee Center origional research.
|2012/10/09
|DKB
|-
|SeriesDiploDiplomaticConnectionIndex
|Pardee Center origional research.
|2012/10/09
|DKB
|-
|SeriesDiploDistanceToEurope
|
|2012/11/06
|
|-
|SeriesDiploEmbassyIn
|Diplometrics
|2022/04/08
|CLP
|-
|SeriesDiploEmbassyMaxGlobal
|Diplometrics
|2016/08/23
|CLP
|-
|SeriesDiploEmbassyOut
|Diplometrics
|2022/04/08
|CLP
|-
|SeriesDiploEmbassyOutStd
|Pardee Center origional research.
|2012/10/09
|DKB
|-
|SeriesDiploEmbassyStandCountry
|
|2012/10/29
|
|-
|SeriesDiploEmbassyStandGlobal
|
|2012/10/29
|
|-
|SeriesDiploEmbassyTotal
|
|2012/07/18
|
|-
|SeriesDiploIGOMembershipWeightedStd
|Pardee Center origional research.
|2012/10/09
|DKB
|-
|SeriesDiploIGOMemberTotal
|Diplometrics
|2022/06/15
|CLP, Data from Adam
|-
|SeriesDiploIGONetworkedTotal
|
|2012/07/17
|
|-
|SeriesDiploIGONetworkedWeighted
|
|2012/07/17
|
|-
|SeriesDiploIGOWeightedStandCountry
|
|2012/10/29
|
|-
|SeriesDiploIGOWeightedTotal
|Diplometrics
|2022/06/15
|CLP, Data from Adam
|-
|SeriesDiploIGOWeightMax
|Diplometrics
|2016/08/23
|CLP
|-
|SeriesDiploINGOAdvocacy
|Diplometrics
|2017/01/04
|JEM
|-
|SeriesDiploINGOAfiodi
|Diplometrics
|2017/01/04
|JEM
|-
|SeriesDiploINGOAfiodiIn
|Diplometrics
|2017/01/04
|JEM
|-
|SeriesDIPLOINGOdsiaicount
|Diplometrics
|2020/12/15
|AM; YX
|-
|SeriesDIPLOINGOdsiaicytotal
|Diplometrics
|2020/07/16
|AM; YX
|-
|SeriesDIPLOINGOdsiodicount
|Diplometrics
|2020/12/15
|AM; YX
|-
|SeriesDIPLOINGOdsiodicytotal
|Diplometrics
|2020/07/16
|AM; YX
|-
|SeriesDiploINGOFiodi
|Diplometrics
|2017/01/04
|JEM
|-
|SeriesDIPLOINGOfiodicount
|Diplometrics
|2020/12/15
|AM; YX
|-
|SeriesDIPLOINGOfiodicytotal
|Diplometrics
|2020/07/16
|AM; YX
|-
|SeriesDIPLOINGOinctypecy
|Diplometrics
|2020/07/16
|AM; YX
|-
|SeriesDIPLOINGOinctypehi
|Diplometrics
|2020/07/16
|AM; YX
|-
|SeriesDIPLOINGOinctypelm
|Diplometrics
|2020/07/16
|AM; YX
|-
|SeriesDIPLOINGOinctypelo
|Diplometrics
|2020/07/16
|AM; YX
|-
|SeriesDIPLOINGOinctypeum
|Diplometrics
|2020/07/16
|AM; YX
|-
|SeriesDIPLOINGOingocount
|Diplometrics
|2020/12/15
|AM; YX
|-
|SeriesDIPLOINGOops12count
|Diplometrics
|2020/12/15
|AM; YX
|-
|SeriesDIPLOINGOops1count
|Diplometrics
|2020/12/15
|AM; YX
|-
|SeriesDIPLOINGOops2count
|Diplometrics
|2020/12/15
|AM; YX
|-
|SeriesDIPLOINGOops2cy
|Diplometrics
|2020/07/16
|AM; YX
|-
|SeriesDIPLOINGOops3cy
|Diplometrics
|2020/07/16
|AM; YX
|-
|SeriesDIPLOINGOops4cy
|Diplometrics
|2020/07/16
|AM; YX
|-
|SeriesDiploINGOPfiodi
|Diplometrics
|2017/01/04
|JEM
|-
|SeriesDIPLOINGOprogramcytotal
|Diplometrics
|2020/07/16
|AM; YX
|-
|SeriesDiploINGOProgrammatic
|Diplometrics
|2017/01/04
|JEM
|-
|SeriesDIPLOINGOregtypean
|Diplometrics
|2020/07/16
|AM; YX
|-
|SeriesDIPLOINGOregtypeat
|Diplometrics
|2020/07/16
|AM; YX
|-
|SeriesDIPLOINGOregtypecy
|Diplometrics
|2020/07/16
|AM; YX
|-
|SeriesDIPLOINGOregtypedm
|Diplometrics
|2020/07/16
|AM; YX
|-
|SeriesDIPLOINGOsource123count
|Diplometrics
|2020/12/15
|AM; YX
|-
|SeriesDIPLOINGOsource12count
|Diplometrics
|2020/12/15
|AM; YX
|-
|SeriesDIPLOINGOsource1count
|Diplometrics
|2020/12/15
|AM; YX
|-
|SeriesDIPLOINGOsource1cytotal
|Diplometrics
|2020/07/16
|AM; YX
|-
|SeriesDIPLOINGOsource2count
|Diplometrics
|2020/12/15
|AM; YX
|-
|SeriesDIPLOINGOsource2cytotal
|Diplometrics
|2020/07/16
|AM; YX
|-
|SeriesDIPLOINGOsource3count
|Diplometrics
|2020/12/15
|AM; YX
|-
|SeriesDIPLOINGOsource3cytotal
|Diplometrics
|2020/07/16
|AM; YX
|-
|SeriesDiploINGOSum
|Diplometrics
|2017/01/04
|JEM
|-
|SeriesDiploTreatyNetworkedSign1Rat1
|
|2012/07/24
|
|-
|SeriesDiploTreatyNetworkedSign1Rat2
|
|2012/07/24
|
|-
|SeriesDiploTreatyNetworkedWeightedSign1Rat1
|
|2012/07/17
|
|-
|SeriesDiploTreatyNetworkedWeightedSign1Rat2
|
|2012/07/17
|
|-
|SeriesDiploTreatySign1Rat2Std
|Pardee Center origional research.
|2012/10/09
|DKB
|-
|SeriesDiploTreatyTotalRat2Sign1
|Diplometrics
|2022/06/01
|CLP import; Prepared by Adam
|-
|SeriesDiploTreatyTotalSign1Rat1
|Diplometrics
|2017/01/04
|JEM
|-
|SeriesDiploTreatyWeightedSign1Rat1
|
|2022/06/01
|CLP import; Prepared by Adam
|-
|SeriesDiploTreatyWeightedSign1Rat2
|Diplometrics
|2022/06/01
|CLP import; Prepared by Adam
|-
|SeriesDiploTreatyWeightedSign1Rat2Max
|Diplometrics
|2022/06/01
|CLP import; Prepared by Adam
|-
|SeriesDiploTreatyWeightedSign1Rat2StandCountry
|
|2022/06/01
|CLP import; Prepared by Adam
|-
|SeriesDiploUNStaffByCountry
|UN General Assembly
|2013/01/21
|Years from 2006-2012, staff is defined by UN as "All Staff", counting only employees grade P+ and higher; before 2005, employees are defined as "staff subject to geographical distribution" with grade P+ and higher
|-
|SeriesDirectPopularVoteIndexVDEM
|Varieties of Democracy
|2019/08/08
|BG
|-
|SeriesDividedPartyControlIndexVDEM
|Varieties of Democracy
|2017/06/28
|
|-
|SeriesDivisionofPowerIndexVDEM
|Varieties of Democracy
|2019/08/06
|AW
|-
|SeriesDrinkWaterSuppClearProcRural
|UN SDG Indicators Global Database
|2019/06/30
|Additional Series; Coded to a binary scale of 0=NO, 1=YES, by combining two series: Countries with/without clearly defined procedures for participation in planning program in drinking-water supply (Rural); JD; AJM
|-
|SeriesDrinkWaterSuppClearProcUrban
|UN SDG Indicators Global Database
|2017/08/08
|Additional Series; Coded to a binary scale of 0=NO, 1=YES, by combining two series: Countries with/without clearly defined procedures for participation in planning program in drinking-water supply (Urban); JD; AJM
|-
|SeriesDrinkWaterSuppParticRural
|UN SDG Indicators Global Database
|2019/06/28
|Additional Series; Coded to a scale of: 1-Low Level, 2-Moderate Level, 3-High Level, by combining three series: Countries with a low/moderate/high level participating in planning programs in drinking-water supply by Rural; JD; AJM
|-
|SeriesDrinkWaterSuppParticUrban
|UN SDG Indicators Global Database
|2017/08/08
|Additional Series; Coded to a scale of: 1-Low Level, 2-Moderate Level, 3-High Level, by combining three series: Countries with a low/moderate/high level participating in planning programs in drinking-water supply by Urban; JD; AJM
|-
|SeriesDrOpiodPrevIHME
|IHME
|2018/01/19
|MPD
|-
|SeriesDrPrevAmphCalcIHME
|IHME
|2018/04/25
|KBN
|-
|SeriesDrPrevCokeCalcIHME
|IHME
|2018/04/25
|KBN
|-
|SeriesDrPrevOpiatesCalcIHME
|IHME
|2018/04/25
|KBN
|-
|SeriesDrPrevPresOpCalcIHME
|IHME
|2018/04/25
|KBN
|-
|SeriesDrugOffences
|UNDP
|
|
|-
|SeriesDrugPrevalenceAmphetaminesIHME
|IHME
|2018/01/14
|KBN
|-
|SeriesDrugPrevalenceCocaineIHME
|IHME
|2018/01/14
|KBN
|-
|SeriesDrugPrevalenceRateAmphetamines
|United Nations Office of Drug and Crime (UNODC)
|2018/10/09
|KM, EM
|-
|SeriesDrugPrevalenceRateCannabis
|United Nations Office of Drug and Crime (UNODC)
|2018/10/09
|KM, EM
|-
|SeriesDrugPrevalenceRateCocaine
|United Nations Office of Drug and Crime (UNODC)
|2018/10/09
|KM, EM
|-
|SeriesDrugPrevalenceRateEcstacy
|United Nations Office of Drug and Crime (UNODC)
|2018/10/09
|KM, EM
|-
|SeriesDrugPrevalenceRateOpiates
|United Nations Office of Drug and Crime (UNODC)
|2018/10/09
|KM, EM
|-
|SeriesDrugPrevalenceRateOpioids
