Pardee Wiki - User contributions [en]

World Population Prospects (WPP), United Nations Population Division (UNPD)

2026-04-08T20:01:47Z

Yutang.Xiong: /* Tables In IFs */

FAOSTAT Food Security and Nutrition

2026-02-24T22:08:41Z

Yutang.Xiong: /* Data Notes */

= Summary =
FAOSTAT gives users access to food and agriculture data for over 245 countries and territories. Additionally, it covers all FAO regional groupings from 1961 to the most recent year available. The Suite of Food Security Indicators presents the core set of food security indicators. The choice of the indicators has been informed by expert judgment and the availability of data with sufficient coverage to enable comparisons across regions and over time. Many of these indicators are produced and published elsewhere by FAO and other international organizations. More indicators will be added to as more data will become available. Indicators are classified along the four dimensions of food security -- availability, access, utilization and stability.

The data team uses food security indicators for many series, including those related to hunger/malnutrition such as Series, Series, Series, Series. To pull data, follow the instructions below.

= Tables In IFs =
{| class="wikitable"
|+
!Variable
!Definition
!Years
!UsedInPreprocessor
!UsedInPreprocessorFileName
|-
|MalnDietEnSup
|National average dietary energy supply (DES) (expressed in calories per caput per day) used in the estimation of prevalence of undernourishment.
|2001-2023
|1
|AGRICULTURE
|-
|MalnCaloricDistribution
|Coefficient of variation of habitual caloric consumption distribution.
|2001-2023
|1
|AGRICULTURE
|-
|MalnMinNutrRequired
|In a specified age/sex category, MDER is the minimum amount of dietary energy per person that is considered adequate to meet the energy needs at a minimum acceptable BMI of an individual engaged in low physical activity.
|2001-2023
|1
|AGRICULTURE
|-
|MalnPop%
|Prevalence of undernourishment (% of population)
|2001-2023
|1
|AGRICULTURE
|}

= Data Pulling Instructions =
Step 1. Navigate to [https://www.fao.org/faostat/en/#data/FS FAOSTAT Food Security]site. This site will display a suite of food security indicators.

[[File:FAO IMG 1.jpg|center|thumb|696x696px|FAO Food Security Homepage]]

Step 2. The page displays four options that must be selected to download data: Countries/Regions/Special Groups, Elements, Years, and Items. For all these options, you can also type in a search box.
[[File:FAO IMG 2.jpg|center|thumb|754x754px|FAO Data Selections]]

Step 3. For '''“Countries”''' options, you will generally want to select '''“Select All”''' to capture all country data.
[[File:FAO IMG 3.jpg|center|thumb|688x688px|Country Selection]]

Step 4. For the '''“Elements”''' options you will most likely want to select '''“Value”''', depending on the desired dataset.
[[File:FAO IMG 4.jpg|center|thumb|684x684px|Elements Selection]]

Step 5. For '''“Years”''', you will generally want to select '''“Select All”''' to capture all years of data available.
[[File:FAO IMG 5.jpg|center|thumb|678x678px|Years Selection]]

Step 6. For '''“Items”''', select the desired indicator. You can also type in the search box for the indicator. This example will use '''“Prevalence of undernourishment (percent)”'''.
[[File:FAO IMG 6.jpg|center|thumb|684x684px|Items Selection]]

Step 7. After selecting for these for options, the page will display the completed selections.
[[File:FAO IMG 7.jpg|center|thumb|716x716px|Completed Selections ]]

Step 8. Scroll down below the options. You also have options to select the “Output Type”, “File Type”, and “Thousand Separator in ‘Show Data’”. It’s recommended to select '''“Pivot”''' under '''“Output Type”''' to have easier data cleaning.
[[File:FAO IMG 8.jpg|center|thumb|701x701px|Additional Data Options]]

Step 9. You may select '''“Show Data”''' to look at the data before downloading. This option will populate data at the bottom of the page.

Step 10. Click '''“Download Data”'''. Either an Excel or CSV sheet will download. Now you can format the data to upload it into IFs. To import data into IFs, please follow the instructions found in the [[Importing data (general instructions)|Importing Data (general instructions)]] page.
[[File:FAO IMG 9.jpg|center|thumb|718x718px|Show and Download Data]]

= Data Notes =
* Any data values that include a symbol (ie. <2.5 for Algeria 2018-2022) need to be changed to NULL.
* For the table MalnMinNutrRequired, we need to make sure Sahrawi has values (use Mali as a proxy). So, only Kosovo is allowed to have nulls.
* MalnPop% has year ranges; only include the mid-year in the range. (ie. If 2000-2002, change it to 2001).
* While exporting data, make sure to select "Null Values" under Output Formatting Options. This is to ensure that all countries are included even if there is no data available.
* For country concordance, use "China, mainland" to represent China in IFs.

Version 8.51 (August 7th, 2025)

2026-02-19T19:04:02Z

Yutang.Xiong: /* Known Issues */

= Version 8.51 (August 7th, 2025) =
[[Version 8.50 (July 16th, 2025)|PREVIOUS VERSION]] | [[Index Page For All Other Version Notes|INDEX PAGE]] | [[Version 8.52 (September 2nd, 2025)|NEXT VERSION]]

You can download IFs Version 8.51 at [https://ifsfiles.du.edu/IFs%20with%20Pardee%208_51%20Bilateral%20August%207%202025.zip IFs 8.51].

== Interface Updates ==

*Fix order in G-Lists WB Income Groups
*Increase font size in axis ticker values in World Value Survey Display.
*Handling problems with strange characters in some descriptions in Cross-Section, for example GDI.

== Model Updates ==
*Added Bilateral Tariff Weighted Elasticities.[[File:New Trade.png|none|thumb]]

== Data Updates ==
*New data on Bilateral Tariff Weighted Elasticities.
*148 Tables inserted & updated in IFsHistSeries
*2 preprocessors updated, GDP2011 & GDP2017 based on IMF's July2025 revision
*99 tables updated, historical data for pov rate by sex & age from WPC
*1 table updated, carbon emissions from GCB
*12 tables created, historical GDP & Population from Fariss et al. (2022)
*15 tables updated, obesity rates from WHO
*19 tables fixed, energy resource & reserve from BGR
*12 Rows updated in DataDict
*7 rows, typos under the [Group] fixed, "SociaPolitical" -> "Sociopolitical"
*5 rows, [Aggregation] for UNPD Med projections are now POPUNPDMED2022

*9 tables updated in IFs.db
*9 tables, pov rates by gender and age groups at 3 thresholds. Values are taken from year 2022, tables are named with prefix PovRateAge

*142 Tables inserted & updated
*InfMortRateIHME in DataDict now has an aggregation rule using BIRTHS
*20 tables, SHAPE scenario based projections of GDPPCPPP & Poverty, updated ones that now have values for Turkey
*18 tables from FAO AquaStat on water resources & water infrastructure
*19 tables from BGR, energy reserves
*5 table from BP, energy production and consumption for gas, oil, and coal
*1 preprocessor, corruption index
*1 table from EIA, total electricity capacity
*26 tables FAO FishJ, fish catch and fish production
*8 tables from FAO TradeStat, crop and meat trade values
* 1 preprocessor from OECD, gov outlay
*11 tables from ILO, labor par by gender & labor size by sector
*2 tables from IMF, gov debt
*9 tables from IMF GFS, gov revenue and gov spending on health & education
*2 tables from SIPRI, military spending
*2 tables from UNHDR, GII and GDI
*8 tables from UNODC, drug prevalence
*2 tables from UNSD, marriage rates
*2 tables from WGI, gov effectiveness and quality
*4 tables from WRS, paved roads

== Known Issues ==
After release of IFs 8.51 an issue with the bilateral tariff weighted elasticities formula was discovered. This error was corrected in IFs 8.53.

[[Category:Versions]]

Version 8.52 (September 2nd, 2025)

2026-02-19T19:03:46Z

Yutang.Xiong: /* Known Issues */

= Version 8.52 (September 2nd, 2025) =
[[Version 8.51 (August 7th, 2025)|PREVIOUS VERSION]] | [[Index Page For All Other Version Notes|INDEX PAGE]] | [[Version 8.53 (September 17th, 2025)|NEXT VERSION]]

You can download IFs Version 8.52 at [https://ifsfiles.du.edu/IFs%20with%20Pardee%208_52%20September%202%202025.zip IFs 8.52].

== Interface Updates ==

*Added new Macro and Meso Categories to Flex Displays, also added new filters.
*[[File:New Flex Displays.png|none|thumb|1076x1076px|New Flex Displays with Macro and Meso Categories]]

* Fix to formulas with repeated terms, for example: Probability(Cluster_0)=e^(-0.6128+(1.8340*a)+(2.2198*b)+(1.7760*c)+(0.7788*(d/e))-(2.6070*f))/(e^(-0.6128+(1.8340*a)+(2.2198*b)+(1.7760*c)+(0.7788*(d/e))-(2.6070*f))+e^(-0.9064-(1.4878*a)-(2.5714*b)-(1.9723*c)-(1.3653*(d/e))+(2.2223*f))+e^(-0.7270-(0.3462*a)+(0.3516*b)+(0.1963*c)+(0.5865*(d/e))+(0.3847*f))), this was producing repeated terms in the selection box:

[[File:Selection box.png|none|thumb]]

* Removing double counting from SAM, variables with Total were being double counted.

== Model Updates ==
*contiguity parameter was not working for groups, like contiguity(World, World).
*Added limit to bilateral trade, it was going negative when tariffs get to 100%.
*Added Politically Relevant Dyad Security Count (PRDSEC):
*[[File:PRDSEC.png|none|thumb|1243x1243px]]

== Data Updates ==
*IFsVar new Descriptions, new SubModule Names and Segment Names.
*Added new Variable Lists to have full list available in Flex Displays.
*25 Tables inserted & updated in IFsHistSeries
*3 new tables, country-year distance to development pattern cluster centers.
*10 new tables, score and counts Character of Bilateral Interaction in the International System.
*8 new tables, civil war onset and its impact from Pardee's Civil War data.
*2 new tables, sovereignty states and geographic distance.
*2 tables updated from WDI, urban population and foreign population.
*34 Tables inserted & updated in IFsHistSeries
*3 new tables, country-year distance to development pattern cluster centers.
*10 new tables, score and counts Character of Bilateral Interaction in the International System.
*8 new tables, civil war onset and its impact from Pardee's Civil War data.
*2 new tables, sovereignty states and geographic distance.
*2 tables updated from WDI, urban population and foreign population.
*7 new tables, poverty tables from PIP at $2021
*2 new tables, GDP2021 & GDP2021PCPPP

== Known Issues ==
After release of IFs 8.52 an issue with the bilateral tariff weighted elasticities formula which was introduced in IFs 8.51 was discovered. This error was corrected in IFs 8.53.

[[Category:Versions]]

Version 8.52 (September 2nd, 2025)

2026-02-19T18:24:29Z

Yutang.Xiong: /* Known Issues */

International Futures (IFs)

2026-02-19T18:23:25Z

Yutang.Xiong:

'''Welcome to the International Futures (IFs) wiki. '''

'''[[Introduction_to_IFs|Introduction to IFs]]''' - Click here for an overview of the IFs system, including a description of the tool's purpose and major assumptions in its Base Case forecasts.

'''[[Use_IFs_(Download_Version)|Use IFs (Download Version)]] '''- Click here for instructions on using the standalone desktop version of IFs.

'''[[Use_IFs_(Online_Version)|Use IFs (Online Version)]] '''- Click here for instructions on using the web (cloud) version of IFs.

'''[[Understand_the_Model|Understand IFs]]''' - Click here to access documentation on each of the major system models in IFs. 

'''[[Guide_to_Scenario_Analysis_in_International_Futures_(IFs)|Guide to Scenario Analysis in IFs]] '''- Click here for instructions on creating and comparing alternative scenarios in IFs. This guide also includes an updated list of IFs parameters.

'''[[Project_Documentation|Project Documentation]] '''- Click here for documentation regarding ongoing projects at the Frederick S. Pardee Center.

'''[[Data]] '''- Click here for data updates

'''[[Additional_resources|Additional Resources]]'''

'''[[Index Page For All Other Version Notes|Version Notes]]''' - Click here to download the latest version of IFs and view an index of previous version notes.

[https://forms.office.com/Pages/ResponsePage.aspx?id=N3A8b8KF5kCd7BiwLSiSiAabQPS0TfVGq22zkCelI51UMko4UFpVRk5BQTVRSTU5NExSV0JBV1BYUSQlQCN0PWcu '''Newsletter Subscription Link''']

== Feedback ==

Feedback on how to improve IFs is always appreciated, especially if you find something that is not working. Compliments are also accepted. To send feedback or if you have any questions about using IFs, please email us at pardee.center [at] du.edu. You can also find additional contact information on our center's website. 

International Futures (IFs)

2026-02-19T00:43:22Z

Yutang.Xiong:

'''Welcome to the International Futures (IFs) wiki. '''

'''[[Introduction_to_IFs|Introduction to IFs]]''' - Click here for an overview of the IFs system, including a description of the tool's purpose and major assumptions in its Base Case forecasts.

'''[[Use_IFs_(Download_Version)|Use IFs (Download Version)]] '''- Click here for instructions on using the standalone desktop version of IFs.

'''[[Use_IFs_(Online_Version)|Use IFs (Online Version)]] '''- Click here for instructions on using the web (cloud) version of IFs.

'''[[Understand_the_Model|Understand IFs]]''' - Click here to access documentation on each of the major system models in IFs. 

'''[[Guide_to_Scenario_Analysis_in_International_Futures_(IFs)|Guide to Scenario Analysis in IFs]] '''- Click here for instructions on creating and comparing alternative scenarios in IFs. This guide also includes an updated list of IFs parameters.

'''[[Project_Documentation|Project Documentation]] '''- Click here for documentation regarding ongoing projects at the Frederick S. Pardee Center.

'''[[Data]] '''- Click here for data updates

'''[[Additional_resources|Additional Resources]]'''

[[Index Page For All Other Version Notes|Version Notes]] - Click here to download the latest version of IFs and view an index of previous version notes.

[https://forms.office.com/Pages/ResponsePage.aspx?id=N3A8b8KF5kCd7BiwLSiSiAabQPS0TfVGq22zkCelI51UMko4UFpVRk5BQTVRSTU5NExSV0JBV1BYUSQlQCN0PWcu '''Newsletter Subscription Link''']

== Feedback ==

Feedback on how to improve IFs is always appreciated, especially if you find something that is not working. Compliments are also accepted. To send feedback or if you have any questions about using IFs, please email us at pardee.center [at] du.edu. You can also find additional contact information on our center's website. 

Gender Statistics, World Bank

2026-02-12T19:56:51Z

Yutang.Xiong: /* Data Notes */

= Summary =
The [https://genderdata.worldbank.org/en/home World Bank's Gender Data Portal] makes the latest gender statistics accessible through compelling narratives and data visualizations to improve the understanding of gender data and facilitate analyses that inform policy choices.

= Table In IFs =
{| class="wikitable"
|+
!Variable
!Definition
!Ext Source Def
!UsedInPreprocessor
!UsedInPreprocessorFileName
!Name in Source
!Code in Source
|-
|AvgAgeAtFirstMarriageMen-WB
|Mean Age at First Marriage, Male
|Mean age at marriage, male shows the average length of single life expressed in years among those males who marry before age 50.
|1
|POPULATION
|Mean age at first marriage, male
|SP.DYN.SMAM.MA
|-
|AvgAgeAtFirstMarriageWomen-WB
|Mean Age at First Marriage, Female
|Mean age at marriage, female shows the average length of single life expressed in years among those females who marry before age 50.
|1
|POPULATION
|Mean age at first marriage, female.
|SP.DYN.SMAM.FE
|-
|LaborInformal%TotalFemaleWB
|Labor, informal, as a share of total, female
|
|1
|POPULATION
|Informal employment, female (% of total nonagricultural employment)
|SL.ISV.IFRM.FE.ZS
|-
|LaborInformal%TotalMaleWB
|Labor, informal, as a share of total, male
|
|1
|POPULATION
|Informal employment, male (% of total nonagricultural employment)
|SL.ISV.IFRM.MA.ZS
|-
|GenderUnpaidDomesticWorkTimeFemale
|Proportion of time spent on unpaid domestic and care work, female (% of 24 hour day).
|The average time women spend on household provision of services for own consumption. Data are expressed as a proportion of time in a day.
|0
|
|Proportion of time spent on unpaid domestic and care work, female (% of 24 hour day)
|SG.TIM.UWRK.FE
|-
|GenderUnpaidDomesticWorkTimeMale
|Proportion of time spent on unpaid domestic and care work, male (% of 24 hour day).
|The average time men spend on household provision of services for own consumption. Data are expressed as a proportion of time in a day.
|1
|POPULATION
|Proportion of time spent on unpaid domestic and care work, male (% of 24 hour day)
|SG.TIM.UWRK.MA
|}

= Data Pulling Instructions =
# You can either search up the indicator in the [https://genderdata.worldbank.org/en/topics topics] or search through them [https://genderdata.worldbank.org/en/indicators#id_employment_and_time_use here in the all indicators page] or download the entire dataset and filter it out for the data you need.
## NOTE: if you choose to download the entire dataset, note that the dataset has the years from 1960-2023/present but the series you are pulling may only have a specific timeframe (ie. 2000-2023). In this case it might be easier to just download the specific indicators so that you do not have to deal with extra years with NULL data. [[File:WB Gender Portal.png|none|thumb|600x600px]]
# Using the example informal employment, I can search up the indicator and click on it to take me to another page to download the data. [[File:Informal labor example WB gender portal.png|none|thumb|600x600px]][[File:Informal labor example WB gender portal download.png|none|thumb|600x600px]]

= Data Notes =

* GenderUnpaidDomesticWorkTimeFemale and GenderUnpaidDomesticWorkTimeMale are often pulled with the [[World Development Indicators, World Bank|World Development Indicators]].
* For women's age of marriage, raw data values for Barbados have an outlier in 2012 that needs to be manually removed, see the [https://github.com/PardeeCenterDU/IFs-Issues-Tracking/issues/527 GitHub ticket here]

Gender Statistics, World Bank

2026-02-12T19:56:17Z

Yutang.Xiong: /* Data Notes */

GDP & GDPPCPPP from IMF World Economic Outlook & WDI

2026-02-12T17:26:55Z

Yutang.Xiong: /* Special Cases */

= Summary =
The IMF World Economic Outlook and WDI hold valuable information about countries' GDP, PPP, etc. Therefore IFs contains that information and uses that information to calculate other indicators.

=== Data Collected From Sources ===

* GDP at current prices in local currency (GDP_current_LCU)
* GDP deflator for each year (GDP_deflator_year)
* GDP deflator for your base year (e.g., GDP_deflator_base for 2021)
* Exchange rate for your base year (Exchange_Rate_base; local currency per USD)
* PPP conversion factor for your base year (PPP_conversion_base; local currency per international dollar)
* Population (for each year; can be millions or actual number)

=== Calculation ===
In the calculation of constant GDP MER at constant price (such as tables GDP2021 and GDP2017), the formal process is to

* Get GDP at local currency unit- GDP_current_LCU
* Use GDP deflator to calculate GDP_constant_LCU. Here, you will need to pick a base year. We were using 2017 but are now moving to 2021. Thus, the result would be GDP at constant 2021 LCU.
** GDP_constant_LCU_base = GDP_current_LCU × (GDP_deflator_base / GDP_deflator_year)
* Use the market exchange rate to calculate GDP_constant_USD. Here, you will need the market exchange rate in 2021.
** GDP_constant_USD_base = GDP_constant_LCU_base / Exchange_Rate_base

To calculate GDP PPP at constant price, the formal process is exactly the same, except for step 3 in which instead of using market exchange rate, we apply PPP conversion rate.

* Same as step 1 above.
* Same as step 2 above.
* Use the PPP conversion rate to calculate GDP_PPP_constant_USD. Here, you will need the PPP conversion rate in 2021.
** GDP_PPP_constant_base = GDP_constant_LCU_base / PPP_conversion_base
We can use a shortcuts because each country's GDP at constant price in a selected year equals to that country's GDP at current price in the same year.

# Take a country's GDP at current USD in year 2021 and treat it as its GDP at constant $2021 in year 2021.
## GDP_constant_USD_2021 = GDP_current_USD_2021
# Calculate GDP growth rates measured at constant price.
## Because GDP growth rates measured at constant price remain the same despite of the base year chosen, i.e., a country's GDP growth rates would be the same across table GDP2011, GDP2017, and GDP2021.
### GDP_constant_USD_year = GDP_current_USD_base × (Growth Rate)
# Apply the growth rates to that country's GDP at current USD in 2021 to calculate other years' GDP.

This short cut also works for calculating GDP PPP at constant price.

# We already calculated GDP MER at constant USD in 2021 for each country for all years above.
# Use the ratio between a country's 2021's market exchange rate and its 2021's PPP conversion factor: you can use the following equation: GDP MER at constant $2021 / price ratio to get GDP PPP. Then GDP PPP / Population gets GDP PC PPP.
## GDP_PPP_constant_year = GDP_constant_USD_year / (Exchange_Rate_base / PPP_conversion_base)
## GDP_PC_PPP_constant_year = GDP_PPP_constant_year / Population_year

=== Related Wiki Pages ===
# [https://pardeewiki.du.edu//index.php?title=Core_Series GDP and GDPPC]
# [https://pardeewiki.du.edu/index.php?title=Economy_Data GDP2011]
# [https://pardeewiki.du.edu/index.php?title=Economy_Data GDP2011PCPPP]

= Tables In IFs =
{| class="wikitable"
|Variable
|Table
|Group
|SubGroup
|Definition
|Units
|Years
|UsedInPreprocessor
|UsedInPreprocessorFileName
|-
|GDP2011
|SeriesGDP2011
|Economy
|Aggregate
|IMF 2025 October Release, projection to 2027; WDI 2025 October version, values up to 2024
|Bil 2011$
|1960-2027
|0
|
|-
|GDP2011PCPPP
|SeriesGDP2011PCPPP
|Economic, Favorites
|GDP per Capita
|WDI 2025 October version, values up to 2024
|2011 PPP$
|1960-2024
|1
|Economy
|-
|GDP2017
|SeriesGDP2017
|Economy
|Aggregate
|IMF 2025 October Release, projection to 2027; WDI 2025 October version, values up to 2024
|Bil 2017$
|1960-2027
|0
|
|-
|GDP2017PCPPP
|SeriesGDP2017PCPPP
|Economic, Favorites
|GDP per Capita
|WDI 2025 October version, values up to 2024
|2017 PPP$
|1960-2024
|1
|Economy
|-
|GDP2021
|SeriesGDP2021
|Economy
|Aggregate
|IMF 2025 October Release, projection to 2027; WDI 2025 October version, values up to 2024
|Bil 2021$
|1960-2027
|0
|
|-
|GDP2021PCPPP
|SeriesGDP2021PCPPP
|Economic, Favorites
|GDP per Capita
|WDI 2025 October version, values up to 2024
|2021 PPP$
|1960-2024
|1
|Economy
|}
Most recent update was in November of 2025.

= Data Pulling Instructions =
If you would like a script to pull navigate to https://github.com/n1shamin/GDP-and-GDPPCPPP-IMF-and-WB. This does '''not''' do the special cases.

=== GDP ===
1. Download data

* GDP (current US$): https://data.worldbank.org/indicator/NY.GDP.MKTP.CD
* GDP growth (annual %): https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG
* IMF: entire dataset https://www.imf.org/en/Publications/SPROLLS/world-economic-outlook-databases#sort=%40imfdate%20descending
** Click the most recent WEO database, eg, World Economic Outlook Database, October 2024.
** Select "Entire Dataset"
** Click "By Countires", it will automatically download the entire data. The download file will be in .xls format. Open the file with Excel and Save as .xlsx for future use.
*** We will only use WEO Subject Code in (NGDP_RPCH, NGDPD). NGDP_RPCH is the percent change of GDP, and NGDPD is GDP in current US$ in Billions).
* IFsHistSeries: SeriesGDP2011 and SeriesGDP2017, in current US$ in Billions. We will use '''2011''' as the example in this instruction.

2. Calculate Growth_Rate from SeriesGDP2011. (Growth_Rate in 2011 = (GDP in 2011 - GDP in 2010)/GDP in 2010; The growth_rate need to *100 as percent value.)

3. Change all the data we downloaded and calculated to the format of [Country, Year, VAL]

* GDP (current US$): Country, Year, wb_curr
* GDP growth (annual %): Country, Year, wb_growth
* IMF_ NGDP_RPCH: Country, Year, imf_growth
* IMF_ NGDPD: Country, Year, imf_curr ('''''Note1''''')
* SeriesGDP2011: Country, Year, ifs_gdp ('''''Note1''''')
* Growth_Rate: Country, Year, ifs_growth

'''''Note1: imf_curr and ifs_gdp need to be *''''' '''''1000000000 due to the Billions Unit.'''''

4. Create a new value, '''GDP_curr,''' primarily using wb_curr.

* If there is a null in wb_curr, then use imf_curr to fill the null.
* If there is a null in both wb_curr and imf_curr, then use ifs_gdp to fill the null.

5. Create a new value, '''Growth,''' primarily using wb_growth

* If there is a null in wb_growth, then use imf_growth to fill the null.
* If there is a null in both wb_growth and imf_growth, then use ifs_growth to fill the null.
* After filling nulls, use forward fill to fill the rest of nulls.

6. Create a new value, '''GDP_new,'''

* The base year is 2011 since we use GDP2011 from the IFsHist. It will change to 2017 when we use GDP2017.
* Fill the base year value in GDP_curr to GDP_new for each country.
* After this step, every country will only have 1 value in GDP_new for the base year, e.g. 2011.

7. Fill the rest of the years for '''GDP_new''' using '''Growth''' and '''GDP_curr''':

* For the years before the base year, 2011, we will calculate the value from 2010 to 1960. E.g. GDP_new in 2010 = GDP_new in 2011*100 / (Growth in 2011 + 100). The year will be rolling for the rest of the calculation.
* For the years from the base year to year_end('''''Note2'''''), 2011, we will calculate the value from 2012 to 2026. E.g. GDP_new in 2012 = GDP_new in 2011*(100 + Growth in 2012) / 100. The year will be rolling for the rest of the calculation.

'''''Note2: year_end is 2 years from now.'''''

'''8. GDP_new is the new value for GDP2011. Need to be /1000000000 and be rounded to 5 decimals.'''

=== GDPPCPPP ===
1. Download Data

* GDP per capita (current LCU): https://data.worldbank.org/indicator/NY.GDP.PCAP.CN
* GDP deflator (base year varies by country): https://data.worldbank.org/indicator/NY.GDP.DEFL.ZS
* PPP conversion factor, GDP (LCU per international $): https://data.worldbank.org/indicator/PA.NUS.PPP
* GDP per capita growth (annual %): https://data.worldbank.org/indicator/NY.GDP.PCAP.KD.ZG
* IFsHistSeries: SeriesGDP2011PCPPP and SeriesGDP2017PCPPP. We will use '''2011''' as the example in this instruction.

2. Calculate PCPPP_Growth_Rate from SeriesGDP2011PCPPP. (PCPPP_Growth_Rate in 2011 = (PCPPP in 2011 - PCPPP in 2010)/PCPPP in 2010; The growth_rate need to *100 as percent value.)

3. Change all the data we downloaded and calculated to the format of [Country, Year, VAL]

* GDP per capita (current LCU): Country, Year, wb_curr
* GDP deflator (base year varies by country): Country, Year, wb_deflat
* PPP conversion factor, GDP (LCU per international $): Country, Year, wb_ppp_cov
* GDP per capita growth (annual %): Country, Year, wb_growth
* SeriesGDP2011PCPPP: Country, Year, ifs_pcppp
* PCPPP_Growth_Rate: Country, Year, ifs_pcppp_growth

4. Create a new value, '''pc_growth,''' primarily using wb_growth.

* If there is a null in wb_growth, then use ifs_pcppp_growth to fill the null.

5. Create a new value, '''2011_deflat''' (if use SeriesGDP2017PCPPP, then the new value is 2017_deflat)

* The base year is 2011 since we use GDP2011PCPPP from the IFsHist. It will change to 2017 when we use GDP2017PCPPP.
* Fill the base year value in wb_deflat in 2011_deflat for each country.
* After this step, every country will only have 1 value in 2011_deflat for the base year, e.g. 2011.
* If there is null value for wb_deflat in the base year, then you can leave the 2011_deflat null.

6. Create a new value, '''2011_ppp_cov''' (if use SeriesGDP2017PCPPP, then the new value is 2017_ppp_cov)

* Fill the base year value in wb_ppp_cov in 2011_ppp_cov for each country.
* After this step, every country will only have 1 value in 2011_ppp_cov for the base year, e.g. 2011.

7. Create a '''new''' '''table''' using [Country, Year, wb_curr] and [Country, Year, wb_deflat]

* Find the most recent year for each country as well as the wb_curr and wb_deflat in that most recent year.

8. Merge the new table with 2011_deflat and 2011_ppp_cov only using Country.

* The output table would be
** Country
** Year (the most recent year for the country)
** wb_curr (the wb_curr in the most recent year for the country)
** wb_deflat (the wb_deflat in the most recent year for the country)
** 2011_deflat (the deflator in the base year, 2011)
** 2011_ppp_cov (the ppp_cov in the base year, 2011)
* Calculate a new value, '''2011_const_pcppp''' (if use SeriesGDP2017PCPPP, then the new value is 2017_const_pcppp) using the formula below.
** '''2011_const_pcppp = (wb_curr * (2011_deflat/wb_deflat))/2011_ppp_cov'''

9. There will be countries having 0 or null values for '''2011_const_pcppp'''. We will use the value in ifs_pcppp in the most recent year for these missing countries to fill 2011_const_pcppp. (In this case, the most recent year will change to the most recent year for ifs_pcppp.) Thus, all 188 countries should have a 2011_const_pcppp for its most recent year.

10. Now we have 2011_const_pcppp in the most recent year for each country. We will calculate the 2011_const_pcppp for the rest of the year.

* year_end = 2022 (The most recent year in GDP per capita (current LCU) from WDI).
* Starting from the most recent year to the earliest year. E.g. The most recent year in Afghanistan is 2021, 2021 – 1960, for 2020
** 2011_const_pcppp in 2020 = 100* 2011_const_pcppp in 2021 / (100 + pc_growth in 2021)
* Starting from the most recent year to the year_end. E.g. The most recent year in Afghanistan is 2021, 2021 – 2022, for 2022
** 2011_const_pcppp in 2022 = 2011_const_pcppp in 2021 * (100 + pc_growth in 2022)/100

'''11. The 2011_const_pcppp is the new value for GDP2011PCPPP. Need to be rounded to 5 decimals.'''

=== Special Cases ===

#We want to have as much data for all regions and coverage, we need to make sure that we use proxies and special cases when we can. For Kosovo, Sahrawi Arab Democratic Republic, and South Sudan use Albania, Morocco (and Mali for GDP switch to Mali in 1967), and Sudan as proxies. Be mindful that for tables with units in rate or percent, proximal values can be directly applied; however, when values are in numbers or headcounts, you will need to borrow the population figures to apply values proportionally. To do this use SeriesPopulation. For instance, Kosovo’s GDP = Albania’s GDP * Kosovo’s Population / Albania’s Population.
#For Taiwan PC PPP use IMF data ''instead of WDI'' which gives you data from 1980-present year. Then use Singapore as a proxy for years 1960-1979.
#For North Korea, you can find their data from their rivalry (South Korea's central bank) at https://www.bok.or.kr/eng/search/search/main.do?menuNo=400493&query=north+korea+gdp. With this, we will be able to construct the data of annual GDP growth rates for NK from 1990 to current year. Please prioritize new releases during this process. For instance, if you have a report in 2010 and another one in 2003 that both have annual growth rates in 1990-2000, we will trust the ones from the 2010 report, and fill in values using 2003 report. For historical values, just use whatever is in IFs. The next step is to read the 2011, 2017, and 2021 report, because they all have NK's GDP at current price. Note that those numbers are reported under South Korean Won, which means you will need the exchange rate between South Korean Won and US Dollars to get NK's GDP at current dollars in 2011, 2017, and 2021. With annual gdp growth rates, and gdp at current dollars in those years, the script will be able to run and get the final data we want.

= Data Notes =

# The IMF updated their NGDP_RPCH projection for part of the countries in July of 2025 (https://www.imf.org/en/Publications/WEO/Issues/2025/07/29/world-economic-outlook-update-july-2025).
# IMF updated their data structure and codes. The script adapted to this.
# GDP2021 and GDP2021PCPPP are new tables introduced in 2025 because the World Bank introduced 2021 PPP.

Global Burdens of Disease (GBD), Institute for Health Metrics and Evaluation (IHME)

2026-02-09T21:36:23Z

Yutang.Xiong: /* Tables Used in Hist+Forecast Display */

Global Burdens of Disease (GBD), Institute for Health Metrics and Evaluation (IHME)

2026-02-09T21:34:10Z

Yutang.Xiong: /* Tables Used in Hist+Forecast Display */

Global Burdens of Disease (GBD), Institute for Health Metrics and Evaluation (IHME)

2026-02-09T21:28:40Z

Yutang.Xiong: /* Tables Used in Hist+Forecast Display */

= Summary =
The Institute for Health Metrics and Evaluation (IHME) provides data on the Global Burdens of Disease that can be accessed here http://ghdx.healthdata.org/gbd-results-tool. IHME provides data on multiple metrics that we pull into IFs, including deaths, DALYs, incidence, prevalence, violence and forecasts for each.

= Table In IFs =
There are 5 groups of data tables we pull from IHME into IFs. You can view the detailed pulling instruction below to see the exact tables needed.

=== Tables on Prevalence and Incidence ===
These are tables related to drug prevalence, deaths from violences, and prevalence of some specific diseases.

=== Tables Used in Hist+Forecast Display ===
There are 45 tables in total related to detailed death tables in IFs. For each of 15 death causes in IFs, we have a total death table and two tables by sex group. These 45 tables can be searched by using the following naming conventions IHMEDths___, IHMEDths___Females, and IHMEDths___Males, where ___ denotes the cause of death in IFs. These 15 causes are, Cardio, Diabet, Diarrh, Digest, HIV, Intlj, Malar, MalNeopl, MentHlth, OthCD, OthNCD, OthUnintlj, Respinfec, Respiratory, Roadacc.

Note that a detailed death cause mapping table can be found [[IFs.db#HealthDetailedDeathsCtry|here]].
{| class="wikitable"
!Table
!Definition
!UsedInPreprocessor
!UsedInPreprocessorFileName
!Name in Source
|-
|IHMEDthsCardio
|Deaths from cardiovascular disease in millions from IHME for both sexes
|0
|
|Cardiovascular diseases
|-
|IHMEDthsCardioFemales
|Deaths from cardiovascular disease in millions from IHME for females
|0
|
|Cardiovascular diseases
|-
|IHMEDthsCardioMales
|Deaths from cardiovascular disease in millions from IHME for males
|0
|
|Cardiovascular diseases
|-
|IHMEDthsDiabet
|Deaths from diabetes in millions from IHME for both sexes
|0
|
|Diabetes mellitus
|-
|IHMEDthsDiabetFemales
|Deaths from diabetes in millions from IHME for females
|0
|
|Diabetes mellitus
|-
|IHMEDthsDiabetMales
|Deaths from diabetes in millions from IHME for males
|0
|
|Diabetes mellitus
|-
|IHMEDthsDiarrh
|Deaths from diarrhoea in millions from IHME for both sexes
|0
|
|Diarrheal diseases
|-
|IHMEDthsDiarrhFemales
|Deaths from diarrhoea in millions from IHME for females
|0
|
|Diarrheal diseases
|-
|IHMEDthsDiarrhMales
|Deaths from diarrhoea in millions from IHME for males
|0
|
|Diarrheal diseases
|-
|IHMEDthsDigest
|Deaths from digestive diseases in millions from IHME for both sexes
|0
|
|Digestive diseases
|-
|IHMEDthsDigestFemales
|Deaths from digestive diseases in millions from IHME for females
|0
|
|Digestive diseases
|-
|IHMEDthsDigestMales
|Deaths from digestive diseases in millions from IHME for males
|0
|
|Digestive diseases
|-
|IHMEDthsHIV
|Deaths from HIV in millions from IHME for both sexes
|1
|HEALTH, POPULATION
|HIV/AIDS
|-
|IHMEDthsHIVFemales
|Deaths from HIV in millions from IHME for females
|0
|
|HIV/AIDS
|-
|IHMEDthsHIVMales
|Deaths from HIV in millions from IHME for males
|0
|
|HIV/AIDS
|-
|IHMEDthsIntIj
|Deaths from Intentional injuries in millions from IHME for both sexes
|0
|
|Self-harm and interpersonal violence
|-
|IHMEDthsIntIjFemales
|Deaths from Intentional injuries in millions from IHME for females
|0
|
|Self-harm and interpersonal violence
|-
|IHMEDthsIntIjMales
|Deaths from Intentional injuries in millions from IHME for males
|0
|
|Self-harm and interpersonal violence
|-
|IHMEDthsMalar
|Deaths from malaria in millions from IHME for both sexes
|1
|HEALTH
|Malaria
|-
|IHMEDthsMalarFemales
|Deaths from malaria in millions from IHME for females
|0
|
|Malaria
|-
|IHMEDthsMalarMales
|Deaths from malaria in millions from IHME for males
|0
|
|Malaria
|-
|IHMEDthsMalNeopl
|Deaths from cancers in millions from IHME for both sexes
|0
|
|Neoplasms
|-
|IHMEDthsMalNeoplFemales
|Deaths from cancers in millions from IHME for females
|0
|
|Neoplasms
|-
|IHMEDthsMalNeoplMales
|Deaths from cancers in millions from IHME for males
|0
|
|Neoplasms
|-
|IHMEDthsMentHlth
|Deaths from mental health in millions from IHME for both sexes
|0
|
|Neurological disorders + Mental disorders + Substance use disorders
|-
|IHMEDthsMentHlthFemales
|Deaths from mental health in millions from IHME for females
|0
|
|Neurological disorders + Mental disorders + Substance use disorders
|-
|IHMEDthsMentHlthMales
|Deaths from mental health in millions from IHME for males
|0
|
|Neurological disorders + Mental disorders + Substance use disorders
|-
|IHMEDthsOthCD
|Deaths from other CD in millions from IHME for both sexes
|0
|
|Communicable, maternal, neonatal, and nutritional diseases, excluding individual causes in IFs
|-
|IHMEDthsOthCDFemales
|Deaths from other CD in millions from IHME for females
|0
|
|Communicable, maternal, neonatal, and nutritional diseases, excluding individual causes in IFs
|-
|IHMEDthsOthCDMales
|Deaths from other CD in millions from IHME for males
|0
|
|Communicable, maternal, neonatal, and nutritional diseases, excluding individual causes in IFs
|-
|IHMEDthsOthNCD
|Deaths from other NCD in millions from IHME for both sexes
|0
|
|Non-communicable diseases, excluding individual causes in IFs
|-
|IHMEDthsOthNCDFemales
|Deaths from other NCD in millions from IHME for females
|0
|
|Non-communicable diseases, excluding individual causes in IFs
|-
|IHMEDthsOthNCDMales
|Deaths from other NCD in millions from IHME for males
|0
|
|Non-communicable diseases, excluding individual causes in IFs
|-
|IHMEDthsOthUnintIj
|Deaths from other unintentional injuries in millions from IHME for both sexes
|0
|
|Unintentional injuries
|-
|IHMEDthsOthUnintIjFemales
|Deaths from other unintentional injuries in millions from IHME for females
|0
|
|Unintentional injuries
|-
|IHMEDthsOthUnintIjMales
|Deaths from other unintentional injuries in millions from IHME for males
|0
|
|Unintentional injuries
|-
|IHMEDthsRespinfec
|Deaths from respiratory infections in millions from IHME for both sexes
|0
|
|Lower respiratory infections + Upper respiratory infections + Otitis media + COVID-19 + Other COVID-19 pandemic-related outcomes
|-
|IHMEDthsRespinfecFemales
|Deaths from respiratory infections in millions from IHME for females
|0
|
|Lower respiratory infections + Upper respiratory infections + Otitis media + COVID-19 + Other COVID-19 pandemic-related outcomes
|-
|IHMEDthsRespinfecMales
|Deaths from respiratory infections in millions from IHME for males
|0
|
|Lower respiratory infections + Upper respiratory infections + Otitis media + COVID-19 + Other COVID-19 pandemic-related outcomes
|-
|IHMEDthsRespiratory
|Deaths from respiratory diseases in millions from IHME for both sexes
|0
|
|Chronic respiratory diseases
|-
|IHMEDthsRespiratoryFemales
|Deaths from respiratory diseases in millions from IHME for females
|0
|
|Chronic respiratory diseases
|-
|IHMEDthsRespiratoryMales
|Deaths from respiratory diseases in millions from IHME for males
|0
|
|Chronic respiratory diseases
|-
|IHMEDthsRoadacc
|Deaths from road accidents in millions from IHME for both sexes
|0
|
|Transport injuries
|-
|IHMEDthsRoadaccFemales
|Deaths from road accidents in millions from IHME for females
|0
|
|Transport injuries
|-
|IHMEDthsRoadaccMales
|Deaths from road accidents in millions from IHME for males
|0
|
|Transport injuries
|}

=== Life Expectancy ===
{| class="wikitable"
|+
!Variable
!Definition
!UsedInPreprocessor
|-
|LifeExpectIHMEBothSexesHistOnly
|Average life expectancy values (history only) for both sexes for ages 0-6 days.
|0
|-
|LifeExpectIHMEFemaleHistOnly
|Average life expectancy values (history only) for female for ages 0-6 days.
|0
|-
|LifeExpectIHMEMaleHistOnly
|Average life expectancy values (history only) for male for ages 0-6 days.
|0
|}

=== Infant Mortality ===
{| class="wikitable"
|+
!Variable
!Definition
!Name in Source
!UsedInPreprocessor
|-
|InfMortRateIHME
|Infant Mortality Rate from IHME GBD project. Summation of mortality categories: 0-6 Days; 7-28 Days; 29-365 Days.
|Probability of death; >1 year
|0
|-
|Under5MortRateIHME
|Under 5 mortality Rate from IHME GBD Project, all causes
|Probability of death; sum of >1 years, 2-4 years
|0
|}

=== Tables in IFs.db ===
'''HealthDetailedDeathsCtry'''

Detailed instruction on pulling number of deaths for IFs death categories can be found [[IFs.db|here]].

= Data Pulling Instructions =

=== Preprocessors on Prevalence and Incidence ===
[https://github.com/shangkexin/The_Institute_for_Health_Metrics_and_Evaluation_IHME Python scripts] used from last data pull can be accessed and adjusted for future data pulls. An updated country concordance table can be found [https://1drv.ms/x/c/64a1920f95dbe644/EWHhcKUK3ldEoIgMqLc6O0QB5ylEO7D509sB2273fFWu-g?e=vbtEsa here]. There are different country concordance for each set of indicators. Double check what format of country concordance you need for each section.