|United Nations Office of Drug and Crime (UNODC)
|2018/10/09
|KM, EM
|-
|SeriesDrugPrevalenceRatePrescription Stimulants
|United Nations Office of Drug and Crime (UNODC)
|2018/10/09
|KM, EM
|-
|SeriesDrugPrevalenceRatePrescriptionOpioids
|United Nations Office of Drug and Crime (UNODC)
|2018/10/09
|KM, EM
|-
|SeriesDrugPrevalenceRateTranquilizersandSedatives
|United Nations Office of Drug and Crime (UNODC)
|2018/10/09
|KM, EM
|-
|SeriesDrugPrevAmphCalc
|Calculated by KBN
|2018/06/02
|KBN
|-
|SeriesDrugPrevAmphetamineRegAvg
|UNODC
|2018/03/28
|KN
|-
|SeriesDrugPrevCocaineRegAvg
|UNODC
|2018/03/28
|KN
|-
|SeriesDrugPrevOpiateRegAvg
|UNODC
|2018/03/28
|KN
|-
|SeriesDrugPrevOpiatesCalc
|Calculated by KBN
|2018/06/02
|KBN
|-
|SeriesDrugPrevPresOpCalc
|Calculated by KBN
|2018/06/02
|KBN
|-
|SeriesDrugPrevPresOpRegAvg
|UNODC
|2018/03/28
|KN
|-
|SeriesEconomicIntegration
|Computed for Productivity Project
|2018/12/09
|KBN
|-
|SeriesEconShadow%GDP
|Schneider, Friedrich and Andreas Buehn. 2007-9. Shadow Economies and Corruption All Over the World: Revised Estimates for 120 Countries. <nowiki>Http://www.economics-ejournal.org/economics/journalarticles/2007-9</nowiki>
|2010/04/09
|PM, EWF
|-
|SeriesEdAttnShrBothSexesNoEd15+IIASASSP2
|WIC/IIASA Data Explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrBothSexesNoEd20to64IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrBothSexesNoEd25+IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrBothSexesPostSec15+IIASASSP2
|WIC/IIASA Data Explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrBothSexesPostSec20to64IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrBothSexesPostSec25+IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrBothSexesPri15+IIASASSP2
|WIC/IIASA Data Explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrBothSexesPri20to64IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrBothSexesPri25+IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrBothSexesPriIncmplt15+IIASASSP2
|WIC/IIASA Data Explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrBothSexesPriIncmplt20to64IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrBothSexesPriIncmplt25+IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrBothSexesSecLowr15+IIASASSP2
|WIC/IIASA Data Explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrBothSexesSecLowr20to64IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrBothSexesSecLowr25+IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrBothSexesSecUppr15+IIASASSP2
|WIC/IIASA Data Explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrBothSexesSecUppr20to64IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrBothSexesSecUppr25+IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrFemaleNoEd15+IIASASSP2
|WIC/IIASA Data Explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrFemaleNoEd20to64IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrFemaleNoEd25+IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrFemalePostSec15+IIASASSP2
|WIC/IIASA Data Explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrFemalePostSec20to64IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrFemalePostSec25+IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrFemalePri15+IIASASSP2
|WIC/IIASA Data Explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrFemalePri20to64IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrFemalePri25+IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrFemalePriIncmplt15+IIASASSP2
|WIC/IIASA Data Explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrFemalePriIncmplt20to64IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrFemalePriIncmplt25+IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrFemaleSecLowr15+IIASASSP2
|WIC/IIASA Data Explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrFemaleSecLowr20to64IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrFemaleSecLowr25+IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrFemaleSecUppr15+IIASASSP2
|WIC/IIASA Data Explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrFemaleSecUppr20to64IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrFemaleSecUppr25+IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrMaleNoEd15+IIASASSP2
|WIC/IIASA Data Explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrMaleNoEd20to64IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrMaleNoEd25+IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrMalePostSec15+IIASASSP2
|WIC/IIASA Data Explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrMalePostSec20to64IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrMalePostSec25+IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrMalePri15+IIASASSP2
|WIC/IIASA Data Explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrMalePri20to64IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrMalePri25+IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrMalePriIncmplt15+IIASASSP2
|WIC/IIASA Data Explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrMalePriIncmplt20to64IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrMalePriIncmplt25+IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrMaleSecLowr15+IIASASSP2
|WIC/IIASA Data Explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrMaleSecLowr20to64IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrMaleSecLowr25+IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrMaleSecUppr15+IIASASSP2
|WIC/IIASA Data Explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrMaleSecUppr20to64IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdAttnShrMaleSecUppr25+IIASASSP2
|WIC data explorer (<nowiki>http://dataexplorer.wittgensteincentre.org/wcde-v2/</nowiki>)
|2019/06/18
|BRM
|-
|SeriesEdBasicEnrollGross%Female
|IFs Calculation
|2008/02/01
|Calculated from primary and lower secondary age population and the enrollment numbers at those levels
|-
|SeriesEdBasicEnrollGross%Male
|IFs Calculation
|2008/02/01
|Calculated from primary and lower secondary age population and the enrollment numbers at those levels
|-
|SeriesEdBasicEnrollGross%Total
|IFs Calculation
|2008/02/01
|Calculated from primary and lower secondary age population and the enrollment numbers at those levels
|-
|SeriesEdEnrollPrivate%Lower2ndaryGeneral
|UIS 2019 Batch Pull
|2019/03/04
|RG
|-
|SeriesEdEnrollPrivate%Lower2ndaryTotal
|UNESCO Institute for Statistics, Global Education Digest 2007
|2008/05/01
|
|-
|SeriesEdEnrollPrivate%Lower2ndaryVocTech
|UIS 2013 BATCH PULL
|2013/07/11
|
|-
|SeriesEdEnrollPrivate%Primary
|UIS
|2019/09/12
|BG
|-
|SeriesEdEnrollPrivate%Upper2ndaryGeneral
|UIS
|2019/09/12
|BG
|-
|SeriesEdEnrollPrivate%Upper2ndaryVocTech
|UIS 2013 BATCH PULL
|2013/07/11
|
|-
|SeriesEdEnrollPrivateAll2ndaryCombined
|UIS 2013 BATCH PULL
|2013/07/11
|
|-
|SeriesEdEnrollPrivateUpper2ndaryGeneral
|UIS 2013 BATCH PULL
|2013/07/11
|
|-
|SeriesEdEnrollPrivateUpper2ndaryVocTech
|UIS 2013 BATCH PULL
|2013/07/11
|
|-
|SeriesEdExp%GDPPrivatePrePrim
|UIS 2013 BATCH PULL
|2013/07/15
|
|-
|SeriesEdExp%GDPPrivatePrim
|UIS 2013 BATCH PULL
|2013/07/15
|
|-
|SeriesEdExp%GDPPrivateSec