Use these filters:
{| class="wikitable"
|Variable
|GBD Estimate
|Measure
|Metric
|Cause
|Location
|Age
|Sex
|Year
|Decimal Places
|-
|ViolencePoliticalConflictTerror
|Cause of death or injury
|Deaths
|Rate
|Conflict and terrorism
|Select all countries and territories
|All Ages
|Both
|Select all
|4
|-
|ViolencePoliticalPoliceExecution
|Cause of death or injury
|Deaths
|Rate
|Executions and police conflict
|Select all countries and territories
|All Ages
|Both
|Select all
|4
|-
|ViolenceSelfHarm
|Cause of death or injury
|Deaths
|Rate
|Self-harm
|Select all countries and territories
|All Ages
|Both
|Select all
|4
|-
|SocietalViolenceDeathsTotal
|Cause of death or injury
|Deaths
|Rate
|Interpersonal violence + Conflict and terrorism + Executions and police conflict
|Select all countries and territories
|All Ages
|Both
|Select all
|4
|-
|ViolenceInterpersonChild
|Cause of death or injury
|Deaths
|Rate
|Interpersonal violence against all children death rate
|Select all countries and territories
|0-14
|Both
|Select all
|4
|-
|ViolenceInterpersonWomenAdult
|Cause of death or injury
|Deaths
|Rate
|Interpersonal violence
|Select all countries and territories
|>= 15
|Female
|Select all
|4
|-
|DrOpiodPrevIHME
|Cause of death or injury
|Prevalence
|Percent
|Opioid Use Disorders
|Select all countries and territories
|All Ages
|Both
|Select all
|4
|-
|DrugPrevalenceAmphetaminesIHME
|Cause of death or injury
|Prevalence
|Percent
|Amphetamine Use Disorders
|Select all countries and territories
|All Ages
|Both
|Select all
|5
|-
|DrugPrevalenceCocaineIHME
|Cause of death or injury
|Prevalence
|Percent
|Cocaine Use Disorders
|Select all countries and territories
|All Ages
|Both
|Select all
|5
|-
|IHMEDthsHIV
|Cause of death or injury
|Deaths
|Number
|HIV/AIDs
|Select all countries and territories
|All Ages
|Both
|Select all
|5
|-
|IHMEDthsMalar
|Cause of death or injury
|Deaths
|Number
|Malaria
|Select all countries and territories
|All Ages
|Both
|Select all
|5
|-
|IHMEInciHIVNumber
|Cause of death or injury
|Incidence
|Number
|HIV/AIDs
|Select all countries and territories
|All Ages
|Both
|Select all
|
|-
|IHMEInciMalariaNumber
|Cause of death or injury
|Incidence
|Number
|Malaria
|Select all countries and territories
|All Ages
|Both
|Select all
|
|-
|IHMEPrevHIV
|Cause of death or injury
|Prevalence
|Percent
|HIV/AIDs
|Select all countries and territories
|All Ages
|Both
|Select all
|3
|-
|IHMEPrevHIVNumber
|Cause of death or injury
|Prevalence
|Number
|HIV/AIDs
|Select all countries and territories
|All Ages
|Both
|Select all
|
|-
|IHMEPrevMalariaNumber
|Cause of death or injury
|Prevalence
|Number
|Malaria
|Select all countries and territories
|All Ages
|Both
|Select all
|
|-
|ViolenceInterpersonOther
|Cause of death or injury
|Deaths
|Rate
|Death rate from Interpersonal violence for males aged over 15
|Select all countries and territories
|>= 15
|Both
|Select all
|
|-
|ViolenceInterpersonWomenChild
|Cause of death or injury
|Deaths
|Rate
|Deaths from interpersonal violence (Violence against women and children)
|Select all countries and territories
|Sum of Child and WomenAdult
|Both
|Select all
|
|}

=== Tables Used in Hist+Forecast Display ===
[https://vizhub.healthdata.org/gbd-results/ Use the filters]:

GBD Estimate Cause of death or injury

Measure Deaths

Metric Rate

Location All territories and countries

Age All ages

Sex Both, Female, Male

Year All

Use the causes below:
{| class="wikitable"
!Table
!Cause
|-
|IHMEDthsCardio
|Cardiovascular diseases
|-
|IHMEDthsCardioFemales
|Cardiovascular diseases
|-
|IHMEDthsCardioMales
|Cardiovascular diseases
|-
|IHMEDthsDiabet
|Diabetes mellitus
|-
|IHMEDthsDiabetFemales
|Diabetes mellitus
|-
|IHMEDthsDiabetMales
|Diabetes mellitus
|-
|IHMEDthsDiarrh
|Diarrheal diseases
|-
|IHMEDthsDiarrhFemales
|Diarrheal diseases
|-
|IHMEDthsDiarrhMales
|Diarrheal diseases
|-
|IHMEDthsDigest
|Digestive diseases
|-
|IHMEDthsDigestFemales
|Digestive diseases
|-
|IHMEDthsDigestMales
|Digestive diseases
|-
|IHMEDthsHIV
|HIV/AIDS
|-
|IHMEDthsHIVFemales
|HIV/AIDS
|-
|IHMEDthsHIVMales
|HIV/AIDS
|-
|IHMEDthsIntIj
|Self-harm and interpersonal violence
|-
|IHMEDthsIntIjFemales
|Self-harm and interpersonal violence
|-
|IHMEDthsIntIjMales
|Self-harm and interpersonal violence
|-
|IHMEDthsMalar
|Malaria
|-
|IHMEDthsMalarFemales
|Malaria
|-
|IHMEDthsMalarMales
|Malaria
|-
|IHMEDthsMalNeopl
|Neoplasms
|-
|IHMEDthsMalNeoplFemales
|Neoplasms
|-
|IHMEDthsMalNeoplMales
|Neoplasms
|-
|IHMEDthsMentHlth
|Neurological disorders + Mental disorders + Substance use disorders
|-
|IHMEDthsMentHlthFemales
|Neurological disorders + Mental disorders + Substance use disorders
|-
|IHMEDthsMentHlthMales
|Neurological disorders + Mental disorders + Substance use disorders
|-
|IHMEDthsOthCD
|Communicable, maternal, neonatal, and nutritional diseases, excluding individual causes in IFs
|-
|IHMEDthsOthCDFemales
|Communicable, maternal, neonatal, and nutritional diseases, excluding individual causes in IFs
|-
|IHMEDthsOthCDMales
|Communicable, maternal, neonatal, and nutritional diseases, excluding individual causes in IFs
|-
|IHMEDthsOthNCD
|Non-communicable diseases, excluding individual causes in IFs
|-
|IHMEDthsOthNCDFemales
|Non-communicable diseases, excluding individual causes in IFs
|-
|IHMEDthsOthNCDMales
|Non-communicable diseases, excluding individual causes in IFs
|-
|IHMEDthsOthUnintIj
|Unintentional injuries
|-
|IHMEDthsOthUnintIjFemales
|Unintentional injuries
|-
|IHMEDthsOthUnintIjMales
|Unintentional injuries
|-
|IHMEDthsRespinfec
|Lower respiratory infections + Upper respiratory infections + Otitis media + COVID-19 + Other COVID-19 pandemic-related outcomes
|-
|IHMEDthsRespinfecFemales
|Lower respiratory infections + Upper respiratory infections + Otitis media + COVID-19 + Other COVID-19 pandemic-related outcomes
|-
|IHMEDthsRespinfecMales
|Lower respiratory infections + Upper respiratory infections + Otitis media + COVID-19 + Other COVID-19 pandemic-related outcomes
|-
|IHMEDthsRespiratory
|Chronic respiratory diseases
|-
|IHMEDthsRespiratoryFemales
|Chronic respiratory diseases
|-
|IHMEDthsRespiratoryMales
|Chronic respiratory diseases
|-
|IHMEDthsRoadacc
|Transport injuries
|-
|IHMEDthsRoadaccFemales
|Transport injuries
|-
|IHMEDthsRoadaccMales
|Transport injuries
|}

=== Life Expectancy ===
Navigate to: https://vizhub.healthdata.org/gbd-results/

Use the filters:
GBD Estimate All-cause mortality

Measure Life Expectancy

Metric Years

Location All territories and countries

Age Stated below

Sex Stated below

Year All
{| class="wikitable"
|Variable
|Age
|Sex
|Year
|Metric Name
|Decimal Places
|-
|LifeExpectIHMEBothSexesHistOnly
|0-6 days
|Both
|1950-2023
|Years
|3
|-
|LifeExpectIHMEMaleHistOnly
|0-6 days
|Male
|1950-2023
|Years
|3
|-
|LifeExpectIHMEFemaleHistOnly
|0-6 days
|Female
|1950-2023
|Years
|3
|}

=== Infant Mortality ===
Life expectancy data can be pulled from http://ghdx.healthdata.org/gbd-results-tool. The filters should be:

GBD Estimate All-cause mortality

Measure Life Tables

Metric Probability of death

Location All territories and countries

Age Stated below

Sex Both

Year All
{| class="wikitable"
|Variable
|Age
|Sex
|Year
|Decimal Places
|-
|InfMortRateIHME
|< 1 year
|Both
|1950-2023
|4
|-
|Under5MortRateIHME
|Calculation of < 1 year, 12-23 months, and 2-4 year
|Both
|1950-2023
|4
|}
'''Calculations:'''

In order to get the right value for Under5MortRateIHME, we have to calculate it. It should be:

PoD under 5 = PoD under 1 + PoD 12-23 months * Probability of not dying under 1+ PoD 2-4 years * Probability of not dying under 2.

An example is posted below for Iraq:
{| class="wikitable"
|Age
|Metric
|Year
|Value
|-
|<1 year
|Probability of death
|2019
|0.019244
|-
|12-23 months
|Probability of death
|2019
|0.001482
|-
|2-4 years
|Probability of death
|2019
|0.002298
|}

The PoD for under 2 would be = 0.019244 + 0.001482 * ( 1 - 0.019244) = 0.020698;

The PoD for under 5 would be = 0.020698 + 0.002298 * ( 1 - 0.020698) = 0.022948.

The final data point is '''0.022948'''.

= Data Notes =

* ViolenceInterpersonChild & ViolenceInterpersonWomenAdult have changed a lot, we identify the Age by comparing the trend of old data in IFsHistSeries and the new data from GBD.
** In March 2025, the ages have shifted from >20 for adults to >15. To get this data, do a summation of 15-19 and 20+

* Be aware of the definition of each preprocessor and its unit so that you can find the exact match in the IHME GBD variables.

=== Proxies ===
* Some regions in IFs are not provided by IHME yet, we are encouraged to use proxies. For example, use Singapore, Albania, and Mali for Hong Kong, Kosovo, and Sahrawi respectively. Be mindful that for tables with units in rate or percent, proximal values can be directly applied; however, when values are in numbers or headcounts, you will need to borrow the population figures to apply values proportionally.
** For instance, LifeExpectIHMEBothSexesHistOnly would not use a calculation, but IHMEDthsHIV will.
** To do this use SeriesPopulation. For instance, Kosovo’s number of deaths = Albania’s number of deaths * Kosovo’s Population / Albania’s Population.

Global Burdens of Disease (GBD), Institute for Health Metrics and Evaluation (IHME)

2026-02-09T21:28:25Z

Yutang.Xiong: /* Preprocessors on Prevalence and Incidence */

= Summary =
The Institute for Health Metrics and Evaluation (IHME) provides data on the Global Burdens of Disease that can be accessed here http://ghdx.healthdata.org/gbd-results-tool. IHME provides data on multiple metrics that we pull into IFs, including deaths, DALYs, incidence, prevalence, violence and forecasts for each.

= Table In IFs =
There are 5 groups of data tables we pull from IHME into IFs. You can view the detailed pulling instruction below to see the exact tables needed.

=== Tables on Prevalence and Incidence ===
These are tables related to drug prevalence, deaths from violences, and prevalence of some specific diseases.

=== Tables Used in Hist+Forecast Display ===
There are 45 tables in total related to detailed death tables in IFs. For each of 15 death causes in IFs, we have a total death table and two tables by sex group. These 45 tables can be searched by using the following naming conventions IHMEDths___, IHMEDths___Females, and IHMEDths___Males, where ___ denotes the cause of death in IFs. These 15 causes are, Cardio, Diabet, Diarrh, Digest, HIV, Intlj, Malar, MalNeopl, MentHlth, OthCD, OthNCD, OthUnintlj, Respinfec, Respiratory, Roadacc. Below is the causes used for each table:

The data can be accessed from the GBD tool at http://ghdx.healthdata.org/gbd-results-tool. You can download the data based on the table below for each series. Note that a detailed death cause mapping table can be found [[IFs.db#HealthDetailedDeathsCtry|here]].
{| class="wikitable"
!Table
!Definition
!UsedInPreprocessor
!UsedInPreprocessorFileName
!Name in Source
|-
|IHMEDthsCardio
|Deaths from cardiovascular disease in millions from IHME for both sexes
|0
|
|Cardiovascular diseases
|-
|IHMEDthsCardioFemales
|Deaths from cardiovascular disease in millions from IHME for females
|0
|
|Cardiovascular diseases
|-
|IHMEDthsCardioMales
|Deaths from cardiovascular disease in millions from IHME for males
|0
|
|Cardiovascular diseases
|-
|IHMEDthsDiabet
|Deaths from diabetes in millions from IHME for both sexes
|0
|
|Diabetes mellitus
|-
|IHMEDthsDiabetFemales
|Deaths from diabetes in millions from IHME for females
|0
|
|Diabetes mellitus
|-
|IHMEDthsDiabetMales
|Deaths from diabetes in millions from IHME for males
|0
|
|Diabetes mellitus
|-
|IHMEDthsDiarrh
|Deaths from diarrhoea in millions from IHME for both sexes
|0
|
|Diarrheal diseases
|-
|IHMEDthsDiarrhFemales
|Deaths from diarrhoea in millions from IHME for females
|0
|
|Diarrheal diseases
|-
|IHMEDthsDiarrhMales
|Deaths from diarrhoea in millions from IHME for males
|0
|
|Diarrheal diseases
|-
|IHMEDthsDigest
|Deaths from digestive diseases in millions from IHME for both sexes
|0
|
|Digestive diseases
|-
|IHMEDthsDigestFemales
|Deaths from digestive diseases in millions from IHME for females
|0
|
|Digestive diseases
|-
|IHMEDthsDigestMales
|Deaths from digestive diseases in millions from IHME for males
|0
|
|Digestive diseases
|-
|IHMEDthsHIV
|Deaths from HIV in millions from IHME for both sexes
|1
|HEALTH, POPULATION
|HIV/AIDS
|-
|IHMEDthsHIVFemales
|Deaths from HIV in millions from IHME for females
|0
|
|HIV/AIDS
|-
|IHMEDthsHIVMales
|Deaths from HIV in millions from IHME for males
|0
|
|HIV/AIDS
|-
|IHMEDthsIntIj
|Deaths from Intentional injuries in millions from IHME for both sexes
|0
|
|Self-harm and interpersonal violence
|-
|IHMEDthsIntIjFemales
|Deaths from Intentional injuries in millions from IHME for females
|0
|
|Self-harm and interpersonal violence
|-
|IHMEDthsIntIjMales
|Deaths from Intentional injuries in millions from IHME for males
|0
|
|Self-harm and interpersonal violence
|-
|IHMEDthsMalar
|Deaths from malaria in millions from IHME for both sexes
|1
|HEALTH
|Malaria
|-
|IHMEDthsMalarFemales
|Deaths from malaria in millions from IHME for females
|0
|
|Malaria
|-
|IHMEDthsMalarMales
|Deaths from malaria in millions from IHME for males
|0
|
|Malaria
|-
|IHMEDthsMalNeopl
|Deaths from cancers in millions from IHME for both sexes
|0
|
|Neoplasms
|-
|IHMEDthsMalNeoplFemales
|Deaths from cancers in millions from IHME for females
|0
|
|Neoplasms
|-
|IHMEDthsMalNeoplMales
|Deaths from cancers in millions from IHME for males
|0
|
|Neoplasms
|-
|IHMEDthsMentHlth
|Deaths from mental health in millions from IHME for both sexes
|0
|
|Neurological disorders + Mental disorders + Substance use disorders
|-
|IHMEDthsMentHlthFemales
|Deaths from mental health in millions from IHME for females
|0
|
|Neurological disorders + Mental disorders + Substance use disorders
|-
|IHMEDthsMentHlthMales
|Deaths from mental health in millions from IHME for males
|0
|
|Neurological disorders + Mental disorders + Substance use disorders
|-
|IHMEDthsOthCD
|Deaths from other CD in millions from IHME for both sexes
|0
|
|Communicable, maternal, neonatal, and nutritional diseases, excluding individual causes in IFs
|-
|IHMEDthsOthCDFemales
|Deaths from other CD in millions from IHME for females
|0
|
|Communicable, maternal, neonatal, and nutritional diseases, excluding individual causes in IFs
|-
|IHMEDthsOthCDMales
|Deaths from other CD in millions from IHME for males
|0
|
|Communicable, maternal, neonatal, and nutritional diseases, excluding individual causes in IFs
|-
|IHMEDthsOthNCD
|Deaths from other NCD in millions from IHME for both sexes
|0
|
|Non-communicable diseases, excluding individual causes in IFs
|-
|IHMEDthsOthNCDFemales
|Deaths from other NCD in millions from IHME for females
|0
|
|Non-communicable diseases, excluding individual causes in IFs
|-
|IHMEDthsOthNCDMales
|Deaths from other NCD in millions from IHME for males
|0
|
|Non-communicable diseases, excluding individual causes in IFs
|-
|IHMEDthsOthUnintIj
|Deaths from other unintentional injuries in millions from IHME for both sexes
|0
|
|Unintentional injuries
|-
|IHMEDthsOthUnintIjFemales
|Deaths from other unintentional injuries in millions from IHME for females
|0
|
|Unintentional injuries
|-
|IHMEDthsOthUnintIjMales
|Deaths from other unintentional injuries in millions from IHME for males
|0
|
|Unintentional injuries
|-
|IHMEDthsRespinfec
|Deaths from respiratory infections in millions from IHME for both sexes
|0
|
|Lower respiratory infections + Upper respiratory infections + Otitis media + COVID-19 + Other COVID-19 pandemic-related outcomes
|-
|IHMEDthsRespinfecFemales
|Deaths from respiratory infections in millions from IHME for females
|0
|
|Lower respiratory infections + Upper respiratory infections + Otitis media + COVID-19 + Other COVID-19 pandemic-related outcomes
|-
|IHMEDthsRespinfecMales
|Deaths from respiratory infections in millions from IHME for males
|0
|
|Lower respiratory infections + Upper respiratory infections + Otitis media + COVID-19 + Other COVID-19 pandemic-related outcomes
|-
|IHMEDthsRespiratory
|Deaths from respiratory diseases in millions from IHME for both sexes
|0
|
|Chronic respiratory diseases
|-
|IHMEDthsRespiratoryFemales
|Deaths from respiratory diseases in millions from IHME for females
|0
|
|Chronic respiratory diseases
|-
|IHMEDthsRespiratoryMales
|Deaths from respiratory diseases in millions from IHME for males
|0
|
|Chronic respiratory diseases
|-
|IHMEDthsRoadacc
|Deaths from road accidents in millions from IHME for both sexes
|0
|
|Transport injuries
|-
|IHMEDthsRoadaccFemales
|Deaths from road accidents in millions from IHME for females
|0
|
|Transport injuries
|-
|IHMEDthsRoadaccMales
|Deaths from road accidents in millions from IHME for males
|0
|
|Transport injuries
|}

=== Life Expectancy ===
{| class="wikitable"
|+
!Variable
!Definition
!UsedInPreprocessor
|-
|LifeExpectIHMEBothSexesHistOnly
|Average life expectancy values (history only) for both sexes for ages 0-6 days.
|0
|-
|LifeExpectIHMEFemaleHistOnly
|Average life expectancy values (history only) for female for ages 0-6 days.
|0
|-
|LifeExpectIHMEMaleHistOnly
|Average life expectancy values (history only) for male for ages 0-6 days.
|0
|}

=== Infant Mortality ===
{| class="wikitable"
|+
!Variable
!Definition
!Name in Source
!UsedInPreprocessor
|-
|InfMortRateIHME
|Infant Mortality Rate from IHME GBD project. Summation of mortality categories: 0-6 Days; 7-28 Days; 29-365 Days.
|Probability of death; >1 year
|0
|-
|Under5MortRateIHME
|Under 5 mortality Rate from IHME GBD Project, all causes
|Probability of death; sum of >1 years, 2-4 years
|0
|}

=== Tables in IFs.db ===
'''HealthDetailedDeathsCtry'''

Detailed instruction on pulling number of deaths for IFs death categories can be found [[IFs.db|here]].

= Data Pulling Instructions =

=== Preprocessors on Prevalence and Incidence ===
[https://github.com/shangkexin/The_Institute_for_Health_Metrics_and_Evaluation_IHME Python scripts] used from last data pull can be accessed and adjusted for future data pulls. An updated country concordance table can be found [https://1drv.ms/x/c/64a1920f95dbe644/EWHhcKUK3ldEoIgMqLc6O0QB5ylEO7D509sB2273fFWu-g?e=vbtEsa here]. There are different country concordance for each set of indicators. Double check what format of country concordance you need for each section.

Use these filters:
{| class="wikitable"
|Variable
|GBD Estimate
|Measure
|Metric
|Cause
|Location
|Age
|Sex
|Year
|Decimal Places
|-
|ViolencePoliticalConflictTerror
|Cause of death or injury
|Deaths
|Rate
|Conflict and terrorism
|Select all countries and territories
|All Ages
|Both
|Select all
|4
|-
|ViolencePoliticalPoliceExecution
|Cause of death or injury
|Deaths
|Rate
|Executions and police conflict
|Select all countries and territories
|All Ages
|Both
|Select all
|4
|-
|ViolenceSelfHarm
|Cause of death or injury
|Deaths
|Rate
|Self-harm
|Select all countries and territories
|All Ages
|Both
|Select all
|4
|-
|SocietalViolenceDeathsTotal
|Cause of death or injury
|Deaths
|Rate
|Interpersonal violence + Conflict and terrorism + Executions and police conflict
|Select all countries and territories
|All Ages
|Both
|Select all
|4
|-
|ViolenceInterpersonChild
|Cause of death or injury
|Deaths
|Rate
|Interpersonal violence against all children death rate
|Select all countries and territories
|0-14
|Both
|Select all
|4
|-
|ViolenceInterpersonWomenAdult
|Cause of death or injury
|Deaths
|Rate
|Interpersonal violence
|Select all countries and territories
|>= 15
|Female
|Select all
|4
|-
|DrOpiodPrevIHME
|Cause of death or injury
|Prevalence
|Percent
|Opioid Use Disorders
|Select all countries and territories
|All Ages
|Both
|Select all
|4
|-
|DrugPrevalenceAmphetaminesIHME
|Cause of death or injury
|Prevalence
|Percent
|Amphetamine Use Disorders
|Select all countries and territories
|All Ages
|Both
|Select all
|5
|-
|DrugPrevalenceCocaineIHME
|Cause of death or injury
|Prevalence
|Percent
|Cocaine Use Disorders
|Select all countries and territories
|All Ages
|Both
|Select all
|5
|-
|IHMEDthsHIV
|Cause of death or injury
|Deaths
|Number
|HIV/AIDs
|Select all countries and territories
|All Ages
|Both
|Select all
|5
|-
|IHMEDthsMalar
|Cause of death or injury
|Deaths
|Number
|Malaria
|Select all countries and territories
|All Ages
|Both
|Select all
|5
|-
|IHMEInciHIVNumber
|Cause of death or injury
|Incidence
|Number
|HIV/AIDs
|Select all countries and territories
|All Ages
|Both
|Select all
|
|-
|IHMEInciMalariaNumber
|Cause of death or injury
|Incidence
|Number
|Malaria
|Select all countries and territories
|All Ages
|Both
|Select all
|
|-
|IHMEPrevHIV
|Cause of death or injury
|Prevalence
|Percent
|HIV/AIDs
|Select all countries and territories
|All Ages
|Both
|Select all
|3
|-
|IHMEPrevHIVNumber
|Cause of death or injury
|Prevalence
|Number
|HIV/AIDs
|Select all countries and territories
|All Ages
|Both
|Select all
|
|-
|IHMEPrevMalariaNumber
|Cause of death or injury
|Prevalence
|Number
|Malaria
|Select all countries and territories
|All Ages
|Both
|Select all
|
|-
|ViolenceInterpersonOther
|Cause of death or injury
|Deaths
|Rate
|Death rate from Interpersonal violence for males aged over 15
|Select all countries and territories
|>= 15
|Both
|Select all
|
|-
|ViolenceInterpersonWomenChild
|Cause of death or injury
|Deaths
|Rate
|Deaths from interpersonal violence (Violence against women and children)
|Select all countries and territories
|Sum of Child and WomenAdult
|Both
|Select all
|
|}

=== Tables Used in Hist+Forecast Display ===
[https://vizhub.healthdata.org/gbd-results/ Use the filters]:

GBD Estimate Cause of death or injury

Measure Deaths

Metric Rate

Location All territories and countries

Age All ages

Sex Both, Female, Male

Year All

Use the causes below:
{| class="wikitable"
!Table
!Cause
|-
|IHMEDthsCardio
|Cardiovascular diseases
|-
|IHMEDthsCardioFemales
|Cardiovascular diseases
|-
|IHMEDthsCardioMales
|Cardiovascular diseases
|-
|IHMEDthsDiabet
|Diabetes mellitus
|-
|IHMEDthsDiabetFemales
|Diabetes mellitus
|-
|IHMEDthsDiabetMales
|Diabetes mellitus
|-
|IHMEDthsDiarrh
|Diarrheal diseases
|-
|IHMEDthsDiarrhFemales
|Diarrheal diseases
|-
|IHMEDthsDiarrhMales
|Diarrheal diseases
|-
|IHMEDthsDigest
|Digestive diseases
|-
|IHMEDthsDigestFemales
|Digestive diseases
|-
|IHMEDthsDigestMales
|Digestive diseases
|-
|IHMEDthsHIV
|HIV/AIDS
|-
|IHMEDthsHIVFemales
|HIV/AIDS
|-
|IHMEDthsHIVMales
|HIV/AIDS
|-
|IHMEDthsIntIj
|Self-harm and interpersonal violence
|-
|IHMEDthsIntIjFemales
|Self-harm and interpersonal violence
|-
|IHMEDthsIntIjMales
|Self-harm and interpersonal violence
|-
|IHMEDthsMalar
|Malaria
|-
|IHMEDthsMalarFemales
|Malaria
|-
|IHMEDthsMalarMales
|Malaria
|-
|IHMEDthsMalNeopl
|Neoplasms
|-
|IHMEDthsMalNeoplFemales
|Neoplasms
|-
|IHMEDthsMalNeoplMales
|Neoplasms
|-
|IHMEDthsMentHlth
|Neurological disorders + Mental disorders + Substance use disorders
|-
|IHMEDthsMentHlthFemales
|Neurological disorders + Mental disorders + Substance use disorders
|-
|IHMEDthsMentHlthMales
|Neurological disorders + Mental disorders + Substance use disorders
|-
|IHMEDthsOthCD
|Communicable, maternal, neonatal, and nutritional diseases, excluding individual causes in IFs
|-
|IHMEDthsOthCDFemales
|Communicable, maternal, neonatal, and nutritional diseases, excluding individual causes in IFs
|-
|IHMEDthsOthCDMales
|Communicable, maternal, neonatal, and nutritional diseases, excluding individual causes in IFs
|-
|IHMEDthsOthNCD
|Non-communicable diseases, excluding individual causes in IFs
|-
|IHMEDthsOthNCDFemales
|Non-communicable diseases, excluding individual causes in IFs
|-
|IHMEDthsOthNCDMales
|Non-communicable diseases, excluding individual causes in IFs
|-
|IHMEDthsOthUnintIj
|Unintentional injuries
|-
|IHMEDthsOthUnintIjFemales
|Unintentional injuries
|-
|IHMEDthsOthUnintIjMales
|Unintentional injuries
|-
|IHMEDthsRespinfec
|Lower respiratory infections + Upper respiratory infections + Otitis media + COVID-19 + Other COVID-19 pandemic-related outcomes
|-
|IHMEDthsRespinfecFemales
|Lower respiratory infections + Upper respiratory infections + Otitis media + COVID-19 + Other COVID-19 pandemic-related outcomes
|-
|IHMEDthsRespinfecMales
|Lower respiratory infections + Upper respiratory infections + Otitis media + COVID-19 + Other COVID-19 pandemic-related outcomes
|-
|IHMEDthsRespiratory
|Chronic respiratory diseases
|-
|IHMEDthsRespiratoryFemales
|Chronic respiratory diseases
|-
|IHMEDthsRespiratoryMales
|Chronic respiratory diseases
|-
|IHMEDthsRoadacc
|Transport injuries
|-
|IHMEDthsRoadaccFemales
|Transport injuries
|-
|IHMEDthsRoadaccMales
|Transport injuries
|}

=== Life Expectancy ===
Navigate to: https://vizhub.healthdata.org/gbd-results/

Use the filters:
GBD Estimate All-cause mortality

Measure Life Expectancy

Metric Years

Location All territories and countries

Age Stated below

Sex Stated below

Year All
{| class="wikitable"
|Variable
|Age
|Sex
|Year
|Metric Name
|Decimal Places
|-
|LifeExpectIHMEBothSexesHistOnly
|0-6 days
|Both
|1950-2023
|Years
|3
|-
|LifeExpectIHMEMaleHistOnly
|0-6 days
|Male
|1950-2023
|Years
|3
|-
|LifeExpectIHMEFemaleHistOnly
|0-6 days
|Female
|1950-2023
|Years
|3
|}

=== Infant Mortality ===
Life expectancy data can be pulled from http://ghdx.healthdata.org/gbd-results-tool. The filters should be:

GBD Estimate All-cause mortality

Measure Life Tables

Metric Probability of death

Location All territories and countries

Age Stated below

Sex Both

Year All
{| class="wikitable"
|Variable
|Age
|Sex
|Year
|Decimal Places
|-
|InfMortRateIHME
|< 1 year
|Both
|1950-2023
|4
|-
|Under5MortRateIHME
|Calculation of < 1 year, 12-23 months, and 2-4 year
|Both
|1950-2023
|4
|}
'''Calculations:'''

In order to get the right value for Under5MortRateIHME, we have to calculate it. It should be:

PoD under 5 = PoD under 1 + PoD 12-23 months * Probability of not dying under 1+ PoD 2-4 years * Probability of not dying under 2.

An example is posted below for Iraq:
{| class="wikitable"
|Age
|Metric
|Year
|Value
|-
|<1 year
|Probability of death
|2019
|0.019244
|-
|12-23 months
|Probability of death
|2019
|0.001482
|-
|2-4 years
|Probability of death
|2019
|0.002298
|}

The PoD for under 2 would be = 0.019244 + 0.001482 * ( 1 - 0.019244) = 0.020698;

The PoD for under 5 would be = 0.020698 + 0.002298 * ( 1 - 0.020698) = 0.022948.

The final data point is '''0.022948'''.

= Data Notes =

* ViolenceInterpersonChild & ViolenceInterpersonWomenAdult have changed a lot, we identify the Age by comparing the trend of old data in IFsHistSeries and the new data from GBD.
** In March 2025, the ages have shifted from >20 for adults to >15. To get this data, do a summation of 15-19 and 20+

* Be aware of the definition of each preprocessor and its unit so that you can find the exact match in the IHME GBD variables.

=== Proxies ===
* Some regions in IFs are not provided by IHME yet, we are encouraged to use proxies. For example, use Singapore, Albania, and Mali for Hong Kong, Kosovo, and Sahrawi respectively. Be mindful that for tables with units in rate or percent, proximal values can be directly applied; however, when values are in numbers or headcounts, you will need to borrow the population figures to apply values proportionally.
** For instance, LifeExpectIHMEBothSexesHistOnly would not use a calculation, but IHMEDthsHIV will.
** To do this use SeriesPopulation. For instance, Kosovo’s number of deaths = Albania’s number of deaths * Kosovo’s Population / Albania’s Population.

Carbon Dioxide Information and Analysis Center (CDIAC)

2026-02-03T23:39:37Z

Yutang.Xiong: /* CDIAC Calculations */

= Summary =
The EmissionsCarbonCDIAC data comes from the Carbon Dioxide Information Analysis Center (CDIAC) and is [https://rieee.appstate.edu/projects-programs/cdiac/ available here.]

The organization publishes a number of datasets, including data on climate, atmospheric gases, and vegetation. The only data from the CDIAC used in IFs is total fossil-fuel CO2 emissions, which is available from 1751 to 2021, but has been imported to IFs beginning in the year 1800.

There is a consistent time series of data on CO2 emissions from fossil fuel use and cement manufacture was for many years generated and updated annually by the Carbon Dioxide Information and Analysis Center (CDIAC) at the U.S. Department of Energy’s (DOE) Oak Ridge National Laboratory (ORNL). The CDIAC annual inventories began in 1984 and were continued through 2021. The CDIAC emissions data set extended from the beginning of the industrial era (1751) to essentially the present. There is a data lag of 2 to 3 years due to the time needed for collecting and processing the primary data. The DOE ceased support for CDIAC in 2017. The last release supported by the DOE included emission estimates for the year 2014.

The last update was in June 2025.

=== CDIAC Calculations ===
The CO2 emissions prior to 1950 were calculated using historical energy statistics found in academic publications. Post-1950 emissions data has mostly come from energy statistics published by the United Nations. See more detail on the CDIAC methods and sources [https://rieee.appstate.edu/projects-programs/cdiac/ here].

Two main equations, derived from the work of [http://journals.co-action.net/index.php/tellusb/article/viewFile/14907/16719 Marland and Rotty (1984)] and [https://www.osti.gov/scitech/biblio/207068/ Boden et. al (1995)], were used in calculating carbon emissions for this data. In order to calculate the CO2 emissions from fossil fuels, three variables were used, including the amount of fuel produced and consumed (P), the fraction of the fuel that becomes oxidized (FO), and a factor for carbon content of the fuel (C). The following equation was used:

<math>CO_2{_i} = (P_i) (FO_i) (C_i) </math>

where <math> _i </math> is the group of fuel being calculated, chosen from gases, liquids, or solids. This equation was used to calculate global CO2 estimates for each fuel group. A total of the caclulations for each group was then used to calculate the total CO2 emissions for each country, used in IFs. In order to estimate the amount of fuel consumed (P), another equation was used:

<math>Consumption = production + imports - exports - bunkers - changes in stocks </math>

where "bunkers" refers to fuel used by ships and aircraft used in international trade.

=== Negative Emissions ===
There are negative emissions for a number of countries in this dataset, for Australia in the 1950s, Venezuela in 1930, and Iran in 1950, among others. A possible explanation has been provided by the CDIAC in their discussion of the CO2 trends for the [http://cdiac.ornl.gov/trends/emis/tre_ira.html Islamic Republic of Iran]:

''Energy statistics for the Islamic Republic of Iran in the early 1950s and the corresponding CO2 emissions estimates should be used with caution. Domestic fuel consumption is calculated as the difference between production plus imports and exports plus changes in stocks. When both production and exports are very large and very similar, a small error in either estimate can make it appear that domestic consumption was negative.''

This is a plausible explanation for other countries with negative emissions, as they were all for years in the late 1800s or early to mid 1900s, or had poor data availability during the years their emissions appear negative.

= Table In IFs =
{| class="wikitable"
|+
!Variable
!Definition
!Years
!UsedInPreprocessor
!UsedInPreprocessorFileName
|-
|EmissionsCarbonCDIAC
|Total carbon emissions from fossil fuel consumption and cement production.
|1751-2021
|1
|ENVIRONMENT
|}

= Data Pulling Instructions =
For general instructions on how to import data into IFs, visit the [http://pardee.du.edu/wiki/index.php?title=Importing_data_(general_instructions) data import page].

To get the data, download the National CO2 emissions from fossil fuels and cement manufacture excel sheet.
[[File:CDIAC.png|none|thumb|400x400px]]
The data is in the column: Total CO2 emissions from fossil-fuels and cement production (thousand metric tons of C).

= Data Notes =
1. The country concordance table to be used for this dataset is the CDIAC table. Every year, the CDIAC adds and deletes some countries out of these tables. Therefore the table must be updated along with the dataset.

2. Data is available from the year 1700 onwards. However, only data from the year 1800 onwards is used in IFs.

3. The user must update the formula column when importing the dataset through IFs so that the data is recorded in billions and not millions.

4. The user or vetter should also check the earliest and most recent data columns in the access file. In case of datasets with a large number of nulls, IFs does not update the earliest and most recent columns accurately in the case of some countries.

World Population Prospects (WPP), United Nations Population Division (UNPD)

2026-02-03T23:15:15Z

Yutang.Xiong: /* Data Notes */

= Summary =
The Population Division was established in the earlier years of the United Nations to serve as the Secretariat of the then Population Commission, created in 1946. Over the years, the Division has played an active role in the intergovernmental dialogue on population and development, producing constantly updated demographic estimates and projections for all countries, including data essential for the monitoring of the progress in achieving the Millennium Development Goals, developing and disseminating new methodologies, leading the substantive preparations for the United Nations major conferences on population and development as well as the annual sessions of the Commission on Population and Development.

= Tables In IFs =
{| class="wikitable"
|+
!Variable
!Definition
!Name In Source
!UsedInPreprocessor
!UsedInPreprocessorFileName
|-
|ForecastUnder5MortUNPDWPP
|Total Population Forecasts, UNPD WPP 2019, No change forecast
|Under 5 mortality; estimates & medium fertility variant
|0
|
|-
|AgeMedianUNPD
|Median Age, as of 1 July (years) - Estimates
|Median Age, as of 1 July (years)
|0
|
|-
|BirthsUNPD
|Births (thousands) - Estimates
|Births (thousands)
|0
|
|-
|CBR
|Crude Birth Rate (births per 1,000 population) - Estimates
|Crude Birth Rate (births per 1,000 population)
|1
|POPULATION
|-
|CBRUNPD
|Crude Birth Rate (births per 1,000 population) - Estimates
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|CDR
|Crude Death Rate (deaths per 1,000 population) - Estimates
|Crude Death Rate (deaths per 1,000 population)
|1
|POPULATION
|-
|CDRUNPD
|Crude Death Rate (deaths per 1,000 population) - Estimates
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ElderDep%UNPD
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastAgeMedianConstFertilityUNPD
|Median Age, as of 1 July (years) - Estimates & Constant-fertility
|Median Age, as of 1 July (years)
|0
|
|-
|ForecastAgeMedianConstMortUNPD
|Median Age, as of 1 July (years) - Estimates & Constant-mortality
|Median Age, as of 1 July (years)
|0
|
|-
|ForecastAgeMedianHighUNPD
|Median Age, as of 1 July (years) - Estimates & High variant
|Median Age, as of 1 July (years)
|0
|
|-
|ForecastAgeMedianLowUNPD
|Median Age, as of 1 July (years) - Estimates & Low variant
|Median Age, as of 1 July (years)
|0
|
|-
|ForecastAgeMedianMedUNPD
|Median Age, as of 1 July (years) - Estimates & Medium variant
|Median Age, as of 1 July (years)
|0
|
|-
|ForecastAgeMedianMomentumUNPD
|Median Age, as of 1 July (years) - Estimates & Momentum
|Median Age, as of 1 July (years)
|0
|
|-
|ForecastAgeMedianNoChangeUNPD
|Median Age, as of 1 July (years) - Estimates & No change
|Median Age, as of 1 July (years)
|0
|
|-
|ForecastAgeMedianReplacementUNPD
|Median Age, as of 1 July (years) - Estimates & Instant-replacement
|Median Age, as of 1 July (years)
|0
|
|-
|ForecastAgeMedianZeroMigrationUNPD
|Median Age, as of 1 July (years) - Estimates & Zero-migration
|Median Age, as of 1 July (years)
|0
|
|-
|ForecastBirthsConstFertilityUNPD
|Births (thousands) - Estimates & Constant-fertility
|Births (thousands)
|0
|
|-
|ForecastBirthsConstMortUNPD
|Births (thousands) - Estimates & Constant-mortality
|Births (thousands)
|0
|
|-
|ForecastBirthsHighUNPD
|Births (thousands) - Estimates & High variant
|Births (thousands)
|0
|
|-
|ForecastBirthsLowUNPD
|Births (thousands) - Estimates & Low variant
|Births (thousands)
|0
|
|-
|ForecastBirthsMedUNPD
|Births (thousands) - Estimates & Medium variant
|Births (thousands)
|0
|
|-
|ForecastBirthsMedUNPD2022Rev
|Births (thousands) - Estimates & Medium variant
|Births (thousands)
|0
|
|-
|ForecastBirthsMomentumUNPD
|Births (thousands) - Estimates & Momentum
|Births (thousands)
|0
|
|-
|ForecastBirthsNoChangeUNPD
|Births (thousands) - Estimates & No change
|Births (thousands)
|0
|
|-
|ForecastBirthsReplacementUNPD
|Births (thousands) - Estimates & Instant-replacement
|Births (thousands)
|0
|
|-
|ForecastBirthsZeroMigrationUNPD
|Births (thousands) - Estimates & Zero-migration
|Births (thousands)
|0
|
|-
|ForecastCBRConstFertilityUNPD
|Crude Birth Rate (births per 1,000 population) - Estimates & Constant-fertility
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|ForecastCBRConstMortUNPD
|Crude Birth Rate (births per 1,000 population) - Estimates & Constant-mortality
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|ForecastCBRHighUNPD
|Crude Birth Rate (births per 1,000 population) - Estimates & High variant
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|ForecastCBRLowUNPD
|Crude Birth Rate (births per 1,000 population) - Estimates & Low variant
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|ForecastCBRMedUNPD
|Crude Birth Rate (births per 1,000 population) - Estimates & Medium variant
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|ForecastCBRMedUNPD2022Rev
|Crude Birth Rate (births per 1,000 population) - Estimates & Medium variant
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|ForecastCBRMomentumUNPD
|Crude Birth Rate (births per 1,000 population) - Estimates & Momentum
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|ForecastCBRNoChangeUNPD
|Crude Birth Rate (births per 1,000 population) - Estimates & No change
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|ForecastCBRReplacementUNPD
|Crude Birth Rate (births per 1,000 population) - Estimates & Instant-replacement
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|ForecastCBRZeroMigrationUNPD
|Crude Birth Rate (births per 1,000 population) - Estimates & Zero-migration
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|ForecastCDRConstFertilityUNPD
|Crude Death Rate (deaths per 1,000 population) - Estimates & Constant-fertility
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRConstFertilityUNPD2022Rev
|Crude Death Rate (deaths per 1,000 population) - Estimates & Constant-fertility
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRConstMortUNPD
|Crude Death Rate (deaths per 1,000 population) - Estimates & Constant-mortality
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRConstMortUNPD2022Rev
|Crude Death Rate (deaths per 1,000 population) - Estimates & Constant-mortality
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRHighUNPD
|Crude Death Rate (deaths per 1,000 population) - Estimates & High variant
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRHighUNPD2022Rev
|Crude Death Rate (deaths per 1,000 population) - Estimates & High variant
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRLowUNPD
|Crude Death Rate (deaths per 1,000 population) - Estimates & Low variant
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRLowUNPD2022Rev
|Crude Death Rate (deaths per 1,000 population) - Estimates & Low variant
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRMedUNPD
|Crude Death Rate (deaths per 1,000 population) - Estimates & Medium variant
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRMedUNPD2022Rev
|Crude Death Rate (deaths per 1,000 population) - Estimates & Medium variant
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRMomentumUNPD
|Crude Death Rate (deaths per 1,000 population) - Estimates & Momentum
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRMomentumUNPD2022Rev
|Crude Death Rate (deaths per 1,000 population) - Estimates & Momentum
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRNoChangeUNPD
|Crude Death Rate (deaths per 1,000 population) - Estimates & No change
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRNoChangeUNPD2022Rev
|Crude Death Rate (deaths per 1,000 population) - Estimates & No change
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRReplacementUNPD
|Crude Death Rate (deaths per 1,000 population) - Estimates & Instant-replacement
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRZeroMigrationUNPD
|Crude Death Rate (deaths per 1,000 population) - Estimates & Zero-migration
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastElderDep%ConstFertilityUNPD
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Constant-fertility
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%ConstFertilityUNPD2022Rev
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Constant-fertility
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%ConstMortUNPD
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Constant-mortality
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%ConstMortUNPD2022Rev
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Constant-mortality
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%HighUNPD
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & High variant
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%HighUNPD2022Rev
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & High variant
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%LowUNPD
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Low variant
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%LowUNPD2022Rev
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Low variant
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%MedUNPD
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Medium variant
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%MedUNPD2022Rev
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Medium variant
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%MomentumUNPD
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Momentum
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%NoChangeUNPD
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & No change
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%NoChangeUNPD2022Rev
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & No change
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%ReplacementUNPD
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Instant-replacement
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%ReplacementUNPD2022Rev
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Instant-replacement
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%ZeroMigrationUNPD
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Zero-migration
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%ZeroMigrationUNPD2022Rev
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Zero-migration
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastInfMortConstFertilityUNPD
|Infant Mortality Rate (infant deaths per 1,000 live births) - Estimates & Constant-fertility
|Infant Mortality Rate (infant deaths per 1,000 live births)
|0
|
|-
|ForecastInfMortConstMortUNPD
|Infant Mortality Rate (infant deaths per 1,000 live births) - Estimates & Constant-mortality
|Infant Mortality Rate (infant deaths per 1,000 live births)
|0
|
|-
|ForecastInfMortHighUNPD
|Infant Mortality Rate (infant deaths per 1,000 live births) - Estimates & High variant
|Infant Mortality Rate (infant deaths per 1,000 live births)
|0
|
|-
|ForecastInfMortLowUNPD
|Infant Mortality Rate (infant deaths per 1,000 live births) - Estimates & Low variant
|Infant Mortality Rate (infant deaths per 1,000 live births)
|0
|
|-
|ForecastInfMortMedUNPD
|Infant Mortality Rate (infant deaths per 1,000 live births) - Estimates & Medium variant
|Infant Mortality Rate (infant deaths per 1,000 live births)
|0
|
|-
|ForecastInfMortMomentumUNPD
|Infant Mortality Rate (infant deaths per 1,000 live births) - Estimates & Momentum
|Infant Mortality Rate (infant deaths per 1,000 live births)
|0
|
|-
|ForecastInfMortNoChangeUNPD
|Infant Mortality Rate (infant deaths per 1,000 live births) - Estimates & No change
|Infant Mortality Rate (infant deaths per 1,000 live births)
|0
|
|-
|ForecastInfMortReplacementUNPD
|Infant Mortality Rate (infant deaths per 1,000 live births) - Estimates & Instant-replacement
|Infant Mortality Rate (infant deaths per 1,000 live births)
|0
|
|-
|ForecastInfMortZeroMigrationUNPD
|Infant Mortality Rate (infant deaths per 1,000 live births) - Estimates & Zero-migration
|Infant Mortality Rate (infant deaths per 1,000 live births)
|0
|
|-
|ForecastLifeExpBothSexesConstFertilityUNPD
|Life Expectancy at Birth, both sexes (years) - Estimates & Constant-fertility
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|ForecastLifeExpBothSexesConstMortUNPD
|Life Expectancy at Birth, both sexes (years) - Estimates & Constant-mortality
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|ForecastLifeExpBothSexesHighUNPD
|Life Expectancy at Birth, both sexes (years) - Estimates & High variant
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|ForecastLifeExpBothSexesLowUNPD
|Life Expectancy at Birth, both sexes (years) - Estimates & Low variant
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|ForecastLifeExpBothSexesMomentumUNPD
|Life Expectancy at Birth, both sexes (years) - Estimates & Momentum
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|ForecastLifeExpBothSexesNoChangeUNPD
|Life Expectancy at Birth, both sexes (years) - Estimates & No change
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|ForecastLifeExpBothSexesReplacementUNPD
|Life Expectancy at Birth, both sexes (years) - Estimates & Instant-replacement
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|ForecastLifeExpBothSexesZeroMigrationUNPD
|Life Expectancy at Birth, both sexes (years) - Estimates & Zero-migration
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|ForecastLifeExpFemaleConstFertilityUNPD
|Female Life Expectancy at Birth (years) - Estimates & Constant-fertility
|Female Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpFemaleConstMortUNPD
|Female Life Expectancy at Birth (years) - Estimates & Constant-mortality
|Female Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpFemaleHighUNPD
|Female Life Expectancy at Birth (years) - Estimates & High variant
|Female Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpFemaleLowUNPD
|Female Life Expectancy at Birth (years) - Estimates & Low variant
|Female Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpFemaleMomentumUNPD
|Female Life Expectancy at Birth (years) - Estimates & Momentum
|Female Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpFemaleNoChangeUNPD
|Female Life Expectancy at Birth (years) - Estimates & No change
|Female Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpFemaleReplacementUNPD
|Female Life Expectancy at Birth (years) - Estimates & Instant-replacement
|Female Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpFemaleZeroMigrationUNPD
|Female Life Expectancy at Birth (years) - Estimates & Zero-migration
|Female Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMaleConstFertilityUNPD
|Male Life Expectancy at Birth (years) - Estimates & Constant-fertility
|Male Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMaleConstMortUNPD
|Male Life Expectancy at Birth (years) - Estimates & Constant-mortality
|Male Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMaleHighUNPD
|Male Life Expectancy at Birth (years) - Estimates & High variant
|Male Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMaleLowUNPD
|Male Life Expectancy at Birth (years) - Estimates & Low variant
|Male Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMaleMomentumUNPD
|Male Life Expectancy at Birth (years) - Estimates & Momentum
|Male Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMaleNoChangeUNPD
|Male Life Expectancy at Birth (years) - Estimates & No change
|Male Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMaleReplacementUNPD
|Male Life Expectancy at Birth (years) - Estimates & Instant-replacement
|Male Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMaleZeroMigrationUNPD
|Male Life Expectancy at Birth (years) - Estimates & Zero-migration
|Male Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMedBothSexesUNPD
|Life Expectancy at Birth, both sexes (years) - Estimates & Medium variant
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|ForecastLifeExpMedBothSexesUNPD2022Rev
|Life Expectancy at Birth, both sexes (years) - Estimates & Medium variant
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|ForecastLifeExpMedFemaleUNPD
|Female Life Expectancy at Birth (years) - Estimates & Medium variant
|Female Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMedFemaleUNPD2022Rev
|Female Life Expectancy at Birth (years) - Estimates & Medium variant
|Female Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMedMaleUNPD
|Male Life Expectancy at Birth (years) - Estimates & Medium variant
|Male Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMedMaleUNPD2022Rev
|Male Life Expectancy at Birth (years) - Estimates & Medium variant
|Male Life Expectancy at Birth (years)
|0
|
|-
|ForecastNetMigrationConstFertilityUNPD
|Net Number of Migrants (thousands) - Estimates & Constant-fertility
|Net Number of Migrants (thousands)
|0
|
|-
|ForecastNetMigrationConstMortUNPD
|Net Number of Migrants (thousands) - Estimates & Constant-mortality
|Net Number of Migrants (thousands)
|0
|
|-
|ForecastNetMigrationHighUNPD
|Net Number of Migrants (thousands) - Estimates & High variant
|Net Number of Migrants (thousands)
|0
|
|-
|ForecastNetMigrationLowUNPD
|Net Number of Migrants (thousands) - Estimates & Low variant
|Net Number of Migrants (thousands)
|0
|
|-
|ForecastNetMigrationMedUNPD
|Net Number of Migrants (thousands) - Estimates & Medium variant
|Net Number of Migrants (thousands)
|0
|
|-
|ForecastNetMigrationMedUNPD2019Rev
|Net migration headcounts by country, medium-fertility variant, every five years 1955-2100
|MIGR_F02_NET_NUMBER_OF_MIGRANTS
|0
|
|-
|ForecastNetMigrationMedUNPD2022Rev
|Net Number of Migrants (thousands) - Estimates & Medium variant
|Net Number of Migrants (thousands)
|0
|
|-
|ForecastNetMigrationMomentumUNPD
|Net Number of Migrants (thousands) - Estimates & Momentum
|Net Number of Migrants (thousands)
|0
|
|-
|ForecastNetMigrationNoChangeUNPD
|Net Number of Migrants (thousands) - Estimates & No change
|Net Number of Migrants (thousands)
|0
|
|-
|ForecastNetMigrationRateConstFertilityUNPD
|Net Migration Rate (per 1,000 population) - Estimates & Constant-fertility
|Net Migration Rate (per 1,000 population)
|0
|
|-
|ForecastNetMigrationRateConstMortUNPD
|Net Migration Rate (per 1,000 population) - Estimates & Constant-mortality
|Net Migration Rate (per 1,000 population)
|0
|
|-
|ForecastNetMigrationRateHighUNPD
|Net Migration Rate (per 1,000 population) - Estimates & High variant
|Net Migration Rate (per 1,000 population)
|0
|
|-
|ForecastNetMigrationRateLowUNPD
|Net Migration Rate (per 1,000 population) - Estimates & Low variant
|Net Migration Rate (per 1,000 population)
|0
|
|-
|ForecastNetMigrationRateMedUNPD
|Net Migration Rate (per 1,000 population) - Estimates & Medium variant
|Net Migration Rate (per 1,000 population)
|0
|
|-
|ForecastNetMigrationRateMedUNPD2022Rev
|Net Migration Rate (per 1,000 population) - Estimates & Medium variant
|Net Migration Rate (per 1,000 population)
|0
|
|-
|ForecastNetMigrationRateMomentumUNPD
|Net Migration Rate (per 1,000 population) - Estimates & Momentum
|Net Migration Rate (per 1,000 population)
|0
|
|-
|ForecastNetMigrationRateNoChangeUNPD
|Net Migration Rate (per 1,000 population) - Estimates & No change
|Net Migration Rate (per 1,000 population)
|0
|
|-
|ForecastNetMigrationRateReplacementUNPD
|Net Migration Rate (per 1,000 population) - Estimates & Instant-replacement
|Net Migration Rate (per 1,000 population)
|0
|
|-
|ForecastNetMigrationRateZeroMigrationUNPD
|Net Migration Rate (per 1,000 population) - Estimates & Zero-migration
|Net Migration Rate (per 1,000 population)
|0
|
|-
|ForecastNetMigrationReplacementUNPD
|Net Number of Migrants (thousands) - Estimates & Instant-replacement
|Net Number of Migrants (thousands)
|0
|
|-
|ForecastNetMigrationZeroMigrationUNPD
|Net Number of Migrants (thousands) - Estimates & Zero-migration
|Net Number of Migrants (thousands)
|0
|
|-
|ForecastPop15PlusConstFertilityUNPD
|Population ages 15 and older in millions
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|ForecastPop15PlusConstMortUNPD
|Population ages 15 and older in millions
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|ForecastPop15PlusHighUNPD
|Population ages 15 and older in millions
- Estimates & High variant
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|ForecastPop15PlusLowUNPD
|Population ages 15 and older in millions
- Estimates & Low variant
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|ForecastPop15PlusMedUNPD
|Population ages 15 and older in millions
- Estimates & Medium variant
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|ForecastPop15PlusMomentumUNPD
|Population ages 15 and older in millions
- Estimates & Momentum
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|ForecastPop15PlusNoChangeUNPD
|Population ages 15 and older in millions
- Estimates & No change
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|ForecastPop15PlusReplacementUNPD
|Population ages 15 and older in millions
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|ForecastPop15PlusZeroMigrationUNPD
|Population ages 15 and older in millions
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|ForecastPop15to29ConstFertilityUNPD
|Population aged 15 to 29 years in millions
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|ForecastPop15to29ConstMortUNPD
|Population aged 15 to 29 years in millions
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|ForecastPop15to29HighUNPD
|Population aged 15 to 29 years in millions
- Estimates & High variant
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|ForecastPop15to29LowUNPD
|Population aged 15 to 29 years in millions
- Estimates & Low variant
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|ForecastPop15to29MedUNPD
|Population aged 15 to 29 years in millions
- Estimates & Medium variant
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|ForecastPop15to29MomentumUNPD
|Population aged 15 to 29 years in millions
- Estimates & Momentum
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|ForecastPop15to29NoChangeUNPD
|Population aged 15 to 29 years in millions
- Estimates & No change
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|ForecastPop15to29ReplacementUNPD
|Population aged 15 to 29 years in millions
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|ForecastPop15to29ZeroMigrationUNPD
|Population aged 15 to 29 years in millions
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|ForecastPop15to64%ConstFertilityUNPD
|Percentage of population aged 15 up to 65 years relative to total population
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64%ConstMortUNPD
|Percentage of population aged 15 up to 65 years relative to total population
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64%HighUNPD
|Percentage of population aged 15 up to 65 years relative to total population
- Estimates & High variant
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64%LowUNPD
|Percentage of population aged 15 up to 65 years relative to total population
- Estimates & Low variant
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64%MedUNPD
|Percentage of population aged 15 up to 65 years relative to total population
- Estimates & Medium variant
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64%MomentumUNPD
|Percentage of population aged 15 up to 65 years relative to total population
- Estimates & Momentum
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64%NoChangeUNPD
|Percentage of population aged 15 up to 65 years relative to total population
- Estimates & No change
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64%ReplacementUNPD
|Percentage of population aged 15 up to 65 years relative to total population
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64%ZeroMigrationUNPD
|Percentage of population aged 15 up to 65 years relative to total population
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64ConstFertilityUNPD
|Population aged 15 to 64 years in millions
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64ConstMortUNPD
|Population aged 15 to 64 years in millions
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64HighUNPD
|Population aged 15 to 64 years in millions
- Estimates & High variant
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64LowUNPD
|Population aged 15 to 64 years in millions
- Estimates & Low variant
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64MedUNPD
|Population aged 15 to 64 years in millions
- Estimates & Medium variant
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64MomentumUNPD
|Population aged 15 to 64 years in millions
- Estimates & Momentum
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64NoChangeUNPD
|Population aged 15 to 64 years in millions
- Estimates & No change
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64ReplacementUNPD
|Population aged 15 to 64 years in millions
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64ZeroMigrationUNPD
|Population aged 15 to 64 years in millions
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop65Plus%ConstFertilityUNPD
|Percentage of population aged 65 years or more relative to total population
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65Plus%ConstMortUNPD
|Percentage of population aged 65 years or more relative to total population
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65Plus%HighUNPD
|Percentage of population aged 65 years or more relative to total population
- Estimates & High variant
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65Plus%LowUNPD
|Percentage of population aged 65 years or more relative to total population
- Estimates & Low variant
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65Plus%MedUNPD
|Percentage of population aged 65 years or more relative to total population
- Estimates & Medium variant
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65Plus%MomentumUNPD
|Percentage of population aged 65 years or more relative to total population
- Estimates & Momentum
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65Plus%NoChangeUNPD
|Percentage of population aged 65 years or more relative to total population
- Estimates & No change
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65Plus%ReplacementUNPD
|Percentage of population aged 65 years or more relative to total population
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65Plus%ZeroMigrationUNPD
|Percentage of population aged 65 years or more relative to total population
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65PlusConstFertilityUNPD
|Population ages 65 and older in millions
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65PlusConstMortUNPD
|Population ages 65 and older in millions
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65PlusHighUNPD
|Population ages 65 and older in millions
- Estimates & High variant
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65PlusLowUNPD
|Population ages 65 and older in millions
- Estimates & Low variant
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65PlusMedUNPD
|Population ages 65 and older in millions
- Estimates & Medium variant
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65PlusMomentumUNPD
|Population ages 65 and older in millions
- Estimates & Momentum
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65PlusNoChangeUNPD
|Population ages 65 and older in millions
- Estimates & No change
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65PlusReplacementUNPD
|Population ages 65 and older in millions
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65PlusZeroMigrationUNPD
|Population ages 65 and older in millions
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPopConstFertilityUNPD
|Total Population, as of 1 July (thousands) - Estimates & Constant-fertility
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopConstFertilityUNPD2022Rev
|Total Population, as of 1 July (thousands) - Estimates & Constant-fertility
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopConstMortUNPD
|Total Population, as of 1 July (thousands) - Estimates & Constant-mortality
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopConstMortUNPD2022Rev
|Total Population, as of 1 July (thousands) - Estimates & Constant-mortality
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopFemaleConstFertilityUNPD
|Female Population, as of 1 July (thousands) - Estimates & Constant-fertility
|Female Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopFemaleConstMortUNPD
|Female Population, as of 1 July (thousands) - Estimates & Constant-mortality
|Female Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopFemaleHighUNPD
|Female Population, as of 1 July (thousands) - Estimates & High variant
|Female Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopFemaleLowUNPD
|Female Population, as of 1 July (thousands) - Estimates & Low variant
|Female Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopFemaleMedUNPD
|Female Population, as of 1 July (thousands) - Estimates & Medium variant
|Female Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopFemaleMedUNPD2022Rev
|Total female population, as of 1 July (thousands) - Estimates & Medium variant
|Female Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopFemaleMomentumUNPD
|Female Population, as of 1 July (thousands) - Estimates & Momentum
|Female Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopFemaleNoChangeUNPD
|Female Population, as of 1 July (thousands) - Estimates & No change
|Female Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopFemaleReplacementUNPD
|Female Population, as of 1 July (thousands) - Estimates & Instant-replacement
|Female Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopFemaleZeroMigrationUNPD
|Female Population, as of 1 July (thousands) - Estimates & Zero-migration
|Female Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopHighUNPD
|Total Population, as of 1 July (thousands) - Estimates & High variant
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopHighUNPD2022Rev
|Total Population, as of 1 July (thousands) - Estimates & High variant
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopLowUNPD
|Total Population, as of 1 July (thousands) - Estimates & Low variant
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopLowUNPD2022Rev
|Total Population, as of 1 July (thousands) - Estimates & Low variant
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMaleConstFertilityUNPD
|Male Population, as of 1 July (thousands) - Estimates & Constant-fertility
|Male Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMaleConstMortUNPD
|Male Population, as of 1 July (thousands) - Estimates & Constant-mortality
|Male Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMaleHighUNPD
|Male Population, as of 1 July (thousands) - Estimates & High variant
|Male Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMaleLowUNPD
|Male Population, as of 1 July (thousands) - Estimates & Low variant
|Male Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMaleMedUNPD
|Male Population, as of 1 July (thousands) - Estimates & Medium variant
|Male Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMaleMedUNPD2022Rev
|Total male population, as of 1 July (thousands) - Estimates & Medium variant
|Male Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMaleMomentumUNPD
|Male Population, as of 1 July (thousands) - Estimates & Momentum
|Male Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMaleNoChangeUNPD
|Male Population, as of 1 July (thousands) - Estimates & No change
|Male Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMaleReplacementUNPD
|Male Population, as of 1 July (thousands) - Estimates & Instant-replacement
|Male Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMaleZeroMigrationUNPD
|Male Population, as of 1 July (thousands) - Estimates & Zero-migration
|Male Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMedUNPD
|Total Population, as of 1 July (thousands) - Estimates & Medium variant
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMedUNPD2022Rev
|Total Population, as of 1 July (thousands) - Estimates & Medium variant
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMomentumUNPD
|Total Population, as of 1 July (thousands) - Estimates & Momentum
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopNoChangeUNPD
|Total Population, as of 1 July (thousands) - Estimates & No change
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopNoChangeUNPD2022Rev
|Total Population, as of 1 July (thousands) - Estimates & No change
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopReplacementUNPD
|Total Population, as of 1 July (thousands) - Estimates & Instant-replacement
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopReplacementUNPD2022Rev
|Total Population, as of 1 July (thousands) - Estimates & Instant-replacement
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopUnder15%ConstFertilityUNPD
|Percentage of population under the age of 15 relative to the total population
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15%ConstMortUNPD
|Percentage of population under the age of 15 relative to the total population
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15%HighUNPD
|Percentage of population under the age of 15 relative to the total population
- Estimates & High variant
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15%LowUNPD
|Percentage of population under the age of 15 relative to the total population
- Estimates & Low variant
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15%MedUNPD
|Percentage of population under the age of 15 relative to the total population
- Estimates & Medium variant
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15%MomentumUNPD
|Percentage of population under the age of 15 relative to the total population
- Estimates & Momentum
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15%NoChangeUNPD
|Percentage of population under the age of 15 relative to the total population
- Estimates & No change
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15%ReplacementUNPD
|Percentage of population under the age of 15 relative to the total population
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15%ZeroMigrationUNPD
|Percentage of population under the age of 15 relative to the total population
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15ConstFertilityUNPD
|Population under the age 15 in millions
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15ConstMortUNPD
|Population under the age 15 in millions
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15HighUNPD
|Population under the age 15 in millions
- Estimates & High variant
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15LowUNPD
|Population under the age 15 in millions
- Estimates & Low variant
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15MedUNPD
|Population under the age 15 in millions
- Estimates & Medium variant
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15MomentumUNPD
|Population under the age 15 in millions
- Estimates & Momentum
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15NoChangeUNPD
|Population under the age 15 in millions
- Estimates & No change
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15ReplacementUNPD
|Population under the age 15 in millions
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15ZeroMigrationUNPD
|Population under the age 15 in millions
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder5ConstFertilityUNPD
|Population under 5 years of age in millions
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - 0-4
|0
|
|-
|ForecastPopUnder5ConstMortUNPD
|Population under 5 years of age in millions
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - 0-4
|0
|
|-
|ForecastPopUnder5HighUNPD
|Population under 5 years of age in millions
- Estimates & High variant
|Annual population by age groups (both sexes) - 0-4
|0
|
|-
|ForecastPopUnder5LowUNPD
|Population under 5 years of age in millions
- Estimates & Low variant
|Annual population by age groups (both sexes) - 0-4
|0
|
|-
|ForecastPopUnder5MedUNPD
|Population under 5 years of age in millions
- Estimates & Medium variant
|Annual population by age groups (both sexes) - 0-4
|0
|
|-
|ForecastPopUnder5MomentumUNPD
|Population under 5 years of age in millions
- Estimates & Momentum
|Annual population by age groups (both sexes) - 0-4
|0
|
|-
|ForecastPopUnder5NoChangeUNPD
|Population under 5 years of age in millions
- Estimates & No change
|Annual population by age groups (both sexes) - 0-4
|0
|
|-
|ForecastPopUnder5ReplacementUNPD
|Population under 5 years of age in millions
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - 0-4
|0
|
|-
|ForecastPopUnder5ZeroMigrationUNPD
|Population under 5 years of age in millions
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - 0-4
|0
|
|-
|ForecastPopZeroMigrationUNPD
|Total Population, as of 1 July (thousands) - Estimates & Zero-migration
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopZeroMigrationUNPD2022Rev
|Total Population, as of 1 July (thousands) - Estimates & Zero-migration
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastTFRConstFertilityUNPD
|Total Fertility Rate (live births per woman) - Estimates & Constant-fertility
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRConstFertilityUNPD2022Rev
|Total Fertility Rate (live births per woman) - Estimates & Constant-fertility
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRConstMortUNPD
|Total Fertility Rate (live births per woman) - Estimates & Constant-mortality
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRConstMortUNPD2022Rev
|Total Fertility Rate (live births per woman) - Estimates & Constant-mortality
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRHighUNPD
|Total Fertility Rate (live births per woman) - Estimates & High variant
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRHighUNPD2022Rev
|Total Fertility Rate (live births per woman) - Estimates & High variant
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRLowUNPD
|Total Fertility Rate (live births per woman) - Estimates & Low variant
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRLowUNPD2022Rev
|Total Fertility Rate (live births per woman) - Estimates & Low variant
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRMedUNPD
|Total Fertility Rate (live births per woman) - Estimates & Medium variant
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRMedUNPD2022Rev
|Total Fertility Rate (live births per woman) - Estimates & Medium variant
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRMomentumUNPD
|Total Fertility Rate (live births per woman) - Estimates & Momentum
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRNoChangeUNPD
|Total Fertility Rate (live births per woman) - Estimates & No change
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRNoChangeUNPD2022Rev
|Total Fertility Rate (live births per woman) - Estimates & No change
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRReplacementUNPD
|Total Fertility Rate (live births per woman) - Estimates & Instant-replacement
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRReplacementUNPD2022Rev
|Total Fertility Rate (live births per woman) - Estimates & Instant-replacement
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRZeroMigrationUNPD
|Total Fertility Rate (live births per woman) - Estimates & Zero-migration
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRZeroMigrationUNPD2022Rev
|Total Fertility Rate (live births per woman) - Estimates & Zero-migration
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastUnder5MortConstFertilityUNPD
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates & Constant-fertility
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|ForecastUnder5MortConstMortUNPD
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates & Constant-mortality
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|ForecastUnder5MortHighUNPD
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates & High variant
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|ForecastUnder5MortLowUNPD
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates & Low variant
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|ForecastUnder5MortMedUNPD
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates & Medium variant
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|ForecastUnder5MortMomentumUNPD
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates & Momentum
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|ForecastUnder5MortNoChangeUNPD
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates & No change
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|ForecastUnder5MortNoChangeUNPD2022Rev
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates & No change
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|ForecastUnder5MortReplacementUNPD
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates & Instant-replacement
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|ForecastUnder5MortZeroMigrationUNPD
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates & Zero-migration
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|ForecastYouthBulgeBy15ConstFertilityUNPD
|Youth bulge calculated with population ages 15-29 over population ages 15 up
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|0
|
|-
|ForecastYouthBulgeBy15ConstMortUNPD
|Youth bulge calculated with population ages 15-29 over population ages 15 up
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|0
|
|-
|ForecastYouthBulgeBy15HighUNPD
|Youth bulge calculated with population ages 15-29 over population ages 15 up
- Estimates & High variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|0
|
|-
|ForecastYouthBulgeBy15LowUNPD
|Youth bulge calculated with population ages 15-29 over population ages 15 up
- Estimates & Low variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|0
|
|-
|ForecastYouthBulgeBy15MedUNPD
|Youth bulge calculated with population ages 15-29 over population ages 15 up
- Estimates & Medium variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|0
|
|-
|ForecastYouthBulgeBy15MomentumUNPD
|Youth bulge calculated with population ages 15-29 over population ages 15 up
- Estimates & Momentum
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|0
|
|-
|ForecastYouthBulgeBy15NoChangeUNPD
|Youth bulge calculated with population ages 15-29 over population ages 15 up
- Estimates & No change
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|0
|
|-
|ForecastYouthBulgeBy15ReplacementUNPD
|Youth bulge calculated with population ages 15-29 over population ages 15 up
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|0
|
|-
|ForecastYouthBulgeBy15ZeroMigrationUNPD
|Youth bulge calculated with population ages 15-29 over population ages 15 up
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|0
|
|-
|ForecastYouthBulgeBy30ConstFertilityUNPD
|Youth bulge calculated with population ages 15-29 over population ages 30 up
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|0
|
|-
|ForecastYouthBulgeBy30ConstMortUNPD
|Youth bulge calculated with population ages 15-29 over population ages 30 up
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|0
|
|-
|ForecastYouthBulgeBy30HighUNPD
|Youth bulge calculated with population ages 15-29 over population ages 30 up
- Estimates & High variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|0
|
|-
|ForecastYouthBulgeBy30LowUNPD
|Youth bulge calculated with population ages 15-29 over population ages 30 up
- Estimates & Low variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|0
|
|-
|ForecastYouthBulgeBy30MedUNPD
|Youth bulge calculated with population ages 15-29 over population ages 30 up
- Estimates & Medium variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|0
|
|-
|ForecastYouthBulgeBy30MomentumUNPD
|Youth bulge calculated with population ages 15-29 over population ages 30 up
- Estimates & Momentum
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|0
|
|-
|ForecastYouthBulgeBy30NoChangeUNPD
|Youth bulge calculated with population ages 15-29 over population ages 30 up
- Estimates & No change
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|0
|
|-
|ForecastYouthBulgeBy30ReplacementUNPD
|Youth bulge calculated with population ages 15-29 over population ages 30 up
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|0
|
|-
|ForecastYouthBulgeBy30ZeroMigrationUNPD
|Youth bulge calculated with population ages 15-29 over population ages 30 up
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|0
|
|-
|ForecastYouthBulgeByTotalConstFertilityUNPD
|Youth bulge calculated with population ages 15-29 over the total population
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - sum(15-29)/total
|0
|
|-
|ForecastYouthBulgeByTotalConstMortUNPD
|Youth bulge calculated with population ages 15-29 over the total population
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - sum(15-29)/total
|0
|
|-
|ForecastYouthBulgeByTotalHighUNPD
|Youth bulge calculated with population ages 15-29 over the total population
- Estimates & High variant
|Annual population by age groups (both sexes) - sum(15-29)/total
|0
|
|-
|ForecastYouthBulgeByTotalLowUNPD
|Youth bulge calculated with population ages 15-29 over the total population
- Estimates & Low variant
|Annual population by age groups (both sexes) - sum(15-29)/total
|0
|
|-
|ForecastYouthBulgeByTotalMedUNPD
|Youth bulge calculated with population ages 15-29 over the total population
- Estimates & Medium variant
|Annual population by age groups (both sexes) - sum(15-29)/total
|0
|
|-
|ForecastYouthBulgeByTotalMomentumUNPD
|Youth bulge calculated with population ages 15-29 over the total population
- Estimates & Momentum
|Annual population by age groups (both sexes) - sum(15-29)/total
|0
|
|-
|ForecastYouthBulgeByTotalNoChangeUNPD
|Youth bulge calculated with population ages 15-29 over the total population
- Estimates & No change
|Annual population by age groups (both sexes) - sum(15-29)/total
|0
|
|-
|ForecastYouthBulgeByTotalReplacementUNPD
|Youth bulge calculated with population ages 15-29 over the total population
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - sum(15-29)/total
|0
|
|-
|ForecastYouthBulgeByTotalZeroMigrationUNPD
|Youth bulge calculated with population ages 15-29 over the total population
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - sum(15-29)/total
|0
|
|-
|ForecastYouthDep%ConstFertilityUNPD
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Constant-fertility
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%ConstFertilityUNPD2022Rev
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Constant-fertility
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%ConstMortUNPD
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Constant-mortality
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%ConstMortUNPD2022Rev
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Constant-mortality
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%HighUNPD
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & High variant
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%HighUNPD2022Rev
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & High variant
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%LowUNPD
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Low variant
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%LowUNPD2022Rev
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Low variant
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%MedUNPD
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Medium variant
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%MedUNPD2022Rev
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Medium variant
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%MomentumUNPD
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Momentum
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%MomentumUNPD2022Rev
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Momentum
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%NoChangeUNPD
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & No change
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%NoChangeUNPD2022Rev
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & No change
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%ReplacementUNPD
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Instant-replacement
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%ReplacementUNPD2022Rev
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Instant-replacement
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%ZeroMigrationUNPD
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Zero-migration
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%ZeroMigrationUNPD2022Rev
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Zero-migration
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|InfMortMedUNPD
|Medium-fertility variant Infant mortality rate by country, every five years 1955-2020 ( infant deaths per 1,000 live births)
|Infant mortality rate by country, every five years 1955-2010 (infant deaths per 1,000 live births)
|0
|
|-
|InfMortUNPD
|Infant Mortality Rate (infant deaths per 1,000 live births) - Estimates
|Infant Mortality Rate (infant deaths per 1,000 live births)
|0
|
|-
|LifeExpBothSexesUNPD
|Life Expectancy at Birth, both sexes (years) - Estimates
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|LifeExpFemaleUNPD
|Female Life Expectancy at Birth (years) - Estimates
|Female Life Expectancy at Birth (years)
|0
|
|-
|LifeExpMaleUNPD
|Male Life Expectancy at Birth (years) - Estimates
|Male Life Expectancy at Birth (years)
|0
|
|-
|LifExpect
|Life expectancy at birth, Both sexes
|Life expectancy at birth, both sexes (years)
|0
|
|-
|LifExpectFemale
|Life expectancy at birth, female
|Life expectancy at birth, female (years)
|0
|
|-
|LifExpectFemaleMedUNPD
|Medium variant- Life expectancy at birth, female
|Life expectancy at birth, female (years)
|0
|
|-
|LifExpectMale
|Life expectancy at birth, Male
|Life expectancy at birth, male (years)
|0
|
|-
|LifExpectMaleMedUNPD
|Medium variant- Life expectancy at birth, Male
|Life expectancy at birth, male (years)
|0
|
|-
|LifExpectMedUNPD
|Medium variant- Life expectancy at birth, Both sexes
|Life expectancy at birth, both sexes (years)
|0
|
|-
|MedAgeUNPD
|Median Age, as of 1 July (years)
|Median Age, as of 1 July (years)
|0
|
|-
|NetMigrationRateUNPD
|Net Migration Rate (per 1,000 population) - Estimates
|Net Migration Rate (per 1,000 population)
|0
|
|-
|NetMigrationUNPD
|Net Number of Migrants (thousands) - Estimates
|Net Number of Migrants (thousands)
|0
|
|-
|Pop15PlusUNPD
|Population ages 15 and older in millions
- Estimates
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|Pop15to29UNPD
|Population aged 15 to 29 years in millions
- Estimates
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|Pop15to64%UNPD
|Percentage of population aged 15 up to 65 years relative to total population
- Estimates
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|Pop15to64UNPD
|Population aged 15 to 64 years in millions
- Estimates
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|Pop65Plus%UNPD
|Percentage of population aged 65 years or more relative to total population
- Estimates
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|Pop65PlusUNPD
|Population ages 65 and older in millions
- Estimates
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|PopFemaleUNPD
|Female Population, as of 1 July (thousands) - Estimates
|Female Population, as of 1 July (thousands)
|0
|
|-
|PopMaleUNPD
|Male Population, as of 1 July (thousands) - Estimates
|Male Population, as of 1 July (thousands)
|0
|
|-
|Population
|Total Population, as of 1 July (thousands) - Estimates
|Total Population, as of 1 July (thousands)
|1
|ECONOMY, HEALTH, INFRASTRUCTURE, POPULATION
|-
|Population15PlusMill
|Population ages 15 and older in millions
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|Population15to29Mill
|Population aged 15 to 29 years in millions
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|Population15to64%
|Percentage of population aged 15 up to 65 years relative to total population
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|Population15to64Mill
|Population aged 15 to 64 years in millions
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|Population65Plus
|Ratio of population aged 65 years or more relative to total population
|Annual population by age groups (both sexes)
|0
|
|-
|Population65Plus%
|Percentage of population aged 65 years or more relative to total population
|Annual population by age groups (both sexes)
|0
|
|-
|Population65PlusMill
|Population ages 65 and older in millions
|Annual population by age groups (both sexes) - 65+
|1
|SOCIOPOL
|-
|PopulationElderlyDependence%
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|PopulationMedian
|Median Age, as of 1 July (years)
|Median Age, as of 1 July (years)
|0
|
|-
|PopulationUnder15Mill
|Population under the age 15 in millions
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|PopulationUnder5Mill
|Population under 5 years of age in millions
|Annual population by age groups (both sexes) - 0-5
|0
|
|-
|PopulationYouthDep%
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates
|Annual child dep. ratio [0-14 / 15-64] (%)
|1
|POPULATION
|-
|PopUnder15%UNPD
|Percentage of population under the age of 15 relative to the total population
- Estimates
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|PopUnder15UNPD
|Population under the age 15 in millions
- Estimates
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|PopUnder5UNPD
|Population under 5 years of age in millions
- Estimates
|Annual population by age groups (both sexes) - 0-4
|0
|
|-
|PopUNPD
|Total Population, as of 1 July (thousands) - Estimates
|Total Population, as of 1 July (thousands)
|0
|
|-
|PopYouthBulgeBy15
|Youth bulge calculated with population ages 15-29 over population ages 15 up
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|1
|SOCIOPOL
|-
|PopYouthBulgeBy30
|Youth bulge calculated with population ages 15-29 over population ages 30 up
|Annual population by age groups (both sexes)
|0
|
|-
|PopYouthBulgeByTotal
|Youth bulge calculated with population ages 15-29 over the total population
|Annual population by age groups (both sexes)
|0
|
|-
|PopYouthDependency%
|Percentage of population under the age of 15 relative to the total population
|Annual population by age groups (both sexes)
|0
|
|-
|TFR
|Total Fertility Rate (live births per woman) - Estimates
|Total Fertility Rate (live births per woman)
|1
|POPULATION
|-
|TFRMedAnnUNDP
|Annualized data of total fertility rate from UNDP
|Fertility Rate by country annually
|0
|
|-
|TFRMedUNPD
|Medium-fertility variant total fertility rate by country, every five years 1955-2020 (births)
|Fertility Rate by country, every five years
|1
|POPULATION
|-
|TFRUNPD
|Total Fertility Rate (live births per woman) - Estimates
|Total Fertility Rate (live births per woman)
|0
|
|-
|Under5MortUNPD
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|Under5MortUNPDWPP
|Probability of dying between birth and exact age of 5
|Under 5 mortality; estimates 1950-2015
|0
|
|-
|YouthBulgeBy15UNPD
|Youth bulge calculated with population ages 15-29 over population ages 15 up
- Estimates
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|0
|
|-
|YouthBulgeBy30UNPD
|Youth bulge calculated with population ages 15-29 over population ages 30 up
- Estimates
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|0
|
|-
|YouthBulgeByTotalUNPD
|Youth bulge calculated with population ages 15-29 over the total population
- Estimates
|Annual population by age groups (both sexes) - sum(15-29)/total
|0
|
|-
|YouthDep%UNPD
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastCDRConstFertilityUNPD2017Rev
|Forecast of Crude Death Rates using Constant Fertility Variant
|Crude death rate
|0
|
|-
|ForecastCDRConstMortUNPD2017Rev
|Forecasts of Death Rates from UNPD using Constant Mortality Variant
|Crude Death Rate
|0
|
|-
|ForecastCDRHighVariantUNPD2017Rev
|Forecasts of Crude death rates using High Variant
|Crude Death Rate
|0
|
|-
|ForecastCDRLowVariantUNPD2017Rev
|Forecasts of death rates from UNPD using Low variant
|Crude Death Rate
|0
|
|-
|ForecastCDRMediumVariantUNPD2017Rev
|Forecasts of death rates from UNPD using 2017 Revision
|Crude Death Rate
|0
|
|-
|ForecastCDRMomentumUNPD2017Rev
|Forecasts of death rates from UNPD using Momentum Variant
|Crude Death Rate
|0
|
|-
|ForecastCDRNochangeUNPD2017Rev
|Forecasts of death rates using No change Variant 2017 Rev
|Crude Death Rate
|0
|
|-
|ForecastConstFertilityUNPD2017Rev
|Total fertility rate, constant-fertility variant, annualized version of UNPD 2017 data and forecast
|Total Fertility rate, constant-fertility variant
|0
|
|-
|ForecastDependencyRatioConstantFertilityUNPD2017Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioConstantMortalityUNPD2017Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioHighUNPD2017Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioInstantReplacementUNPD2017Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioLowUNPD2017Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioMediumUNPD2017Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioMomentumUNPD2017Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioNoChangeUNPD2017Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioZeroMigrationUNPD2017Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastFertilityConstMortalityUNPD2017Rev
|Total fertility rate, constant-mortality variant, annualized version of UNPD 2017 data and forecast
|Total Fertility rate, constant-mortality variant
|0
|
|-
|ForecastFertilityConstMortalityUNPD2019Rev
|Total fertility rate, constant-mortality variant, annualized version of UNPD 2019 data and forecast
|Total Fertility rate, constant-mortality variant
|0
|
|-
|ForecastFertilityHighUNPD2017Rev
|Total fertility rate, high-fertility variant, annualized version of UNPD 2017 data and forecast
|Total Fertility rate, high-fertility variant
|0
|
|-
|ForecastFertilityLowUNPD2017Rev
|Total fertility rate, low-fertility variant, annualized version of UNPD 2017 data and forecast
|Total Fertility rate, low-fertility variant
|0
|
|-
|ForecastFertilityMedUNPD2017Rev
|Total fertility rate, medium-fertility variant, annualized version of UNPD 2017 data and forecast
|Total Fertility rate, medium-fertility variant
|0
|
|-
|ForecastFertilityNoChangeUNPD2017Rev
|Total fertility rate, no change variant, annualized version of UNPD 2017 data and forecast
|Total Fertility rate, no change variant
|0
|
|-
|ForecastFertilityReplacementUNPD2017Rev
|Total fertility rate, instant replacement variant, annualized version of UNPD 2017 data and forecast
|Total Fertility rate, instant replacement variant
|0
|
|-
|ForecastFertilityZeroMigrationUNPD2017Rev
|Total fertility rate, zero migration variant, annualized version of UNPD 2017 data and forecast
|Total Fertility rate, zero-migration variant
|0
|
|-
|ForecastInfantMortalityMediumUNPD2017Rev
|Infant mortality rate for both sexes combined (infant deaths per 1,000 live births), annualized version of Medium Variant of UNPD 2017
|Infant Mortality Rate - Medium
|0
|
|-
|ForecastLifeExpMedBothSexesUNPD2017Rev
|Life expectancy at birth, medium variant, both sexes. Annualized version of UNPD 2017 data and forecast
|Life expectancy at birth, both sexes; Historical data and medium variant
|0
|
|-
|ForecastLifeExpMedFemaleUNPD2017Rev
|Life expectancy at birth, medium variant, female. Annualized version of UNPD 2017 data and forecast
|Life expectancy at birth, female; Historical data and medium variant
|0
|
|-
|ForecastLifeExpMedMaleUNPD2017Rev
|Life expectancy at birth, medium variant, male. Annualized version of UNPD 2017 data and forecast
|Life Expectancy at Birth, Male; Historical data and medium variant
|0
|
|-
|ForecastMigrationUNPD2017Rev
|Migrants from UNPD in millions
|
|0
|
|-
|ForecastOldageDependencyRatioConstFertUNPD2017Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2017 Constant Fertility forecast
|Old age dependency ratio - Constant Fertility
|0
|
|-
|ForecastOldageDependencyRatioConstMortUNPD2017Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2017 Constant Mortality forecast
|Old age dependency ratio - Constant Mortality
|0
|
|-
|ForecastOldageDependencyRatioHighUNPD2017Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2017 High Variant forecast
|Old age dependency ratio - High Variant
|0
|
|-
|ForecastOldageDependencyRatioInstRepUNPD2017Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2017 Instant Replacement forecast
|Old age dependency ratio - Instant Replacement
|0
|
|-
|ForecastOldageDependencyRatioLowUNPD2017Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2017 Low Variant forecast
|Old age dependency ratio - Low Variant
|0
|
|-
|ForecastOldageDependencyRatioMediumUNPD2017Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2017 Medium Variant forecast
|Old age dependency ratio - Medium Variant
|0
|
|-
|ForecastOldageDependencyRatioNoChangeUNPD2017Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2017 No Change forecast
|Old age dependency ratio - No Change
|0
|
|-
|ForecastOldageDependencyRatioZeroMigrationUNPD2017Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2017 Zero Migration forecast
|Old age dependency ratio - Zero Migration
|0
|
|-
|ForecastPopBothSexesConstFertilityUNPD2017Rev
|Total Population Forecasts, UNPD WPP 2017 , Constant Fertility Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopBothSexesConstMortalityUNPD2017Rev
|Total Population Forecasts, UNPD WPP 2017, Constant Mortality Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopBothSexesInstantRepUNPD2017Rev
|Total Population Forecasts, UNPD WPP 2017, Instant Replacement Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopBothSexesZeroMigrationUNPD2017Rev
|Total Population Forecasts, UNPD WPP 2017, Zero Migration Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopulationBothSexesHighUNPD2017Rev
|Total Population Forecasts, UNPD WPP 2017, High Variant Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopulationBothSexesLowUNPD2017Rev
|Total Population Forecasts, UNPD WPP 2017, Low Variant Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopulationBothSexesMediumUNPD2017Rev
|Total Population Forecasts, UNPD WPP 2017, Medium Variant Forecast
|Population (Both Sexes)
|0
|
|-
|ForecastPopulationBothSexesNoChangeUNPD2017Rev
|Total Population Forecasts, UNPD WPP 2017, No change forecast
|Population (both sexes)
|0
|
|-
|ForecastYouthDependencyConstantFertilityUNPD2017Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2017 Constant Fertility Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyConstantMortalityUNPD2017Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2017 Zero Migration Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyHighUNPD2017Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2017 High Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyInstantReplacementUNPD2017Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2017 Instant Replacement Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyLowUNPD2017Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2017 Low Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyMediumUNPD2017Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2017 Medium Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyNoChangeUNPD2017Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2017 Zero Migration Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyZeroMigrationUNPD2017Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2017 Zero Migration Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastCDRConstFertilityUNPD2019Rev
|Forecast of Crude Death Rates using Constant Fertility Variant
|Crude death rate
|0
|
|-
|ForecastCDRConstMortUNPD2019Rev
|Forecasts of Death Rates from UNPD using Constant Mortality Variant
|Crude Death Rate
|0
|
|-
|ForecastCDRHighVariantUNPD2019Rev
|Forecasts of Crude death rates using High Variant
|Crude Death Rate
|0
|
|-
|ForecastCDRLowVariantUNPD2019Rev
|Forecasts of death rates from UNPD using Low variant
|Crude Death Rate
|0
|
|-
|ForecastCDRMediumVariantUNPD2019Rev
|Forecasts of death rates from UNPD medium variant
|Crude Death Rate
|0
|
|-
|ForecastCDRMomentumUNPD2019Rev
|Forecasts of death rates from UNPD using Momentum Variant
|Crude Death Rate
|0
|
|-
|ForecastCDRNochangeUNPD2019Rev
|Forecasts of death rates using No change Variant
|Crude Death Rate
|0
|
|-
|ForecastConstFertilityUNPD2019Rev
|Total fertility rate, constant-fertility variant, annualized version of UNPD 2019 data and forecast
|Total Fertility rate, constant-fertility variant
|0
|
|-
|ForecastDependencyRatioConstantFertilityUNPD2019Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioHighUNPD2019Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioInstantReplacementUNPD2019Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioLowUNPD2019Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioMediumUNPD2019Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioMomentumUNPD2019Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioNoChangeUNPD2019Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioZeroMigrationUNPD2019Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastFertilityHighUNPD2019Rev
|Total fertility rate, high-fertility variant, annualized version of UNPD 2019 data and forecast
|Total Fertility rate, high-fertility variant
|0
|
|-
|ForecastFertilityLowUNPD2019Rev
|Total fertility rate, low-fertility variant, annualized version of UNPD 2019 data and forecast
|Total Fertility rate, low-fertility variant
|0
|
|-
|ForecastFertilityMedUNPD2019Rev
|Total fertility rate, medium-fertility variant, annualized version of UNPD 2019 data and forecast
|Total Fertility rate, medium-fertility variant
|0
|
|-
|ForecastFertilityNoChangeUNPD2019Rev
|Total fertility rate, no change variant, annualized version of UNPD 2019 data and forecast
|Total Fertility rate, no change variant
|0
|
|-
|ForecastFertilityReplacementUNPD2019Rev
|Total fertility rate, instant replacement variant, annualized version of UNPD 2019 data and forecast
|Total Fertility rate, instant replacement variant
|0
|
|-
|ForecastFertilityZeroMigrationUNPD2019Rev
|Total fertility rate, zero migration variant, annualized version of UNPD 2019 data and forecast
|Total Fertility rate, zero-migration variant
|0
|
|-
|ForecastLifeExpMedBothSexesUNPD2019Rev
|Life expectancy at birth, medium variant, both sexes. Annualized version of UNPD 2017 data and forecast
|Life expectancy at birth, both sexes; Historical data and medium variant
|0
|
|-
|ForecastLifeExpMedFemaleUNPD2019Rev
|Life expectancy at birth, medium variant, female. Annualized version of UNPD 2017 data and forecast
|Life expectancy at birth, female; Historical data and medium variant
|0
|
|-
|ForecastLifeExpMedMaleUNPD2019Rev
|Life expectancy at birth, medium variant, male. Annualized version of UNPD 2017 data and forecast
|Life Expectancy at Birth, Male; Historical data and medium variant
|0
|
|-
|ForecastMigrationUNPD2019Rev
|Migrants from UNPD in millions
|Net number of migrants
|0
|
|-
|ForecastNetMigrationMedUNPD2019RevAnn
|Net migration headcounts by country, medium-fertility variant, every five years 1955-2100 (annualized using IFs spread function)
|MIGR_F02_NET_NUMBER_OF_MIGRANTS
|0
|
|-
|ForecastNetMigrationRateMedUNPD2019Rev
|Net migration rates by country, medium-fertility variant, every five years 1955-2100
|MIGR_F01_NET_MIGRATION_RATE
|0
|
|-
|ForecastNetMigrationRateMedUNPD2019RevAnn
|Net migration rates by country, medium-fertility variant, every five years 1955-2100 (annualized using IFs interpolate function)
|MIGR_F01_NET_MIGRATION_RATE
|0
|
|-
|ForecastNetMigrationUNPD
|Net migration headcounts by country, medium-fertility variant, every five years 1955-2100 (annualized using IFs spread function)
|MIGR_F02_NET_NUMBER_OF_MIGRANTS
|0
|
|-
|ForecastOldageDependencyRatioConstFertUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 Constant Fertility forecast
|Old age dependency ratio - Constant Fertility
|0
|
|-
|ForecastOldageDependencyRatioConstMortUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 Constant Mortality forecast
|Old age dependency ratio - Constant Mortality
|0
|
|-
|ForecastOldageDependencyRatioHighUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 High Variant forecast
|Old age dependency ratio - High Variant
|0
|
|-
|ForecastOldageDependencyRatioInstRepUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 Instant Replacement forecast
|Old age dependency ratio - Instant Replacement
|0
|
|-
|ForecastOldageDependencyRatioLowUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 Low Variant forecast
|Old age dependency ratio - Low Variant
|0
|
|-
|ForecastOldageDependencyRatioMediumUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 Medium Variant forecast
|Old age dependency ratio - Medium Variant
|0
|
|-
|ForecastOldageDependencyRatioNoChangeUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 No Change forecast
|Old age dependency ratio - No Change
|0
|
|-
|ForecastOldageDependencyRatioZeroMigrationUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 Zero Migration forecast
|Old age dependency ratio - Zero Migration
|0
|
|-
|ForecastPopBothSexesConstFertilityUNPD2019Rev
|Total Population Forecasts, UNPD WPP 2019 , Constant Fertility Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopBothSexesConstMortalityUNPD2019Rev
|Total Population Forecasts, UNPD WPP 2019, Constant Mortality Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopBothSexesInstantRepUNPD2019Rev
|Total Population Forecasts, UNPD WPP 2019, Instant Replacement Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopBothSexesZeroMigrationUNPD2019Rev
|Total Population Forecasts, UNPD WPP 2019, Zero Migration Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopulationBothSexesHighUNPD2019Rev
|Total Population Forecasts, UNPD WPP 2019, High Variant Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopulationBothSexesLowUNPD2019Rev
|Total Population Forecasts, UNPD WPP 2019, Low Variant Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopulationBothSexesMediumUNPD2019Rev
|Total Population Forecasts, UNPD WPP 2019, Medium Variant Forecast
|Population (Both Sexes)
|0
|
|-
|ForecastPopulationBothSexesNoChangeUNPD2019Rev
|Total Population Forecasts, UNPD WPP 2019, No change forecast
|Population (both sexes)
|0
|
|-
|ForecastYouthDependencyConstantFertilityUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Constant Fertility Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyConstantMortalityUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Zero Migration Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyHighUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 High Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyInstantReplacementUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Instant Replacement Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyLowUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Low Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyMediumUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Medium Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyNoChangeUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Zero Migration Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyZeroMigrationUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Zero Migration Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastNetMigrationRateUNPD
|Net migration rates by country, medium-fertility variant, yearly data from SeriesForecastNetMigrationRateMedUNPD, unit converted to per 100 instead of the original per 1,000
|MIGR_F01_NET_MIGRATION_RATE
|0
|
|-
|ForecastDependencyRatioOld0MigrationUNPD2015Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2015 Zero Migration forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldConstFertUNPD2015Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2015 Constant Fertility forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldConstMortalityUNPD2015Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2015 Constant Mortality forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldHighUNPD2015Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2015 High Variant forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldLowUNPD2015Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2015 Low Variant forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldMedUNPD2015Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2015 Medium Variant forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldNoChangeUNPD2015Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2015 No Change forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldReplacementUNPD2015Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2015 Instant Replacement forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastYouthDependencyRatio0MigrationUNPD2015Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2015 Zero Migration forecast
|Child Dependency Ratio 1 - WPP2015_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioConstFertUNPD2015Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2015 Constant Fertility forecast
|Child Dependency Ratio 1 - WPP2015_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioConstMortalityUNPD2015Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2015 Constant Mortality forecast
|Child Dependency Ratio 1 - WPP2015_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioHighUNPD2015Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2015 High Variant forecast
|Child Dependency Ratio 1 - WPP2015_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioLowUNPD2015Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2015 Low Variant forecast
|Child Dependency Ratio 1 - WPP2015_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioMedUNPD2015Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2015 Medium Variant forecast
|Child Dependency Ratio 1 - WPP2015_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioNoChangeUNPD2015Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2015 No Change forecast
|Child Dependency Ratio 1 - WPP2015_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioNoChangeUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 No Change forecast
|Child Dependency Ratio 1 - WPP2019_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioReplacementUNPD2015Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2015 Instant Replacement forecast
|Child Dependency Ratio 1 - WPP2015_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastDependencyRatioOld0MigrationUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2015 Zero Migration forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldConstFertUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2015 Constant Fertility forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldConstMortalityUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 Constant Mortality forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldHighUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 High Variant forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2019_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldLowUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 Low Variant forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2019_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldNoChangeUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 No Change forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2019_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldReplacementUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 Instant Replacement forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2019_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastYouthDependencyRatio0MigrationUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Zero Migration forecast
|Child Dependency Ratio 1 - WPP2019_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioConstFertUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Constant Fertility forecast
|Child Dependency Ratio 1 - WPP2019_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioConstMortalityUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Constant Mortality forecast
|Child Dependency Ratio 1 - WPP2019_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioHighUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 High Variant forecast
|Child Dependency Ratio 1 - WPP2019_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioLowUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Low Variant forecast
|Child Dependency Ratio 1 - WPP2019_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioMedUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Medium Variant forecast
|Child Dependency Ratio 1 - WPP2019_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioReplacementUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Instant Replacement forecast
|Child Dependency Ratio 1 - WPP2019_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastBirthsUNPDMed
|Births (in thousands), from UN World Population Prospects 2019, values include historical estimation and medium variant projection. Kosovo values from WDI.
|Annual Demographic Indicators-> Births (thousands)
|0
|
|-
|ForecastCBRUNPDMed
|Crude birth rate, from UN World Population Prospects 2019, values include historical estimation and medium variant projection. Kosovo values from WDI.
|Annual Demographic Indicators-> Crude birth rate (births per 1,000 population)
|0
|
|-
|ForecastCDRUNPDMed
|Crude death rate, from UN World Population Prospects 2019, values include historical estimation and medium variant projection. Kosovo values from WDI.
|Annual Demographic Indicators-> Crude death rate (deaths per 1,000 population)
|0
|
|-
|ForecastPopulationUNPDMed
|Total population (in millions), from UN World Population Prospects 2019, values include historical estimation and medium variant projection. Kosovo values from WDI.
|Annual Population Indicators - Both Sexes-> Population, Total
|0
|
|-
|ForecastTFRUNPDMed
|Total fertility rate (births per woman), from UN World Population Prospects 2019, values include historical estimation and medium variant projection. Kosovo values from WDI.
|Annual Demographic Indicators-> Total fertility (live births per woman)
|0
|
|}