|UIS 2013 BATCH PULL
|2013/07/15
|
|-
|SeriesEdExp%GDPPrivateTer
|UIS 2013 BATCH PULL
|2013/07/15
|
|-
|SeriesEdExp%GDPPrivateTot
|UIS 2013 BATCH PULL
|2013/07/15
|
|-
|SeriesEdExp%GDPPublic
|UIS 2013 BATCH PULL
|2013/07/15
|
|-
|SeriesEdExp%GDPTotal
|OECD Education Database
|
|
|-
|SeriesEdExpbyLevel
|Computed by Functions
|
|
|-
|SeriesEdExpectedYearsofSchooling
|World Bank Edstats
|2016/04/07
|BV; mti; Also called School Life Expectancy
|-
|SeriesEdExpectedYearsofSchoolingFemale
|UIS
|2016/04/08
|BV
|-
|SeriesEdExpectedYearsofSchoolingMale
|UIS
|2016/04/08
|BV;
|-
|SeriesEdExpISCED1-4Cap%
|UIS 2013 BATCH PULL
|2013/07/15
|
|-
|SeriesEdExpISCED1-4NonSalCur%
|UIS 2013 BATCH PULL
|2013/07/15
|
|-
|SeriesEdExpISCED1-4Sal%
|UIS 2013 BATCH PULL
|2013/07/15
|
|-
|SeriesEdExpISCED1-4TotalCur%
|UIS 2013 BATCH PULL
|2013/07/15
|
|-
|SeriesEdExpPrePri%EducTot
|UIS 2013 BATCH PULL
|2013/07/15
|Not sure whether public expenditure only
|-
|SeriesEdExpPrePri%GDP
|UNESCO
|2015/08/28
|CLP; Alex Porter
|-
|SeriesEdExpPri%EducTot
|UIS 2013 BATCH PULL
|2013/07/15
|Extracted from <nowiki>http://stats.uis.unesco.org/TableViewer/tableView.aspx?ReportId=219</nowiki>
|-
|SeriesEdExpPri%GDPPC
|World Development Indicators
|2022/03/21
|CP
|-
|SeriesEdExpSec%EducTot
|UIS 2013 BATCH PULL
|2013/07/15
|Extracted from UIS website
|-
|SeriesEdExpSec%GDPPC
|World Development Indicators
|2022/03/21
|YX; JS
|-
|SeriesEdExpSecLowr%EducTot
|UIS 2013 BATCH PULL
|2013/07/15
|Not sure whether public expenditure only
|-
|SeriesEdExpSecLowr%GDPPC
|UIS Single Pull
|2019/09/18
|LW,JM,DK
|-
|SeriesEdExpSecUppr%EducTot
|UIS 2013 BATCH PULL
|2013/07/15
|Not sure whether public expenditure only
|-
|SeriesEdExpSecUppr%GDPPC
|UIS, UNESCO Institute for Statistics; <nowiki>http://data.uis.unesco.org/</nowiki>
|2019/09/18
|LW,JM,DK
|-
|SeriesEdExpTer%EducTot
|UIS 2013 BATCH PULL
|2013/07/15
|Extracted from UIS website
|-
|SeriesEdExpTer%GDPPC
|World Development Indicators
|2022/03/21
|YX; JS
|-
|SeriesEdExpTerCap%
|UIS 2013 BATCH PULL
|2013/07/15
|
|-
|SeriesEdExpTertNonSalCur%
|UIS 2013 BATCH PULL
|2013/07/15
|
|-
|SeriesEdExpTertSal%
|UIS 2013 BATCH PULL
|2013/07/15
|
|-
|SeriesEdExpTertTotalCur%
|UIS 2013 BATCH PULL
|2013/07/15
|
|-
|SeriesEdGrad15to19Ter
|Barro and Lee
|2014/04/21
|DAB; CN
|-
|SeriesEdHCStock15to64FemaleBLLong
|Barro-Lee
|2017/02/19
|SK;HF: HC reflects educational attainment of the female population (15-64), disaggregated by gender and age
|-
|SeriesEdHCStock15to64MaleBLLong
|Barro-Lee
|2017/02/19
|SK;HF: Adult population (15to64) educational attainment, male population disaggregated by gender and age
|-
|SeriesEdHCStock15to64TotalBLLong
|Barro-Lee
|2017/02/19
|SK;HF: Adult population (15to64)education attainment , both sexes disaggregated by gender and age
|-
|SeriesEdlowSecGradBothSexes
|UIS
|2019/09/30
|KN, CG, MN
|-
|SeriesEdlowSecGradFemale
|UIS
|2019/09/30
|KN, CG, MN
|-
|SeriesEdLowSecGradMale
|UIS
|2019/09/30
|KN, CG, MN
|-
|SeriesEdNoSchoolingAdult15Female%
|Barro-Lee
|2014/04/21
|DAB; AT;CN-entire series updated to reflect Barro-Lee revisions to 2010 data made in late 2011
|-
|SeriesEdNoSchoolingAdult15Male%
|Barro-Lee
|2014/04/29
|dab; AT;CN Calcualted from Barro-Lee using female and total-entire series updated to reflect Barro-Lee revisions to 2010 data made in late 2011
|-
|SeriesEdNoSchoolingAdult15to24Female%
|Barro-Lee
|2016/01/04
|mti; No Schooling + (Primary Total - Primary Completed) of B-L Spreadsheet Columns
|-
|SeriesEdNoSchoolingAdult15to24Female%BLLong
|Barro-Lee
|2017/02/11
|SK;HF: No Schooling + (Primary Total - Primary Completed) of B-L Spreadsheet Columns
|-
|SeriesEdNoSchoolingAdult15to24Male%
|Barro-Lee
|2016/01/04
|mti; No Schholing + (Primary Total - Primary Completed ) of B-L Spreadsheet Columns
|-
|SeriesEdNoSchoolingAdult15to24Male%BLLong
|Barro-Lee
|2017/02/11
|SK;HF: No Schholing + (Primary Total - Primary Completed ) of B-L Spreadsheet Columns
|-
|SeriesEdNoSchoolingAdult15to24Total%
|Barro-Lee
|2016/01/04
|mti; No Schooling + (Primary Total - Primary Completed) of B-L Spreadsheet Columns
|-
|SeriesEdNoSchoolingAdult15to24Total%BLLong
|Barro-Lee
|2017/02/11
|SK;HF: No Schooling + (Primary Total - Primary Completed) of B-L Spreadsheet Columns
|-
|SeriesEdNoSchoolingAdult15to64Female%BLLong
|Barro-Lee
|2017/02/12
|SK;HF: No Schooling + (Primary Total - Primary Completed) of B-L Spreadsheet Columns
|-
|SeriesEdNoSchoolingAdult15to64Male%BLLong
|Barro-Lee
|2017/02/12
|SK;HF: No Schholing + (Primary Total - Primary Completed ) of B-L Spreadsheet Columns
|-
|SeriesEdNoSchoolingAdult15Total%
|Barro-Lee
|2014/04/21
|DAB; AT;CN-entire series updated to reflect Barro-Lee revisions to 2010 data made in late 2011
|-
|SeriesEdNoSchoolingAdult25Female%
|Barro-Lee
|2014/04/21
|DAB; AT;CN-entire series updated to reflect Barro-Lee revisions to 2010 data made in late 2011
|-
|SeriesEdNoSchoolingAdult25Male%
|Barro-Lee
|2014/04/29
|AT;CN Calcualted from Barro-Lee using female and total-entire series updated to reflect Barro-Lee revisions to 2010 data made in late 2011
|-
|SeriesEdNoSchoolingAdult25to64Female%BLLong
|Barro-Lee
|2017/02/12
|SK;HF: No Schholing + (Primary Total - Primary Completed ) of B-L Spreadsheet Columns
|-
|SeriesEdNoSchoolingAdult25to64Male%BLLong
|Barro-Lee
|2017/02/13
|SK;HF: No Schooling + (Primary Total - Primary Completed ) of B-L Spreadsheet Columns
|-
|SeriesEdNoSchoolingAdult25Total%
|Barro-Lee
|2014/04/21
|DAB; AT;CN-entire series updated to reflect Barro-Lee revisions to 2010 data made in late 2011
|-
|SeriesEdNoSchoolingAdults15to64Total%BLLong
|Barro-Lee
|2017/02/12
|SK;HF: No Schholing + (Primary Total - Primary Completed ) of B-L Spreadsheet Columns
|-
|SeriesEdNoSchoolingAdults25to64Total%BLLong
|Barro-Lee
|2017/02/19
|SK;HF: No Schooling + (Primary Total - Primary Completed ) of B-L Spreadsheet Columns
|-
|SeriesEdParityGenderLiteracy
|UN SDG Indicators Global Database
|2019/06/28
|JD; Extended Source Def truncated; name in source truncated
|-
|SeriesEdParityGenderNumeracy
|UN SDG Indicators Global Database
|2017/07/26
|AJM, MM; Extended Source Def truncated; name in source truncated
|-
|SeriesEdParitySocioeconLiteracy
|UN SDG Indicators Global Database
|2019/06/28
|AJM, MM; Extended Source Def truncated
|-
|SeriesEdParitySocioeconNumeracy
|UN SDG Indicators Global Database
|2019/06/28
|AJM, MM; Extended Source Def truncated
|-
|SeriesEdPostSecDuration
|UIS 2015 Pull
|2015/09/10
|AER, Mohammod requested that I import this variable
|-
|SeriesEdPrePriDuration
|UIS 2015 Pull
|2015/09/10
|AER, I created this variable at Mohammod's request, he wants all the UIS duration data
|-
|SeriesEdPrePriEnrollGrossFemalePcnt
|UIS
|2019/09/12
|BG
|-
|SeriesEdPrePriEnrollGrossMalePcnt
|UIS
|2019/09/12
|BG
|-
|SeriesEdPrePriEnrollGrossTotalPcnt
|UIS
|2019/09/13
|BG
|-
|SeriesEdPrePriOrgLearnFemale%
|UN SDG Indicators Global Database
|2019/05/21
|MN, AJM, MM; Extended Source Def truncated
|-
|SeriesEdPrePriOrgLearnMale%
|UN SDG Indicators Global Database
|2019/05/21
|MN, AJM, MM; Extended Source Def truncated
|-