= Data Pulling Instructions =
Use the table below for filtering and gathering data. [https://github.com/shangkexin/United_Nations_Population_Division_UNPD?tab=readme-ov-file There is a python script if you would like to use it]
{| class="wikitable"
|Variable
|File
|Variant/Tab
|Column
|Original Source
|Units
|-
|ForecastAgeMedianConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|MedianAgePop
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|Years
|-
|ForecastBirthsConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|Births
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|Thousands
|-
|ForecastCBRConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|CBR
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|per 1000
|-
|ForecastCDRConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|CDR
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|per 1000
|-
|ForecastInfMortConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|IMR
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|per 1000
|-
|ForecastLifeExpBothSexesConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|LEx
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|Years
|-
|ForecastLifeExpFemaleConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|LExFemale
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|Years
|-
|ForecastLifeExpMaleConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|LExMale
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|Years
|-
|ForecastNetMigrationConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|NetMigrations
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|Millions
|-
|ForecastNetMigrationRateConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|CNMR
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|per 1000
|-
|ForecastPopConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|TPopulation1July
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|Millions
|-
|ForecastPopFemaleConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|TPopulationFemale1July
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|Millions
|-
|ForecastPopMaleConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|TPopulationMale1July
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|Millions
|-
|ForecastTFRConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|TFR
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|Births
|-
|ForecastUnder5MortConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|Under5Deaths
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|per 1000
|-
|ForecastAgeMedianConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Median Age, as of 1 July (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastBirthsConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Births (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Thousands
|-
|ForecastCBRConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Crude Birth Rate (births per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastCDRConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Crude Death Rate (deaths per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastInfMortConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Infant Mortality Rate (infant deaths per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastLifeExpBothSexesConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Life Expectancy at Birth, both sexes (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpFemaleConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Female Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpMaleConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Male Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastNetMigrationConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Net Number of Migrants (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastNetMigrationRateConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Net Migration Rate (per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastPopConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Total Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopFemaleConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Female Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopMaleConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Male Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastTFRConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Total Fertility Rate (live births per woman)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Births
|-
|ForecastUnder5MortConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|AgeMedianUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Median Age, as of 1 July (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|BirthsUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Births (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Thousands
|-
|CBR
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Crude Birth Rate (births per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|CDR
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Crude Death Rate (deaths per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|InfMortUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Infant Mortality Rate (infant deaths per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|LifeExpBothSexesUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Life Expectancy at Birth, both sexes (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|LifeExpFemaleUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Female Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|LifeExpMaleUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Male Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|NetMigrationRateUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Net Migration Rate (per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|NetMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Net Number of Migrants (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|PopFemaleUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Female Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|PopMaleUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Male Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|Population
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Total Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|TFR
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Total Fertility Rate (live births per woman)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Births
|-
|Under5MortUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastAgeMedianHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Median Age, as of 1 July (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastBirthsHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Births (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Thousands
|-
|ForecastCBRHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Crude Birth Rate (births per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastCDRHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Crude Death Rate (deaths per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastInfMortHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Infant Mortality Rate (infant deaths per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastLifeExpBothSexesHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Life Expectancy at Birth, both sexes (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpFemaleHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Female Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpMaleHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Male Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastNetMigrationHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Net Number of Migrants (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastNetMigrationRateHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Net Migration Rate (per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastPopFemaleHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Female Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Total Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopMaleHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Male Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastTFRHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Total Fertility Rate (live births per woman)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Births
|-
|ForecastUnder5MortHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastAgeMedianReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Median Age, as of 1 July (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastBirthsReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Births (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Thousands
|-
|ForecastCBRReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Crude Birth Rate (births per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastCDRReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Crude Death Rate (deaths per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastInfMortReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Infant Mortality Rate (infant deaths per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastLifeExpBothSexesReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Life Expectancy at Birth, both sexes (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpFemaleReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Female Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpMaleReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Male Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastNetMigrationRateReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Net Migration Rate (per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastNetMigrationReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Net Number of Migrants (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopFemaleReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Female Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopMaleReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Male Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Total Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastTFRReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Total Fertility Rate (live births per woman)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Births
|-
|ForecastUnder5MortReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastAgeMedianLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Median Age, as of 1 July (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastBirthsLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Births (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Thousands
|-
|ForecastCBRLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Crude Birth Rate (births per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastCDRLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Crude Death Rate (deaths per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastInfMortLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Infant Mortality Rate (infant deaths per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastLifeExpBothSexesLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Life Expectancy at Birth, both sexes (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpFemaleLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Female Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpMaleLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Male Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastNetMigrationLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Net Number of Migrants (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastNetMigrationRateLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Net Migration Rate (per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastPopFemaleLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Female Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Total Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopMaleLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Male Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastTFRLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Total Fertility Rate (live births per woman)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Births
|-
|ForecastUnder5MortLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastAgeMedianMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Median Age, as of 1 July (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastBirthsMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Births (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Thousands
|-
|ForecastCBRMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Crude Birth Rate (births per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastCDRMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Crude Death Rate (deaths per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastInfMortMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Infant Mortality Rate (infant deaths per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastLifeExpMedBothSexesUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Life Expectancy at Birth, both sexes (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpMedFemaleUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Female Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpMedMaleUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Male Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastNetMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Net Number of Migrants (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastNetMigrationRateMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Net Migration Rate (per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastPopFemaleMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Female Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopMaleMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Male Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Total Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastTFRMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Total Fertility Rate (live births per woman)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Births
|-
|ForecastUnder5MortMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastAgeMedianMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Median Age, as of 1 July (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastBirthsMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Births (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Thousands
|-
|ForecastCBRMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Crude Birth Rate (births per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastCDRMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Crude Death Rate (deaths per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastInfMortMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Infant Mortality Rate (infant deaths per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastLifeExpBothSexesMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Life Expectancy at Birth, both sexes (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpFemaleMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Female Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpMaleMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Male Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastNetMigrationMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Net Number of Migrants (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastNetMigrationRateMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Net Migration Rate (per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastPopFemaleMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Female Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopMaleMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Male Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Total Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastTFRMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Total Fertility Rate (live births per woman)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Births
|-
|ForecastUnder5MortMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastAgeMedianNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Median Age, as of 1 July (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastBirthsNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Births (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Thousands
|-
|ForecastCBRNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Crude Birth Rate (births per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastCDRNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Crude Death Rate (deaths per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastInfMortNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Infant Mortality Rate (infant deaths per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastLifeExpBothSexesNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Life Expectancy at Birth, both sexes (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpFemaleNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Female Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpMaleNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Male Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastNetMigrationNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Net Number of Migrants (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastNetMigrationRateNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Net Migration Rate (per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastPopFemaleNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Female Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopMaleNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Male Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Total Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastTFRNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Total Fertility Rate (live births per woman)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Births
|-
|ForecastUnder5MortNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastAgeMedianZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Median Age, as of 1 July (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastBirthsZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Births (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Thousands
|-
|ForecastCBRZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Crude Birth Rate (births per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastCDRZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Crude Death Rate (deaths per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastInfMortZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Infant Mortality Rate (infant deaths per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastLifeExpBothSexesZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Life Expectancy at Birth, both sexes (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpFemaleZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Female Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpMaleZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Male Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastNetMigrationRateZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Net Migration Rate (per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastNetMigrationZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Net Number of Migrants (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopFemaleZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Female Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopMaleZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Male Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Total Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastTFRZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Total Fertility Rate (live births per woman)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Births
|-
|ForecastUnder5MortZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastPop15to29ConstFertilityUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|Annual population by age groups (both sexes) - sum(15-29)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastYouthBulgeBy15ConstFertilityUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeBy30ConstFertilityUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeByTotalConstFertilityUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|Annual population by age groups (both sexes) - sum(15-29)/total
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to29ConstMortUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|Annual population by age groups (both sexes) - sum(15-29)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastYouthBulgeBy15ConstMortUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeBy30ConstMortUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeByTotalConstMortUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|Annual population by age groups (both sexes) - sum(15-29)/total
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|Pop15to29UNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|Annual population by age groups (both sexes) - sum(15-29)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|YouthBulgeBy15UNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|YouthBulgeBy30UNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|YouthBulgeByTotalUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|Annual population by age groups (both sexes) - sum(15-29)/total
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to29HighUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|Annual population by age groups (both sexes) - sum(15-29)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastYouthBulgeBy15HighUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeBy30HighUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeByTotalHighUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|Annual population by age groups (both sexes) - sum(15-29)/total
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to29ReplacementUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|Annual population by age groups (both sexes) - sum(15-29)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastYouthBulgeBy15ReplacementUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeBy30ReplacementUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeByTotalReplacementUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|Annual population by age groups (both sexes) - sum(15-29)/total
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to29LowUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|Annual population by age groups (both sexes) - sum(15-29)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastYouthBulgeBy15LowUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeBy30LowUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeByTotalLowUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|Annual population by age groups (both sexes) - sum(15-29)/total
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to29MedUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|Annual population by age groups (both sexes) - sum(15-29)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastYouthBulgeBy15MedUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeBy30MedUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeByTotalMedUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|Annual population by age groups (both sexes) - sum(15-29)/total
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to29MomentumUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|Annual population by age groups (both sexes) - sum(15-29)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastYouthBulgeBy15MomentumUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeBy30MomentumUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeByTotalMomentumUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|Annual population by age groups (both sexes) - sum(15-29)/total
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to29NoChangeUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|Annual population by age groups (both sexes) - sum(15-29)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastYouthBulgeBy15NoChangeUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeBy30NoChangeUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeByTotalNoChangeUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|Annual population by age groups (both sexes) - sum(15-29)/total
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to29ZeroMigrationUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|Annual population by age groups (both sexes) - sum(15-29)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastYouthBulgeBy15ZeroMigrationUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeBy30ZeroMigrationUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeByTotalZeroMigrationUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|Annual population by age groups (both sexes) - sum(15-29)/total
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15PlusConstFertilityUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|15+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15to64ConstFertilityUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop65PlusConstFertilityUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder15ConstFertilityUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder5ConstFertilityUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|0-4
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15PlusConstMortUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|15+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15to64ConstMortUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop65PlusConstMortUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder15ConstMortUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder5ConstMortUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|0-4
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|Pop15PlusUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|15+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|Pop15to64UNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|Pop65PlusUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|PopUnder15UNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|PopUnder5UNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|0-4
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15PlusHighUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|15+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15to64HighUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop65PlusHighUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder15HighUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder5HighUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|0-4
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15PlusReplacementUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|15+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15to64ReplacementUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop65PlusReplacementUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder15ReplacementUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder5ReplacementUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|0-4
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15PlusLowUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|15+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15to64LowUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop65PlusLowUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder15LowUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder5LowUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|0-4
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15PlusMedUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|15+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15to64MedUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop65PlusMedUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder15MedUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder5MedUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|0-4
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15PlusMomentumUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|15+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15to64MomentumUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop65PlusMomentumUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder15MomentumUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder5MomentumUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|0-4
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15PlusNoChangeUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|15+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15to64NoChangeUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop65PlusNoChangeUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder15NoChangeUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder5NoChangeUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|0-4
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15PlusZeroMigrationUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|15+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15to64ZeroMigrationUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop65PlusZeroMigrationUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder15ZeroMigrationUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder5ZeroMigrationUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|0-4
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15to64%ConstFertilityUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop65Plus%ConstFertilityUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPopUnder15%ConstFertilityUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to64%ConstMortUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop65Plus%ConstMortUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPopUnder15%ConstMortUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|Pop15to64%UNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|Pop65Plus%UNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|PopUnder15%UNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to64%HighUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop65Plus%HighUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPopUnder15%HighUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to64%ReplacementUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop65Plus%ReplacementUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPopUnder15%ReplacementUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to64%LowUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop65Plus%LowUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPopUnder15%LowUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to64%MedUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop65Plus%MedUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPopUnder15%MedUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to64%MomentumUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop65Plus%MomentumUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPopUnder15%MomentumUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to64%NoChangeUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop65Plus%NoChangeUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPopUnder15%NoChangeUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to64%ZeroMigrationUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop65Plus%ZeroMigrationUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPopUnder15%ZeroMigrationUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastElderDep%ConstFertilityUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Constant-fertility
|Annual old-age dep. ratio [65+ / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastYouthDep%ConstFertilityUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Constant-fertility
|Annual child dep. ratio [0-14 / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastElderDep%ConstMortUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Constant-mortality
|Annual old-age dep. ratio [65+ / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastYouthDep%ConstMortUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Constant-mortality
|Annual child dep. ratio [0-14 / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ElderDep%UNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Estimates
|Annual old-age dep. ratio [65+ / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|YouthDep%UNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Estimates
|Annual child dep. ratio [0-14 / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastElderDep%HighUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|High variant
|Annual old-age dep. ratio [65+ / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastYouthDep%HighUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|High variant
|Annual child dep. ratio [0-14 / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastElderDep%ReplacementUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Instant-replacement
|Annual old-age dep. ratio [65+ / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastYouthDep%ReplacementUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Instant-replacement
|Annual child dep. ratio [0-14 / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastElderDep%LowUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Low variant
|Annual old-age dep. ratio [65+ / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastYouthDep%LowUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Low variant
|Annual child dep. ratio [0-14 / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastElderDep%MedUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Medium variant
|Annual old-age dep. ratio [65+ / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastYouthDep%MedUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Medium variant
|Annual child dep. ratio [0-14 / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastElderDep%MomentumUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Momentum
|Annual old-age dep. ratio [65+ / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastYouthDep%MomentumUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Momentum
|Annual child dep. ratio [0-14 / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastElderDep%NoChangeUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|No change
|Annual old-age dep. ratio [65+ / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastYouthDep%NoChangeUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|No change
|Annual child dep. ratio [0-14 / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastElderDep%ZeroMigrationUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Zero-migration
|Annual old-age dep. ratio [65+ / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastYouthDep%ZeroMigrationUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Zero-migration
|Annual child dep. ratio [0-14 / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|}
# There are 290 series in total. 4 preprocessors: CBR, CDR, TFR, and Population.

# WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx does not contain "Constant-mortality" variant/tab. Thus, we use the CSV file to import the series.
# Besides "Estimates", we have 9 variants: Medium, High, Low, Constant-fertility, Constant-mortality, Instant-replacement, Momentum, No change and Zero-migration. You may check the definition [https://population.un.org/wpp/DefinitionOfProjectionScenarios/ here].[[File:Image UNPD 1.png|none|thumb|569x569px]]
# Series from WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx needs extra calculation. The rest of the series can be imported directly according to the "Column" for each file by variant/tab listed above.
# Please keep in mind, "Estimates" value needs to be combined with each variant/tab.
# The python scripts for data pulling can be accessed [https://github.com/shangkexin/United_Nations_Population_Division_UNPD here].

= Data Notes =
Besides "Estimate", all the data with variant contains data from 1950-2100, 151 years in total. There are 9 variants. Thus, there are 261 tables (29*9) containing data for 151 years and no missing values.

Thus, we found that the processing time took way longer than we expected.

To save the time, we have recommend that removing half of the tables at a time from IFsImport.db.

'''Naming convention for data tables needs follow the rules below:'''

# For forecast series, when a new update is done, tables from the previous versions need to be renamed with their version name, i.e., 2022Rev for Forecast series from WPP 2022 version.
# In 2024 update, we make a copy of the ForecastNetMigrationMedUNPD table and rename it to ForecastNetMigrationUNPD.

World Population Prospects (WPP), United Nations Population Division (UNPD)

2026-02-03T23:13:24Z

Yutang.Xiong: /* Life expectancy and TFR data */ delete old instruction for 5-year-interval data

= Summary =
The Population Division was established in the earlier years of the United Nations to serve as the Secretariat of the then Population Commission, created in 1946. Over the years, the Division has played an active role in the intergovernmental dialogue on population and development, producing constantly updated demographic estimates and projections for all countries, including data essential for the monitoring of the progress in achieving the Millennium Development Goals, developing and disseminating new methodologies, leading the substantive preparations for the United Nations major conferences on population and development as well as the annual sessions of the Commission on Population and Development.

= Tables In IFs =
{| class="wikitable"
|+
!Variable
!Definition
!Name In Source
!UsedInPreprocessor
!UsedInPreprocessorFileName
|-
|ForecastUnder5MortUNPDWPP
|Total Population Forecasts, UNPD WPP 2019, No change forecast
|Under 5 mortality; estimates & medium fertility variant
|0
|
|-
|AgeMedianUNPD
|Median Age, as of 1 July (years) - Estimates
|Median Age, as of 1 July (years)
|0
|
|-
|BirthsUNPD
|Births (thousands) - Estimates
|Births (thousands)
|0
|
|-
|CBR
|Crude Birth Rate (births per 1,000 population) - Estimates
|Crude Birth Rate (births per 1,000 population)
|1
|POPULATION
|-
|CBRUNPD
|Crude Birth Rate (births per 1,000 population) - Estimates
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|CDR
|Crude Death Rate (deaths per 1,000 population) - Estimates
|Crude Death Rate (deaths per 1,000 population)
|1
|POPULATION
|-
|CDRUNPD
|Crude Death Rate (deaths per 1,000 population) - Estimates
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ElderDep%UNPD
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastAgeMedianConstFertilityUNPD
|Median Age, as of 1 July (years) - Estimates & Constant-fertility
|Median Age, as of 1 July (years)
|0
|
|-
|ForecastAgeMedianConstMortUNPD
|Median Age, as of 1 July (years) - Estimates & Constant-mortality
|Median Age, as of 1 July (years)
|0
|
|-
|ForecastAgeMedianHighUNPD
|Median Age, as of 1 July (years) - Estimates & High variant
|Median Age, as of 1 July (years)
|0
|
|-
|ForecastAgeMedianLowUNPD
|Median Age, as of 1 July (years) - Estimates & Low variant
|Median Age, as of 1 July (years)
|0
|
|-
|ForecastAgeMedianMedUNPD
|Median Age, as of 1 July (years) - Estimates & Medium variant
|Median Age, as of 1 July (years)
|0
|
|-
|ForecastAgeMedianMomentumUNPD
|Median Age, as of 1 July (years) - Estimates & Momentum
|Median Age, as of 1 July (years)
|0
|
|-
|ForecastAgeMedianNoChangeUNPD
|Median Age, as of 1 July (years) - Estimates & No change
|Median Age, as of 1 July (years)
|0
|
|-
|ForecastAgeMedianReplacementUNPD
|Median Age, as of 1 July (years) - Estimates & Instant-replacement
|Median Age, as of 1 July (years)
|0
|
|-
|ForecastAgeMedianZeroMigrationUNPD
|Median Age, as of 1 July (years) - Estimates & Zero-migration
|Median Age, as of 1 July (years)
|0
|
|-
|ForecastBirthsConstFertilityUNPD
|Births (thousands) - Estimates & Constant-fertility
|Births (thousands)
|0
|
|-
|ForecastBirthsConstMortUNPD
|Births (thousands) - Estimates & Constant-mortality
|Births (thousands)
|0
|
|-
|ForecastBirthsHighUNPD
|Births (thousands) - Estimates & High variant
|Births (thousands)
|0
|
|-
|ForecastBirthsLowUNPD
|Births (thousands) - Estimates & Low variant
|Births (thousands)
|0
|
|-
|ForecastBirthsMedUNPD
|Births (thousands) - Estimates & Medium variant
|Births (thousands)
|0
|
|-
|ForecastBirthsMedUNPD2022Rev
|Births (thousands) - Estimates & Medium variant
|Births (thousands)
|0
|
|-
|ForecastBirthsMomentumUNPD
|Births (thousands) - Estimates & Momentum
|Births (thousands)
|0
|
|-
|ForecastBirthsNoChangeUNPD
|Births (thousands) - Estimates & No change
|Births (thousands)
|0
|
|-
|ForecastBirthsReplacementUNPD
|Births (thousands) - Estimates & Instant-replacement
|Births (thousands)
|0
|
|-
|ForecastBirthsZeroMigrationUNPD
|Births (thousands) - Estimates & Zero-migration
|Births (thousands)
|0
|
|-
|ForecastCBRConstFertilityUNPD
|Crude Birth Rate (births per 1,000 population) - Estimates & Constant-fertility
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|ForecastCBRConstMortUNPD
|Crude Birth Rate (births per 1,000 population) - Estimates & Constant-mortality
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|ForecastCBRHighUNPD
|Crude Birth Rate (births per 1,000 population) - Estimates & High variant
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|ForecastCBRLowUNPD
|Crude Birth Rate (births per 1,000 population) - Estimates & Low variant
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|ForecastCBRMedUNPD
|Crude Birth Rate (births per 1,000 population) - Estimates & Medium variant
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|ForecastCBRMedUNPD2022Rev
|Crude Birth Rate (births per 1,000 population) - Estimates & Medium variant
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|ForecastCBRMomentumUNPD
|Crude Birth Rate (births per 1,000 population) - Estimates & Momentum
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|ForecastCBRNoChangeUNPD
|Crude Birth Rate (births per 1,000 population) - Estimates & No change
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|ForecastCBRReplacementUNPD
|Crude Birth Rate (births per 1,000 population) - Estimates & Instant-replacement
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|ForecastCBRZeroMigrationUNPD
|Crude Birth Rate (births per 1,000 population) - Estimates & Zero-migration
|Crude Birth Rate (births per 1,000 population)
|0
|
|-
|ForecastCDRConstFertilityUNPD
|Crude Death Rate (deaths per 1,000 population) - Estimates & Constant-fertility
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRConstFertilityUNPD2022Rev
|Crude Death Rate (deaths per 1,000 population) - Estimates & Constant-fertility
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRConstMortUNPD
|Crude Death Rate (deaths per 1,000 population) - Estimates & Constant-mortality
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRConstMortUNPD2022Rev
|Crude Death Rate (deaths per 1,000 population) - Estimates & Constant-mortality
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRHighUNPD
|Crude Death Rate (deaths per 1,000 population) - Estimates & High variant
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRHighUNPD2022Rev
|Crude Death Rate (deaths per 1,000 population) - Estimates & High variant
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRLowUNPD
|Crude Death Rate (deaths per 1,000 population) - Estimates & Low variant
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRLowUNPD2022Rev
|Crude Death Rate (deaths per 1,000 population) - Estimates & Low variant
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRMedUNPD
|Crude Death Rate (deaths per 1,000 population) - Estimates & Medium variant
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRMedUNPD2022Rev
|Crude Death Rate (deaths per 1,000 population) - Estimates & Medium variant
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRMomentumUNPD
|Crude Death Rate (deaths per 1,000 population) - Estimates & Momentum
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRMomentumUNPD2022Rev
|Crude Death Rate (deaths per 1,000 population) - Estimates & Momentum
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRNoChangeUNPD
|Crude Death Rate (deaths per 1,000 population) - Estimates & No change
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRNoChangeUNPD2022Rev
|Crude Death Rate (deaths per 1,000 population) - Estimates & No change
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRReplacementUNPD
|Crude Death Rate (deaths per 1,000 population) - Estimates & Instant-replacement
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastCDRZeroMigrationUNPD
|Crude Death Rate (deaths per 1,000 population) - Estimates & Zero-migration
|Crude Death Rate (deaths per 1,000 population)
|0
|
|-
|ForecastElderDep%ConstFertilityUNPD
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Constant-fertility
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%ConstFertilityUNPD2022Rev
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Constant-fertility
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%ConstMortUNPD
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Constant-mortality
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%ConstMortUNPD2022Rev
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Constant-mortality
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%HighUNPD
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & High variant
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%HighUNPD2022Rev
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & High variant
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%LowUNPD
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Low variant
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%LowUNPD2022Rev
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Low variant
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%MedUNPD
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Medium variant
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%MedUNPD2022Rev
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Medium variant
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%MomentumUNPD
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Momentum
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%NoChangeUNPD
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & No change
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%NoChangeUNPD2022Rev
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & No change
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%ReplacementUNPD
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Instant-replacement
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%ReplacementUNPD2022Rev
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Instant-replacement
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%ZeroMigrationUNPD
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Zero-migration
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastElderDep%ZeroMigrationUNPD2022Rev
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates & Zero-migration
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|ForecastInfMortConstFertilityUNPD
|Infant Mortality Rate (infant deaths per 1,000 live births) - Estimates & Constant-fertility
|Infant Mortality Rate (infant deaths per 1,000 live births)
|0
|
|-
|ForecastInfMortConstMortUNPD
|Infant Mortality Rate (infant deaths per 1,000 live births) - Estimates & Constant-mortality
|Infant Mortality Rate (infant deaths per 1,000 live births)
|0
|
|-
|ForecastInfMortHighUNPD
|Infant Mortality Rate (infant deaths per 1,000 live births) - Estimates & High variant
|Infant Mortality Rate (infant deaths per 1,000 live births)
|0
|
|-
|ForecastInfMortLowUNPD
|Infant Mortality Rate (infant deaths per 1,000 live births) - Estimates & Low variant
|Infant Mortality Rate (infant deaths per 1,000 live births)
|0
|
|-
|ForecastInfMortMedUNPD
|Infant Mortality Rate (infant deaths per 1,000 live births) - Estimates & Medium variant
|Infant Mortality Rate (infant deaths per 1,000 live births)
|0
|
|-
|ForecastInfMortMomentumUNPD
|Infant Mortality Rate (infant deaths per 1,000 live births) - Estimates & Momentum
|Infant Mortality Rate (infant deaths per 1,000 live births)
|0
|
|-
|ForecastInfMortNoChangeUNPD
|Infant Mortality Rate (infant deaths per 1,000 live births) - Estimates & No change
|Infant Mortality Rate (infant deaths per 1,000 live births)
|0
|
|-
|ForecastInfMortReplacementUNPD
|Infant Mortality Rate (infant deaths per 1,000 live births) - Estimates & Instant-replacement
|Infant Mortality Rate (infant deaths per 1,000 live births)
|0
|
|-
|ForecastInfMortZeroMigrationUNPD
|Infant Mortality Rate (infant deaths per 1,000 live births) - Estimates & Zero-migration
|Infant Mortality Rate (infant deaths per 1,000 live births)
|0
|
|-
|ForecastLifeExpBothSexesConstFertilityUNPD
|Life Expectancy at Birth, both sexes (years) - Estimates & Constant-fertility
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|ForecastLifeExpBothSexesConstMortUNPD
|Life Expectancy at Birth, both sexes (years) - Estimates & Constant-mortality
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|ForecastLifeExpBothSexesHighUNPD
|Life Expectancy at Birth, both sexes (years) - Estimates & High variant
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|ForecastLifeExpBothSexesLowUNPD
|Life Expectancy at Birth, both sexes (years) - Estimates & Low variant
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|ForecastLifeExpBothSexesMomentumUNPD
|Life Expectancy at Birth, both sexes (years) - Estimates & Momentum
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|ForecastLifeExpBothSexesNoChangeUNPD
|Life Expectancy at Birth, both sexes (years) - Estimates & No change
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|ForecastLifeExpBothSexesReplacementUNPD
|Life Expectancy at Birth, both sexes (years) - Estimates & Instant-replacement
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|ForecastLifeExpBothSexesZeroMigrationUNPD
|Life Expectancy at Birth, both sexes (years) - Estimates & Zero-migration
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|ForecastLifeExpFemaleConstFertilityUNPD
|Female Life Expectancy at Birth (years) - Estimates & Constant-fertility
|Female Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpFemaleConstMortUNPD
|Female Life Expectancy at Birth (years) - Estimates & Constant-mortality
|Female Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpFemaleHighUNPD
|Female Life Expectancy at Birth (years) - Estimates & High variant
|Female Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpFemaleLowUNPD
|Female Life Expectancy at Birth (years) - Estimates & Low variant
|Female Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpFemaleMomentumUNPD
|Female Life Expectancy at Birth (years) - Estimates & Momentum
|Female Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpFemaleNoChangeUNPD
|Female Life Expectancy at Birth (years) - Estimates & No change
|Female Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpFemaleReplacementUNPD
|Female Life Expectancy at Birth (years) - Estimates & Instant-replacement
|Female Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpFemaleZeroMigrationUNPD
|Female Life Expectancy at Birth (years) - Estimates & Zero-migration
|Female Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMaleConstFertilityUNPD
|Male Life Expectancy at Birth (years) - Estimates & Constant-fertility
|Male Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMaleConstMortUNPD
|Male Life Expectancy at Birth (years) - Estimates & Constant-mortality
|Male Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMaleHighUNPD
|Male Life Expectancy at Birth (years) - Estimates & High variant
|Male Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMaleLowUNPD
|Male Life Expectancy at Birth (years) - Estimates & Low variant
|Male Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMaleMomentumUNPD
|Male Life Expectancy at Birth (years) - Estimates & Momentum
|Male Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMaleNoChangeUNPD
|Male Life Expectancy at Birth (years) - Estimates & No change
|Male Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMaleReplacementUNPD
|Male Life Expectancy at Birth (years) - Estimates & Instant-replacement
|Male Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMaleZeroMigrationUNPD
|Male Life Expectancy at Birth (years) - Estimates & Zero-migration
|Male Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMedBothSexesUNPD
|Life Expectancy at Birth, both sexes (years) - Estimates & Medium variant
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|ForecastLifeExpMedBothSexesUNPD2022Rev
|Life Expectancy at Birth, both sexes (years) - Estimates & Medium variant
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|ForecastLifeExpMedFemaleUNPD
|Female Life Expectancy at Birth (years) - Estimates & Medium variant
|Female Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMedFemaleUNPD2022Rev
|Female Life Expectancy at Birth (years) - Estimates & Medium variant
|Female Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMedMaleUNPD
|Male Life Expectancy at Birth (years) - Estimates & Medium variant
|Male Life Expectancy at Birth (years)
|0
|
|-
|ForecastLifeExpMedMaleUNPD2022Rev
|Male Life Expectancy at Birth (years) - Estimates & Medium variant
|Male Life Expectancy at Birth (years)
|0
|
|-
|ForecastNetMigrationConstFertilityUNPD
|Net Number of Migrants (thousands) - Estimates & Constant-fertility
|Net Number of Migrants (thousands)
|0
|
|-
|ForecastNetMigrationConstMortUNPD
|Net Number of Migrants (thousands) - Estimates & Constant-mortality
|Net Number of Migrants (thousands)
|0
|
|-
|ForecastNetMigrationHighUNPD
|Net Number of Migrants (thousands) - Estimates & High variant
|Net Number of Migrants (thousands)
|0
|
|-
|ForecastNetMigrationLowUNPD
|Net Number of Migrants (thousands) - Estimates & Low variant
|Net Number of Migrants (thousands)
|0
|
|-
|ForecastNetMigrationMedUNPD
|Net Number of Migrants (thousands) - Estimates & Medium variant
|Net Number of Migrants (thousands)
|0
|
|-
|ForecastNetMigrationMedUNPD2019Rev
|Net migration headcounts by country, medium-fertility variant, every five years 1955-2100
|MIGR_F02_NET_NUMBER_OF_MIGRANTS
|0
|
|-
|ForecastNetMigrationMedUNPD2022Rev
|Net Number of Migrants (thousands) - Estimates & Medium variant
|Net Number of Migrants (thousands)
|0
|
|-
|ForecastNetMigrationMomentumUNPD
|Net Number of Migrants (thousands) - Estimates & Momentum
|Net Number of Migrants (thousands)
|0
|
|-
|ForecastNetMigrationNoChangeUNPD
|Net Number of Migrants (thousands) - Estimates & No change
|Net Number of Migrants (thousands)
|0
|
|-
|ForecastNetMigrationRateConstFertilityUNPD
|Net Migration Rate (per 1,000 population) - Estimates & Constant-fertility
|Net Migration Rate (per 1,000 population)
|0
|
|-
|ForecastNetMigrationRateConstMortUNPD
|Net Migration Rate (per 1,000 population) - Estimates & Constant-mortality
|Net Migration Rate (per 1,000 population)
|0
|
|-
|ForecastNetMigrationRateHighUNPD
|Net Migration Rate (per 1,000 population) - Estimates & High variant
|Net Migration Rate (per 1,000 population)
|0
|
|-
|ForecastNetMigrationRateLowUNPD
|Net Migration Rate (per 1,000 population) - Estimates & Low variant
|Net Migration Rate (per 1,000 population)
|0
|
|-
|ForecastNetMigrationRateMedUNPD
|Net Migration Rate (per 1,000 population) - Estimates & Medium variant
|Net Migration Rate (per 1,000 population)
|0
|
|-
|ForecastNetMigrationRateMedUNPD2022Rev
|Net Migration Rate (per 1,000 population) - Estimates & Medium variant
|Net Migration Rate (per 1,000 population)
|0
|
|-
|ForecastNetMigrationRateMomentumUNPD
|Net Migration Rate (per 1,000 population) - Estimates & Momentum
|Net Migration Rate (per 1,000 population)
|0
|
|-
|ForecastNetMigrationRateNoChangeUNPD
|Net Migration Rate (per 1,000 population) - Estimates & No change
|Net Migration Rate (per 1,000 population)
|0
|
|-
|ForecastNetMigrationRateReplacementUNPD
|Net Migration Rate (per 1,000 population) - Estimates & Instant-replacement
|Net Migration Rate (per 1,000 population)
|0
|
|-
|ForecastNetMigrationRateZeroMigrationUNPD
|Net Migration Rate (per 1,000 population) - Estimates & Zero-migration
|Net Migration Rate (per 1,000 population)
|0
|
|-
|ForecastNetMigrationReplacementUNPD
|Net Number of Migrants (thousands) - Estimates & Instant-replacement
|Net Number of Migrants (thousands)
|0
|
|-
|ForecastNetMigrationZeroMigrationUNPD
|Net Number of Migrants (thousands) - Estimates & Zero-migration
|Net Number of Migrants (thousands)
|0
|
|-
|ForecastPop15PlusConstFertilityUNPD
|Population ages 15 and older in millions
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|ForecastPop15PlusConstMortUNPD
|Population ages 15 and older in millions
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|ForecastPop15PlusHighUNPD
|Population ages 15 and older in millions
- Estimates & High variant
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|ForecastPop15PlusLowUNPD
|Population ages 15 and older in millions
- Estimates & Low variant
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|ForecastPop15PlusMedUNPD
|Population ages 15 and older in millions
- Estimates & Medium variant
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|ForecastPop15PlusMomentumUNPD
|Population ages 15 and older in millions
- Estimates & Momentum
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|ForecastPop15PlusNoChangeUNPD
|Population ages 15 and older in millions
- Estimates & No change
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|ForecastPop15PlusReplacementUNPD
|Population ages 15 and older in millions
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|ForecastPop15PlusZeroMigrationUNPD
|Population ages 15 and older in millions
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|ForecastPop15to29ConstFertilityUNPD
|Population aged 15 to 29 years in millions
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|ForecastPop15to29ConstMortUNPD
|Population aged 15 to 29 years in millions
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|ForecastPop15to29HighUNPD
|Population aged 15 to 29 years in millions
- Estimates & High variant
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|ForecastPop15to29LowUNPD
|Population aged 15 to 29 years in millions
- Estimates & Low variant
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|ForecastPop15to29MedUNPD
|Population aged 15 to 29 years in millions
- Estimates & Medium variant
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|ForecastPop15to29MomentumUNPD
|Population aged 15 to 29 years in millions
- Estimates & Momentum
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|ForecastPop15to29NoChangeUNPD
|Population aged 15 to 29 years in millions
- Estimates & No change
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|ForecastPop15to29ReplacementUNPD
|Population aged 15 to 29 years in millions
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|ForecastPop15to29ZeroMigrationUNPD
|Population aged 15 to 29 years in millions
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|ForecastPop15to64%ConstFertilityUNPD
|Percentage of population aged 15 up to 65 years relative to total population
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64%ConstMortUNPD
|Percentage of population aged 15 up to 65 years relative to total population
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64%HighUNPD
|Percentage of population aged 15 up to 65 years relative to total population
- Estimates & High variant
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64%LowUNPD
|Percentage of population aged 15 up to 65 years relative to total population
- Estimates & Low variant
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64%MedUNPD
|Percentage of population aged 15 up to 65 years relative to total population
- Estimates & Medium variant
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64%MomentumUNPD
|Percentage of population aged 15 up to 65 years relative to total population
- Estimates & Momentum
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64%NoChangeUNPD
|Percentage of population aged 15 up to 65 years relative to total population
- Estimates & No change
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64%ReplacementUNPD
|Percentage of population aged 15 up to 65 years relative to total population
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64%ZeroMigrationUNPD
|Percentage of population aged 15 up to 65 years relative to total population
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64ConstFertilityUNPD
|Population aged 15 to 64 years in millions
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64ConstMortUNPD
|Population aged 15 to 64 years in millions
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64HighUNPD
|Population aged 15 to 64 years in millions
- Estimates & High variant
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64LowUNPD
|Population aged 15 to 64 years in millions
- Estimates & Low variant
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64MedUNPD
|Population aged 15 to 64 years in millions
- Estimates & Medium variant
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64MomentumUNPD
|Population aged 15 to 64 years in millions
- Estimates & Momentum
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64NoChangeUNPD
|Population aged 15 to 64 years in millions
- Estimates & No change
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64ReplacementUNPD
|Population aged 15 to 64 years in millions
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop15to64ZeroMigrationUNPD
|Population aged 15 to 64 years in millions
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|ForecastPop65Plus%ConstFertilityUNPD
|Percentage of population aged 65 years or more relative to total population
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65Plus%ConstMortUNPD
|Percentage of population aged 65 years or more relative to total population
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65Plus%HighUNPD
|Percentage of population aged 65 years or more relative to total population
- Estimates & High variant
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65Plus%LowUNPD
|Percentage of population aged 65 years or more relative to total population
- Estimates & Low variant
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65Plus%MedUNPD
|Percentage of population aged 65 years or more relative to total population
- Estimates & Medium variant
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65Plus%MomentumUNPD
|Percentage of population aged 65 years or more relative to total population
- Estimates & Momentum
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65Plus%NoChangeUNPD
|Percentage of population aged 65 years or more relative to total population
- Estimates & No change
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65Plus%ReplacementUNPD
|Percentage of population aged 65 years or more relative to total population
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65Plus%ZeroMigrationUNPD
|Percentage of population aged 65 years or more relative to total population
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65PlusConstFertilityUNPD
|Population ages 65 and older in millions
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65PlusConstMortUNPD
|Population ages 65 and older in millions
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65PlusHighUNPD
|Population ages 65 and older in millions
- Estimates & High variant
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65PlusLowUNPD
|Population ages 65 and older in millions
- Estimates & Low variant
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65PlusMedUNPD
|Population ages 65 and older in millions
- Estimates & Medium variant
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65PlusMomentumUNPD
|Population ages 65 and older in millions
- Estimates & Momentum
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65PlusNoChangeUNPD
|Population ages 65 and older in millions
- Estimates & No change
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65PlusReplacementUNPD
|Population ages 65 and older in millions
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPop65PlusZeroMigrationUNPD
|Population ages 65 and older in millions
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|ForecastPopConstFertilityUNPD
|Total Population, as of 1 July (thousands) - Estimates & Constant-fertility
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopConstFertilityUNPD2022Rev
|Total Population, as of 1 July (thousands) - Estimates & Constant-fertility
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopConstMortUNPD
|Total Population, as of 1 July (thousands) - Estimates & Constant-mortality
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopConstMortUNPD2022Rev
|Total Population, as of 1 July (thousands) - Estimates & Constant-mortality
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopFemaleConstFertilityUNPD
|Female Population, as of 1 July (thousands) - Estimates & Constant-fertility
|Female Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopFemaleConstMortUNPD
|Female Population, as of 1 July (thousands) - Estimates & Constant-mortality
|Female Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopFemaleHighUNPD
|Female Population, as of 1 July (thousands) - Estimates & High variant
|Female Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopFemaleLowUNPD
|Female Population, as of 1 July (thousands) - Estimates & Low variant
|Female Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopFemaleMedUNPD
|Female Population, as of 1 July (thousands) - Estimates & Medium variant
|Female Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopFemaleMedUNPD2022Rev
|Total female population, as of 1 July (thousands) - Estimates & Medium variant
|Female Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopFemaleMomentumUNPD
|Female Population, as of 1 July (thousands) - Estimates & Momentum
|Female Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopFemaleNoChangeUNPD
|Female Population, as of 1 July (thousands) - Estimates & No change
|Female Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopFemaleReplacementUNPD
|Female Population, as of 1 July (thousands) - Estimates & Instant-replacement
|Female Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopFemaleZeroMigrationUNPD
|Female Population, as of 1 July (thousands) - Estimates & Zero-migration
|Female Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopHighUNPD
|Total Population, as of 1 July (thousands) - Estimates & High variant
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopHighUNPD2022Rev
|Total Population, as of 1 July (thousands) - Estimates & High variant
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopLowUNPD
|Total Population, as of 1 July (thousands) - Estimates & Low variant
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopLowUNPD2022Rev
|Total Population, as of 1 July (thousands) - Estimates & Low variant
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMaleConstFertilityUNPD
|Male Population, as of 1 July (thousands) - Estimates & Constant-fertility
|Male Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMaleConstMortUNPD
|Male Population, as of 1 July (thousands) - Estimates & Constant-mortality
|Male Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMaleHighUNPD
|Male Population, as of 1 July (thousands) - Estimates & High variant
|Male Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMaleLowUNPD
|Male Population, as of 1 July (thousands) - Estimates & Low variant
|Male Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMaleMedUNPD
|Male Population, as of 1 July (thousands) - Estimates & Medium variant
|Male Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMaleMedUNPD2022Rev
|Total male population, as of 1 July (thousands) - Estimates & Medium variant
|Male Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMaleMomentumUNPD
|Male Population, as of 1 July (thousands) - Estimates & Momentum
|Male Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMaleNoChangeUNPD
|Male Population, as of 1 July (thousands) - Estimates & No change
|Male Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMaleReplacementUNPD
|Male Population, as of 1 July (thousands) - Estimates & Instant-replacement
|Male Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMaleZeroMigrationUNPD
|Male Population, as of 1 July (thousands) - Estimates & Zero-migration
|Male Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMedUNPD
|Total Population, as of 1 July (thousands) - Estimates & Medium variant
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMedUNPD2022Rev
|Total Population, as of 1 July (thousands) - Estimates & Medium variant
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopMomentumUNPD
|Total Population, as of 1 July (thousands) - Estimates & Momentum
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopNoChangeUNPD
|Total Population, as of 1 July (thousands) - Estimates & No change
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopNoChangeUNPD2022Rev
|Total Population, as of 1 July (thousands) - Estimates & No change
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopReplacementUNPD
|Total Population, as of 1 July (thousands) - Estimates & Instant-replacement
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopReplacementUNPD2022Rev
|Total Population, as of 1 July (thousands) - Estimates & Instant-replacement
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopUnder15%ConstFertilityUNPD
|Percentage of population under the age of 15 relative to the total population
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15%ConstMortUNPD
|Percentage of population under the age of 15 relative to the total population
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15%HighUNPD
|Percentage of population under the age of 15 relative to the total population
- Estimates & High variant
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15%LowUNPD
|Percentage of population under the age of 15 relative to the total population
- Estimates & Low variant
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15%MedUNPD
|Percentage of population under the age of 15 relative to the total population
- Estimates & Medium variant
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15%MomentumUNPD
|Percentage of population under the age of 15 relative to the total population
- Estimates & Momentum
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15%NoChangeUNPD
|Percentage of population under the age of 15 relative to the total population
- Estimates & No change
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15%ReplacementUNPD
|Percentage of population under the age of 15 relative to the total population
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15%ZeroMigrationUNPD
|Percentage of population under the age of 15 relative to the total population
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15ConstFertilityUNPD
|Population under the age 15 in millions
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15ConstMortUNPD
|Population under the age 15 in millions
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15HighUNPD
|Population under the age 15 in millions
- Estimates & High variant
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15LowUNPD
|Population under the age 15 in millions
- Estimates & Low variant
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15MedUNPD
|Population under the age 15 in millions
- Estimates & Medium variant
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15MomentumUNPD
|Population under the age 15 in millions
- Estimates & Momentum
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15NoChangeUNPD
|Population under the age 15 in millions
- Estimates & No change
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15ReplacementUNPD
|Population under the age 15 in millions
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder15ZeroMigrationUNPD
|Population under the age 15 in millions
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|ForecastPopUnder5ConstFertilityUNPD
|Population under 5 years of age in millions
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - 0-4
|0
|
|-
|ForecastPopUnder5ConstMortUNPD
|Population under 5 years of age in millions
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - 0-4
|0
|
|-
|ForecastPopUnder5HighUNPD
|Population under 5 years of age in millions
- Estimates & High variant
|Annual population by age groups (both sexes) - 0-4
|0
|
|-
|ForecastPopUnder5LowUNPD
|Population under 5 years of age in millions
- Estimates & Low variant
|Annual population by age groups (both sexes) - 0-4
|0
|
|-
|ForecastPopUnder5MedUNPD
|Population under 5 years of age in millions
- Estimates & Medium variant
|Annual population by age groups (both sexes) - 0-4
|0
|
|-
|ForecastPopUnder5MomentumUNPD
|Population under 5 years of age in millions
- Estimates & Momentum
|Annual population by age groups (both sexes) - 0-4
|0
|
|-
|ForecastPopUnder5NoChangeUNPD
|Population under 5 years of age in millions
- Estimates & No change
|Annual population by age groups (both sexes) - 0-4
|0
|
|-
|ForecastPopUnder5ReplacementUNPD
|Population under 5 years of age in millions
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - 0-4
|0
|
|-
|ForecastPopUnder5ZeroMigrationUNPD
|Population under 5 years of age in millions
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - 0-4
|0
|
|-
|ForecastPopZeroMigrationUNPD
|Total Population, as of 1 July (thousands) - Estimates & Zero-migration
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastPopZeroMigrationUNPD2022Rev
|Total Population, as of 1 July (thousands) - Estimates & Zero-migration
|Total Population, as of 1 July (thousands)
|0
|
|-
|ForecastTFRConstFertilityUNPD
|Total Fertility Rate (live births per woman) - Estimates & Constant-fertility
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRConstFertilityUNPD2022Rev
|Total Fertility Rate (live births per woman) - Estimates & Constant-fertility
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRConstMortUNPD
|Total Fertility Rate (live births per woman) - Estimates & Constant-mortality
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRConstMortUNPD2022Rev
|Total Fertility Rate (live births per woman) - Estimates & Constant-mortality
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRHighUNPD
|Total Fertility Rate (live births per woman) - Estimates & High variant
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRHighUNPD2022Rev
|Total Fertility Rate (live births per woman) - Estimates & High variant
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRLowUNPD
|Total Fertility Rate (live births per woman) - Estimates & Low variant
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRLowUNPD2022Rev
|Total Fertility Rate (live births per woman) - Estimates & Low variant
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRMedUNPD
|Total Fertility Rate (live births per woman) - Estimates & Medium variant
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRMedUNPD2022Rev
|Total Fertility Rate (live births per woman) - Estimates & Medium variant
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRMomentumUNPD
|Total Fertility Rate (live births per woman) - Estimates & Momentum
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRNoChangeUNPD
|Total Fertility Rate (live births per woman) - Estimates & No change
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRNoChangeUNPD2022Rev
|Total Fertility Rate (live births per woman) - Estimates & No change
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRReplacementUNPD
|Total Fertility Rate (live births per woman) - Estimates & Instant-replacement
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRReplacementUNPD2022Rev
|Total Fertility Rate (live births per woman) - Estimates & Instant-replacement
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRZeroMigrationUNPD
|Total Fertility Rate (live births per woman) - Estimates & Zero-migration
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastTFRZeroMigrationUNPD2022Rev
|Total Fertility Rate (live births per woman) - Estimates & Zero-migration
|Total Fertility Rate (live births per woman)
|0
|
|-
|ForecastUnder5MortConstFertilityUNPD
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates & Constant-fertility
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|ForecastUnder5MortConstMortUNPD
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates & Constant-mortality
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|ForecastUnder5MortHighUNPD
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates & High variant
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|ForecastUnder5MortLowUNPD
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates & Low variant
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|ForecastUnder5MortMedUNPD
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates & Medium variant
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|ForecastUnder5MortMomentumUNPD
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates & Momentum
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|ForecastUnder5MortNoChangeUNPD
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates & No change
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|ForecastUnder5MortNoChangeUNPD2022Rev
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates & No change
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|ForecastUnder5MortReplacementUNPD
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates & Instant-replacement
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|ForecastUnder5MortZeroMigrationUNPD
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates & Zero-migration
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|ForecastYouthBulgeBy15ConstFertilityUNPD
|Youth bulge calculated with population ages 15-29 over population ages 15 up
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|0
|
|-
|ForecastYouthBulgeBy15ConstMortUNPD
|Youth bulge calculated with population ages 15-29 over population ages 15 up
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|0
|
|-
|ForecastYouthBulgeBy15HighUNPD
|Youth bulge calculated with population ages 15-29 over population ages 15 up
- Estimates & High variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|0
|
|-
|ForecastYouthBulgeBy15LowUNPD
|Youth bulge calculated with population ages 15-29 over population ages 15 up
- Estimates & Low variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|0
|
|-
|ForecastYouthBulgeBy15MedUNPD
|Youth bulge calculated with population ages 15-29 over population ages 15 up
- Estimates & Medium variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|0
|
|-
|ForecastYouthBulgeBy15MomentumUNPD
|Youth bulge calculated with population ages 15-29 over population ages 15 up
- Estimates & Momentum
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|0
|
|-
|ForecastYouthBulgeBy15NoChangeUNPD
|Youth bulge calculated with population ages 15-29 over population ages 15 up
- Estimates & No change
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|0
|
|-
|ForecastYouthBulgeBy15ReplacementUNPD
|Youth bulge calculated with population ages 15-29 over population ages 15 up
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|0
|
|-
|ForecastYouthBulgeBy15ZeroMigrationUNPD
|Youth bulge calculated with population ages 15-29 over population ages 15 up
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|0
|
|-
|ForecastYouthBulgeBy30ConstFertilityUNPD
|Youth bulge calculated with population ages 15-29 over population ages 30 up
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|0
|
|-
|ForecastYouthBulgeBy30ConstMortUNPD
|Youth bulge calculated with population ages 15-29 over population ages 30 up
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|0
|
|-
|ForecastYouthBulgeBy30HighUNPD
|Youth bulge calculated with population ages 15-29 over population ages 30 up
- Estimates & High variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|0
|
|-
|ForecastYouthBulgeBy30LowUNPD
|Youth bulge calculated with population ages 15-29 over population ages 30 up
- Estimates & Low variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|0
|
|-
|ForecastYouthBulgeBy30MedUNPD
|Youth bulge calculated with population ages 15-29 over population ages 30 up
- Estimates & Medium variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|0
|
|-
|ForecastYouthBulgeBy30MomentumUNPD
|Youth bulge calculated with population ages 15-29 over population ages 30 up
- Estimates & Momentum
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|0
|
|-
|ForecastYouthBulgeBy30NoChangeUNPD
|Youth bulge calculated with population ages 15-29 over population ages 30 up
- Estimates & No change
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|0
|
|-
|ForecastYouthBulgeBy30ReplacementUNPD
|Youth bulge calculated with population ages 15-29 over population ages 30 up
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|0
|
|-
|ForecastYouthBulgeBy30ZeroMigrationUNPD
|Youth bulge calculated with population ages 15-29 over population ages 30 up
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|0
|
|-
|ForecastYouthBulgeByTotalConstFertilityUNPD
|Youth bulge calculated with population ages 15-29 over the total population
- Estimates & Constant-fertility
|Annual population by age groups (both sexes) - sum(15-29)/total
|0
|
|-
|ForecastYouthBulgeByTotalConstMortUNPD
|Youth bulge calculated with population ages 15-29 over the total population
- Estimates & Constant-mortality
|Annual population by age groups (both sexes) - sum(15-29)/total
|0
|
|-
|ForecastYouthBulgeByTotalHighUNPD
|Youth bulge calculated with population ages 15-29 over the total population
- Estimates & High variant
|Annual population by age groups (both sexes) - sum(15-29)/total
|0
|
|-
|ForecastYouthBulgeByTotalLowUNPD
|Youth bulge calculated with population ages 15-29 over the total population
- Estimates & Low variant
|Annual population by age groups (both sexes) - sum(15-29)/total
|0
|
|-
|ForecastYouthBulgeByTotalMedUNPD
|Youth bulge calculated with population ages 15-29 over the total population
- Estimates & Medium variant
|Annual population by age groups (both sexes) - sum(15-29)/total
|0
|
|-
|ForecastYouthBulgeByTotalMomentumUNPD
|Youth bulge calculated with population ages 15-29 over the total population
- Estimates & Momentum
|Annual population by age groups (both sexes) - sum(15-29)/total
|0
|
|-
|ForecastYouthBulgeByTotalNoChangeUNPD
|Youth bulge calculated with population ages 15-29 over the total population
- Estimates & No change
|Annual population by age groups (both sexes) - sum(15-29)/total
|0
|
|-
|ForecastYouthBulgeByTotalReplacementUNPD
|Youth bulge calculated with population ages 15-29 over the total population
- Estimates & Instant-replacement
|Annual population by age groups (both sexes) - sum(15-29)/total
|0
|
|-
|ForecastYouthBulgeByTotalZeroMigrationUNPD
|Youth bulge calculated with population ages 15-29 over the total population
- Estimates & Zero-migration
|Annual population by age groups (both sexes) - sum(15-29)/total
|0
|
|-
|ForecastYouthDep%ConstFertilityUNPD
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Constant-fertility
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%ConstFertilityUNPD2022Rev
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Constant-fertility
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%ConstMortUNPD
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Constant-mortality
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%ConstMortUNPD2022Rev
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Constant-mortality
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%HighUNPD
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & High variant
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%HighUNPD2022Rev
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & High variant
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%LowUNPD
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Low variant
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%LowUNPD2022Rev
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Low variant
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%MedUNPD
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Medium variant
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%MedUNPD2022Rev
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Medium variant
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%MomentumUNPD
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Momentum
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%MomentumUNPD2022Rev
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Momentum
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%NoChangeUNPD
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & No change
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%NoChangeUNPD2022Rev
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & No change
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%ReplacementUNPD
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Instant-replacement
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%ReplacementUNPD2022Rev
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Instant-replacement
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%ZeroMigrationUNPD
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Zero-migration
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastYouthDep%ZeroMigrationUNPD2022Rev
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates & Zero-migration
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|InfMortMedUNPD
|Medium-fertility variant Infant mortality rate by country, every five years 1955-2020 ( infant deaths per 1,000 live births)
|Infant mortality rate by country, every five years 1955-2010 (infant deaths per 1,000 live births)
|0
|
|-
|InfMortUNPD
|Infant Mortality Rate (infant deaths per 1,000 live births) - Estimates
|Infant Mortality Rate (infant deaths per 1,000 live births)
|0
|
|-
|LifeExpBothSexesUNPD
|Life Expectancy at Birth, both sexes (years) - Estimates
|Life Expectancy at Birth, both sexes (years)
|0
|
|-
|LifeExpFemaleUNPD
|Female Life Expectancy at Birth (years) - Estimates
|Female Life Expectancy at Birth (years)
|0
|
|-
|LifeExpMaleUNPD
|Male Life Expectancy at Birth (years) - Estimates
|Male Life Expectancy at Birth (years)
|0
|
|-
|LifExpect
|Life expectancy at birth, Both sexes
|Life expectancy at birth, both sexes (years)
|0
|
|-
|LifExpectFemale
|Life expectancy at birth, female
|Life expectancy at birth, female (years)
|0
|
|-
|LifExpectFemaleMedUNPD
|Medium variant- Life expectancy at birth, female
|Life expectancy at birth, female (years)
|0
|
|-
|LifExpectMale
|Life expectancy at birth, Male
|Life expectancy at birth, male (years)
|0
|
|-
|LifExpectMaleMedUNPD
|Medium variant- Life expectancy at birth, Male
|Life expectancy at birth, male (years)
|0
|
|-
|LifExpectMedUNPD
|Medium variant- Life expectancy at birth, Both sexes
|Life expectancy at birth, both sexes (years)
|0
|
|-
|MedAgeUNPD
|Median Age, as of 1 July (years)
|Median Age, as of 1 July (years)
|0
|
|-
|NetMigrationRateUNPD
|Net Migration Rate (per 1,000 population) - Estimates
|Net Migration Rate (per 1,000 population)
|0
|
|-
|NetMigrationUNPD
|Net Number of Migrants (thousands) - Estimates
|Net Number of Migrants (thousands)
|0
|
|-
|Pop15PlusUNPD
|Population ages 15 and older in millions
- Estimates
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|Pop15to29UNPD
|Population aged 15 to 29 years in millions
- Estimates
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|Pop15to64%UNPD
|Percentage of population aged 15 up to 65 years relative to total population
- Estimates
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|Pop15to64UNPD
|Population aged 15 to 64 years in millions
- Estimates
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|Pop65Plus%UNPD
|Percentage of population aged 65 years or more relative to total population
- Estimates
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|Pop65PlusUNPD
|Population ages 65 and older in millions
- Estimates
|Annual population by age groups (both sexes) - 65+
|0
|
|-
|PopFemaleUNPD
|Female Population, as of 1 July (thousands) - Estimates
|Female Population, as of 1 July (thousands)
|0
|
|-
|PopMaleUNPD
|Male Population, as of 1 July (thousands) - Estimates
|Male Population, as of 1 July (thousands)
|0
|
|-
|Population
|Total Population, as of 1 July (thousands) - Estimates
|Total Population, as of 1 July (thousands)
|1
|ECONOMY, HEALTH, INFRASTRUCTURE, POPULATION
|-
|Population15PlusMill
|Population ages 15 and older in millions
|Annual population by age groups (both sexes) - 15+
|0
|
|-
|Population15to29Mill
|Population aged 15 to 29 years in millions
|Annual population by age groups (both sexes) - sum(15-29)
|0
|
|-
|Population15to64%
|Percentage of population aged 15 up to 65 years relative to total population
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|Population15to64Mill
|Population aged 15 to 64 years in millions
|Annual population by age groups (both sexes) - 15-64
|0
|
|-
|Population65Plus
|Ratio of population aged 65 years or more relative to total population
|Annual population by age groups (both sexes)
|0
|
|-
|Population65Plus%
|Percentage of population aged 65 years or more relative to total population
|Annual population by age groups (both sexes)
|0
|
|-
|Population65PlusMill
|Population ages 65 and older in millions
|Annual population by age groups (both sexes) - 65+
|1
|SOCIOPOL
|-
|PopulationElderlyDependence%
|Annual old-age dep. ratio [65+ / 15-64] (%) - Estimates
|Annual old-age dep. ratio [65+ / 15-64] (%)
|0
|
|-
|PopulationMedian
|Median Age, as of 1 July (years)
|Median Age, as of 1 July (years)
|0
|
|-
|PopulationUnder15Mill
|Population under the age 15 in millions
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|PopulationUnder5Mill
|Population under 5 years of age in millions
|Annual population by age groups (both sexes) - 0-5
|0
|
|-
|PopulationYouthDep%
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates
|Annual child dep. ratio [0-14 / 15-64] (%)
|1
|POPULATION
|-
|PopUnder15%UNPD
|Percentage of population under the age of 15 relative to the total population
- Estimates
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|PopUnder15UNPD
|Population under the age 15 in millions
- Estimates
|Annual population by age groups (both sexes) - 0-14
|0
|
|-
|PopUnder5UNPD
|Population under 5 years of age in millions
- Estimates
|Annual population by age groups (both sexes) - 0-4
|0
|
|-
|PopUNPD
|Total Population, as of 1 July (thousands) - Estimates
|Total Population, as of 1 July (thousands)
|0
|
|-
|PopYouthBulgeBy15
|Youth bulge calculated with population ages 15-29 over population ages 15 up
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|1
|SOCIOPOL
|-
|PopYouthBulgeBy30
|Youth bulge calculated with population ages 15-29 over population ages 30 up
|Annual population by age groups (both sexes)
|0
|
|-
|PopYouthBulgeByTotal
|Youth bulge calculated with population ages 15-29 over the total population
|Annual population by age groups (both sexes)
|0
|
|-
|PopYouthDependency%
|Percentage of population under the age of 15 relative to the total population
|Annual population by age groups (both sexes)
|0
|
|-
|TFR
|Total Fertility Rate (live births per woman) - Estimates
|Total Fertility Rate (live births per woman)
|1
|POPULATION
|-
|TFRMedAnnUNDP
|Annualized data of total fertility rate from UNDP
|Fertility Rate by country annually
|0
|
|-
|TFRMedUNPD
|Medium-fertility variant total fertility rate by country, every five years 1955-2020 (births)
|Fertility Rate by country, every five years
|1
|POPULATION
|-
|TFRUNPD
|Total Fertility Rate (live births per woman) - Estimates
|Total Fertility Rate (live births per woman)
|0
|
|-
|Under5MortUNPD
|Under-Five Mortality (deaths under age 5 per 1,000 live births) - Estimates
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|0
|
|-
|Under5MortUNPDWPP
|Probability of dying between birth and exact age of 5
|Under 5 mortality; estimates 1950-2015
|0
|
|-
|YouthBulgeBy15UNPD
|Youth bulge calculated with population ages 15-29 over population ages 15 up
- Estimates
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|0
|
|-
|YouthBulgeBy30UNPD
|Youth bulge calculated with population ages 15-29 over population ages 30 up
- Estimates
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|0
|
|-
|YouthBulgeByTotalUNPD
|Youth bulge calculated with population ages 15-29 over the total population
- Estimates
|Annual population by age groups (both sexes) - sum(15-29)/total
|0
|
|-
|YouthDep%UNPD
|Annual child dep. ratio [0-14 / 15-64] (%) - Estimates
|Annual child dep. ratio [0-14 / 15-64] (%)
|0
|
|-
|ForecastCDRConstFertilityUNPD2017Rev
|Forecast of Crude Death Rates using Constant Fertility Variant
|Crude death rate
|0
|
|-
|ForecastCDRConstMortUNPD2017Rev
|Forecasts of Death Rates from UNPD using Constant Mortality Variant
|Crude Death Rate
|0
|
|-
|ForecastCDRHighVariantUNPD2017Rev
|Forecasts of Crude death rates using High Variant
|Crude Death Rate
|0
|
|-
|ForecastCDRLowVariantUNPD2017Rev
|Forecasts of death rates from UNPD using Low variant
|Crude Death Rate
|0
|
|-
|ForecastCDRMediumVariantUNPD2017Rev
|Forecasts of death rates from UNPD using 2017 Revision
|Crude Death Rate
|0
|
|-
|ForecastCDRMomentumUNPD2017Rev
|Forecasts of death rates from UNPD using Momentum Variant
|Crude Death Rate
|0
|
|-
|ForecastCDRNochangeUNPD2017Rev
|Forecasts of death rates using No change Variant 2017 Rev
|Crude Death Rate
|0
|
|-
|ForecastConstFertilityUNPD2017Rev
|Total fertility rate, constant-fertility variant, annualized version of UNPD 2017 data and forecast
|Total Fertility rate, constant-fertility variant
|0
|
|-
|ForecastDependencyRatioConstantFertilityUNPD2017Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioConstantMortalityUNPD2017Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioHighUNPD2017Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioInstantReplacementUNPD2017Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioLowUNPD2017Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioMediumUNPD2017Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioMomentumUNPD2017Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioNoChangeUNPD2017Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioZeroMigrationUNPD2017Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastFertilityConstMortalityUNPD2017Rev
|Total fertility rate, constant-mortality variant, annualized version of UNPD 2017 data and forecast
|Total Fertility rate, constant-mortality variant
|0
|
|-
|ForecastFertilityConstMortalityUNPD2019Rev
|Total fertility rate, constant-mortality variant, annualized version of UNPD 2019 data and forecast
|Total Fertility rate, constant-mortality variant
|0
|
|-
|ForecastFertilityHighUNPD2017Rev
|Total fertility rate, high-fertility variant, annualized version of UNPD 2017 data and forecast
|Total Fertility rate, high-fertility variant
|0
|
|-
|ForecastFertilityLowUNPD2017Rev
|Total fertility rate, low-fertility variant, annualized version of UNPD 2017 data and forecast
|Total Fertility rate, low-fertility variant
|0
|
|-
|ForecastFertilityMedUNPD2017Rev
|Total fertility rate, medium-fertility variant, annualized version of UNPD 2017 data and forecast
|Total Fertility rate, medium-fertility variant
|0
|
|-
|ForecastFertilityNoChangeUNPD2017Rev
|Total fertility rate, no change variant, annualized version of UNPD 2017 data and forecast
|Total Fertility rate, no change variant
|0
|
|-
|ForecastFertilityReplacementUNPD2017Rev
|Total fertility rate, instant replacement variant, annualized version of UNPD 2017 data and forecast
|Total Fertility rate, instant replacement variant
|0
|
|-
|ForecastFertilityZeroMigrationUNPD2017Rev
|Total fertility rate, zero migration variant, annualized version of UNPD 2017 data and forecast
|Total Fertility rate, zero-migration variant
|0
|
|-
|ForecastInfantMortalityMediumUNPD2017Rev
|Infant mortality rate for both sexes combined (infant deaths per 1,000 live births), annualized version of Medium Variant of UNPD 2017
|Infant Mortality Rate - Medium
|0
|
|-
|ForecastLifeExpMedBothSexesUNPD2017Rev
|Life expectancy at birth, medium variant, both sexes. Annualized version of UNPD 2017 data and forecast
|Life expectancy at birth, both sexes; Historical data and medium variant
|0
|
|-
|ForecastLifeExpMedFemaleUNPD2017Rev
|Life expectancy at birth, medium variant, female. Annualized version of UNPD 2017 data and forecast
|Life expectancy at birth, female; Historical data and medium variant
|0
|
|-
|ForecastLifeExpMedMaleUNPD2017Rev
|Life expectancy at birth, medium variant, male. Annualized version of UNPD 2017 data and forecast
|Life Expectancy at Birth, Male; Historical data and medium variant
|0
|
|-
|ForecastMigrationUNPD2017Rev
|Migrants from UNPD in millions
|
|0
|
|-
|ForecastOldageDependencyRatioConstFertUNPD2017Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2017 Constant Fertility forecast
|Old age dependency ratio - Constant Fertility
|0
|
|-
|ForecastOldageDependencyRatioConstMortUNPD2017Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2017 Constant Mortality forecast
|Old age dependency ratio - Constant Mortality
|0
|
|-
|ForecastOldageDependencyRatioHighUNPD2017Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2017 High Variant forecast
|Old age dependency ratio - High Variant
|0
|
|-
|ForecastOldageDependencyRatioInstRepUNPD2017Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2017 Instant Replacement forecast
|Old age dependency ratio - Instant Replacement
|0
|
|-
|ForecastOldageDependencyRatioLowUNPD2017Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2017 Low Variant forecast
|Old age dependency ratio - Low Variant
|0
|
|-
|ForecastOldageDependencyRatioMediumUNPD2017Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2017 Medium Variant forecast
|Old age dependency ratio - Medium Variant
|0
|
|-
|ForecastOldageDependencyRatioNoChangeUNPD2017Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2017 No Change forecast
|Old age dependency ratio - No Change
|0
|
|-
|ForecastOldageDependencyRatioZeroMigrationUNPD2017Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2017 Zero Migration forecast
|Old age dependency ratio - Zero Migration
|0
|
|-
|ForecastPopBothSexesConstFertilityUNPD2017Rev
|Total Population Forecasts, UNPD WPP 2017 , Constant Fertility Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopBothSexesConstMortalityUNPD2017Rev
|Total Population Forecasts, UNPD WPP 2017, Constant Mortality Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopBothSexesInstantRepUNPD2017Rev
|Total Population Forecasts, UNPD WPP 2017, Instant Replacement Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopBothSexesZeroMigrationUNPD2017Rev
|Total Population Forecasts, UNPD WPP 2017, Zero Migration Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopulationBothSexesHighUNPD2017Rev
|Total Population Forecasts, UNPD WPP 2017, High Variant Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopulationBothSexesLowUNPD2017Rev
|Total Population Forecasts, UNPD WPP 2017, Low Variant Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopulationBothSexesMediumUNPD2017Rev
|Total Population Forecasts, UNPD WPP 2017, Medium Variant Forecast
|Population (Both Sexes)
|0
|
|-
|ForecastPopulationBothSexesNoChangeUNPD2017Rev
|Total Population Forecasts, UNPD WPP 2017, No change forecast
|Population (both sexes)
|0
|
|-
|ForecastYouthDependencyConstantFertilityUNPD2017Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2017 Constant Fertility Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyConstantMortalityUNPD2017Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2017 Zero Migration Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyHighUNPD2017Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2017 High Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyInstantReplacementUNPD2017Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2017 Instant Replacement Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyLowUNPD2017Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2017 Low Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyMediumUNPD2017Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2017 Medium Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyNoChangeUNPD2017Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2017 Zero Migration Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyZeroMigrationUNPD2017Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2017 Zero Migration Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastCDRConstFertilityUNPD2019Rev
|Forecast of Crude Death Rates using Constant Fertility Variant
|Crude death rate
|0
|
|-
|ForecastCDRConstMortUNPD2019Rev
|Forecasts of Death Rates from UNPD using Constant Mortality Variant
|Crude Death Rate
|0
|
|-
|ForecastCDRHighVariantUNPD2019Rev
|Forecasts of Crude death rates using High Variant
|Crude Death Rate
|0
|
|-
|ForecastCDRLowVariantUNPD2019Rev
|Forecasts of death rates from UNPD using Low variant
|Crude Death Rate
|0
|
|-
|ForecastCDRMediumVariantUNPD2019Rev
|Forecasts of death rates from UNPD medium variant
|Crude Death Rate
|0
|
|-
|ForecastCDRMomentumUNPD2019Rev
|Forecasts of death rates from UNPD using Momentum Variant
|Crude Death Rate
|0
|
|-
|ForecastCDRNochangeUNPD2019Rev
|Forecasts of death rates using No change Variant
|Crude Death Rate
|0
|
|-
|ForecastConstFertilityUNPD2019Rev
|Total fertility rate, constant-fertility variant, annualized version of UNPD 2019 data and forecast
|Total Fertility rate, constant-fertility variant
|0
|
|-
|ForecastDependencyRatioConstantFertilityUNPD2019Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioHighUNPD2019Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioInstantReplacementUNPD2019Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioLowUNPD2019Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioMediumUNPD2019Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioMomentumUNPD2019Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioNoChangeUNPD2019Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastDependencyRatioZeroMigrationUNPD2019Rev
|Child dependency ratio <15/(15-64) by major area, region and country, 1950-2100 (ratio of population 0-14 per 100 population 15-64)
|Child dependency ratio <15/(15-64) by region
|0
|
|-
|ForecastFertilityHighUNPD2019Rev
|Total fertility rate, high-fertility variant, annualized version of UNPD 2019 data and forecast
|Total Fertility rate, high-fertility variant
|0
|
|-
|ForecastFertilityLowUNPD2019Rev
|Total fertility rate, low-fertility variant, annualized version of UNPD 2019 data and forecast
|Total Fertility rate, low-fertility variant
|0
|
|-
|ForecastFertilityMedUNPD2019Rev
|Total fertility rate, medium-fertility variant, annualized version of UNPD 2019 data and forecast
|Total Fertility rate, medium-fertility variant
|0
|
|-
|ForecastFertilityNoChangeUNPD2019Rev
|Total fertility rate, no change variant, annualized version of UNPD 2019 data and forecast
|Total Fertility rate, no change variant
|0
|
|-
|ForecastFertilityReplacementUNPD2019Rev
|Total fertility rate, instant replacement variant, annualized version of UNPD 2019 data and forecast
|Total Fertility rate, instant replacement variant
|0
|
|-
|ForecastFertilityZeroMigrationUNPD2019Rev
|Total fertility rate, zero migration variant, annualized version of UNPD 2019 data and forecast
|Total Fertility rate, zero-migration variant
|0
|
|-
|ForecastLifeExpMedBothSexesUNPD2019Rev
|Life expectancy at birth, medium variant, both sexes. Annualized version of UNPD 2017 data and forecast
|Life expectancy at birth, both sexes; Historical data and medium variant
|0
|
|-
|ForecastLifeExpMedFemaleUNPD2019Rev
|Life expectancy at birth, medium variant, female. Annualized version of UNPD 2017 data and forecast
|Life expectancy at birth, female; Historical data and medium variant
|0
|
|-
|ForecastLifeExpMedMaleUNPD2019Rev
|Life expectancy at birth, medium variant, male. Annualized version of UNPD 2017 data and forecast
|Life Expectancy at Birth, Male; Historical data and medium variant
|0
|
|-
|ForecastMigrationUNPD2019Rev
|Migrants from UNPD in millions
|Net number of migrants
|0
|
|-
|ForecastNetMigrationMedUNPD2019RevAnn
|Net migration headcounts by country, medium-fertility variant, every five years 1955-2100 (annualized using IFs spread function)
|MIGR_F02_NET_NUMBER_OF_MIGRANTS
|0
|
|-
|ForecastNetMigrationRateMedUNPD2019Rev
|Net migration rates by country, medium-fertility variant, every five years 1955-2100
|MIGR_F01_NET_MIGRATION_RATE
|0
|
|-
|ForecastNetMigrationRateMedUNPD2019RevAnn
|Net migration rates by country, medium-fertility variant, every five years 1955-2100 (annualized using IFs interpolate function)
|MIGR_F01_NET_MIGRATION_RATE
|0
|
|-
|ForecastNetMigrationUNPD
|Net migration headcounts by country, medium-fertility variant, every five years 1955-2100 (annualized using IFs spread function)
|MIGR_F02_NET_NUMBER_OF_MIGRANTS
|0
|
|-
|ForecastOldageDependencyRatioConstFertUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 Constant Fertility forecast
|Old age dependency ratio - Constant Fertility
|0
|
|-
|ForecastOldageDependencyRatioConstMortUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 Constant Mortality forecast
|Old age dependency ratio - Constant Mortality
|0
|
|-
|ForecastOldageDependencyRatioHighUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 High Variant forecast
|Old age dependency ratio - High Variant
|0
|
|-
|ForecastOldageDependencyRatioInstRepUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 Instant Replacement forecast
|Old age dependency ratio - Instant Replacement
|0
|
|-
|ForecastOldageDependencyRatioLowUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 Low Variant forecast
|Old age dependency ratio - Low Variant
|0
|
|-
|ForecastOldageDependencyRatioMediumUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 Medium Variant forecast
|Old age dependency ratio - Medium Variant
|0
|
|-
|ForecastOldageDependencyRatioNoChangeUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 No Change forecast
|Old age dependency ratio - No Change
|0
|
|-
|ForecastOldageDependencyRatioZeroMigrationUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 Zero Migration forecast
|Old age dependency ratio - Zero Migration
|0
|
|-
|ForecastPopBothSexesConstFertilityUNPD2019Rev
|Total Population Forecasts, UNPD WPP 2019 , Constant Fertility Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopBothSexesConstMortalityUNPD2019Rev
|Total Population Forecasts, UNPD WPP 2019, Constant Mortality Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopBothSexesInstantRepUNPD2019Rev
|Total Population Forecasts, UNPD WPP 2019, Instant Replacement Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopBothSexesZeroMigrationUNPD2019Rev
|Total Population Forecasts, UNPD WPP 2019, Zero Migration Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopulationBothSexesHighUNPD2019Rev
|Total Population Forecasts, UNPD WPP 2019, High Variant Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopulationBothSexesLowUNPD2019Rev
|Total Population Forecasts, UNPD WPP 2019, Low Variant Forecast
|Population (both sexes)
|0
|
|-
|ForecastPopulationBothSexesMediumUNPD2019Rev
|Total Population Forecasts, UNPD WPP 2019, Medium Variant Forecast
|Population (Both Sexes)
|0
|
|-
|ForecastPopulationBothSexesNoChangeUNPD2019Rev
|Total Population Forecasts, UNPD WPP 2019, No change forecast
|Population (both sexes)
|0
|
|-
|ForecastYouthDependencyConstantFertilityUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Constant Fertility Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyConstantMortalityUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Zero Migration Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyHighUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 High Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyInstantReplacementUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Instant Replacement Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyLowUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Low Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyMediumUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Medium Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyNoChangeUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Zero Migration Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastYouthDependencyZeroMigrationUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Zero Migration Variant forecast
|Child Dependency Ratio 1
|0
|
|-
|ForecastNetMigrationRateUNPD
|Net migration rates by country, medium-fertility variant, yearly data from SeriesForecastNetMigrationRateMedUNPD, unit converted to per 100 instead of the original per 1,000
|MIGR_F01_NET_MIGRATION_RATE
|0
|
|-
|ForecastDependencyRatioOld0MigrationUNPD2015Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2015 Zero Migration forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldConstFertUNPD2015Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2015 Constant Fertility forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldConstMortalityUNPD2015Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2015 Constant Mortality forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldHighUNPD2015Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2015 High Variant forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldLowUNPD2015Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2015 Low Variant forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldMedUNPD2015Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2015 Medium Variant forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldNoChangeUNPD2015Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2015 No Change forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldReplacementUNPD2015Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2015 Instant Replacement forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastYouthDependencyRatio0MigrationUNPD2015Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2015 Zero Migration forecast
|Child Dependency Ratio 1 - WPP2015_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioConstFertUNPD2015Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2015 Constant Fertility forecast
|Child Dependency Ratio 1 - WPP2015_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioConstMortalityUNPD2015Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2015 Constant Mortality forecast
|Child Dependency Ratio 1 - WPP2015_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioHighUNPD2015Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2015 High Variant forecast
|Child Dependency Ratio 1 - WPP2015_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioLowUNPD2015Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2015 Low Variant forecast
|Child Dependency Ratio 1 - WPP2015_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioMedUNPD2015Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2015 Medium Variant forecast
|Child Dependency Ratio 1 - WPP2015_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioNoChangeUNPD2015Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2015 No Change forecast
|Child Dependency Ratio 1 - WPP2015_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioNoChangeUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 No Change forecast
|Child Dependency Ratio 1 - WPP2019_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioReplacementUNPD2015Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2015 Instant Replacement forecast
|Child Dependency Ratio 1 - WPP2015_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastDependencyRatioOld0MigrationUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2015 Zero Migration forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldConstFertUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2015 Constant Fertility forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldConstMortalityUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 Constant Mortality forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2015_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldHighUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 High Variant forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2019_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldLowUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 Low Variant forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2019_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldNoChangeUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 No Change forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2019_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastDependencyRatioOldReplacementUNPD2019Rev
|Dependency ratio, old age, ratio of population aged 65+ to population 15-64 per 100, UNPD WPP 2019 Instant Replacement forecast
|Old-Age Dependency Ratio 1 (XLS) - WPP2019_POP_F13_A_OLD_AGE_DEPENDENCY_RATIO_1564.XLS
|0
|
|-
|ForecastYouthDependencyRatio0MigrationUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Zero Migration forecast
|Child Dependency Ratio 1 - WPP2019_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioConstFertUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Constant Fertility forecast
|Child Dependency Ratio 1 - WPP2019_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioConstMortalityUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Constant Mortality forecast
|Child Dependency Ratio 1 - WPP2019_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioHighUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 High Variant forecast
|Child Dependency Ratio 1 - WPP2019_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioLowUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Low Variant forecast
|Child Dependency Ratio 1 - WPP2019_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioMedUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Medium Variant forecast
|Child Dependency Ratio 1 - WPP2019_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastYouthDependencyRatioReplacementUNPD2019Rev
|Dependency ratio, child, ratio of population aged 0-14 to population 15-64 per 100, UNPD WPP 2019 Instant Replacement forecast
|Child Dependency Ratio 1 - WPP2019_POP_F12_A_CHILD_DEPENDENCY_RATIO_1564
|0
|
|-
|ForecastBirthsUNPDMed
|Births (in thousands), from UN World Population Prospects 2019, values include historical estimation and medium variant projection. Kosovo values from WDI.
|Annual Demographic Indicators-> Births (thousands)
|0
|
|-
|ForecastCBRUNPDMed
|Crude birth rate, from UN World Population Prospects 2019, values include historical estimation and medium variant projection. Kosovo values from WDI.
|Annual Demographic Indicators-> Crude birth rate (births per 1,000 population)
|0
|
|-
|ForecastCDRUNPDMed
|Crude death rate, from UN World Population Prospects 2019, values include historical estimation and medium variant projection. Kosovo values from WDI.
|Annual Demographic Indicators-> Crude death rate (deaths per 1,000 population)
|0
|
|-
|ForecastPopulationUNPDMed
|Total population (in millions), from UN World Population Prospects 2019, values include historical estimation and medium variant projection. Kosovo values from WDI.
|Annual Population Indicators - Both Sexes-> Population, Total
|0
|
|-
|ForecastTFRUNPDMed
|Total fertility rate (births per woman), from UN World Population Prospects 2019, values include historical estimation and medium variant projection. Kosovo values from WDI.
|Annual Demographic Indicators-> Total fertility (live births per woman)
|0
|
|}