|SeriesEdPrePriOrgLearnTotal%
|UN SDG Indicators Global Database
|2019/05/21
|MN, AJM, MM; Extended Source Def truncated
|-
|SeriesEdPrePriParityGenderOrgLearn
|UN SDG Indicators Global Database
|2019/05/21
|MN, AJM, MM; Extended Source Def truncated
|-
|SeriesEdPrePriParityGenderTeachers
|UN SDG Indicators Global Database
|2019/06/28
|JD; Extended Source Def truncated
|-
|SeriesEdPrePriTeachTrainFemale%
|UN SDG Indicators Global Database
|2019/06/28
|JD; Extended Source Def truncated
|-
|SeriesEdPrePriTeachTrainMale%
|UN SDG Indicators Global Database
|2019/06/28
|JD; Extended Source Def truncated
|-
|SeriesEdPrePriTeachTrainTotal%
|UN SDG Indicators Global Database
|2019/06/28
|JD; Extended Source Def truncated
|-
|SeriesEdPri2and3ParityGenderMath
|UN SDG Indicators Global Database
|2019/06/28
|JD; Extended Source Def truncated
|-
|SeriesEdPri2and3ParityGenderRead
|UN SDG Indicators Global Database
|2019/06/28
|JD; Extended Source Def truncated
|-
|SeriesEdPri2and3ParityRurUrbMath
|UN SDG Indicators Global Database
|2019/06/28
|JD; Extended Source Def truncated
|-
|SeriesEdPri2and3ParityRurUrbRead
|UN SDG Indicators Global Database
|2019/06/28
|JD; Extended Source Def truncated
|-
|SeriesEdPri2and3ParitySocioeconMath
|UN SDG Indicators Global Database
|2019/06/28
|JD; Extended Source Def truncated
|-
|SeriesEdPri2and3ParitySocioeconRead
|UN SDG Indicators Global Database
|2019/06/28
|JD; Extended Source Def truncated
|-
|SeriesEdPriAdultGrads15Female%
|Barro-Lee
|2018/02/11
|JW,MD; entire series updated to reflect Barro-Lee revisions to 2010 data made in late 2011
|-
|SeriesEdPriAdultGrads15Male%
|Barro-Lee
|2018/02/11
|JW,MD; Calcualted from Barro-Lee using female and total-entire series updated to reflect Barro-Lee revisions to 2010 data made in late 2011
|-
|SeriesEdPriAdultGrads15to24Female%
|Barro-Lee
|2018/02/12
|JW,MD; Primary Completed + Sec Total + Ter total of B-L Spreadsheet Columns
|-
|SeriesEdPriAdultGrads15to24Female%BLLong
|Barro-Lee
|2017/02/19
|SK;HF: Primary Completed + Sec Total + Ter total of B-L Spreadsheet Columns
|-
|SeriesEdPriAdultGrads15to24Male%
|Barro-Lee
|2018/02/12
|JW,MD; Primary Completed + Sec Total + Tertiary Total of B-L Spreadsheet Columns
|-
|SeriesEdPriAdultGrads15to24Total%
|Barro-Lee
|2018/02/12
|JW,MD; Primary Completed + Sec Total + Ter Total of B-L Spreadsheet Columns
|-
|SeriesEdPriAdultGrads15to24Total%BLLong
|Barro-Lee
|2017/02/11
|SK;HF: Primary Completed + Sec Total + Ter Total of B-L Spreadsheet Columns
|-
|SeriesEdPriAdultGrads15to64Female%BLLong
|Barro-Lee
|2017/02/12
|SK;HF: Primary Completed + Sec Total + Ter total of B-L Spreadsheet Columns
|-
|SeriesEdPriAdultGrads15to64Male%BLLong
|Barro-Lee
|2017/02/12
|SK;HF: Primary Completed + Sec Total + Ter total of B-L Spreadsheet Columns
|-
|SeriesEdPriAdultGrads15to64Total%BLLong
|Barro-Lee
|2017/02/12
|SK;HF: Primary Completed + Sec Total + Ter total of B-L Spreadsheet Columns
|-
|SeriesEdPriAdultGrads15Total%
|Barro-Lee
|2018/02/11
|JW,MD; entire series updated to reflect Barro-Lee revisions to 2010 data made in late 2011
|-
|SeriesEdPriAdultGrads25Female%
|Barro-Lee
|2014/04/21
|DAB; AT;CN-entire series updated to reflect Barro-Lee revisions to 2010 data made in late 2011
|-
|SeriesEdPriAdultGrads25Male%
|Barro-Lee
|2014/04/21
|DAB; AT;CN Calcualted from Barro-Lee using female and total-entire series updated to reflect Barro-Lee revisions to 2010 data made in late 2011
|-
|SeriesEdPriAdultGrads25to64Female%BLLong
|Barro-Lee
|2017/02/12
|SK;HF: Primary Completed + Sec Total + Ter total of B-L Spreadsheet Columns
|-
|SeriesEdPriAdultGrads25to64Male%BLLong
|Barro-Lee
|2017/02/13
|SK;HF: Primary Completed + Sec Total + Ter total of B-L Spreadsheet Columns
|-
|SeriesEdPriAdultGrads25to64Total%BLLong
|Barro-Lee
|2017/02/13
|SK;HF: Primary Completed + Sec Total + Ter total of B-L Spreadsheet Columns
|-
|SeriesEdPriAdultGrads25Total%
|Barro-Lee
|2014/04/21
|DAB; AT;CN-entire series updated to reflect Barro-Lee revisions to 2010 data made in late 2011
|-
|SeriesEdPriAIRFemale%
|UIS
|2019/11/08
|KS;
|-
|SeriesEdPriAIRMale%
|UIS
|2019/11/08
|KS;
|-
|SeriesEdPriAIRParity
|UIS
|2019/09/13
|BG
|-
|SeriesEdPriAIRTotal%
|UIS
|2019/09/17
|EB,HF,DK
|-
|SeriesEdPriCompletionFemale
|UIS
|2019/11/08
|KM
|-
|SeriesEdPriCompletionFemale%
|UIS
|2019/09/17
|EB,HF,DK
|-
|SeriesEdPriCompletionMale
|UIS
|2019/11/08
|KM
|-
|SeriesEdPriCompletionMale%
|UIS
|2019/09/17
|EB,HF,DK
|-
|SeriesEdPriCompletionParity
|UIS 2013 BATCH PULL
|2013/07/12
|
|-
|SeriesEdPriCompletionTotal
|UIS
|2019/11/08
|KM
|-
|SeriesEdPriCompletionTotal%
|UIS
|2019/09/17
|EB,HF,DK
|-
|SeriesEdPriDrpGr1BothSexes
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDrpGr1Female
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDrpGr1Male
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDrpGr2BothSexes
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDrpGr2Female
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDrpGr2Male
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDrpGr3BothSexes
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDrpGr3Female
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDrpGr3Male
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDrpGr4BothSexes
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDrpGr4Female
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDrpGr4Male
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDrpGr5BothSexes
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDrpGr5Female
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDrpGr5Male
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDrpGr6BothSexes
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDrpGr6Female
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDrpGr6Male
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDrpGrLastCumBothSexes
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDrpGrLastCumFemale
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDrpGrLastCumMale
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriDuration
|UIS
|2019/09/17
|EB,HF,DK
|-
|SeriesEdPriEnrGr1BothSexesNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGr1FemaleNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGr1MaleNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGr2BothSexesNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGr2FemaleNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGr2MaleNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGr3BothSexesNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGr3FemaleNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGr3MaleNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGr4BothSexesNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGr4FemaleNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGr4MaleNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGr5BothSexesNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGr5FemaleNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGr5MaleNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGr6BothSexesNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGr6FemaleNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGr6MaleNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGr7BothSexesNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGr7FemaleNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGr7MaleNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGrUnSpBothSexesNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGrUnSpFemaleNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrGrUnSpMaleNo
|UIS
|2020/11/24
|YX
|-
|SeriesEdPriEnrollFemale%
|Millennium Indicators Database, UN <nowiki>http://unstats.un.org/unsd/mi/mi_goals.asp</nowiki>
|2004/10/01
|
|-
|SeriesEdPriEnrollFemaleRateBLLong
|Barro-Lee
|2017/02/15
|SK;HF: Enrollment ratios, subdivided by education level (Primary)and gender (female), at five-year intervals.
|-
|SeriesEdPriEnrollFemaleRatioBLLong
|Barro-Lee
|2017/02/15
|SK;HF: Enrollment ratios, subdivided by education level (Primary)and gender (female), at five-year intervals.
|-
|SeriesEdPriEnrollGrossFemalePcnt
|UIS
|2019/09/17
|EB,HF,DK
|-
|SeriesEdPriEnrollGrossMalePcnt
|UIS
|2019/09/17
|EB,HF,DK
|-
|SeriesEdPriEnrollGrossTotalPcnt
|UIS
|2019/09/17
|EB,HF,DK
|-
|SeriesEdPriEnrollHeadcountFem
|UIS 2013 BATCH PULL
|2013/07/17
|No Notes
|-
|SeriesEdPriEnrollHeadcountMal
|UNESCO Institute for Statistics
|2008/11/16
|No Notes
|-
|SeriesEdPriEnrollHeadCountTtl
|UIS 2013 BATCH PULL
|2013/07/17
|No Notes
|-
|SeriesEdPriEnrollMale%
|Millennium Indicators Database, UN <nowiki>http://unstats.un.org/unsd/mi/mi_goals.asp</nowiki>
|2004/10/01
|
|-
|SeriesEdPriEnrollMaleRateBLLong
|Barro-Lee
|2017/02/15
|SK;HF: Enrollment ratios, subdivided by education level (Primary)and gender (male), at five-year intervals
|-
|SeriesEdPriEnrollMaleRatioBLLong
|Barro-Lee
|2017/02/15
|SK;HF: Enrollment ratios, subdivided by education level (Primary)and gender (male), at five-year intervals
|-
|SeriesEdPriEnrollNetFemalePcnt
|UIS
|2019/09/17
|EB,HF,DK
|-
|SeriesEdPriEnrollNetMalePcnt
|UIS
|2019/09/17
|EB,HF,DK
|-
|SeriesEdPriEnrollNetParity
|UIS
|2019/09/12
|BG
|-
|SeriesEdPriEnrollNetTotalPcnt
|UIS
|2019/09/17
|EB,HF,DK
|-
|SeriesEdPriEnrollTotal%
|Millennium Indicators Database, UN <nowiki>http://unstats.un.org/unsd/mi/mi_goals.asp</nowiki>
|2004/10/01
|
|-
|SeriesEdPriEnrollTotalRateBLLong
|Barro-Lee
|2017/02/15
|SK;HF: Enrollment ratios, subdivided by education level (Primary), at five-year intervals
|-
|SeriesEdPriEnrollTotalRatioBLLong
|Barro-Lee
|2017/02/15
|SK;HF: Enrollment ratios, subdivided by education level (Primary), at five-year intervals
|-
|SeriesEdPriEntranceAge
|UIS
|2019/09/17
|EB,HF,DK
|-
|SeriesEdPriGirlBoyRatio
|1990 UN Statistical Division; rest from United Nations Statistics Division available at <nowiki>http://mdgs.un.org/unsd/mdg/</nowiki>
|2006/12/01
|Years with no data points at all omitted
|-
|SeriesEDPRINIRADJFemale
|UIS
|2020/03/10
|BG, YX
|-
|SeriesEDPRINIRADJMale
|UIS
|2020/03/10
|BG, YX
|-
|SeriesEDPRINIRADJTotal
|UIS
|2020/03/10
|BG, YX
|-
|SeriesEDPRINIRENTRNFemale
|UIS
|2020/03/10
|BG, YX
|-
|SeriesEDPRINIRENTRNM1Female
|UIS
|2020/03/10
|BG, YX
|-
|SeriesEDPRINIRENTRNM1Male
|UIS
|2020/03/10
|BG, YX
|-
|SeriesEDPRINIRENTRNM1P1Female
|UIS
|2020/03/10
|BG, YX
|-
|SeriesEDPRINIRENTRNM1P1Male
|UIS
|2020/03/10
|BG, YX
|-
|SeriesEDPRINIRENTRNM1P1Total
|UIS
|2020/03/10
|BG, YX
|-
|SeriesEDPRINIRENTRNM1Total
|UIS
|2020/03/10
|BG, YX
|-
|SeriesEDPRINIRENTRNMale
|UIS
|2020/03/10
|BG, YX
|-
|SeriesEDPRINIRENTRNP1Female
|UIS
|2020/03/10
|BG, YX
|-
|SeriesEDPRINIRENTRNP1Male
|UIS
|2020/03/10
|BG, YX
|-
|SeriesEDPRINIRENTRNP1Total
|UIS
|2020/03/10
|BG, YX
|-
|SeriesEDPRINIRENTRNTotal
|UIS
|2020/03/10
|BG, YX
|-
|SeriesEdPriNIRFemale%
|UIS
|2019/12/17
|YX
|-
|SeriesEdPriNIRMale%
|UIS
|2019/12/17
|YX
|-
|SeriesEdPriNIROfAgeFemale
|UIS
|2019/09/12
|BG
|-
|SeriesEdPriNIROfAgeMale
|UIS
|2019/09/12
|BG
|-
|SeriesEdPriNIROfAgeTotal
|UIS
|2019/09/12
|BG
|-
|SeriesEdPriNIROverAgeFemale
|UIS
|2019/09/12
|BG
|-
|SeriesEdPriNIROverAgeMale
|UIS
|2019/09/12
|BG
|-
|SeriesEdPriNIROverAgeTotal
|UIS
|2019/09/12
|BG
|-
|SeriesEdPriNIRTotal%
|UIS
|2019/12/17
|YX
|-
|SeriesEdPriNIRUnderAgeFemale
|UIS
|2019/09/12
|BG
|-
|SeriesEdPriNIRUnderAgeMale
|UIS
|2019/09/12
|BG
|-
|SeriesEdPriNIRUnderAgeTotal
|UIS
|2019/09/12
|BG
|-
|SeriesEdPriOutofSchoolFemale%
|UIS
|2019/09/12
|BG
|-
|SeriesEdPriOutofSchoolMale%
|UIS 2013 BATCH PULL
|2013/07/12
|Net rate of out of schoolers in primary, Male
|-
|SeriesEdPriOutofSchoolTotal%
|UIS 2013 BATCH PULL
|2013/07/12
|Net rate of out of schoolers in primary, total
|-
|SeriesEdPriParityGenderMath
|UN SDG Indicators Global Database
|2019/06/28
|JD; Extended Source Def truncated
|-
|SeriesEdPriParityGenderRead
|UN SDG Indicators Global Database
|2019/06/28
|JD; Extended Source Def truncated
|-
|SeriesEdPriParityGenderTeachers
|UN SDG Indicators Global Database
|2019/06/28
|JD; Extended Source Def truncated
|-
|SeriesEdPriParityRurUrbMath
|UN SDG Indicators Global Database
|2019/06/28
|JD; Extended Source Def truncated
|-
|SeriesEdPriParityRurUrbRead
|UN SDG Indicators Global Database
|2019/06/28
|JD; Extended Source Def truncated
|-
|SeriesEdPriParitySocioeconMath
|UN SDG Indicators Global Database
|2017/07/26
|AJM, MM; Extended Source Def truncated
|-
|SeriesEdPriParitySocioeconRead
|UN SDG Indicators Global Database
|2019/06/28
|JD; Extended Source Def truncated
|-
|SeriesEDPriPTR
|UIS
|2019/09/07
|EB,HF,DK,AA
|-
|SeriesEdPriRepeater1stYearFemale%
|UIS
|2019/09/12
|BG
|-
|SeriesEdPriRepeater1stYearMale%
|UIS
|2019/09/12
|BG
|-
|SeriesEdPriRepeater1stYearTotal%
|UIS
|2019/09/12
|BG
|-
|SeriesEdPriRepeaterFemale%
|UIS
|2019/09/12
|BG
|-
|SeriesEdPriRepeaterMale%
|UIS
|2019/09/12
|BG
|-
|SeriesEdPriRepeaterTotal%
|UIS
|2019/09/13
|BG
|-
|SeriesEdPriRpt%Gr1BothSexes
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%Gr1Female
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%Gr1Male
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%Gr2BothSexes
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%Gr2Female
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%Gr2Male
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%Gr3BothSexes
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%Gr3Female
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%Gr3Male
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%Gr4BothSexes
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%Gr4Female
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%Gr4Male
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%Gr5BothSexes
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%Gr5Female
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%Gr5Male
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%Gr6BothSexes
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%Gr6Female
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%Gr6Male
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%Gr7BothSexes
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%Gr7Female
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%Gr7Male
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%GrAllBothSexes
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%GrAllFemale
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriRpt%GrAllMale
|The World Bank Education Statistics (EdStats)
|2021/05/07
|
|-
|SeriesEdPriSurvivalFemale%
|UIS
|2019/11/08
|KS;
|-
|SeriesEdPriSurvivalMale%
|UIS
|2019/11/08
|KS;
|-
|SeriesEdPriSurvivalParity
|UIS
|2019/11/08
|KS;
|-
|SeriesEdPriSurvivalTotal%
|UIS
|2019/11/08
|KS;
|-
|SeriesEdPriTeachTrainFemale%
|UN SDG Indicators Global Database
|2019/06/28
|JD; Extended Source Def truncated
|-
|SeriesEdPriTeachTrainMale%
|UN SDG Indicators Global Database
|2019/06/28
|JD; Extended Source Def truncated
|-
|SeriesEdPriTeachTrainTotal%
|UN SDG Indicators Global Database
|2019/06/28
|JD; Extended Source Def truncated
|-
|SeriesEdPriTransition2Sec%Female
|UIS
|2021/11/01
|SN, CP
|-
|SeriesEdPriTransition2Sec%Male
|UIS
|2021/11/01
|SN, CP
|-
|SeriesEdPriTransition2Sec%Parity
|UIS
|2019/09/15
|AW
|-
|SeriesEdPriTransition2Sec%Total
|UIS
|2021/11/01
|SN, CP
|-
|SeriesEdPriWilsGPI1991
|John Cheeseboro's Extraction
|2008/05/27
|No Notes
|-
|SeriesEdPriWilsGPI1999
|John Cheeseboro's Extraction
|2008/05/27
|Uses Post 1999 data; few of the countries use one data point close to 1999
|-
|SeriesEdPriWilsGPICohort
|John Cheeseboro's Extraction
|2008/05/27
|Uses Cohort Projection
|-
|SeriesEdPriWilsNER1991Total%
|John Cheeseboro's Extraction
|2008/05/27
|No Notes
|-
|SeriesEdPriWilsNER1999Total%
|John Cheeseboro's Extraction
|2008/05/27
|Uses Post 1999 data; few of the countries use one data point close to 1999
|-
|SeriesEdPriWilsNERCohortTotal%
|John Cheeseboro's Extraction
|2008/05/27
|Uses Cohort Projection
|-
|SeriesEdPriWilsStudentsTotal
|John Cheeseboro's Extraction
|2008/05/27
|No Notes
|-
|SeriesEdQualADJ0F1
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ0F2
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ0G1
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ0G2
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ0M1
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ0M2
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ0R1
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ0R2
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ0U1
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ0U2
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ1F1
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ1F2
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ1G1
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ1G2
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ1M1
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ1M2
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ1R1
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ1R2
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ1U1
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ1U2
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ2F1
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ2F2
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ2G1
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ2G2
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ2M1
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ2M2
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ2R1
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ2R2
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ2U1
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualADJ2U2
|<nowiki>http://www.beta-umr7522.fr/Datasets</nowiki>
|2015/01/29
|JEM
|-
|SeriesEdQualAdultsAg15Total
|IFs pre-proc estimation
|2018/06/03
|mti; from IFs
|-
|SeriesEDQualMeanScorePrimaryFemale
|An Updated Global Dataset on Education Quality (1965-2015)
|2017/11/27
|JD,EB
|-
|SeriesEDQualMeanScorePrimaryMale
|An Updated Global Dataset on Education Quality (1965-2015)
|2017/11/27
|JD,EB
|-
|SeriesEDQualMeanScorePrimaryTotal
|An Updated Global Dataset on Education Quality (1965-2015)
|2017/11/27
|JD,EB
|-
|SeriesEDQualMeanScoreSecondaryFemale
|An Updated Global Dataset on Education Quality (1965-2015)
|2017/11/27
|JD,EB
|-
|SeriesEDQualMeanScoreSecondaryMale
|An Updated Global Dataset on Education Quality (1965-2015)
|2017/11/27
|JD,EB
|-
|SeriesEDQualMeanScoreSecondaryTotal
|An Updated Global Dataset on Education Quality (1965-2015)
|2017/11/27
|JD,EB
|-
|SeriesEdQualPIRLSReadingBot25%
|<nowiki>http://isc.bc.edu/PDF/P06_IR_Ch2.pdf</nowiki>
|2008/02/01
|
|-
|SeriesEdQualPIRLSReadingFemales
|<nowiki>http://timssandpirls.bc.edu/pirls2011/international-results-pirls.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualPIRLSReadingMales
|<nowiki>http://timssandpirls.bc.edu/pirls2011/international-results-pirls.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualPIRLSReadingMark400%
|<nowiki>http://timssandpirls.bc.edu/pirls2011/international-results-pirls.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualPIRLSReadingMark475%
|<nowiki>http://timssandpirls.bc.edu/pirls2011/international-results-pirls.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualPIRLSReadingMark550%
|<nowiki>http://timssandpirls.bc.edu/pirls2011/international-results-pirls.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualPIRLSReadingMark625%
|<nowiki>http://timssandpirls.bc.edu/pirls2011/international-results-pirls.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualPIRLSReadingTop10%
|<nowiki>http://isc.bc.edu/PDF/P06_IR_Ch2.pdf</nowiki>
|2008/02/01
|
|-
|SeriesEdQualPIRLSReadingTop25%
|<nowiki>http://isc.bc.edu/PDF/P06_IR_Ch2.pdf</nowiki>
|2008/02/01
|
|-
|SeriesEdQualPIRLSReadingTop50%
|<nowiki>http://isc.bc.edu/PDF/P06_IR_Ch2.pdf</nowiki>
|2008/02/01
|
|-
|SeriesEdQualPIRLSReadingTotal
|<nowiki>http://isc.bc.edu/PDF/P06_IR_Ch2.pdf</nowiki>
|2008/02/01
|
|-
|SeriesEdQualPISAMathFemales
|OECD, Programme for International Student Assessment, <nowiki>http://pisadata.acer.edu.au/2006</nowiki>
|2013/12/13
|DAB (vetter); JEM
|-
|SeriesEdQualPISAMathMales
|OECD, Programme for International Student Assessment, <nowiki>http://pisadata.acer.edu.au/2006</nowiki>
|2013/12/13
|DAB (vetter); JEM
|-
|SeriesEdQualPISAMathTotal
|OECD, Programme for International Student Assessment, <nowiki>http://pisadata.acer.edu.au/2006</nowiki>
|2013/12/13
|DAB (vetter); JEM
|-
|SeriesEdQualPISAMathTotalWCI
|World Competitiveness Yearbook 2013, IMD (Penrose Sub)
|2013/05/21
|GE;CN
|-
|SeriesEdQualPISAReadingFemales
|OECD, Programme for International Student Assessment, <nowiki>http://pisadata.acer.edu.au/2006</nowiki>
|2013/12/13
|DAB (vetter); JEM
|-
|SeriesEdQualPISAReadingMales
|OECD, Programme for International Student Assessment, <nowiki>http://pisadata.acer.edu.au/2006</nowiki>
|2013/12/13
|DAB (vetter); JEM
|-
|SeriesEdQualPISAReadingTotal
|OECD, Programme for International Student Assessment, <nowiki>http://pisadata.acer.edu.au/2006</nowiki>
|2013/12/13
|DAB (vetter); JEM
|-
|SeriesEdQualPISAScienceFemales
|OECD, Programme for International Student Assessment, <nowiki>http://pisadata.acer.edu.au/2006</nowiki>