= Data Pulling Instructions =
Use the table below for filtering and gathering data. [https://github.com/shangkexin/United_Nations_Population_Division_UNPD?tab=readme-ov-file There is a python script if you would like to use it]
{| class="wikitable"
|Variable
|File
|Variant/Tab
|Column
|Original Source
|Units
|-
|ForecastAgeMedianConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|MedianAgePop
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|Years
|-
|ForecastBirthsConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|Births
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|Thousands
|-
|ForecastCBRConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|CBR
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|per 1000
|-
|ForecastCDRConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|CDR
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|per 1000
|-
|ForecastInfMortConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|IMR
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|per 1000
|-
|ForecastLifeExpBothSexesConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|LEx
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|Years
|-
|ForecastLifeExpFemaleConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|LExFemale
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|Years
|-
|ForecastLifeExpMaleConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|LExMale
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|Years
|-
|ForecastNetMigrationConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|NetMigrations
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|Millions
|-
|ForecastNetMigrationRateConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|CNMR
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|per 1000
|-
|ForecastPopConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|TPopulation1July
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|Millions
|-
|ForecastPopFemaleConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|TPopulationFemale1July
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|Millions
|-
|ForecastPopMaleConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|TPopulationMale1July
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|Millions
|-
|ForecastTFRConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|TFR
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|Births
|-
|ForecastUnder5MortConstMortUNPD
|WPP2024_Demographic_Indicators_OtherVariants.csv
|Constant-mortality
|Under5Deaths
|<nowiki>https://population.un.org/wpp/Download/Standard/CSV/</nowiki>
|per 1000
|-
|ForecastAgeMedianConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Median Age, as of 1 July (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastBirthsConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Births (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Thousands
|-
|ForecastCBRConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Crude Birth Rate (births per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastCDRConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Crude Death Rate (deaths per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastInfMortConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Infant Mortality Rate (infant deaths per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastLifeExpBothSexesConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Life Expectancy at Birth, both sexes (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpFemaleConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Female Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpMaleConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Male Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastNetMigrationConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Net Number of Migrants (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastNetMigrationRateConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Net Migration Rate (per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastPopConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Total Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopFemaleConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Female Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopMaleConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Male Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastTFRConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Total Fertility Rate (live births per woman)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Births
|-
|ForecastUnder5MortConstFertilityUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Constant-fertility
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|AgeMedianUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Median Age, as of 1 July (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|BirthsUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Births (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Thousands
|-
|CBR
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Crude Birth Rate (births per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|CDR
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Crude Death Rate (deaths per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|InfMortUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Infant Mortality Rate (infant deaths per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|LifeExpBothSexesUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Life Expectancy at Birth, both sexes (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|LifeExpFemaleUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Female Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|LifeExpMaleUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Male Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|NetMigrationRateUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Net Migration Rate (per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|NetMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Net Number of Migrants (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|PopFemaleUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Female Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|PopMaleUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Male Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|Population
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Total Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|TFR
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Total Fertility Rate (live births per woman)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Births
|-
|Under5MortUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Estimates
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastAgeMedianHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Median Age, as of 1 July (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastBirthsHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Births (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Thousands
|-
|ForecastCBRHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Crude Birth Rate (births per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastCDRHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Crude Death Rate (deaths per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastInfMortHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Infant Mortality Rate (infant deaths per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastLifeExpBothSexesHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Life Expectancy at Birth, both sexes (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpFemaleHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Female Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpMaleHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Male Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastNetMigrationHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Net Number of Migrants (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastNetMigrationRateHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Net Migration Rate (per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastPopFemaleHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Female Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Total Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopMaleHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Male Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastTFRHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Total Fertility Rate (live births per woman)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Births
|-
|ForecastUnder5MortHighUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|High variant
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastAgeMedianReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Median Age, as of 1 July (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastBirthsReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Births (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Thousands
|-
|ForecastCBRReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Crude Birth Rate (births per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastCDRReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Crude Death Rate (deaths per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastInfMortReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Infant Mortality Rate (infant deaths per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastLifeExpBothSexesReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Life Expectancy at Birth, both sexes (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpFemaleReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Female Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpMaleReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Male Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastNetMigrationRateReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Net Migration Rate (per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastNetMigrationReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Net Number of Migrants (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopFemaleReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Female Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopMaleReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Male Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Total Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastTFRReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Total Fertility Rate (live births per woman)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Births
|-
|ForecastUnder5MortReplacementUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Instant-replacement
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastAgeMedianLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Median Age, as of 1 July (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastBirthsLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Births (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Thousands
|-
|ForecastCBRLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Crude Birth Rate (births per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastCDRLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Crude Death Rate (deaths per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastInfMortLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Infant Mortality Rate (infant deaths per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastLifeExpBothSexesLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Life Expectancy at Birth, both sexes (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpFemaleLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Female Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpMaleLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Male Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastNetMigrationLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Net Number of Migrants (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastNetMigrationRateLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Net Migration Rate (per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastPopFemaleLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Female Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Total Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopMaleLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Male Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastTFRLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Total Fertility Rate (live births per woman)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Births
|-
|ForecastUnder5MortLowUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Low variant
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastAgeMedianMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Median Age, as of 1 July (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastBirthsMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Births (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Thousands
|-
|ForecastCBRMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Crude Birth Rate (births per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastCDRMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Crude Death Rate (deaths per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastInfMortMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Infant Mortality Rate (infant deaths per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastLifeExpMedBothSexesUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Life Expectancy at Birth, both sexes (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpMedFemaleUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Female Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpMedMaleUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Male Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastNetMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Net Number of Migrants (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastNetMigrationRateMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Net Migration Rate (per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastPopFemaleMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Female Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopMaleMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Male Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Total Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastTFRMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Total Fertility Rate (live births per woman)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Births
|-
|ForecastUnder5MortMedUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Medium variant
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastAgeMedianMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Median Age, as of 1 July (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastBirthsMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Births (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Thousands
|-
|ForecastCBRMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Crude Birth Rate (births per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastCDRMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Crude Death Rate (deaths per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastInfMortMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Infant Mortality Rate (infant deaths per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastLifeExpBothSexesMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Life Expectancy at Birth, both sexes (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpFemaleMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Female Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpMaleMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Male Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastNetMigrationMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Net Number of Migrants (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastNetMigrationRateMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Net Migration Rate (per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastPopFemaleMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Female Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopMaleMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Male Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Total Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastTFRMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Total Fertility Rate (live births per woman)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Births
|-
|ForecastUnder5MortMomentumUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Momentum
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastAgeMedianNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Median Age, as of 1 July (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastBirthsNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Births (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Thousands
|-
|ForecastCBRNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Crude Birth Rate (births per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastCDRNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Crude Death Rate (deaths per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastInfMortNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Infant Mortality Rate (infant deaths per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastLifeExpBothSexesNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Life Expectancy at Birth, both sexes (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpFemaleNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Female Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpMaleNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Male Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastNetMigrationNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Net Number of Migrants (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastNetMigrationRateNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Net Migration Rate (per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastPopFemaleNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Female Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopMaleNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Male Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Total Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastTFRNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Total Fertility Rate (live births per woman)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Births
|-
|ForecastUnder5MortNoChangeUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|No change
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastAgeMedianZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Median Age, as of 1 July (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastBirthsZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Births (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Thousands
|-
|ForecastCBRZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Crude Birth Rate (births per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastCDRZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Crude Death Rate (deaths per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastInfMortZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Infant Mortality Rate (infant deaths per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastLifeExpBothSexesZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Life Expectancy at Birth, both sexes (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpFemaleZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Female Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastLifeExpMaleZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Male Life Expectancy at Birth (years)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Years
|-
|ForecastNetMigrationRateZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Net Migration Rate (per 1,000 population)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastNetMigrationZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Net Number of Migrants (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopFemaleZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Female Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopMaleZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Male Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastPopZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Total Population, as of 1 July (thousands)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Millions
|-
|ForecastTFRZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Total Fertility Rate (live births per woman)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|Births
|-
|ForecastUnder5MortZeroMigrationUNPD
|WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx
|Zero-migration
|Under-Five Mortality (deaths under age 5 per 1,000 live births)
|<nowiki>https://population.un.org/wpp/Download/Standard/MostUsed/</nowiki>
|per 1000
|-
|ForecastPop15to29ConstFertilityUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|Annual population by age groups (both sexes) - sum(15-29)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastYouthBulgeBy15ConstFertilityUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeBy30ConstFertilityUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeByTotalConstFertilityUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|Annual population by age groups (both sexes) - sum(15-29)/total
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to29ConstMortUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|Annual population by age groups (both sexes) - sum(15-29)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastYouthBulgeBy15ConstMortUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeBy30ConstMortUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeByTotalConstMortUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|Annual population by age groups (both sexes) - sum(15-29)/total
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|Pop15to29UNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|Annual population by age groups (both sexes) - sum(15-29)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|YouthBulgeBy15UNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|YouthBulgeBy30UNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|YouthBulgeByTotalUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|Annual population by age groups (both sexes) - sum(15-29)/total
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to29HighUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|Annual population by age groups (both sexes) - sum(15-29)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastYouthBulgeBy15HighUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeBy30HighUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeByTotalHighUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|Annual population by age groups (both sexes) - sum(15-29)/total
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to29ReplacementUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|Annual population by age groups (both sexes) - sum(15-29)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastYouthBulgeBy15ReplacementUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeBy30ReplacementUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeByTotalReplacementUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|Annual population by age groups (both sexes) - sum(15-29)/total
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to29LowUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|Annual population by age groups (both sexes) - sum(15-29)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastYouthBulgeBy15LowUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeBy30LowUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeByTotalLowUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|Annual population by age groups (both sexes) - sum(15-29)/total
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to29MedUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|Annual population by age groups (both sexes) - sum(15-29)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastYouthBulgeBy15MedUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeBy30MedUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeByTotalMedUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|Annual population by age groups (both sexes) - sum(15-29)/total
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to29MomentumUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|Annual population by age groups (both sexes) - sum(15-29)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastYouthBulgeBy15MomentumUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeBy30MomentumUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeByTotalMomentumUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|Annual population by age groups (both sexes) - sum(15-29)/total
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to29NoChangeUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|Annual population by age groups (both sexes) - sum(15-29)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastYouthBulgeBy15NoChangeUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeBy30NoChangeUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeByTotalNoChangeUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|Annual population by age groups (both sexes) - sum(15-29)/total
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to29ZeroMigrationUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|Annual population by age groups (both sexes) - sum(15-29)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastYouthBulgeBy15ZeroMigrationUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-14))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeBy30ZeroMigrationUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|Annual population by age groups (both sexes) - sum(15-29)/(total-sum(0-29))
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastYouthBulgeByTotalZeroMigrationUNPD
|WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|Annual population by age groups (both sexes) - sum(15-29)/total
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15PlusConstFertilityUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|15+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15to64ConstFertilityUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop65PlusConstFertilityUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder15ConstFertilityUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder5ConstFertilityUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|0-4
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15PlusConstMortUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|15+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15to64ConstMortUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop65PlusConstMortUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder15ConstMortUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder5ConstMortUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|0-4
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|Pop15PlusUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|15+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|Pop15to64UNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|Pop65PlusUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|PopUnder15UNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|PopUnder5UNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|0-4
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15PlusHighUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|15+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15to64HighUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop65PlusHighUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder15HighUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder5HighUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|0-4
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15PlusReplacementUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|15+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15to64ReplacementUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop65PlusReplacementUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder15ReplacementUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder5ReplacementUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|0-4
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15PlusLowUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|15+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15to64LowUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop65PlusLowUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder15LowUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder5LowUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|0-4
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15PlusMedUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|15+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15to64MedUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop65PlusMedUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder15MedUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder5MedUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|0-4
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15PlusMomentumUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|15+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15to64MomentumUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop65PlusMomentumUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder15MomentumUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder5MomentumUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|0-4
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15PlusNoChangeUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|15+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15to64NoChangeUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop65PlusNoChangeUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder15NoChangeUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder5NoChangeUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|0-4
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15PlusZeroMigrationUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|15+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15to64ZeroMigrationUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop65PlusZeroMigrationUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder15ZeroMigrationUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPopUnder5ZeroMigrationUNPD
|WPP2024_POP_F03_1_POPULATION_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|0-4
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Millions
|-
|ForecastPop15to64%ConstFertilityUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop65Plus%ConstFertilityUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPopUnder15%ConstFertilityUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-fertility
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to64%ConstMortUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop65Plus%ConstMortUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPopUnder15%ConstMortUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Constant-mortality
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|Pop15to64%UNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|Pop65Plus%UNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|PopUnder15%UNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Estimates
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to64%HighUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop65Plus%HighUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPopUnder15%HighUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|High variant
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to64%ReplacementUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop65Plus%ReplacementUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPopUnder15%ReplacementUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Instant-replacement
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to64%LowUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop65Plus%LowUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPopUnder15%LowUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Low variant
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to64%MedUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop65Plus%MedUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPopUnder15%MedUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Medium variant
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to64%MomentumUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop65Plus%MomentumUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPopUnder15%MomentumUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Momentum
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to64%NoChangeUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop65Plus%NoChangeUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPopUnder15%NoChangeUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|No change
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop15to64%ZeroMigrationUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|15-64
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPop65Plus%ZeroMigrationUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|65+
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastPopUnder15%ZeroMigrationUNPD
|WPP2024_POP_F06_1_POPULATION_PERCENTAGE_SELECT_AGE_GROUPS_BOTH_SEXES.xlsx
|Zero-migration
|0-14
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Percent
|-
|ForecastElderDep%ConstFertilityUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Constant-fertility
|Annual old-age dep. ratio [65+ / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastYouthDep%ConstFertilityUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Constant-fertility
|Annual child dep. ratio [0-14 / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastElderDep%ConstMortUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Constant-mortality
|Annual old-age dep. ratio [65+ / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastYouthDep%ConstMortUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Constant-mortality
|Annual child dep. ratio [0-14 / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ElderDep%UNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Estimates
|Annual old-age dep. ratio [65+ / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|YouthDep%UNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Estimates
|Annual child dep. ratio [0-14 / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastElderDep%HighUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|High variant
|Annual old-age dep. ratio [65+ / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastYouthDep%HighUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|High variant
|Annual child dep. ratio [0-14 / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastElderDep%ReplacementUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Instant-replacement
|Annual old-age dep. ratio [65+ / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastYouthDep%ReplacementUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Instant-replacement
|Annual child dep. ratio [0-14 / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastElderDep%LowUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Low variant
|Annual old-age dep. ratio [65+ / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastYouthDep%LowUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Low variant
|Annual child dep. ratio [0-14 / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastElderDep%MedUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Medium variant
|Annual old-age dep. ratio [65+ / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastYouthDep%MedUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Medium variant
|Annual child dep. ratio [0-14 / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastElderDep%MomentumUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Momentum
|Annual old-age dep. ratio [65+ / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastYouthDep%MomentumUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Momentum
|Annual child dep. ratio [0-14 / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastElderDep%NoChangeUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|No change
|Annual old-age dep. ratio [65+ / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastYouthDep%NoChangeUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|No change
|Annual child dep. ratio [0-14 / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastElderDep%ZeroMigrationUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Zero-migration
|Annual old-age dep. ratio [65+ / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|-
|ForecastYouthDep%ZeroMigrationUNPD
|WPP2024_POP_F07_1_DEPENDENCY_RATIOS_BOTH_SEXES.xlsx
|Zero-migration
|Annual child dep. ratio [0-14 / 15-64] (%)
|<nowiki>https://population.un.org/wpp/Download/Standard/Population/</nowiki>
|Ratio
|}
# There are 290 series in total. 4 preprocessors: CBR, CDR, TFR, and Population.

# WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx does not contain "Constant-mortality" variant/tab. Thus, we use the CSV file to import the series.
# Besides "Estimates", we have 9 variants: Medium, High, Low, Constant-fertility, Constant-mortality, Instant-replacement, Momentum, No change and Zero-migration. You may check the definition [https://population.un.org/wpp/DefinitionOfProjectionScenarios/ here].[[File:Image UNPD 1.png|none|thumb|569x569px]]
# Series from WPP2024_POP_F02_1_POPULATION_5-YEAR_AGE_GROUPS_BOTH_SEXES.xlsx needs extra calculation. The rest of the series can be imported directly according to the "Column" for each file by variant/tab listed above.
# Please keep in mind, "Estimates" value needs to be combined with each variant/tab.
# The python scripts for data pulling can be accessed [https://github.com/shangkexin/United_Nations_Population_Division_UNPD here].

= Data Notes =
Besides "Estimate", all the data with variant contains data from 1950-2100, 151 years in total. There are 9 variants. Thus, there are 261 tables (29*9) containing data for 151 years and no missing values.

Thus, we found that the processing time took way longer than we expected.

To save the time, we have recommend that removing half of the tables at a time from IFsImport.db.

'''SeriesNameing needs follow the rules below:'''

# For forecast series, when a new update is done, tables from the previous versions need to be renamed with their version name, i.e., 2022Rev for Forecast series from WPP 2022 version.
# In 2024 update, we make a copy of the ForecastNetMigrationMedUNPD table and rename it to ForecastNetMigrationUNPD.

World Revenue Longitudinal Database, IMF

2026-02-02T23:24:12Z

Yutang.Xiong: /* Tables In IFs */

<div style = "display:flex; flex-flow: column wrap; text-align: left;">
= Summary =
The IMF’s World Revenue Longitudinal Database (WoRLD) tracks government revenue trends in 193 countries since the early 1990s. WoRLD tracks directly 9 key components of tax and non-tax revenues, which together contribute about 82 percent of government revenue globally.

= Tables In IFs =
{| class="wikitable"
|+
!Variable
!Definition
!UsedInPreprocessor
!UsedInPreprocessorFileName
!Code in Source
|-
|GovRevGenTotRev%GDP
|Revenue, general government, total revenue, percent of GDP
|0
|ECONOMY
|TotRev
|-
|GovRevGenTaxRev%GDP
|Revenue, general government, tax revenue, percent of GDP
|0
|ECONOMY
|I.TaxRev
|-
|GovRevGenTaxInc%GDP
|Revenue, general government, taxes on income and profit, percent of GDP
|0
|ECONOMY
|I.1.TaxInc
|-
|GovRevGenTaxIncI%GDP
|Revenue, general government, taxes on income and profit of individuals, percent of GDP
|0
|ECONOMY
|I.1.a.TaxIncI
|-
|GovRevGenTaxIncC%GDP
|Revenue, general government, taxes on income and profits of corporations, percent of GDP
|0
|ECONOMY
|I.1.b.TaxIncC
|-
|GovRevGenTaxIncCRes%GDP
|Revenue, general government, taxes on income and profits of extractive industries, percent of GDP
|0
|ECONOMY
|I.1.b.i.TaxIncCRes
|-
|GovRevGenTaxIncO%GDP
|Revenue, general government, other taxes on income and profit, percent of GDP
|0
|ECONOMY
|I.1.c.TaxIncO
|-
|GovRevGenTaxPro%GDP
|Revenue, general government, taxes on property, percent of GDP
|0
|ECONOMY
|I.2.TaxPro
|-
|GovRevGenTaxSal%GDP
|Revenue, general government, taxes on sales and production, percent of GDP
|0
|ECONOMY
|I.3.TaxSal
|-
|GovRevGenTaxSalG%GDP
|Revenue, general government, general sales taxes, percent of GDP
|0
|ECONOMY
|I.3.a.TaxSalG
|-
|GovRevGenTaxSalGI%GDP
|Revenue, general government, general sales taxes collected on imports, percent of GDP
|0
|ECONOMY
|I.3.a.i.TaxSalGI
|-
|GovRevGenTaxSalExc%GDP
|Revenue, general government, excise taxes, percent of GDP
|0
|ECONOMY
|I.3.b.TaxSalExc
|-
|GovRevGenTaxSalUnal%GDP
|Revenue, general government, unallocated taxes on sales and production, percent of GDP
|0
|ECONOMY
|I.3.c.TaxSalUnal
|-
|GovRevGenTaxTra%GDP
|Revenue, general government, taxes on international trade, percent of GDP
|0
|ECONOMY
|I.4.TaxTra
|-
|GovRevGenTaxOth%GDP
|Revenue, general government, taxes not elsewhere classified, percent of GDP
|0
|ECONOMY
|I.5.TaxOth
|-
|GovRevGenSocialCon%GDP
|Revenue, general government, social contributions, percent of GDP
|0
|ECONOMY
|II.SocialCon
|-
|GovRevGenGrants%GDP
|Revenue, general government, grants revenue, percent of GDP
|0
|ECONOMY
|III.Grants
|-
|GovRevGenRevOth%GDP
|Revenue, general government, other revenue, percent of GDP
|0
|ECONOMY
|IV.RevOth
|-
|GovRevGenNonTaxRes%GDP
|Revenue, general government, non-tax revenue from extractive industries, percent of GDP
|0
|ECONOMY
|IV.1.NonTaxRes
|-
|GovRevGenNonTaxOth%GDP
|Revenue, general government, other non-tax revenue, percent of GDP
|0
|ECONOMY
|IV.2.NonTaxOth
|}

= Data Pulling Instructions =

= Data Notes =
If you are downloading the excel or CSV file for the data, some countries spelling gets messed up (ex. CÃ´te d'Ivoire, "SÃ£o TomÃ© and PrÃncipe", TÃ¼rkiye) and need to be changed to match to the IMF WEO Countries naming conventions before importing them to the IFs Database.

World Revenue Longitudinal Database, IMF

2026-02-02T23:23:37Z

Yutang.Xiong: /* Data Pulling Instructions */

World Revenue Longitudinal Database, IMF

2026-02-02T23:17:44Z

Yutang.Xiong: /* Tables In IFs */

<div style = "display:flex; flex-flow: column wrap; text-align: left;">
= Summary =
The IMF’s World Revenue Longitudinal Database (WoRLD) tracks government revenue trends in 193 countries since the early 1990s. WoRLD tracks directly 9 key components of tax and non-tax revenues, which together contribute about 82 percent of government revenue globally.

= Tables In IFs =
{| class="wikitable"
|+
!Variable
!Definition
!UsedInPreprocessor
!UsedInPreprocessorFileName
!Code in Source
|-
|GovtGenGDebt%GDPFM
|Gross debt, General government, Percent of GDP
|0
|
|GGXWDG_NGDP.A
|-
|GovtGenNDebt%GDPFM
|Net debt, General government, Percent of GDP
|0
|
|GGXWDN_NGDP.A
|-
|GovtGenExp%GDPFM
|Expenditure, General government, Percent of GDP
|0
|ECONOMY
|GGX_NGDP.A
|-
|GovtGenRev%GDPFM
|Revenue, General government, Percent of GDP
|0
|ECONOMY
|GGR_NGDP.A
|}

= Data Pulling Instructions =
'''Step 1:''' To pull WEO data, first navigate to the [https://www.imf.org/en/Publications/SPROLLs/world-economic-outlook-databases#sort=%40imfdate%20descending WEO Databases] page [[File:Screenshot 2026-01-21 193435.png|center|thumb|800x800px|IMF WEO Database website]]'''Step 2:''' To pull the most recent data (October 2025). Click on the anchor text '''IMF Data Portal.''' [[File:Screenshot 2026-01-21 .png|center|thumb|800x800px|IMF WEO Database Website]]'''Step 3:''' After getting to the IMF Data Portal, Click on the '''View Data''' button. [[File:Screenshot 2026-01-21 195816.png|center|thumb|800x800px|IMF Data Platform]]

'''Step 4:''' After clicking the View Data, you'll be at the IMF WEO data page. From there you can adjust the filters(like Indicator, Country, Time Period...etc). Then you can click download. [[File:Screenshot 2026-01-21 202817.png|center|thumb|800x800px|IMF WEO Data]]

'''Optional(After Step 1):''' If you want pull specific years dataset Scroll down and click the Database with the month and year desired (ex. October 2016). After adjusting the filters, you can go a head and download the data by clicking on the '''Download''' button.[[File:IMFWEO IMG STEP 02.jpg|center|thumb|750x750px|IMFWEO Database Selection Screen]]

You will be navigated to this page:[[File:IMFWEO IMG STEP 03.jpg|center|thumb|750x750px|Report Main Page]]

Scroll down to the heading titled Download WEO Data and click By Countries for country-level data[[File:IMFWEO IMG STEP 04.jpg|center|thumb|750x750px|Report Data Type Selection]]

You will be navigated to this page:[[File:IMFWEO IMG STEP 05.jpg|center|thumb|750x750px|Country Group Selection]]

Scroll down and click All Countries[[File:IMFWEO IMG STEP 06.jpg|center|thumb|750x750px|Country Group Selection]]

You will be navigated to this page:[[File:IMFWEO IMG STEP 07.jpg|center|thumb|750x750px|Country Selection Page]]

Scroll down to select countries. You can either pre-select all countries in the IFs Country Concordance or click Select All and remove non-IFs countries later, then click Continue[[File:IMFWEO IMG STEP 08.jpg|center|thumb|750x750px|Country Selection Page]]

You will be navigated to this page:[[File:IMFWEO IMG STEP 09.jpg|center|thumb|750x750px|Subject Selection Page]]

Scroll down to view available subjects and select the subject in the units desired (ex. Gross domestic product, constant prices in percent change)[[File:IMFWEO IMG STEP 10.jpg|center|thumb|750x750px|Subject Selection Page]]
Once a subject is selected, the page will look like this:[[File:IMFWEO IMG STEP 11.jpg|center|thumb|750x750px|Subject Selection Page]]
Click Continue and you will be navigated to this page:[[File:IMFWEO IMG STEP 12.jpg|center|thumb|750x750px|Data Range Selection Page]]

Use the dropdowns to select the desired data range (ex. 1980-2022) and adjust any advanced options as needed (ex. if ISO Country Codes will be used for Concordance)[[File:IMFWEO IMG STEP 14.jpg|center|thumb|750x750px|Data Range & Advanced Options Selection Page]]
After you've selected your data range and adjusted advanced options, click Prepare Report and you will be navigated to this page:[[File:IMFWEO IMG STEP 15.jpg|center|thumb|750x750px|Report Download Page]]

Click Download Report to begin downloading the data, which will download in an .xls format

= Data Notes =
If you are downloading the excel or CSV file for the data, some countries spelling gets messed up (ex. CÃ´te d'Ivoire, "SÃ£o TomÃ© and PrÃncipe", TÃ¼rkiye) and need to be changed to match to the IMF WEO Countries naming conventions before importing them to the IFs Database.

World Revenue Longitudinal Database, IMF

2026-02-02T23:17:15Z

Yutang.Xiong:

<div style = "display:flex; flex-flow: column wrap; text-align: left;">
= Summary =
The IMF’s World Revenue Longitudinal Database (WoRLD) tracks government revenue trends in 193 countries since the early 1990s. WoRLD tracks directly 9 key components of tax and non-tax revenues, which together contribute about 82 percent of government revenue globally.

= Tables In IFs =
{| class="wikitable"
|+
!Variable
!Definition
!UsedInPreprocessor
!UsedInPreprocessorFileName
!Code in Source
|-
|GovtGenGDebt%GDPFM
|Gross debt, General government, Percent of GDP
|0
|
|GGXWDG_NGDP.A
|-
|GovtGenNDebt%GDPFM
|Net debt, General government, Percent of GDP
|0
|
|GGXWDN_NGDP.A
|-
|GovtGenExp%GDPFM
|Expenditure, General government, Percent of GDP
|1
|ECONOMY
|GGX_NGDP.A
|-
|GovtGenRev%GDPFM
|Revenue, General government, Percent of GDP
|1
|ECONOMY
|GGR_NGDP.A
|}

= Data Pulling Instructions =
'''Step 1:''' To pull WEO data, first navigate to the [https://www.imf.org/en/Publications/SPROLLs/world-economic-outlook-databases#sort=%40imfdate%20descending WEO Databases] page [[File:Screenshot 2026-01-21 193435.png|center|thumb|800x800px|IMF WEO Database website]]'''Step 2:''' To pull the most recent data (October 2025). Click on the anchor text '''IMF Data Portal.''' [[File:Screenshot 2026-01-21 .png|center|thumb|800x800px|IMF WEO Database Website]]'''Step 3:''' After getting to the IMF Data Portal, Click on the '''View Data''' button. [[File:Screenshot 2026-01-21 195816.png|center|thumb|800x800px|IMF Data Platform]]

'''Step 4:''' After clicking the View Data, you'll be at the IMF WEO data page. From there you can adjust the filters(like Indicator, Country, Time Period...etc). Then you can click download. [[File:Screenshot 2026-01-21 202817.png|center|thumb|800x800px|IMF WEO Data]]

'''Optional(After Step 1):''' If you want pull specific years dataset Scroll down and click the Database with the month and year desired (ex. October 2016). After adjusting the filters, you can go a head and download the data by clicking on the '''Download''' button.[[File:IMFWEO IMG STEP 02.jpg|center|thumb|750x750px|IMFWEO Database Selection Screen]]

You will be navigated to this page:[[File:IMFWEO IMG STEP 03.jpg|center|thumb|750x750px|Report Main Page]]

Scroll down to the heading titled Download WEO Data and click By Countries for country-level data[[File:IMFWEO IMG STEP 04.jpg|center|thumb|750x750px|Report Data Type Selection]]

You will be navigated to this page:[[File:IMFWEO IMG STEP 05.jpg|center|thumb|750x750px|Country Group Selection]]

Scroll down and click All Countries[[File:IMFWEO IMG STEP 06.jpg|center|thumb|750x750px|Country Group Selection]]

You will be navigated to this page:[[File:IMFWEO IMG STEP 07.jpg|center|thumb|750x750px|Country Selection Page]]

Scroll down to select countries. You can either pre-select all countries in the IFs Country Concordance or click Select All and remove non-IFs countries later, then click Continue[[File:IMFWEO IMG STEP 08.jpg|center|thumb|750x750px|Country Selection Page]]

You will be navigated to this page:[[File:IMFWEO IMG STEP 09.jpg|center|thumb|750x750px|Subject Selection Page]]

Scroll down to view available subjects and select the subject in the units desired (ex. Gross domestic product, constant prices in percent change)[[File:IMFWEO IMG STEP 10.jpg|center|thumb|750x750px|Subject Selection Page]]
Once a subject is selected, the page will look like this:[[File:IMFWEO IMG STEP 11.jpg|center|thumb|750x750px|Subject Selection Page]]
Click Continue and you will be navigated to this page:[[File:IMFWEO IMG STEP 12.jpg|center|thumb|750x750px|Data Range Selection Page]]

Use the dropdowns to select the desired data range (ex. 1980-2022) and adjust any advanced options as needed (ex. if ISO Country Codes will be used for Concordance)[[File:IMFWEO IMG STEP 14.jpg|center|thumb|750x750px|Data Range & Advanced Options Selection Page]]
After you've selected your data range and adjusted advanced options, click Prepare Report and you will be navigated to this page:[[File:IMFWEO IMG STEP 15.jpg|center|thumb|750x750px|Report Download Page]]

Click Download Report to begin downloading the data, which will download in an .xls format

= Data Notes =
If you are downloading the excel or CSV file for the data, some countries spelling gets messed up (ex. CÃ´te d'Ivoire, "SÃ£o TomÃ© and PrÃncipe", TÃ¼rkiye) and need to be changed to match to the IMF WEO Countries naming conventions before importing them to the IFs Database.