|2013/12/13
|DAB (vetter); JEM
|-
|SeriesEdQualPISAScienceMales
|OECD, Programme for International Student Assessment, <nowiki>http://pisadata.acer.edu.au/2006</nowiki>
|2013/12/13
|DAB (vetter); JEM
|-
|SeriesEdQualPISAScienceTotal
|OECD, Programme for International Student Assessment, <nowiki>http://pisadata.acer.edu.au/2006</nowiki>
|2013/12/13
|DAB (vetter); JEM
|-
|SeriesEdQualPISAScienceTotalWCI
|World Competitiveness Yearbook 2013, IMD (Penrose Sub)
|2013/05/21
|GE;CN
|-
|SeriesEdQualPriAllAvgScrWBGA
|World Bank EDSTATS
|2018/01/07
|mti;
|-
|SeriesEdQualPriMathAvgScrWBGA
|World Bank EDSTATS
|2018/05/24
|MTI,KBN: Updated with MIDEH conversions
|-
|SeriesEdQualPriMathRatioNAEK
|Nadir Altinok (draft)
|2018/01/23
|KN
|-
|SeriesEdQualPriReadAvgScrWBGA
|World Bank EDSTATS
|2018/05/24
|MTI,KBN: Updated with MIDEH conversions for Honduras
|-
|SeriesEdQualPriReadRatioNAEK
|Nadir Altinok (draft)
|2018/01/23
|KN
|-
|SeriesEdQualPriSciAvgScrWBGA
|World Bank EDSTATS
|2018/01/07
|mti;
|-
|SeriesEdQualPriSciRatioNAEK
|Nadir Altinok (draft)
|2018/01/23
|KN
|-
|SeriesEdQualPriSecAllAvgScrWBGA
|World Bank EDSTATS
|2018/01/07
|mti;
|-
|SeriesEdQualPriTotalRatioNAEK
|Nadir Altinok (draft)
|2018/01/23
|KN
|-
|SeriesEdQualSchlsConInternet%
|WDI CD 06
|2006/08/01
|
|-
|SeriesEdQualSecAllAvgScrWBGA
|World Bank EDSTATS
|2018/01/07
|mti;
|-
|SeriesEdQualSecMathAvgScrWBGA
|World Bank EDSTATS
|2018/05/24
|MTI,KBN: Updated with MIDEH conversions
|-
|SeriesEdQualSecMathRatioNAEK
|Nadir Altinok (draft)
|2018/01/23
|KN
|-
|SeriesEdQualSecReadAvgScrWBGA
|World Bank EDSTATS
|2018/05/24
|mti,kbn
|-
|SeriesEdQualSecReadRatioNAEK
|Nadir Altinok (draft)
|2018/01/23
|KN
|-
|SeriesEdQualSecSciAvgScrWBGA
|World Bank EDSTATS
|2018/01/07
|mti;
|-
|SeriesEdQualSecSciRatioNAEK
|Nadir Altinok (draft)
|2018/01/23
|KN
|-
|SeriesEdQualSecTotalRatioNAEK
|Nadir Altinok (draft)
|2018/01/23
|KN
|-
|SeriesEdQualTIMSSMath4thFemales
|<nowiki>http://timssandpirls.bc.edu/timss2011/international-results-mathematics.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualTIMSSMath4thMales
|<nowiki>http://timssandpirls.bc.edu/timss2011/international-results-mathematics.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualTIMSSMath4thMark400%
|<nowiki>http://timssandpirls.bc.edu/timss2011/international-results-mathematics.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualTIMSSMath4thMark475%
|<nowiki>http://timssandpirls.bc.edu/timss2011/international-results-mathematics.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualTIMSSMath4thMark550%
|<nowiki>http://timssandpirls.bc.edu/timss2011/international-results-mathematics.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualTIMSSMath4thMark625%
|<nowiki>http://timssandpirls.bc.edu/timss2011/international-results-mathematics.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualTIMSSMath4thTop10%
|<nowiki>http://isc.bc.edu/timss1995i</nowiki>
|2008/02/01
|Belgium is average of Flemish and French scores
|-
|SeriesEdQualTIMSSMath4thTop25%
|<nowiki>http://isc.bc.edu/timss1995i</nowiki>
|2008/02/01
|Belgium is average of Flemish and French scores
|-
|SeriesEdQualTIMSSMath4thTop50%
|<nowiki>http://isc.bc.edu/timss1995i</nowiki>
|2008/02/01
|Belgium is average of Flemish and French scores
|-
|SeriesEdQualTIMSSMath4thTotal
|<nowiki>http://timssandpirls.bc.edu/timss2011/international-results-mathematics.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualTIMSSMath8thFemales
|<nowiki>http://timssandpirls.bc.edu/timss2011/international-results-mathematics.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualTIMSSMath8thMales
|<nowiki>http://timssandpirls.bc.edu/timss2011/international-results-mathematics.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualTIMSSMath8thTop10%
|<nowiki>http://isc.bc.edu/timss1995i</nowiki>
|2008/02/01
|Belgium is average of Flemish and French scores
|-
|SeriesEdQualTIMSSMath8thTop25%
|<nowiki>http://isc.bc.edu/timss1995i</nowiki>
|2008/02/01
|Belgium is average of Flemish and French scores
|-
|SeriesEdQualTIMSSMath8thTop50%
|<nowiki>http://isc.bc.edu/timss1995i</nowiki>
|2008/02/01
|Belgium is average of Flemish and French scores
|-
|SeriesEdQualTIMSSMath8thTotal
|<nowiki>http://timssandpirls.bc.edu/timss2011/international-results-mathematics.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualTIMSSScience4thFemales
|<nowiki>http://timssandpirls.bc.edu/timss2011/international-results-science.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualTIMSSScience4thMales
|<nowiki>http://timssandpirls.bc.edu/timss2011/international-results-science.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualTIMSSScience4thMark400%
|<nowiki>http://timssandpirls.bc.edu/timss2011/international-results-science.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualTIMSSScience4thMark475%
|<nowiki>http://timssandpirls.bc.edu/timss2011/international-results-science.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualTIMSSScience4thMark550%
|<nowiki>http://timssandpirls.bc.edu/timss2011/international-results-science.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualTIMSSScience4thMark625%
|<nowiki>http://timssandpirls.bc.edu/timss2011/international-results-science.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualTIMSSScience4thTop10%
|<nowiki>http://isc.bc.edu/timss1995i</nowiki>
|2008/02/01
|Belgium is average of Flemish and French scores
|-
|SeriesEdQualTIMSSScience4thTop25%
|<nowiki>http://isc.bc.edu/timss1995i</nowiki>
|2008/02/01
|Belgium is average of Flemish and French scores
|-
|SeriesEdQualTIMSSScience4thTop50%
|<nowiki>http://isc.bc.edu/timss1995i</nowiki>
|2008/02/01
|Belgium is average of Flemish and French scores
|-
|SeriesEdQualTIMSSScience4thTotal
|<nowiki>http://timssandpirls.bc.edu/timss2011/international-results-science.html</nowiki>
|2013/02/23
|
|-
|SeriesEdQualTIMSSScience8thFemales
|<nowiki>http://timssandpirls.bc.edu/timss2011/international-results-science.html</nowiki>
|2013/02/23
|Belgium is average of Flemish and French scores
|-
|SeriesEdQualTIMSSScience8thMales
|<nowiki>http://timssandpirls.bc.edu/timss2011/international-results-science.html</nowiki>
|2013/02/23
|Belgium is average of Flemish and French scores
|-
|SeriesEdQualTIMSSScience8thTop10%
|<nowiki>http://isc.bc.edu/timss1995i</nowiki>
|2008/02/01
|Belgium is average of Flemish and French scores
|-
|SeriesEdQualTIMSSScience8thTop25%
|<nowiki>http://isc.bc.edu/timss1995i</nowiki>
|2008/02/01
|Belgium is average of Flemish and French scores
|-
|SeriesEdQualTIMSSScience8thTop50%
|<nowiki>http://isc.bc.edu/timss1995i</nowiki>
|2008/02/01
|Belgium is average of Flemish and French scores
|-
|SeriesEdQualTIMSSScience8thTotal
|<nowiki>http://timssandpirls.bc.edu/timss2011/international-results-science.html</nowiki>
|2013/02/23
|Belgium is average of Flemish and French scores
|-
|SeriesEdQualWEFRank
|World Economic Forum. <nowiki>http://www.weforum.org/site/homepublic.nsf/Content/Global+CompetitGlobal</nowiki> Competitiveness Report 2004-2005
|2005/02/01
|Survey Results
|}

Data

2022-08-10T16:53:18Z

Audrey.Pirzadeh:

International Futures (IFs) modeling application contains a large historical database that enables model users to explore patterns, understand relationships and estimate parameters. The database consists of a large collection of country level time series tables covering a large number of variables on IFs model issue areas like population, education, health, agriculture, energy, economics, environment, infrastructure, governance and international politics. The database is compiled, in most cases, from publicly available cross-country data series, e.g., World Development Indicators, UNESCO Institute for Education database on education, FAO database on agricultural production, Correlates of War data on conflicts etc. IFs data team add new series to the database and update the existing ones on a regular basis.

At present IFs data tables contain data for 186 countries. Temporal coverage, in most cases, start in 1960.

*[[Database_files_in_IFs|Database files in IFs]]
*[[Importing_data_(general_instructions)|Importing data (general instructions)]]
*[[Vetting_data|Vetting data]]
*[[Consolidating_data|Consolidating data]]
*[[Sub-regional_data|Sub-Regional data]]
*[[IFs_Base_comparson|IFs Base comparison]]
[[File:something went wrong.png|thumb]]

= Data Source =
*[[Master Sheet]]
*[[New Page|Country Concordance]]
*[[Add New Page]]

= Preprocessor =

*[[Agriculture_preprocessor|Agriculture preprocessor]]
*[[Economic_preprocessor|Economic_preprocessor]]
*[[Education_data|Education data]]
*[[Energy_data|Energy data]]
*[[Environmental_data|Environmental data]]
*[[Health_preprocessor|Health_preprocessor]]
*[[Infrastructure_data|Infrastructure data]]
*[[Population_data|Population data]]
*[[Socio-Political_data|Socio-Political data]]
*[[State_failure_data|State failure data]]
*[[Water_data|Water data]]
*[[World_Value_Survey_data|World Value Survey data]]

[[EconDash|EconDash]]

[[SDG_data|SDG_data]]

File:Something went wrong.png

2022-08-10T16:47:36Z

Audrey.Pirzadeh:

Data

2022-08-10T16:42:03Z

Audrey.Pirzadeh: /* Data Sources */

International Futures (IFs) modeling application contains a large historical database that enables model users to explore patterns, understand relationships and estimate parameters. The database consists of a large collection of country level time series tables covering a large number of variables on IFs model issue areas like population, education, health, agriculture, energy, economics, environment, infrastructure, governance and international politics. The database is compiled, in most cases, from publicly available cross-country data series, e.g., World Development Indicators, UNESCO Institute for Education database on education, FAO database on agricultural production, Correlates of War data on conflicts etc. IFs data team add new series to the database and update the existing ones on a regular basis.

At present IFs data tables contain data for 186 countries. Temporal coverage, in most cases, start in 1960.

*[[Database_files_in_IFs|Database files in IFs]]
*[[Importing_data_(general_instructions)|Importing data (general instructions)]]
*[[Vetting_data|Vetting data]]
*[[Consolidating_data|Consolidating data]]
*[[Sub-regional_data|Sub-Regional data]]
*[[IFs_Base_comparson|IFs Base comparison]]
[[File:World Bank.png|thumb|World Bank Logo (pulling data demo)]]

= Data Sources =
*[[Audrey]]
*[[Test]]
*[[Master Sheet]]
*[[New Page|Country Concordance]]
*[[Add New Page]]

= Preprocessor =

*[[Agriculture_preprocessor|Agriculture preprocessor]]
*[[Economic_preprocessor|Economic_preprocessor]]
*[[Education_data|Education data]]
*[[Energy_data|Energy data]]
*[[Environmental_data|Environmental data]]
*[[Health_preprocessor|Health_preprocessor]]
*[[Infrastructure_data|Infrastructure data]]
*[[Population_data|Population data]]
*[[Socio-Political_data|Socio-Political data]]
*[[State_failure_data|State failure data]]
*[[Water_data|Water data]]
*[[World_Value_Survey_data|World Value Survey data]]

[[EconDash|EconDash]]

[[SDG_data|SDG_data]]

Data

2022-08-10T16:41:25Z

Audrey.Pirzadeh: /* Data Sources */

International Futures (IFs) modeling application contains a large historical database that enables model users to explore patterns, understand relationships and estimate parameters. The database consists of a large collection of country level time series tables covering a large number of variables on IFs model issue areas like population, education, health, agriculture, energy, economics, environment, infrastructure, governance and international politics. The database is compiled, in most cases, from publicly available cross-country data series, e.g., World Development Indicators, UNESCO Institute for Education database on education, FAO database on agricultural production, Correlates of War data on conflicts etc. IFs data team add new series to the database and update the existing ones on a regular basis.

At present IFs data tables contain data for 186 countries. Temporal coverage, in most cases, start in 1960.

*[[Database_files_in_IFs|Database files in IFs]]
*[[Importing_data_(general_instructions)|Importing data (general instructions)]]
*[[Vetting_data|Vetting data]]
*[[Consolidating_data|Consolidating data]]
*[[Sub-regional_data|Sub-Regional data]]
*[[IFs_Base_comparson|IFs Base comparison]]
[[File:World Bank.png|thumb|World Bank Logo (pulling data demo)]]

= Data Sources =
*[[Test]]
*[[Master Sheet]]
*[[New Page|Country Concordance]]
*[[Add New Page]]

= Preprocessor =

*[[Agriculture_preprocessor|Agriculture preprocessor]]
*[[Economic_preprocessor|Economic_preprocessor]]
*[[Education_data|Education data]]
*[[Energy_data|Energy data]]
*[[Environmental_data|Environmental data]]
*[[Health_preprocessor|Health_preprocessor]]
*[[Infrastructure_data|Infrastructure data]]
*[[Population_data|Population data]]
*[[Socio-Political_data|Socio-Political data]]
*[[State_failure_data|State failure data]]
*[[Water_data|Water data]]
*[[World_Value_Survey_data|World Value Survey data]]

[[EconDash|EconDash]]

[[SDG_data|SDG_data]]

Data

2022-08-10T16:40:42Z

Audrey.Pirzadeh: /* Data Sources */

International Futures (IFs) modeling application contains a large historical database that enables model users to explore patterns, understand relationships and estimate parameters. The database consists of a large collection of country level time series tables covering a large number of variables on IFs model issue areas like population, education, health, agriculture, energy, economics, environment, infrastructure, governance and international politics. The database is compiled, in most cases, from publicly available cross-country data series, e.g., World Development Indicators, UNESCO Institute for Education database on education, FAO database on agricultural production, Correlates of War data on conflicts etc. IFs data team add new series to the database and update the existing ones on a regular basis.

At present IFs data tables contain data for 186 countries. Temporal coverage, in most cases, start in 1960.

*[[Database_files_in_IFs|Database files in IFs]]
*[[Importing_data_(general_instructions)|Importing data (general instructions)]]
*[[Vetting_data|Vetting data]]
*[[Consolidating_data|Consolidating data]]
*[[Sub-regional_data|Sub-Regional data]]
*[[IFs_Base_comparson|IFs Base comparison]]
[[File:World Bank.png|thumb|World Bank Logo (pulling data demo)]]

= Data Sources =
*Test
*[[Master Sheet]]
*[[New Page|Country Concordance]]
*[[Add New Page]]

= Preprocessor =

*[[Agriculture_preprocessor|Agriculture preprocessor]]
*[[Economic_preprocessor|Economic_preprocessor]]
*[[Education_data|Education data]]
*[[Energy_data|Energy data]]
*[[Environmental_data|Environmental data]]
*[[Health_preprocessor|Health_preprocessor]]
*[[Infrastructure_data|Infrastructure data]]
*[[Population_data|Population data]]
*[[Socio-Political_data|Socio-Political data]]
*[[State_failure_data|State failure data]]
*[[Water_data|Water data]]
*[[World_Value_Survey_data|World Value Survey data]]

[[EconDash|EconDash]]

[[SDG_data|SDG_data]]