Data Sources

2026-02-02T23:16:23Z

Yutang.Xiong: /* International Monetary Fund (IMF) */

=== [[Country Concordances]] ===

=== [[Research Papers/Projects]] ===

=== [[Preprocessor|Preprocessors]] ===

=== [[Template]] ===

== Data Sources ==

===== Barro-Lee =====
* [[Educational Attainment Dataset, Barro-Lee|Education Attainment Dataset]]

===== Bertelsmann Stiftung =====
* [[The Transformation Index (BTI)]]

===== Carbon Dioxide Information and Analysis Center (CDIAC) =====

* [[Carbon Dioxide Information and Analysis Center (CDIAC)|Carbon Emissions Data]]

===== Center for Systemic Peace =====

* [[Polity5]]
* [[Major Episodes of Political Violence (MEPV)]]
* [[State Fragility Index, Center for Systemic Peace|State Fragility Index]]
* [[Political Instability Task Force (PITF), Center for Systematic Peace|Political Instability Task Force (PITF)]]

===== Centre d'Etudes Prospectives et d'Informations Internationales (CEPII) =====

* [[BACI]]
* [[GeoDist, CEPII|GeoDist]]

===== CIA =====

* [[CIA World Factbook]]

===== Climate Watch =====
* [[Emissions Data, Climate Watch|Emissions Data]]

===== Correlates of War =====
* [[The Correlates of War Project, Correlates of War|The Correlates of War Project]]

===== Demographic and Health Surveys (DHS) =====

* [[STATcompiler, Demographic and Health Surveys (DHS)|STATcompiler]]

===== Economist Intelligence Unit (EIU) =====

* [[Economist Intelligence Unit (EIU)|Economist Intelligence Unit (EIU) Data]]
* [[The Political Instability Index, Economist Intelligence Unit (EIU)|The Political Instability Index]]

===== EMBER =====
* [[Electricity Data, EMBER|Electricity Data]]

===== Emissions Database for Global Atmospheric Research =====

* [[Emissions Database for Global Atmospheric Research Data]]

===== Energy Institute =====

* [[Statistical Review of World Energy, Energy Institute|Statistical Review of World Energy]]

===== Europa World Yearbook =====

* [[Europa World Yearbook Data]]

===== European Commission =====

* [[Eurostat European Commission|Eurostat, European Commission]]

===== Federation of American Scientists =====

* [[Federation of American Scientists Data]]

===== Federal Institute for Geosciences and Natural Resources (BGR) =====

* [[Energy Data, BGR|Energy Data]]

===== Food and Agriculture Organization (FAO) =====

* [[AQUASTAT, FAO|AQUASTAT - FAO's Global Information System on Water and Agriculture]]
* [[FishstatJ, FAO|FishStatJ]]
* [[FAOSTAT Food Balance Sheet]]
* [[FAOSTAT Food Security and Nutrition]]
* [[FAOSTAT Land Use|FAOSTAT Land]]
* [[FAOSTAT SDG Indicators]]
* [[FAOSTAT Trade Crops and livestock products]]

===== Fraser Institute =====

* [https://pardeewiki.du.edu//index.php?title=Fraser_International Economic Data]

===== Freedom House =====

* [[Countries at the Crossroads, Freedom House|Countries at the Crossroads]]
* [[FreedomHouse|Freedom in the World]]
* [[Freedom on the Net, Freedom House|Freedom on the Net]]

===== Fund for Peace =====
* [[Fragile States Index, Fund for Peace|Fragile States Index]]

===== Global Carbon Budget (GCB) =====

* [[Carbon Data, Global Carbon Budget (GCB)|Carbon Data]]

===== Global Footprint Network =====

* [[Global Footprint Network|Global Footprint Network Data]]

===== Global Trade Analysis Project (GTAP) =====

* [[Global Trade Analysis Project (GTAP)|Global Trade Analysis Project (GTAP) Data]]

===== Heritage Foundation =====

* [[Heritage Foundation Data]]

===== Institute for Employment Research (IAB) =====

* [[Brain Drain Data, Institute for Employment Research (IAB)|Brain Drain Data]]

===== Inter-American Development Bank (IDB) =====

* [[Database of Political Institutions, World Bank|Database of Political Institutions (DPI)]]

===== International Groundwater Resources Assessment Centre (IGRAC) =====

* [[Global Depletion of Groundwater Resources, International Groundwater Resources Assessment Centre (IGRAC)|Global Depletion of Groundwater Resources]]

===== International Centre for Tax & Development (ICTD) =====

* [[International Centre for Tax & Development (ICTD)|International Centre for Tax & Development (ICTD) Data]]

===== International Diabetes Federation =====

* [[Diabetes Atlas, International Diabetes Federation|Diabetes Atlas]]

===== [[International Energy Agency (IEA)]] =====
* [[End-Use Prices Data Explorer, International Energy Agency (IEA)|End-Use Prices Data Explorer]]
* [[World Energy Balances, International Energy Agency (IEA)|World Energy Balances]]
* [[World Energy Outlook, International Energy Agency (IEA)|World Energy Outlook]]
* [[World Energy Statistics, International Energy Agency (IEA)|World Energy Statistics]]

===== International Food Policy Research Institute =====

* [[International Food Policy Research Institute Data]]

===== Institute for Health Metrics and Evaluation (IHME) =====

* [[IHME|Global Burdens of Disease (GBD)]]

===== International Institute for Applied Systems Analysis (IIASA) =====

* [[Shared Socioeconomic Pathway (SSP) Extensions Explorer, International Institute for Applied Systems Analysis (IIASA)|Shared Socioeconomic Pathway (SSP) Extensions Explorer]]
* [[RCP Representative Concentration Pathways Database|RCP Database]]

===== International Institute for Management Development (IMD) =====

* [[International Institute for Management Development (IMD) Data]]

===== International Labor Organization (ILO) =====
*[[ILOstat]]
*[[ILO-WIEGO Statistical Reports]]

=====International Monetary Fund (IMF)=====

* [[Balance of Payments and International Investment Position statistics (BoP/IIP), IMF|Balance of Payments and International Investment Position statistics (BoP/IIP)]]
* [[IMF COFOG Classifications of Functions of Government|Classification of the Functions of Government (COFOG)]]
* [[Fiscal Monitor, IMF|Fiscal Monitor]]
* [[GDP & GDPPCPPP from IMF World Economic Outlook & WDI|GDP and GDPPCPPP]]
* [[IMF GFS Government Finance Statistics|Government Finance Statistics (GFS)]]
* [[International Financial Statistics, IMF|International Financial Statistics]]
* [[World Economic Outlook, IMF|World Economic Outlook (WEO)]]
* [[World Revenue Longitudinal Database, IMF|World Revenue Longitudinal Database (WoRLD)]]

===== Inter-Parliamentary Union =====

* [[Women in Parliaments, Inter-Parliamentary Union|Women in Parliaments]]

===== International Renewable Energy Agency (IRENA) =====

* [[Energy Data, International Renewable Energy Agency (IRENA)|Energy Data]]

===== International Research & Exchanges Board (IREX) =====

* [[International Research & Exchanges Board (IREX) Data]]

===== International Study Center, Boston College =====

* [[TIMMS & PIRLS, International Study Center, Boston College|TIMSS & PIRLS]]

===== International Telecommunications Union (ITU) =====

* [[International Telecommunication Union DataHub]]
* [[Global Cybersecurity Index (GCI), ITU|Global Cybersecurity Index (GCI)]]

===== International Organization of Motor Vehicle Manufacturers (OICA) =====

* [[International Organization of Motor Vehicle Manufacturers (OICA) Data]]

===== International Road Federation =====

* [[IRF World Road Statistics|World Road Statistics]]

===== Kellogg Institute For International Studies =====

* [[Kellogg Institute Data]]

===== LIS Cross-National Data Center =====

* [[LIS Cross-National Data Center Data]]

===== Mo Ibrahim Foundation =====

* [[Ibrahim Index of African Governance, Mo Ibrahim Foundation|Ibrahim Index of African Governance]]

===== MyGeoHub =====

* [[MY GEO HUB|Historical Irrigation Dataset (HID)]]

===== National Science Foundation (NSF) =====

* [[Science and Engineering Indicators, National Science Foundation (NSF)|Science and Engineering Indicators]]
* [[Science and Technology Studies (STS), National Science Foundation (NSF)|Science and Technology Studies (STS)]]

===== Organisation of Economic Cooperation and Development (OECD) =====
* International Transport Forum (ITF)
** [[Transport Data and Statistics, International Transport Forum (ITF)|Transport Data and Statistics]]
*[[OECD The Organisation for Economic Co-operation and Development|OECD Data Explorer]]
*[[OECD Education at a glance|OECD Education At a Glance]]
*[[OECD Factbook]]
*[[OECD Information Technology Outlook (ITO)|OECD Information Technology Outlook]]
* [[Measuring the Internet Economy, Organisation for Economic Co-operation and Development (OECD)|Measuring the Internet Economy]]
* [[Pensions at a Glance, Organisation for Economic Co-operation and Development (OECD)|Pensions at a Glance]]
* [[OECD Science & Technology|Science & Technology]]
* [[Social Institutions and Gender Index, OECD|Social Institutions and Gender Index]]
*[[Structural Analysis Database (STAN), OECD|Structural Analysis Database (STAN)]]

===== Political Terror Scale (PTS) =====

* [[Political Terror Scale (PTS) Data]]

===== Population Reference Bureau (PRB) =====

* [[Population Reference Bureau (PRB)|Assorted Annual Population Data Sheets]]

===== Potsdam Institute for Climate Impact Research (PIK) =====

* [[Climate Data, Potsdam Institute for Climate Impact Research (PIK)|Climate Data]]

===== PRIS Project =====

* [[IRIS, PRIS Project|IRIS]]

===== Reporters Without Borders =====

* [[Worldwide Press Freedom Index, Reporters Without Borders|Worldwide Press Freedom Index]]

===== Royal Netherlands Meteorological Institute (WMO KNMI) =====

* [[World Meteorological Organization Climate Explorer Reports]]

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

* [[Stockholm International Peace Research Institute (SIPRI)|Military Expenditure Data]]

===== SpeedTest =====

* [[SpeedTest Data]]

===== The Conference Board =====

* [[Total Economy Database, The Conference Board|Total Economy Database]]

===== Transparency International (TI) =====

* [[Transparency International|Corruption Perceptions Index]]

===== UBS Group AG =====

* [[Global Wealth Report, UBS Group AG|Global Wealth Report]]

===== Uppsala Conflict Data Program (UCDP) =====

* [[Uppsala Conflict Data Program (UCDP) Datasets]]
* [[UCDP/PRIO Armed Conflict Database|Uppsala Conflict Data Program (UCDP)/Peace Research Institute Oslo (PRIO) Armed Conflict Dataset]]

===== United Nations Economic Commission for Europe (UNECE) =====

* [[United Nations Economic Commission for Europe (UNECE) Data|United Nations Economic Commission for Europe Data]]

===== United Nations =====

* [[General Assembly of the United Nations (UNGA)]]
* [[Joint United Nations Programme on HIV/AIDS (UNAIDS)]]
* [[Millennium Development Goals Database, UN|Millennium Development Goals Database]]
* United Nations Department of Economic and Social Affairs
** [[United Nation World Marriage Data, United Nations Department of Economic and Social Affairs|United Nation World Marriage Data]]
* United Nations Educational, Scientific and Cultural Organization (UNESCO)
** [[Institute for Statistics, UNESCO (UIS)]]
* [[United Nations Industrial Development Organization (UNIDO)]]
* United Nations International Children's Emergency Fund (UNICEF)
** [[Child protection and development, UNICEF|Child protection and development]]
*[[United Nations Office of Drug and Crime (UNODC)]]
*United Nations Population Division (UNPD)
** [[United Nations Population Division Department of Economic and Social Affairs]]
** [[World Population Prospects (WPP), United Nations Population Division (UNPD)|World Population Prospects (WPP)]]
** [[UNDP Human Development Reports]]
*[[United Nation SDG Indicators Global Database (UNSDG)]]
*[[United Nations Statistics Division (UNSD)]]
**[[Environmental Indicators, UNSD|Environmental Indicators]]
*[[UN Trade and Development (UNCTAD)|United Nations Trade and Development (UNCTAD)]]
*[[United Nations Treaty Collection]]
*[[World Tourism Organization (UN Tourism)]]

===== University of Maryland =====

* [[Global Terrorism Database, University of Maryland|Global Terrorism Database]]
* [[Peace and Conflict Instability Ledger, University of Maryland/Center for International Development and Conflict Management (CIDCM)|Peace and Conflict Instability Ledger]]
* [[Minorities at risk|All Minorities at Risk (AMAR) Project]]

===== University of Pennsylvania =====

* [[The Political Constraint Index (POLCON), University of Pennsylvania|The Political Constraint Index (POLCON)]]

===== UN University World Institute for Development Economics Research (UNU WIDER) =====

* [[World Income Inequality Database, UN University World Institute for Development Economics Research (UNU WIDER)|World Income Inequality Database]]

===== US Energy Information Administration (EIA) =====

* [[Energy Information Administration (EIA)|Energy Information Administration Data]]

===== US Geological Survey =====

* [[World Petroleum Assessment, U.S. Geological Survey|World Petroleum Assessment]]

===== Vision of Humanity =====

* [[Vision of Humanity Data]]

===== Wittgenstein Centre =====

* [[Wittgenstein Centre Human Capital Data Explorer]]

===== World Bank =====

* [[The Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE), World Bank|Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE)]]
* [[Business Ready (B-READY), World Bank|Business Ready (B-READY)]]
* [[Climate Change Action Plan (CCAP), World Bank|Climate Change Action Plan (CCAP)]]
* [[Climate Change Knowledge Portal, World Bank|Climate Change Knowledge Portal]]
* [[Country Policy and Institutional Assessment (CPIA), World Bank|Country Policy and Institutional Assessment (CPIA)]]
* [[Education Statistics (EdStats), World Bank|Education Statistics (EdStats)]]
* [[GDP & GDPPCPPP from IMF World Economic Outlook & WDI|GDP and GDPPCPPP]]
*[[Gender Statistics, World Bank|Gender Statistics]]
*[[Global Financial Development, World Bank|Global Financial Development]]
*[[Global Integrity Index]]
*[[Informal Economy Database]]
*[[International Development Association, World Bank|International Development Association]]
*[[Poverty and Inequality Platform, World Bank|Poverty and Inequality Platform]]
*[[Private Participation in Infrastructure (PPI) Project Database, World Bank|Private Participation in Infrastructure (PPI) Project Database]]
*[[Road Costs Knowledge System (ROCKS), World Bank|Road Costs Knowledge System (ROCKS)]]
*[[World Development Indicators, World Bank|World Development Indicators (WDI)]]
*[[World Development Report, World Bank|World Development Report]]
*[[Worldwide Governance Indicators|World Governance Indicators (WGI)]]

===== World Democratic Forum (WDF) =====

* [[World Democratic Forum (WDF) Data]]

===== World Economic Forum =====

*[[Global Competitiveness Index, World Economic Forum|Global Competitiveness Index]]

===== [[World Energy Council (WEC)]]=====

* [[Oil & Gas Journal, WEC]]

===== World Health Organization (WHO)=====

*[[Global Health Expenditure Database, WHO|Global Health Expenditure Database]]
*[[Joint Monitoring Programme, WHO/UNICEF|Joint Monitoring Programme (JMP)]]
*[[Surveillance of chronic disease Risk Factors (SuRF), WHO|Surveillance of chronic disease Risk Factors (SuRF)]]
*[[World Malaria Report, WHO|World Malaria Report]]

* [[Global Health Observatory (GHO)]]
** [[Obesity, adult (18+) and child and adolescent (5-19)|Obesity]]
** [[WHO Tobacco Free Initiative (TFI)|Tobacco Free Initiative (TFI)]]

===== World Intellectual Property Organization (WIPO) =====
* [[The Global Innovation Index, World Intellectual Property Organization (WIPO)|The Global Innovation Index]]

===== World Justice Project =====

*[[World Justice Project Rule of Law Index|Rule of Law Index]]

===== World Poverty Clock =====

* [[World Poverty Clock|World Poverty Clock Data]]

=====World Military Expenditures and Arms Transfers (WMEAT)=====

* [[World Military Expenditures and Arms Transfers (WMEAT)|World Military Expenditures and Arms Transfers (WMEAT) Reports]]

=====World Trade Organization (WTO)=====

* [[Dispute Statistics, WTO|Dispute Statistics]]

=====World Value Survey (WVS)=====

* [[World Value Survey|World Value Survey Data]]

Data Sources

2026-02-02T23:15:55Z

Yutang.Xiong: /* International Monetary Fund (IMF) */

=== [[Country Concordances]] ===

=== [[Research Papers/Projects]] ===

=== [[Preprocessor|Preprocessors]] ===

=== [[Template]] ===

== Data Sources ==

===== Barro-Lee =====
* [[Educational Attainment Dataset, Barro-Lee|Education Attainment Dataset]]

===== Bertelsmann Stiftung =====
* [[The Transformation Index (BTI)]]

===== Carbon Dioxide Information and Analysis Center (CDIAC) =====

* [[Carbon Dioxide Information and Analysis Center (CDIAC)|Carbon Emissions Data]]

===== Center for Systemic Peace =====

* [[Polity5]]
* [[Major Episodes of Political Violence (MEPV)]]
* [[State Fragility Index, Center for Systemic Peace|State Fragility Index]]
* [[Political Instability Task Force (PITF), Center for Systematic Peace|Political Instability Task Force (PITF)]]

===== Centre d'Etudes Prospectives et d'Informations Internationales (CEPII) =====

* [[BACI]]
* [[GeoDist, CEPII|GeoDist]]

===== CIA =====

* [[CIA World Factbook]]

===== Climate Watch =====
* [[Emissions Data, Climate Watch|Emissions Data]]

===== Correlates of War =====
* [[The Correlates of War Project, Correlates of War|The Correlates of War Project]]

===== Demographic and Health Surveys (DHS) =====

* [[STATcompiler, Demographic and Health Surveys (DHS)|STATcompiler]]

===== Economist Intelligence Unit (EIU) =====

* [[Economist Intelligence Unit (EIU)|Economist Intelligence Unit (EIU) Data]]
* [[The Political Instability Index, Economist Intelligence Unit (EIU)|The Political Instability Index]]

===== EMBER =====
* [[Electricity Data, EMBER|Electricity Data]]

===== Emissions Database for Global Atmospheric Research =====

* [[Emissions Database for Global Atmospheric Research Data]]

===== Energy Institute =====

* [[Statistical Review of World Energy, Energy Institute|Statistical Review of World Energy]]

===== Europa World Yearbook =====

* [[Europa World Yearbook Data]]

===== European Commission =====

* [[Eurostat European Commission|Eurostat, European Commission]]

===== Federation of American Scientists =====

* [[Federation of American Scientists Data]]

===== Federal Institute for Geosciences and Natural Resources (BGR) =====

* [[Energy Data, BGR|Energy Data]]

===== Food and Agriculture Organization (FAO) =====

* [[AQUASTAT, FAO|AQUASTAT - FAO's Global Information System on Water and Agriculture]]
* [[FishstatJ, FAO|FishStatJ]]
* [[FAOSTAT Food Balance Sheet]]
* [[FAOSTAT Food Security and Nutrition]]
* [[FAOSTAT Land Use|FAOSTAT Land]]
* [[FAOSTAT SDG Indicators]]
* [[FAOSTAT Trade Crops and livestock products]]

===== Fraser Institute =====

* [https://pardeewiki.du.edu//index.php?title=Fraser_International Economic Data]

===== Freedom House =====

* [[Countries at the Crossroads, Freedom House|Countries at the Crossroads]]
* [[FreedomHouse|Freedom in the World]]
* [[Freedom on the Net, Freedom House|Freedom on the Net]]

===== Fund for Peace =====
* [[Fragile States Index, Fund for Peace|Fragile States Index]]

===== Global Carbon Budget (GCB) =====

* [[Carbon Data, Global Carbon Budget (GCB)|Carbon Data]]

===== Global Footprint Network =====

* [[Global Footprint Network|Global Footprint Network Data]]

===== Global Trade Analysis Project (GTAP) =====

* [[Global Trade Analysis Project (GTAP)|Global Trade Analysis Project (GTAP) Data]]

===== Heritage Foundation =====

* [[Heritage Foundation Data]]

===== Institute for Employment Research (IAB) =====

* [[Brain Drain Data, Institute for Employment Research (IAB)|Brain Drain Data]]

===== Inter-American Development Bank (IDB) =====

* [[Database of Political Institutions, World Bank|Database of Political Institutions (DPI)]]

===== International Groundwater Resources Assessment Centre (IGRAC) =====

* [[Global Depletion of Groundwater Resources, International Groundwater Resources Assessment Centre (IGRAC)|Global Depletion of Groundwater Resources]]

===== International Centre for Tax & Development (ICTD) =====

* [[International Centre for Tax & Development (ICTD)|International Centre for Tax & Development (ICTD) Data]]

===== International Diabetes Federation =====

* [[Diabetes Atlas, International Diabetes Federation|Diabetes Atlas]]

===== [[International Energy Agency (IEA)]] =====
* [[End-Use Prices Data Explorer, International Energy Agency (IEA)|End-Use Prices Data Explorer]]
* [[World Energy Balances, International Energy Agency (IEA)|World Energy Balances]]
* [[World Energy Outlook, International Energy Agency (IEA)|World Energy Outlook]]
* [[World Energy Statistics, International Energy Agency (IEA)|World Energy Statistics]]

===== International Food Policy Research Institute =====

* [[International Food Policy Research Institute Data]]

===== Institute for Health Metrics and Evaluation (IHME) =====

* [[IHME|Global Burdens of Disease (GBD)]]

===== International Institute for Applied Systems Analysis (IIASA) =====

* [[Shared Socioeconomic Pathway (SSP) Extensions Explorer, International Institute for Applied Systems Analysis (IIASA)|Shared Socioeconomic Pathway (SSP) Extensions Explorer]]
* [[RCP Representative Concentration Pathways Database|RCP Database]]

===== International Institute for Management Development (IMD) =====

* [[International Institute for Management Development (IMD) Data]]

===== International Labor Organization (ILO) =====
*[[ILOstat]]
*[[ILO-WIEGO Statistical Reports]]

=====International Monetary Fund (IMF)=====

* [[Balance of Payments and International Investment Position statistics (BoP/IIP), IMF|Balance of Payments and International Investment Position statistics (BoP/IIP)]]
* [[IMF COFOG Classifications of Functions of Government|Classification of the Functions of Government (COFOG)]]
* [[Fiscal Monitor, IMF|Fiscal Monitor]]
* [[GDP & GDPPCPPP from IMF World Economic Outlook & WDI|GDP and GDPPCPPP]]
* [[IMF GFS Government Finance Statistics|Government Finance Statistics (GFS)]]
* [[International Financial Statistics, IMF|International Financial Statistics]]
* [[World Economic Outlook, IMF|World Economic Outlook (WEO)]]
* [[World Revenue Longitudinal Database (WoRLD)]]

===== Inter-Parliamentary Union =====

* [[Women in Parliaments, Inter-Parliamentary Union|Women in Parliaments]]

===== International Renewable Energy Agency (IRENA) =====

* [[Energy Data, International Renewable Energy Agency (IRENA)|Energy Data]]

===== International Research & Exchanges Board (IREX) =====

* [[International Research & Exchanges Board (IREX) Data]]

===== International Study Center, Boston College =====

* [[TIMMS & PIRLS, International Study Center, Boston College|TIMSS & PIRLS]]

===== International Telecommunications Union (ITU) =====

* [[International Telecommunication Union DataHub]]
* [[Global Cybersecurity Index (GCI), ITU|Global Cybersecurity Index (GCI)]]

===== International Organization of Motor Vehicle Manufacturers (OICA) =====

* [[International Organization of Motor Vehicle Manufacturers (OICA) Data]]

===== International Road Federation =====

* [[IRF World Road Statistics|World Road Statistics]]

===== Kellogg Institute For International Studies =====

* [[Kellogg Institute Data]]

===== LIS Cross-National Data Center =====

* [[LIS Cross-National Data Center Data]]

===== Mo Ibrahim Foundation =====

* [[Ibrahim Index of African Governance, Mo Ibrahim Foundation|Ibrahim Index of African Governance]]

===== MyGeoHub =====

* [[MY GEO HUB|Historical Irrigation Dataset (HID)]]

===== National Science Foundation (NSF) =====

* [[Science and Engineering Indicators, National Science Foundation (NSF)|Science and Engineering Indicators]]
* [[Science and Technology Studies (STS), National Science Foundation (NSF)|Science and Technology Studies (STS)]]

===== Organisation of Economic Cooperation and Development (OECD) =====
* International Transport Forum (ITF)
** [[Transport Data and Statistics, International Transport Forum (ITF)|Transport Data and Statistics]]
*[[OECD The Organisation for Economic Co-operation and Development|OECD Data Explorer]]
*[[OECD Education at a glance|OECD Education At a Glance]]
*[[OECD Factbook]]
*[[OECD Information Technology Outlook (ITO)|OECD Information Technology Outlook]]
* [[Measuring the Internet Economy, Organisation for Economic Co-operation and Development (OECD)|Measuring the Internet Economy]]
* [[Pensions at a Glance, Organisation for Economic Co-operation and Development (OECD)|Pensions at a Glance]]
* [[OECD Science & Technology|Science & Technology]]
* [[Social Institutions and Gender Index, OECD|Social Institutions and Gender Index]]
*[[Structural Analysis Database (STAN), OECD|Structural Analysis Database (STAN)]]

===== Political Terror Scale (PTS) =====

* [[Political Terror Scale (PTS) Data]]

===== Population Reference Bureau (PRB) =====

* [[Population Reference Bureau (PRB)|Assorted Annual Population Data Sheets]]

===== Potsdam Institute for Climate Impact Research (PIK) =====

* [[Climate Data, Potsdam Institute for Climate Impact Research (PIK)|Climate Data]]

===== PRIS Project =====

* [[IRIS, PRIS Project|IRIS]]

===== Reporters Without Borders =====

* [[Worldwide Press Freedom Index, Reporters Without Borders|Worldwide Press Freedom Index]]

===== Royal Netherlands Meteorological Institute (WMO KNMI) =====

* [[World Meteorological Organization Climate Explorer Reports]]

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

* [[Stockholm International Peace Research Institute (SIPRI)|Military Expenditure Data]]

===== SpeedTest =====

* [[SpeedTest Data]]

===== The Conference Board =====

* [[Total Economy Database, The Conference Board|Total Economy Database]]

===== Transparency International (TI) =====

* [[Transparency International|Corruption Perceptions Index]]

===== UBS Group AG =====

* [[Global Wealth Report, UBS Group AG|Global Wealth Report]]

===== Uppsala Conflict Data Program (UCDP) =====

* [[Uppsala Conflict Data Program (UCDP) Datasets]]
* [[UCDP/PRIO Armed Conflict Database|Uppsala Conflict Data Program (UCDP)/Peace Research Institute Oslo (PRIO) Armed Conflict Dataset]]

===== United Nations Economic Commission for Europe (UNECE) =====

* [[United Nations Economic Commission for Europe (UNECE) Data|United Nations Economic Commission for Europe Data]]

===== United Nations =====

* [[General Assembly of the United Nations (UNGA)]]
* [[Joint United Nations Programme on HIV/AIDS (UNAIDS)]]
* [[Millennium Development Goals Database, UN|Millennium Development Goals Database]]
* United Nations Department of Economic and Social Affairs
** [[United Nation World Marriage Data, United Nations Department of Economic and Social Affairs|United Nation World Marriage Data]]
* United Nations Educational, Scientific and Cultural Organization (UNESCO)
** [[Institute for Statistics, UNESCO (UIS)]]
* [[United Nations Industrial Development Organization (UNIDO)]]
* United Nations International Children's Emergency Fund (UNICEF)
** [[Child protection and development, UNICEF|Child protection and development]]
*[[United Nations Office of Drug and Crime (UNODC)]]
*United Nations Population Division (UNPD)
** [[United Nations Population Division Department of Economic and Social Affairs]]
** [[World Population Prospects (WPP), United Nations Population Division (UNPD)|World Population Prospects (WPP)]]
** [[UNDP Human Development Reports]]
*[[United Nation SDG Indicators Global Database (UNSDG)]]
*[[United Nations Statistics Division (UNSD)]]
**[[Environmental Indicators, UNSD|Environmental Indicators]]
*[[UN Trade and Development (UNCTAD)|United Nations Trade and Development (UNCTAD)]]
*[[United Nations Treaty Collection]]
*[[World Tourism Organization (UN Tourism)]]

===== University of Maryland =====

* [[Global Terrorism Database, University of Maryland|Global Terrorism Database]]
* [[Peace and Conflict Instability Ledger, University of Maryland/Center for International Development and Conflict Management (CIDCM)|Peace and Conflict Instability Ledger]]
* [[Minorities at risk|All Minorities at Risk (AMAR) Project]]

===== University of Pennsylvania =====

* [[The Political Constraint Index (POLCON), University of Pennsylvania|The Political Constraint Index (POLCON)]]

===== UN University World Institute for Development Economics Research (UNU WIDER) =====

* [[World Income Inequality Database, UN University World Institute for Development Economics Research (UNU WIDER)|World Income Inequality Database]]

===== US Energy Information Administration (EIA) =====

* [[Energy Information Administration (EIA)|Energy Information Administration Data]]

===== US Geological Survey =====

* [[World Petroleum Assessment, U.S. Geological Survey|World Petroleum Assessment]]

===== Vision of Humanity =====

* [[Vision of Humanity Data]]

===== Wittgenstein Centre =====

* [[Wittgenstein Centre Human Capital Data Explorer]]

===== World Bank =====

* [[The Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE), World Bank|Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE)]]
* [[Business Ready (B-READY), World Bank|Business Ready (B-READY)]]
* [[Climate Change Action Plan (CCAP), World Bank|Climate Change Action Plan (CCAP)]]
* [[Climate Change Knowledge Portal, World Bank|Climate Change Knowledge Portal]]
* [[Country Policy and Institutional Assessment (CPIA), World Bank|Country Policy and Institutional Assessment (CPIA)]]
* [[Education Statistics (EdStats), World Bank|Education Statistics (EdStats)]]
* [[GDP & GDPPCPPP from IMF World Economic Outlook & WDI|GDP and GDPPCPPP]]
*[[Gender Statistics, World Bank|Gender Statistics]]
*[[Global Financial Development, World Bank|Global Financial Development]]
*[[Global Integrity Index]]
*[[Informal Economy Database]]
*[[International Development Association, World Bank|International Development Association]]
*[[Poverty and Inequality Platform, World Bank|Poverty and Inequality Platform]]
*[[Private Participation in Infrastructure (PPI) Project Database, World Bank|Private Participation in Infrastructure (PPI) Project Database]]
*[[Road Costs Knowledge System (ROCKS), World Bank|Road Costs Knowledge System (ROCKS)]]
*[[World Development Indicators, World Bank|World Development Indicators (WDI)]]
*[[World Development Report, World Bank|World Development Report]]
*[[Worldwide Governance Indicators|World Governance Indicators (WGI)]]

===== World Democratic Forum (WDF) =====

* [[World Democratic Forum (WDF) Data]]

===== World Economic Forum =====

*[[Global Competitiveness Index, World Economic Forum|Global Competitiveness Index]]

===== [[World Energy Council (WEC)]]=====

* [[Oil & Gas Journal, WEC]]

===== World Health Organization (WHO)=====

*[[Global Health Expenditure Database, WHO|Global Health Expenditure Database]]
*[[Joint Monitoring Programme, WHO/UNICEF|Joint Monitoring Programme (JMP)]]
*[[Surveillance of chronic disease Risk Factors (SuRF), WHO|Surveillance of chronic disease Risk Factors (SuRF)]]
*[[World Malaria Report, WHO|World Malaria Report]]

* [[Global Health Observatory (GHO)]]
** [[Obesity, adult (18+) and child and adolescent (5-19)|Obesity]]
** [[WHO Tobacco Free Initiative (TFI)|Tobacco Free Initiative (TFI)]]

===== World Intellectual Property Organization (WIPO) =====
* [[The Global Innovation Index, World Intellectual Property Organization (WIPO)|The Global Innovation Index]]

===== World Justice Project =====

*[[World Justice Project Rule of Law Index|Rule of Law Index]]

===== World Poverty Clock =====

* [[World Poverty Clock|World Poverty Clock Data]]

=====World Military Expenditures and Arms Transfers (WMEAT)=====

* [[World Military Expenditures and Arms Transfers (WMEAT)|World Military Expenditures and Arms Transfers (WMEAT) Reports]]

=====World Trade Organization (WTO)=====

* [[Dispute Statistics, WTO|Dispute Statistics]]

=====World Value Survey (WVS)=====

* [[World Value Survey|World Value Survey Data]]

World Revenue Longitudinal Database, IMF

2026-02-02T23:15:34Z

Yutang.Xiong: Created page with "The IMF’s World Revenue Longitudinal Database (WoRLD) tracks government revenue trends in 193 countries since the early 1990s. WoRLD tracks directly 9 key components of tax and non-tax revenues, which together contribute about 82 percent of government revenue globally. Some key insights include:"

The IMF’s World Revenue Longitudinal Database (WoRLD) tracks government revenue trends in 193 countries since the early 1990s. WoRLD tracks directly 9 key components of tax and non-tax revenues, which together contribute about 82 percent of government revenue globally. Some key insights include:

Data Sources

2026-02-02T23:14:29Z

Yutang.Xiong: /* International Monetary Fund (IMF) */

=== [[Country Concordances]] ===

=== [[Research Papers/Projects]] ===

=== [[Preprocessor|Preprocessors]] ===

=== [[Template]] ===

== Data Sources ==

===== Barro-Lee =====
* [[Educational Attainment Dataset, Barro-Lee|Education Attainment Dataset]]

===== Bertelsmann Stiftung =====
* [[The Transformation Index (BTI)]]

===== Carbon Dioxide Information and Analysis Center (CDIAC) =====

* [[Carbon Dioxide Information and Analysis Center (CDIAC)|Carbon Emissions Data]]

===== Center for Systemic Peace =====

* [[Polity5]]
* [[Major Episodes of Political Violence (MEPV)]]
* [[State Fragility Index, Center for Systemic Peace|State Fragility Index]]
* [[Political Instability Task Force (PITF), Center for Systematic Peace|Political Instability Task Force (PITF)]]

===== Centre d'Etudes Prospectives et d'Informations Internationales (CEPII) =====

* [[BACI]]
* [[GeoDist, CEPII|GeoDist]]

===== CIA =====

* [[CIA World Factbook]]

===== Climate Watch =====
* [[Emissions Data, Climate Watch|Emissions Data]]

===== Correlates of War =====
* [[The Correlates of War Project, Correlates of War|The Correlates of War Project]]

===== Demographic and Health Surveys (DHS) =====

* [[STATcompiler, Demographic and Health Surveys (DHS)|STATcompiler]]

===== Economist Intelligence Unit (EIU) =====

* [[Economist Intelligence Unit (EIU)|Economist Intelligence Unit (EIU) Data]]
* [[The Political Instability Index, Economist Intelligence Unit (EIU)|The Political Instability Index]]

===== EMBER =====
* [[Electricity Data, EMBER|Electricity Data]]

===== Emissions Database for Global Atmospheric Research =====

* [[Emissions Database for Global Atmospheric Research Data]]

===== Energy Institute =====

* [[Statistical Review of World Energy, Energy Institute|Statistical Review of World Energy]]

===== Europa World Yearbook =====

* [[Europa World Yearbook Data]]

===== European Commission =====

* [[Eurostat European Commission|Eurostat, European Commission]]

===== Federation of American Scientists =====

* [[Federation of American Scientists Data]]

===== Federal Institute for Geosciences and Natural Resources (BGR) =====

* [[Energy Data, BGR|Energy Data]]

===== Food and Agriculture Organization (FAO) =====

* [[AQUASTAT, FAO|AQUASTAT - FAO's Global Information System on Water and Agriculture]]
* [[FishstatJ, FAO|FishStatJ]]
* [[FAOSTAT Food Balance Sheet]]
* [[FAOSTAT Food Security and Nutrition]]
* [[FAOSTAT Land Use|FAOSTAT Land]]
* [[FAOSTAT SDG Indicators]]
* [[FAOSTAT Trade Crops and livestock products]]

===== Fraser Institute =====

* [https://pardeewiki.du.edu//index.php?title=Fraser_International Economic Data]

===== Freedom House =====

* [[Countries at the Crossroads, Freedom House|Countries at the Crossroads]]
* [[FreedomHouse|Freedom in the World]]
* [[Freedom on the Net, Freedom House|Freedom on the Net]]

===== Fund for Peace =====
* [[Fragile States Index, Fund for Peace|Fragile States Index]]

===== Global Carbon Budget (GCB) =====

* [[Carbon Data, Global Carbon Budget (GCB)|Carbon Data]]

===== Global Footprint Network =====

* [[Global Footprint Network|Global Footprint Network Data]]

===== Global Trade Analysis Project (GTAP) =====

* [[Global Trade Analysis Project (GTAP)|Global Trade Analysis Project (GTAP) Data]]

===== Heritage Foundation =====

* [[Heritage Foundation Data]]

===== Institute for Employment Research (IAB) =====

* [[Brain Drain Data, Institute for Employment Research (IAB)|Brain Drain Data]]

===== Inter-American Development Bank (IDB) =====

* [[Database of Political Institutions, World Bank|Database of Political Institutions (DPI)]]

===== International Groundwater Resources Assessment Centre (IGRAC) =====

* [[Global Depletion of Groundwater Resources, International Groundwater Resources Assessment Centre (IGRAC)|Global Depletion of Groundwater Resources]]

===== International Centre for Tax & Development (ICTD) =====

* [[International Centre for Tax & Development (ICTD)|International Centre for Tax & Development (ICTD) Data]]

===== International Diabetes Federation =====

* [[Diabetes Atlas, International Diabetes Federation|Diabetes Atlas]]

===== [[International Energy Agency (IEA)]] =====
* [[End-Use Prices Data Explorer, International Energy Agency (IEA)|End-Use Prices Data Explorer]]
* [[World Energy Balances, International Energy Agency (IEA)|World Energy Balances]]
* [[World Energy Outlook, International Energy Agency (IEA)|World Energy Outlook]]
* [[World Energy Statistics, International Energy Agency (IEA)|World Energy Statistics]]

===== International Food Policy Research Institute =====

* [[International Food Policy Research Institute Data]]

===== Institute for Health Metrics and Evaluation (IHME) =====

* [[IHME|Global Burdens of Disease (GBD)]]

===== International Institute for Applied Systems Analysis (IIASA) =====

* [[Shared Socioeconomic Pathway (SSP) Extensions Explorer, International Institute for Applied Systems Analysis (IIASA)|Shared Socioeconomic Pathway (SSP) Extensions Explorer]]
* [[RCP Representative Concentration Pathways Database|RCP Database]]

===== International Institute for Management Development (IMD) =====

* [[International Institute for Management Development (IMD) Data]]

===== International Labor Organization (ILO) =====
*[[ILOstat]]
*[[ILO-WIEGO Statistical Reports]]

=====International Monetary Fund (IMF)=====

* [[Balance of Payments and International Investment Position statistics (BoP/IIP), IMF|Balance of Payments and International Investment Position statistics (BoP/IIP)]]
* [[IMF COFOG Classifications of Functions of Government|Classification of the Functions of Government (COFOG)]]
* [[Fiscal Monitor, IMF|Fiscal Monitor]]
* [[GDP & GDPPCPPP from IMF World Economic Outlook & WDI|GDP and GDPPCPPP]]
* [[IMF GFS Government Finance Statistics|Government Finance Statistics (GFS)]]
* [[International Financial Statistics, IMF|International Financial Statistics]]
* [[World Economic Outlook, IMF|World Economic Outlook (WEO)]]
* World Revenue Longitudinal Database (WoRLD)

===== Inter-Parliamentary Union =====

* [[Women in Parliaments, Inter-Parliamentary Union|Women in Parliaments]]

===== International Renewable Energy Agency (IRENA) =====

* [[Energy Data, International Renewable Energy Agency (IRENA)|Energy Data]]

===== International Research & Exchanges Board (IREX) =====

* [[International Research & Exchanges Board (IREX) Data]]

===== International Study Center, Boston College =====

* [[TIMMS & PIRLS, International Study Center, Boston College|TIMSS & PIRLS]]

===== International Telecommunications Union (ITU) =====

* [[International Telecommunication Union DataHub]]
* [[Global Cybersecurity Index (GCI), ITU|Global Cybersecurity Index (GCI)]]

===== International Organization of Motor Vehicle Manufacturers (OICA) =====

* [[International Organization of Motor Vehicle Manufacturers (OICA) Data]]

===== International Road Federation =====

* [[IRF World Road Statistics|World Road Statistics]]

===== Kellogg Institute For International Studies =====

* [[Kellogg Institute Data]]

===== LIS Cross-National Data Center =====

* [[LIS Cross-National Data Center Data]]

===== Mo Ibrahim Foundation =====

* [[Ibrahim Index of African Governance, Mo Ibrahim Foundation|Ibrahim Index of African Governance]]

===== MyGeoHub =====

* [[MY GEO HUB|Historical Irrigation Dataset (HID)]]

===== National Science Foundation (NSF) =====

* [[Science and Engineering Indicators, National Science Foundation (NSF)|Science and Engineering Indicators]]
* [[Science and Technology Studies (STS), National Science Foundation (NSF)|Science and Technology Studies (STS)]]

===== Organisation of Economic Cooperation and Development (OECD) =====
* International Transport Forum (ITF)
** [[Transport Data and Statistics, International Transport Forum (ITF)|Transport Data and Statistics]]
*[[OECD The Organisation for Economic Co-operation and Development|OECD Data Explorer]]
*[[OECD Education at a glance|OECD Education At a Glance]]
*[[OECD Factbook]]
*[[OECD Information Technology Outlook (ITO)|OECD Information Technology Outlook]]
* [[Measuring the Internet Economy, Organisation for Economic Co-operation and Development (OECD)|Measuring the Internet Economy]]
* [[Pensions at a Glance, Organisation for Economic Co-operation and Development (OECD)|Pensions at a Glance]]
* [[OECD Science & Technology|Science & Technology]]
* [[Social Institutions and Gender Index, OECD|Social Institutions and Gender Index]]
*[[Structural Analysis Database (STAN), OECD|Structural Analysis Database (STAN)]]

===== Political Terror Scale (PTS) =====

* [[Political Terror Scale (PTS) Data]]

===== Population Reference Bureau (PRB) =====

* [[Population Reference Bureau (PRB)|Assorted Annual Population Data Sheets]]

===== Potsdam Institute for Climate Impact Research (PIK) =====

* [[Climate Data, Potsdam Institute for Climate Impact Research (PIK)|Climate Data]]

===== PRIS Project =====

* [[IRIS, PRIS Project|IRIS]]

===== Reporters Without Borders =====

* [[Worldwide Press Freedom Index, Reporters Without Borders|Worldwide Press Freedom Index]]

===== Royal Netherlands Meteorological Institute (WMO KNMI) =====

* [[World Meteorological Organization Climate Explorer Reports]]

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

* [[Stockholm International Peace Research Institute (SIPRI)|Military Expenditure Data]]

===== SpeedTest =====

* [[SpeedTest Data]]

===== The Conference Board =====

* [[Total Economy Database, The Conference Board|Total Economy Database]]

===== Transparency International (TI) =====

* [[Transparency International|Corruption Perceptions Index]]

===== UBS Group AG =====

* [[Global Wealth Report, UBS Group AG|Global Wealth Report]]

===== Uppsala Conflict Data Program (UCDP) =====

* [[Uppsala Conflict Data Program (UCDP) Datasets]]
* [[UCDP/PRIO Armed Conflict Database|Uppsala Conflict Data Program (UCDP)/Peace Research Institute Oslo (PRIO) Armed Conflict Dataset]]

===== United Nations Economic Commission for Europe (UNECE) =====

* [[United Nations Economic Commission for Europe (UNECE) Data|United Nations Economic Commission for Europe Data]]

===== United Nations =====

* [[General Assembly of the United Nations (UNGA)]]
* [[Joint United Nations Programme on HIV/AIDS (UNAIDS)]]
* [[Millennium Development Goals Database, UN|Millennium Development Goals Database]]
* United Nations Department of Economic and Social Affairs
** [[United Nation World Marriage Data, United Nations Department of Economic and Social Affairs|United Nation World Marriage Data]]
* United Nations Educational, Scientific and Cultural Organization (UNESCO)
** [[Institute for Statistics, UNESCO (UIS)]]
* [[United Nations Industrial Development Organization (UNIDO)]]
* United Nations International Children's Emergency Fund (UNICEF)
** [[Child protection and development, UNICEF|Child protection and development]]
*[[United Nations Office of Drug and Crime (UNODC)]]
*United Nations Population Division (UNPD)
** [[United Nations Population Division Department of Economic and Social Affairs]]
** [[World Population Prospects (WPP), United Nations Population Division (UNPD)|World Population Prospects (WPP)]]
** [[UNDP Human Development Reports]]
*[[United Nation SDG Indicators Global Database (UNSDG)]]
*[[United Nations Statistics Division (UNSD)]]
**[[Environmental Indicators, UNSD|Environmental Indicators]]
*[[UN Trade and Development (UNCTAD)|United Nations Trade and Development (UNCTAD)]]
*[[United Nations Treaty Collection]]
*[[World Tourism Organization (UN Tourism)]]

===== University of Maryland =====

* [[Global Terrorism Database, University of Maryland|Global Terrorism Database]]
* [[Peace and Conflict Instability Ledger, University of Maryland/Center for International Development and Conflict Management (CIDCM)|Peace and Conflict Instability Ledger]]
* [[Minorities at risk|All Minorities at Risk (AMAR) Project]]

===== University of Pennsylvania =====

* [[The Political Constraint Index (POLCON), University of Pennsylvania|The Political Constraint Index (POLCON)]]

===== UN University World Institute for Development Economics Research (UNU WIDER) =====

* [[World Income Inequality Database, UN University World Institute for Development Economics Research (UNU WIDER)|World Income Inequality Database]]

===== US Energy Information Administration (EIA) =====

* [[Energy Information Administration (EIA)|Energy Information Administration Data]]

===== US Geological Survey =====

* [[World Petroleum Assessment, U.S. Geological Survey|World Petroleum Assessment]]

===== Vision of Humanity =====

* [[Vision of Humanity Data]]

===== Wittgenstein Centre =====

* [[Wittgenstein Centre Human Capital Data Explorer]]

===== World Bank =====

* [[The Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE), World Bank|Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE)]]
* [[Business Ready (B-READY), World Bank|Business Ready (B-READY)]]
* [[Climate Change Action Plan (CCAP), World Bank|Climate Change Action Plan (CCAP)]]
* [[Climate Change Knowledge Portal, World Bank|Climate Change Knowledge Portal]]
* [[Country Policy and Institutional Assessment (CPIA), World Bank|Country Policy and Institutional Assessment (CPIA)]]
* [[Education Statistics (EdStats), World Bank|Education Statistics (EdStats)]]
* [[GDP & GDPPCPPP from IMF World Economic Outlook & WDI|GDP and GDPPCPPP]]
*[[Gender Statistics, World Bank|Gender Statistics]]
*[[Global Financial Development, World Bank|Global Financial Development]]
*[[Global Integrity Index]]
*[[Informal Economy Database]]
*[[International Development Association, World Bank|International Development Association]]
*[[Poverty and Inequality Platform, World Bank|Poverty and Inequality Platform]]
*[[Private Participation in Infrastructure (PPI) Project Database, World Bank|Private Participation in Infrastructure (PPI) Project Database]]
*[[Road Costs Knowledge System (ROCKS), World Bank|Road Costs Knowledge System (ROCKS)]]
*[[World Development Indicators, World Bank|World Development Indicators (WDI)]]
*[[World Development Report, World Bank|World Development Report]]
*[[Worldwide Governance Indicators|World Governance Indicators (WGI)]]

===== World Democratic Forum (WDF) =====

* [[World Democratic Forum (WDF) Data]]

===== World Economic Forum =====

*[[Global Competitiveness Index, World Economic Forum|Global Competitiveness Index]]

===== [[World Energy Council (WEC)]]=====

* [[Oil & Gas Journal, WEC]]

===== World Health Organization (WHO)=====

*[[Global Health Expenditure Database, WHO|Global Health Expenditure Database]]
*[[Joint Monitoring Programme, WHO/UNICEF|Joint Monitoring Programme (JMP)]]
*[[Surveillance of chronic disease Risk Factors (SuRF), WHO|Surveillance of chronic disease Risk Factors (SuRF)]]
*[[World Malaria Report, WHO|World Malaria Report]]

* [[Global Health Observatory (GHO)]]
** [[Obesity, adult (18+) and child and adolescent (5-19)|Obesity]]
** [[WHO Tobacco Free Initiative (TFI)|Tobacco Free Initiative (TFI)]]

===== World Intellectual Property Organization (WIPO) =====
* [[The Global Innovation Index, World Intellectual Property Organization (WIPO)|The Global Innovation Index]]

===== World Justice Project =====

*[[World Justice Project Rule of Law Index|Rule of Law Index]]

===== World Poverty Clock =====

* [[World Poverty Clock|World Poverty Clock Data]]

=====World Military Expenditures and Arms Transfers (WMEAT)=====

* [[World Military Expenditures and Arms Transfers (WMEAT)|World Military Expenditures and Arms Transfers (WMEAT) Reports]]

=====World Trade Organization (WTO)=====

* [[Dispute Statistics, WTO|Dispute Statistics]]

=====World Value Survey (WVS)=====

* [[World Value Survey|World Value Survey Data]]

Poverty and Inequality Platform, World Bank

2026-01-29T22:03:12Z

Yutang.Xiong: /* Summary */

== Summary ==
The [https://pip.worldbank.org/home Poverty and Inequality Platform (PIP)] is an interactive computational tool that offers users quick access to the World Bank’s estimates of poverty, inequality, and shared prosperity. PIP provides a comprehensive view of global, regional, and country-level trends for more than 160 economies around the world. All poverty and inequality indicators are organized under the dropdown “Data”, where users can access the different measures. The visualizations available allow for multiple ways to explore PIP indicators, from cross-country and regional analyses, to country-specific and subnational, while other pages provide in-depth analyses by topic.

The last update was in June 2025. This update brought an additional 74 country-year datapoints (countries such as Barbados and Equatorial Guinea) to the PIP database and improved existing data for another 90 country-years. A detailed update can be found [https://documents1.worldbank.org/curated/en/099510306052516849/pdf/IDU-eb272b02-ecd1-4633-9e37-9297e20a711c.pdf here].

Another update in August 2025 which led to the correction of three new tables. This is due to the World Bank adding a new modeling around 2021 PPP.

Note that throughout update from World Bank, data pertain to old poverty thresholds might not be available anymore. It is suggested to check throughly and mark old tables as legacy tables if World Bank stops updating them, or if IFs does not use them anymore.
== Tables in IFs ==

{| class="wikitable"
|+
!Variable
!Definition
!Name In Source
!UsedInPreprocessor
!UsedInPreprocessorFileName
!UsedInHistAnalog
|-
|IncBelow2D15c%PIP
|Percent of population living on less than $2.15 per day, estimation by PIP
|headcount
|1
|ECONOMY
|TRUE
|-
|IncBelow3D65c%PIP
|Percent of population living on less than $3.65 per day, estimation by PIP
|headcount
|1
|ECONOMY
|TRUE
|-
|IncBelow6D85c%PIP
|Percent of population living on less than $6.85 per day, estimation by PIP
|headcount
|1
|ECONOMY
|TRUE
|-
|IncMedPIP
|Median income estimated by PIP from WorldBank, $2017 PPP per day
|median
|0
|
|FALSE
|-
|IncMed2021PPPPIP
|Median income estimated by PIP from WorldBank, $2021 PPP per day
|median
|0
|
|FALSE
|-
|PovGap2D15cperDayPIP
|Mean shortfall in income or consumption from the poverty line $2.15 a day, expressed as a percentage of the poverty line. Estimation by PIP
|poverty_gap
|0
|
|FALSE
|-
|PovGap3D55cperDayPIP
|Mean shortfall in income or consumption from the poverty line $3.65 a day, expressed as a percentage of the poverty line. Estimation by PIP
|poverty_gap
|0
|
|FALSE
|-
|PovGap6D85cperDayPIP
|Mean shortfall in income or consumption from the poverty line $6.85 a day, expressed as a percentage of the poverty line. Estimation by PIP
|poverty_gap
|0
|
|FALSE
|-
|IncBelow3D00c%PIP
|Percent of population living on less than $3.00 per day, estimation by PIP
|headcount
|1
|ECONOMY
|TRUE
|-
|IncBelow4D20c%PIP
|Percent of population living on less than $4.20 per day, estimation by PIP
|headcount
|1
|ECONOMY
|TRUE
|-
|IncBelow8D30c%PIP
|Percent of population living on less than $8.30 per day, estimation by PIP
|headcount
|1
|ECONOMY
|TRUE
|-
|IncMed2021PPPPIP
|Median income estimated by PIP from WorldBank, $2021 PPP per day
|median
|0
|
|FALSE
|-
|PovGap3D00cperDayPIP
|Mean shortfall in income or consumption from the poverty line $3.00 a day, expressed as a percentage of the poverty line. Estimation by PIP
|poverty_gap
|0
|
|FALSE
|-
|PovGap4D20cperDayPIP
|Mean shortfall in income or consumption from the poverty line $4.20 a day, expressed as a percentage of the poverty line. Estimation by PIP
|poverty_gap
|0
|
|FALSE
|-
|PovGap8D30cperDayPIP
|Mean shortfall in income or consumption from the poverty line $8.30 a day, expressed as a percentage of the poverty line. Estimation by PIP
|poverty_gap
|0
|
|FALSE
|-
|PovLineNationalWB
|Poverty, income/consumption threshold per person per day, national, in 2011 PPP$
|
|1
|ECONOMY
|FALSE
|-
|PovLineNationalWB2017
|Poverty, income/consumption threshold per person per day, national, in 2011 PPP$
|
|1
|ECONOMY
|FALSE
|-
|PovLineNationalWB2021
|Poverty, income/consumption threshold per person per day, national, in 2021 PPP$
|harm_npl_all_2021ppp
|1
|ECONOMY
|FALSE
|}

== Data Pulling Instructions ==

# Go to this site (https://pip.worldbank.org/home). Make sure the mode is 2017 PPP or 2021 PPP depending on the tables you are updating. Change this in the top right. [[File:Poverty and Inequality Platform.png|none|thumb|700x700px]]
# Select Interpolated Values
# Select Poverty Rate. The data you need will show up in one excel (ie if you need the poverty rate and gap for $3.00, it will show up in one excel). You do not need to select any other indicator or countries.
# Click download and choose download '''all country data'''. While the excel file has different indicators you do need to download different excels for the different dollar amounts (you will have one excel for $4.20, another for $3.00, etc).
## To select different dollar amounts shift the slider in the top left corner or type in the value.
# Filter your reporting data by national (except for Argentina, we can use urban data. For all the other countries it must be national.), filter survey by consumption [and income separately to blend values], headcount, poverty_gap, and median (median is the same in all excel files) will be the indicators for your data.
# After that select survey type income to blend these values in. Consumption should be the preferred survey type but to make sure we have as many values as possible use income has a backdrop.
# To use a script, navigate to https://github.com/n1shamin/Poverty-and-Inequality-Platform. This does the entire process (including Argentina, blending consumption and income, etc).

== Notes ==
For the series:

* IncBelow2D15c%PIP
* IncBelow3D65c%PIP
* IncBelow6D85c%PIP
* PovGap2D15cperDayPIP
* PovGap3D55cperDayPIP
* PovGap6D85cperDayPIP

Multiply the data points by 100 to get the percentage values.

The survey type must be consumption first then income second.

For Argentina, we can use urban data. For all the other countries it must be national.

Make sure that you choose consumption and not income for type of survey at first.

For a country concordance table for the World Bank check this [https://github.com/n1shamin/World-Development-Indicators-Update-2025 GitHub Repo].

Poverty and Inequality Platform, World Bank

2026-01-29T22:00:49Z

Yutang.Xiong: /* Tables in IFs */

== Summary ==
The [https://pip.worldbank.org/home Poverty and Inequality Platform (PIP)] is an interactive computational tool that offers users quick access to the World Bank’s estimates of poverty, inequality, and shared prosperity. PIP provides a comprehensive view of global, regional, and country-level trends for more than 160 economies around the world. All poverty and inequality indicators are organized under the dropdown “Data”, where users can access the different measures. The visualizations available allow for multiple ways to explore PIP indicators, from cross-country and regional analyses, to country-specific and subnational, while other pages provide in-depth analyses by topic.

The last update was in June 2025. This update brought an additional 74 country-year datapoints (countries such as Barbados and Equatorial Guinea) to the PIP database and improved existing data for another 90 country-years. A detailed update can be found [https://documents1.worldbank.org/curated/en/099510306052516849/pdf/IDU-eb272b02-ecd1-4633-9e37-9297e20a711c.pdf here].

Another update in August 2025 which led to the correction of three new tables. This is due to the World Bank adding a new modeling around 2021 PPP.
== Tables in IFs ==

{| class="wikitable"
|+
!Variable
!Definition
!Name In Source
!UsedInPreprocessor
!UsedInPreprocessorFileName
!UsedInHistAnalog
|-
|IncBelow2D15c%PIP
|Percent of population living on less than $2.15 per day, estimation by PIP
|headcount
|1
|ECONOMY
|TRUE
|-
|IncBelow3D65c%PIP
|Percent of population living on less than $3.65 per day, estimation by PIP
|headcount
|1
|ECONOMY
|TRUE
|-
|IncBelow6D85c%PIP
|Percent of population living on less than $6.85 per day, estimation by PIP
|headcount
|1
|ECONOMY
|TRUE
|-
|IncMedPIP
|Median income estimated by PIP from WorldBank, $2017 PPP per day
|median
|0
|
|FALSE
|-
|IncMed2021PPPPIP
|Median income estimated by PIP from WorldBank, $2021 PPP per day
|median
|0
|
|FALSE
|-
|PovGap2D15cperDayPIP
|Mean shortfall in income or consumption from the poverty line $2.15 a day, expressed as a percentage of the poverty line. Estimation by PIP
|poverty_gap
|0
|
|FALSE
|-
|PovGap3D55cperDayPIP
|Mean shortfall in income or consumption from the poverty line $3.65 a day, expressed as a percentage of the poverty line. Estimation by PIP
|poverty_gap
|0
|
|FALSE
|-
|PovGap6D85cperDayPIP
|Mean shortfall in income or consumption from the poverty line $6.85 a day, expressed as a percentage of the poverty line. Estimation by PIP
|poverty_gap
|0
|
|FALSE
|-
|IncBelow3D00c%PIP
|Percent of population living on less than $3.00 per day, estimation by PIP
|headcount
|1
|ECONOMY
|TRUE
|-
|IncBelow4D20c%PIP
|Percent of population living on less than $4.20 per day, estimation by PIP
|headcount
|1
|ECONOMY
|TRUE
|-
|IncBelow8D30c%PIP
|Percent of population living on less than $8.30 per day, estimation by PIP
|headcount
|1
|ECONOMY
|TRUE
|-
|IncMed2021PPPPIP
|Median income estimated by PIP from WorldBank, $2021 PPP per day
|median
|0
|
|FALSE
|-
|PovGap3D00cperDayPIP
|Mean shortfall in income or consumption from the poverty line $3.00 a day, expressed as a percentage of the poverty line. Estimation by PIP
|poverty_gap
|0
|
|FALSE
|-
|PovGap4D20cperDayPIP
|Mean shortfall in income or consumption from the poverty line $4.20 a day, expressed as a percentage of the poverty line. Estimation by PIP
|poverty_gap
|0
|
|FALSE
|-
|PovGap8D30cperDayPIP
|Mean shortfall in income or consumption from the poverty line $8.30 a day, expressed as a percentage of the poverty line. Estimation by PIP
|poverty_gap
|0
|
|FALSE
|-
|PovLineNationalWB
|Poverty, income/consumption threshold per person per day, national, in 2011 PPP$
|
|1
|ECONOMY
|FALSE
|-
|PovLineNationalWB2017
|Poverty, income/consumption threshold per person per day, national, in 2011 PPP$
|
|1
|ECONOMY
|FALSE
|-
|PovLineNationalWB2021
|Poverty, income/consumption threshold per person per day, national, in 2021 PPP$
|harm_npl_all_2021ppp
|1
|ECONOMY
|FALSE
|}

== Data Pulling Instructions ==

# Go to this site (https://pip.worldbank.org/home). Make sure the mode is 2017 PPP or 2021 PPP depending on the tables you are updating. Change this in the top right. [[File:Poverty and Inequality Platform.png|none|thumb|700x700px]]
# Select Interpolated Values
# Select Poverty Rate. The data you need will show up in one excel (ie if you need the poverty rate and gap for $3.00, it will show up in one excel). You do not need to select any other indicator or countries.
# Click download and choose download '''all country data'''. While the excel file has different indicators you do need to download different excels for the different dollar amounts (you will have one excel for $4.20, another for $3.00, etc).
## To select different dollar amounts shift the slider in the top left corner or type in the value.
# Filter your reporting data by national (except for Argentina, we can use urban data. For all the other countries it must be national.), filter survey by consumption [and income separately to blend values], headcount, poverty_gap, and median (median is the same in all excel files) will be the indicators for your data.
# After that select survey type income to blend these values in. Consumption should be the preferred survey type but to make sure we have as many values as possible use income has a backdrop.
# To use a script, navigate to https://github.com/n1shamin/Poverty-and-Inequality-Platform. This does the entire process (including Argentina, blending consumption and income, etc).

== Notes ==
For the series:

* IncBelow2D15c%PIP
* IncBelow3D65c%PIP
* IncBelow6D85c%PIP
* PovGap2D15cperDayPIP
* PovGap3D55cperDayPIP
* PovGap6D85cperDayPIP

Multiply the data points by 100 to get the percentage values.

The survey type must be consumption first then income second.

For Argentina, we can use urban data. For all the other countries it must be national.

Make sure that you choose consumption and not income for type of survey at first.

For a country concordance table for the World Bank check this [https://github.com/n1shamin/World-Development-Indicators-Update-2025 GitHub Repo].

Joint United Nations Programme on HIV/AIDS (UNAIDS)

2026-01-29T18:45:04Z

Yutang.Xiong: /* Tables in IFs */

= Summary =
The '''[https://aidsinfo.unaids.org Joint United Nations Programme on HIV/AIDS (UNAIDS)]''' is a global partnership that leads and coordinates international efforts to end HIV/AIDS as a public health threat. Established in 1996, it brings together 11 UN agencies to respond to the HIV/AIDS epidemic. UNAIDS plays a pivotal role in maintaining global solidarity, reducing stigma and discrimination, and promoting sustainable development goals related to health. The data is located under "Epidemic and Response" on the homepage of UNAIDS, where users can access data for each indicator through "Data Sheets".
Last updated in May of 2025.

= Tables in IFs =
The following 6 tables in IFs are pulled from UNAIDS.
{| class="wikitable"
|+
!Variable
!Definition
!Formula
!Name In Source
!UsedInPreprocessor
!UsedInPreprocessorFileName
|-
|HealthUNAIDSTotalHIVHighEst
|High estimate of nuber of people living with HIV
|/1,000,000
|People living with HIV - All ages
|1
|POPULATION
|-
|HealthUNAIDSTotalHIVMidEst
|Mid-range estimate of number of total population with HIV
|/1,000,000
|People living with HIV - All ages
|1
|POPULATION
|-
|HealthUNAIDSTotalHIVLowEst
|Low estimate of number of total population living with HIV
|/1,000,000
|People living with HIV - All ages
|1
|POPULATION
|-
|HealthUNAIDSDeathsLowEst
|Low estimate of number of deaths from AIDS
|/1,000,000
|AIDS-related deaths - All ages
|1
|POPULATION
|-
|HealthUNAIDSDeathsHighEst
|High estimate of number of deaths from AIDS
|/1,000,000
|AIDS-related deaths - All ages
|1
|POPULATION
|-
|HealthUNAIDSARTCoverMidEst
|Mid-range estimate antiretroviral therapy coverage (%)
|/100
|People living with HIV receiving ART (%)
|1
|HEALTH
|}

= Data Pulling Instructions =
# Go to this site: https://aidsinfo.unaids.org/ and click on Epidemic and Responses
# Then in the indicator search bar, search up the indicators you need.
# The data sheets you will need are under "Data Sheet." You can either export a CSV or XLS file. For this update, '''CSV''' files were used.[[File:UNAIDS.png|none|thumb|700x700px]]
# This is what the data will look like upon downloading the CSV file from UNAIDS.[[File:UNAIDS Data Pull.png|thumb|1147x1147px|none]]
# Each file will have four types of data for the years: 2010 2010_lower 2010_upper. The year in the numerical format (2010) is the mid range estimate. The year_upper is the high estimate number and the year_lower is the low estimate number. This is how we will organize the data for the respective series. '''Make sure to delete the footnote column.'''
# It is imperative that all symbols are removed from the file (this includes <, > signs, ... that acts as NULLs,) so that the data can import properly.
# '''To pull the following tables:'''
## SeriesHealthUNAIDSTotalHIVHighEst
## HealthUNAIDSTotalHIVMidEst
## HealthUNAIDSTotalHIVLowEst
### Search "People living with HIV - All ages" in the bar.
### For these tables, the unit is Millions. Divide the data in the excel by /1,000,000 to get the correct unit.
# '''To pull the following tables:'''
## HealthUNAIDSDeathsLowEst
## HealthUNAIDSDeathsHighEst
### Search "AIDS-related deaths - All ages" in the bar.
### For these tables, the unit is Millions. Divide the data in the excel by /1,000,000 to get the correct unit.
# '''To pull the following table:'''
## HealthUNAIDSARTCoverMidEst
### Search "People living with HIV receiving ART" in the bar.
### For these tables, the unit is percent in decimal form. Divide the data in the excel by /100 to get the correct unit.

= Notes =
# Blending will be imperative as some of the data from countries was not included in the latest update of their data.
# "UNAIDS changes their data because they incorporate new surveillance and program data into their models each year"
# If the data is downloaded into a '''XLS''' file, the sheet will be formatted with LowEst, MidEst, and HighEst in the same cells. Visit this GitHub repository for a python script to separate the data into their own sheets: https://github.com/fr3nch1/Automations/blob/main/%23%20Data%20Separation%20Script.py

Global Health Expenditure Database, WHO

2026-01-29T18:17:26Z

Yutang.Xiong: /* November 2024 Update: */

= Summary =
The Global Health Expenditure Database (GHED), maintained by the World Health Organization (WHO), provides country-level data on health financing, health expenditures, demographics, economic conditions, and health for 194 countries and territories since 2000 with open access to the public.

The data are available here: http://apps.who.int/nha/database

= Table In IFs =
We pull three preprocessors from this site: HealthExpGov%Tot, HealthExpPriv%Tot, HealthExpTot%GDP. These tables were updated in April of 2025.
{| class="wikitable"
|Tables
|Definition
|Unit
|Years
|Source
|Original Source
|Preprocessor
|Preprocessor Name
|Code
|Decimal Points
|-
|HealthExpTot%GDP
|Current health expenditure (CHE) as percentage of gross domestic product (GDP) (%)
|Percent
|2000-2023
|WHO Global Health Expenditure Database
|<nowiki>https://apps.who.int/nha/database</nowiki>
|1
|HEALTH
|che_gdp
|2
|-
|HealthExpPriv%Tot
|Domestic private health expenditure (PVT-D) as percentage of current health expenditure (CHE) (%)
|Percent
|2000-2023
|WHO Global Health Expenditure Database
|<nowiki>https://apps.who.int/nha/database</nowiki>
|1
|HEALTH
|pvtd_che
|2
|-
|HealthExpGov%Tot
|Domestic General Government Health Expenditure (GGHE-D) as % Current Health Expenditure (CHE)
|Percent
|2000-2023
|WHO Global Health Expenditure Database
|<nowiki>https://apps.who.int/nha/database</nowiki>
|1
|HEALTH
|gghed_che
|2
|}

===== November 2024 Update: =====
Created 9 tables:

# SeriesHealthExpCurrTot%GDP
# SeriesHealthExpCurrDomPrivate%GDP
# SeriesHealthExpCurrDomGenGovt%GDP
# SeriesHealthExpCurrExt%GDP
# SeriesHealthExpCapTot%GDP
# SeriesHealthExpCapDomTot%GDP
# SeriesHealthExpCapExt%GDP
# SeriesHealthExpCapDomPublic%GDP
# SeriesHealthExpCapDomPrivate%GDP
These tables are in IFs since version 8.54, and should be updated as well in future updates.

= Data Pulling Instructions =
There are two ways you can pull data. The first way is through the Global Health Expenditure Database bulk data download.

Go to https://apps.who.int/nha/database/Select/Indicators/en

1) Select download all data in XLSX format
[[File:Global health Expenditure Database.png|none|thumb|500x500px]]
2) Search for your indicator in the tab 'Codebook'
[[File:Global health Expenditure Database excel sheet.png|none|thumb|700x700px]]
3) The variable code is the first column, use this code to found your data in the tab 'Data'. For example the code 'che_gdp' refers to Current Health Expenditure (CHE) as % Gross Domestic Product (GDP)

'''Please do not pull the data through their interface as that data is not accurate. The bulk data will be the most accurate data.'''

The second way is to use GHO indicators page

Go to: https://www.who.int/data/gho/data/indicators

1) Search your indicator. It is very case sensitive.
[[File:WHO Indicator.png|none|thumb|500x500px]]

2) The excel file you need will be in the tab Data
[[File:WHO Data.png|none|thumb|500x500px]]
3) On the right side there will be the link that states EXPORT DATA in CSV. Right click on that link and save the link. You will get the csv excel file doing this.
[[File:WHO Data Link.png|none|thumb|400x400px]]

= Data Notes =
* WDI Revision states that indicators SH.XPD.TOTL.ZS Health expenditure, total (% of GDP) and SH.XPD.PRIV.ZS Health expenditure, private (% of GDP) have been deleted in April of 2018.
* To replace these we pulled Current health expenditure (CHE) as percentage of gross domestic product (GDP) (%) for HealthExpTot%GDP and Domestic private health expenditure (PVT-D) as percentage of current health expenditure (CHE) (%) for HealthExpPriv%Tot
* ''The aggregation rule for these three series is set to "none" which creates issues when viewing historical data.''

Agriculture

2026-01-23T03:26:26Z

Yutang.Xiong:

Please cite as: Dale S. Rothman, Hughes, Barry B., and Kanishka Narayan. 2017. ''"IFs Agriculture Model Documentation." ''Working paper 2017.07.04. Pardee Center for International Futures, Josef Korbel School of International Studies, University of Denver, Denver, CO. Accessed DD Month YYYY <[https://pardeewiki.du.edu/index.php?title=Agriculture]>

The IFs agricultural model tracks the supply and demand, including imports, exports, and prices, of three agricultural commodities: crops, meat, and fish. Crops, meat and fish have direct food, animal feed, industrial and food manufacturing uses. The agricultural model is also where land use dynamics and water use are tracked in IFs, as these are key resources for the agricultural sector.

The structure of the agriculture model is very much like that of the economic model. It combines a growth process with a partial economic equilibrium process using stocks and prices to seek a balance between the demand and supply sides. As in the economic model, no effort is made in the standard adjustment mechanism to obtain a precise equilibrium in any time step. Instead stocks serve as a temporary buffer and the model chases equilibrium over time.

The most important linkages between the agriculture model and other models within IFs are with the economic model. The economic model provides forecasts of average income levels, labor supply, total consumer spending, and agricultural investment, all of which are used in the agriculture model. In turn, the agriculture model provides forecasts on agricultural production, imports, exports, and demand for investment, which override the sectoral computations in the economic model. The agricultural model also has important links to the population and health models, using population forecasts and providing forecasts of calorie availability.

= Dominant Relations =

Agricultural production is a function of the availability of resources, e.g. land, livestock, capital, and labor, as well as climate factors and technology. Technology is most directly seen in the changing productivity of land in terms of crop yields, and in the production of meat relative to the input level of feed grain. The model also accounts for lost production (such as spoilage in the fields or in the first stages of the food supply chain), distribution and transformation losses and consumption losses (which account for food lost at the household levels) which are all determined by average income.

Agricultural demand depends on average incomes, prices, and a number of other factors. For example, changing diets can affect the demand for meat, which in turn affects the demand for feed crops. The industrial demand for crops, some of which is directed to the production of biofuels, is also affected by energy prices.

Production and demand, along with existing and desired stocks and historical trade patterns determine the trade in agricultural products. The differences in the supply of crops, meat, and fish (production after accounting for losses and trade) and the demand for these commodities are reflected in shifts in agricultural stocks. Stock shortages feed forward to actual consumption, which is addressed in the population model of IFs. Stocks, particularly changes in stocks, are a key driver of changes in crop prices. Crop prices are also influenced by the returns to agricultural investment and therefore to the basic underlying cost structure. Meat prices are tied to, and track world crop prices, while changes in fish prices are driven by changes in fish stocks.

Stocks and stock changes also play a role, along with general economic and agricultural demand growth, in driving the demand for agricultural investment. The actual levels of investment are finalized in the economic model of IFs and subject to constraints there. The investment can be of two types – investment for expanding and maintaining cropland (extensification) and investment for increasing crop yields per unit area (intensification). The expected relative rates of return determine the split.

The final key dynamics addressed in the agriculture model relate to land, livestock, and water. The latter of these is very straightforward, driven only by crop production. Changes in livestock are determined by changes in the amount of available grazing land, changes in the demand for meat, and the ability of countries to meet this demand as reflected in changing stocks.

In the IFs model, land is divided into 5 categories: crop land, grazing land, forest land, ’other’ land, and urban or built-up land. First, changes in urban land are driven by changes in average income and population, and draws from all other land types. Second, the investment in cropland development is the primary driver of changes in cropland, with shifts being compensated by changes in forest and "other" land. Third, changes in grazing land are a function of average income, with shifts again being compensated by changes in forest and "other" land. Finally, conservation policies can influence the amount of forest land, with any necessary adjustments coming from crop and grazing land. 

= Structure and Agent System =

{| class="tableGrid" style="width:100%;" cellspacing="0" cellpadding="5" border="1"
|-
| style="width: 50%" | <div>'''System/Subsystem''' </div>
| style="text-align: left; padding-left: 10px" align="center" | <div>Agriculture</div>
|-
| style="text-align: left" | <div>'''Organizing Structure'''</div>
| style="text-align: left; padding-left: 10px" align="center" | <div>Partial market equilibrium</div>
|-
| style="text-align: left" | <div>'''Stocks'''</div>
| style="text-align: left; padding-left: 10px" align="center" | <div>Capital, labor, accumulated technology, agricultural commodities, land</div>
|-
| style="text-align: left" | <div>'''Flows'''</div>
| style="text-align: left; padding-left: 10px" align="center" | <div>Production, loss, consumption, trade, investment</div>
|-
| style="text-align: left" | <div>'''Key Aggregate ''' '''Relationships '''</div><div>(illustrative, not comprehensive)</div>
| style="text-align: left; padding-left: 10px" align="center" | <div>Production function with endogenous technological change  </div><div> </div><div>Price determination</div>
|-
| style="text-align: left" | <div style="text-align: left">'''Key Agent-Class Behavioral ''' '''Relationships'''</div><div style="text-align: left">(illustrative, not comprehensive)</div>
| style="text-align: left; padding-left: 10px" align="center" |
Household crop, meat, and fish consumption

Industry crop use

Livestock producers crop use

|}

= Flow Charts =

== Overview ==

The agriculture model combines a growth process in production with a partial equilibrium process that replaces the agricultural sector in the full-equilibrium economic model unless the user disconnects it. The model represents three agricultural commodities: crop, meat, and fish.

The key equilibrating variables are the stocks of the three commodities. Equilibration works via investment to control capital stock and via prices to control domestic demand.

Specifically, as food stocks rise, investment falls, restraining capital stock and agricultural production, and thus holding down stocks. Also, as stocks rise, prices fall, thereby increasing domestic demand, further holding down stocks. Domestic production and demand also influence imports and exports directly, which further affect stocks.

== Agricultural Production ==

=== Crop Production ===

Crop production is most simply a product of the land under cultivation (cropland) and the crop yield per hectare of land. Yield is determined in a Cobb-Douglas type production function, the inputs to which are agricultural capital, labor, and technical change. Technical change is conceptualized as being responsive to price signals, but the model uses food stocks in the computation to enhance control over the temporal dynamics of responsiveness.  Specifically, technology responds to the imbalance between desired and actual food stocks globally.  In addition there is a direct response of yield change to domestic food stocks that represents not so much technical change as farmer behavior in the fact of market conditions (e.g. planting more intensively). Overall, basic annual yield growth is bound by the maximum of the initial model year's yield growth and an exogenous parameter of maximum growth.

This basic yield function is further subject to a saturation factor that is computed internally to the model̶–investments in increasing yield are subject to diminishing rather than constant returns to scale. Moreover, changes in atmospheric carbon dioxide (CO2) will affect agricultural yields both directly through CO2 and indirectly through changes in temperature and precipitation. Finally, the user can rely on parameters to increase or decrease yield patterns indirectly with a multiplier or to use parameters to control the saturation effect and the direct and indirect effects of CO2 on crop yield.

[[File:CropproductionFlowchartKN.png|frame|center|text-bottom|571x500px|Agricultural Production Flowchart]]

=== Meat and Fish Production ===

Meat and fish production are represented far more simply than crop production. Meat production is simply the product of livestock herd size and the slaughter rate. Meat production includes production of non-meat animal products (eg. Milk and eggs). The herd size changes over time in response to global and domestic meat stocks, as well as changes in the demand for meat and the amount of grazing land.

Fish production has two components: wild catch and aquaculture. The former is based on actual data and an exogenous parameter that allows the user to influence rate of catch. Aquaculture is assumed to continue to grow at a country-specific growth rate; a multiplier can also be used to increase or decrease aquaculture production.   

[[File:Meat and fish production FlowchartKN.png|frame|center|text-bottom|571x500px|Meat and Fish Production Flowchart]]

== Agricultural Demand ==

=== Overview ===

Agricultural demand is divided into crops, meat, and fish. Crop demand is further divided into industrial, animal feed, and human food demand. 

Food demand from crops, meat and fish are responsive to calorie demand, which in turn responds to GDP per capita (as a proxy for income).  The division of calorie demand between demand for calories from crops and from meat and fish changes in response also to GDP per capita (increasing with income). Caloric demand is used as the basis to compute food demand through conversion to food demand in terms of grams per capita. The caloric value of demand is also used to compute food demand in terms of proteins per capita. 

In addition to food demand, demand for feed, industrial demand for meat, crops and fish and food manufacturing demand are also computed. When all components of agricultural demand are computed, the price of the food elements of it are checked to assure that the total household demand for food does not exceed a high percentage of total country-level household consumption expenditures.

=== Calorie Demand ===

Crop use for food and meat demand are both influenced by calorie demand. Total per capita calorie demand is driven by GDP per capita, but can be limited by calorie availability as well as by an exogenous parameter specifying maximum calorie need.[[File:Calorie Demand FlowchartKN.png|frame|center|text-bottom|571x500px|Calorie demand flowchart]]

The calculations of demand for meat, fish and food crop determine the ultimate division of calorie sources.  There is also a limit to the share of calories that can come from meat. The demand for calories from crops is simply the residual obtained by subtracting the demand for calories from meat and fish from the demand for total calories. Caloric value of demand is used to compute food demand in terms of grams per capita and in terms of proteins per capita.  Caloric value of demand is adjusted for elasticities to prices for all three categories namely crops, meat and fish.

The user can manipulate calorie demand through the use of an exogenous calorie multiplier and can reduce undernourishment to 5 percent of the population over time through the usage of two other hunger elimination parameters.

=== Food Demand for Crops, Meat and Fish ===

Food demand is driven by the demand for calories. A conversion factor translates calorie demand into food demand in terms of grams per capita.  Crop prices and an elasticity affect the resultant food demand.  So too does a constraint on the maximum calories per capita and the size of the population. 

[[File:Food demand flowchart KN.png|frame|center|text-top|571x500px|Food demand flowchart]]

=== Industrial Demand ===

Industrial demand (examples would be textile use of cotton or beverage inputs use of barley) is driven primarily by GDP per capita and population.   Another important use in recent years has been for biofuels, and that demand component is responsive to world energy price and an elasticity.

Crop prices also influence total industrial demand for crops.  A maximum per capita demand parameter constrains the total and an exogenous multiplier allows users to alter the total.

[[File:IndustrialdemflowchartKN.png|frame|center|text-bottom|200x300px|Industrial demand flowchart]]

=== Feed Demand  ===

The total feed demand for the livestock herd is dependent on the weight of the livestock herd and per unit weight feed requirements.  The per unit feed requirements increase with GDP per capita as populations move from meat sources such as chickens to more feed intensive ones such as pork and especially beef.  But they also are reduced by change in the efficiency of converting feed to animal weight.

Some of the food requirements of livestock are met by grazing, thereby reducing the feed requirements.  The feed equivalent of grazing depends on the amount of grazing land, the productivity of that land (computed in the initial year and highly variable across countries), and grazing intensity (which increases with crop prices).

Finally, the feed demand can be modified directly by an exogenous demand parameter that modifies industrial crop demand. The feed demand for meat and fish are calculated using ratios of the food demand to feed demand which are calculated in the initial years of the model. In addition to industrial demand and feed demand, food manufacturing demand is also calculated in the model on the basis of the food demand for all three categories (meat, crops and fish)

[[File:FeeddemandKN.png|frame|center|564x476px|Feed demand flowchart]]

=== Total Agricultural Demand ===

Total Agricultural demand is the sum of demand for crops to serve industrial, animal feed, food manufacturing and human food purposes. 

[[File:Total Ag demand KN.png|frame|center|564x500px|Total Agricultural Demand Flowchart]]

=== Financial Constraint on Food Demand ===

Total food demand in million metric tons consists of the sum of crop demand, meat demand and food demand and fish demand.  It can be, however, that the monetary value of those calculated demands is greater than the financial ability of households to pay for them.  When that is the case, the food ,meat and fish demand are proportionately reduced. [[File:Financial constraint on food demand KN.png|frame|center|575x400px|Visual representation of financial constraint on food demand]]

=== Agricultural Investment and Capital ===

The level of total desired agricultural investment are driven by the rate of past investment as a portion of GDP, changes in global crop demand as a portion of GDP, and global crop stocks relative to desired levels. We have experimented also with tying investment to profit rates in agriculture, thereby linking it also to prices relative to costs. The user can use a multiplier to increase or decrease the desired level of investment.  This desired amount of investment is passed to the economic model, where it must ‘compete’ with demands for investments in other sectors.  The economic model returns a final investment level for use in agriculture. 

Investment in agriculture has two possible targets. The first is capital stock. The second is land. The split between the two destinations is a function of the relative returns to cropland development and agricultural capital, the latter of which is determined by the increased yield that could be expected from an additional unit of agricultural capital.

[[File:AginvandcapitalFlowchartKN.png|frame|center|564x476px|Visual representation of agricultural investment and capital]]

== Land Dynamics ==

In IFs, land use is divided into 5 categories: cropland, grazing land, forest land, "other" land, and urban or built-up land. Four key dynamics are involved in land use change. First, changes in urban land are driven by changes in average income and population, and draws from all other land types. Second, the investment in cropland development is the primary driver of changes in cropland, but this is also influenced by the cost of developing cropland, the depreciation rate, or maintenance cost, of cropland investment, and a user-controllable multiplier. The costs of developing cropland increase as the amount of cropland increases and, therefore, there is less other land available for conversion. Shifts in cropland are compensated by changes in forest and "other" land. Third, changes in grazing land are a function of average income, with shifts again being compensated by changes in forest and "other" land. Finally, conservation policies can influence the amount of forest land, with any necessary adjustments coming from crop and grazing land. [[File:Land dynamics.png|frame|center|Visual representation of land dynamics]]

= Agricultural Equations =

=== Overview ===

Briefly, each year the agriculture model begins by estimating the production (pre and post-production loss) of crops, meat, and fish. It then turns to the demand for these commodities. This begins with a computation of caloric demand from crops, meat, and fish, which is translated into demand for food going directly to consumers. Other demands for crops, meat, and fish are for feed, industrial uses (e.g. biofuels), and food manufacturing. Losses in the production, distribution and consumption of agricultural commodities are also accounted for. This is followed by computations for trade. The model then considers the balance between the demands and the available supply based on production, imports, and exports. Any excess supply increases stocks. In the case of excess demand, stocks are drawn down; this can result in shortages if there are not enough stocks, which leads to an inability to meet all of the demands. Levels of, and changes in, stocks influence prices for the coming year, as well as desired investment, which are passed to the economic model, which determines the actual amount of investment that will be available. With this knowledge, the model can then estimate values for changes in land development, agricultural capital, and livestock for the coming year.

=== Agricultural Supply ===

Crop, meat, and fish supply have very different bases and IFs determines them in separate procedures.

==== Crop Production ====

Crop production, pre-loss, (AGPpplf=1) i is the product of total yield and land devoted to crops (LDl=1).

<math>AGPppl_{r,f=1}= YL_r*LD_{r,l=1}</math>

We focus here on the determination of yield; the amount of land devoted to crops is addressed in the sections below.Yield functions are almost invariably some kind of saturating exponential that represents decreasing marginal returns on inputs such as fertilizer or farm machinery. Such functions have been used, for instance in World 3<ref>Meadows, Dennis L. et al. 1974. Dynamics of Growth in a Finite World. Cambridge, Mass: Wright-Allen Press.</ref> , SARUM<ref>Systems Analysis Research Unit (SARU). 1977. SARUM 76 Global Modeling Project. Departments of the Environment and Transport, 2 Marsham Street, London, 3WIP 3EB</ref>, the Bariloche Model <ref>Herrera, Amilcar O., et al. 1976. Catastrophe or New Society? A Latin American World Model. Ottawa: International Development Research Centre.</ref>, and AGRIMOD <ref>Levis, Alexander H., and Elizabeth R. Ducot. 1976. "AGRIMOD: A Simulation Model for the Analysis of U.S. Food Policies." Paper delivered at Conference on Systems Analysis of Grain Reserves, Joint Annual Meeting of GRSA and TIMS, Philadelphia, Pa., March 31-April 2.</ref>. IFs also uses a saturating exponential, but relies on a Cobb-Douglas form. The Cobb-Douglas function is used in part to maintain symmetry with the economic model but more fundamentally to introduce labor as a factor of production. Especially in less developed countries (LDCs) where a rural labor surplus exists, there is little question that labor, and especially labor efficiency improvement, can be an important production factor.

===== Pre-processor and first year =====

In the pre-processor, agricultural production is initialized using data from the FAO food balance sheets. For details of the series that are used in this initialization, refer Annex 1 of this document. In the first year of the model, total crop production is calculated by adjusting the initialized value of crop production for production losses, as the FAO data are for post-loss production. Yield (YL) is computed simply as the ratio of total crop production (AGPpplf=1) to cropland (LDl=1). It is bound, however, to be no greater than 100 tons per hectare in any country.

In addition to yield, a number of other values related to production are calculated in the first year of the model that are used in forecast years.

First, a scaling factor cD is calculated in the first year of the model. This is basically the constant in the Cobb-Douglas formulation for estimating yields. It is based upon the base year yield (YL), capital (KAG), and labor supply (LABS). The labor supply is adjusted using a Cobb-Douglass alpha exponent (CDALF) which is explained in detail below.  cD is similar to the shift factors elsewhere in the model, which are used to match predicted values in the base year to actual values.  It does not change over time. It is computed using the following equation,

<math>cD_r= YL_{r,t=1}/ KAG_{r,t=1} ^ {CDALF_{r,s=1}} * LABS_{r,S=1,t=1} ^ {(1-CDALF_{r,s=1})}</math>

Second, a target growth rate in yield is computed (TgrYli) which is used in forecast years to restrict the growth rate of the yield. This target growth is a function of current crop demand (AGDEM), expected crop demand (Etdem), and a target growth rate in cropland.

<math>Tgryli_{r}= (Etdem/AGDEM_{r,s=1}) -1-tgrld_{r} </math>

where,

'''''tgrld''''' is a country-specific parameter indicating target growth in crop land

Etdem is an initial year estimate of the sum of industrial, feed and food demand for crops in the following year

===== Forecast years =====

In forecast years, IFs computes yield in stages. The first provides a basic yield (Byl) representing change in long-term factors such as capital, labor and technology. The second stage uses this basic yield as an input and modifies it based on prices, so as to represent changes in shorter-term factors (e.g. amounts of fertilizer used, even the percentage of land actually under cultivation). Finally, in a third stage, yields are adjusted in response to changing climate conditions.

'''''First stage (Adjustment for long-term factors)'''''

The basic yield (Byl) relates yield to agriculture capital (KAG), agricultural labor (LABS), technological advance (Agtec), a scaling parameter (cD), an exponent (CDALF), and a saturation coefficient (Satk).

<math>Byl_{r}= cD_{r}*(1+Agtec_{r} )_{t-1}* KAG_{r}^ {CDALF_{r,s=1}} * LABS_{r,s=1} ^ {(1-CDALF)_{r,s=1}} * Satk_{r} </math>

The equations for KAG and LABS are described elsewhere (see the sections below  and the economic model, respectively). 

*cD is the scaling factor calculated in the first year of the model. Its calculation is described in the section above

*CDALF is the standard Cobb-Douglas alpha reflecting the relative elasticities of yield to capital and labor.  It is computed each year in a function, rooted in data on factor shares from the Global Trade and Analysis Project, driven by GDP per capita at PPP.<ref>Following table is used to update CDALF, GDP/Capita (PPP) Versus Cobb-Douglas Alpha (GTAP 5)</ref>

Agtec is a factor-neutral technological progress coefficient similar to a multifactor productivity coefficient. It is initially set to 1 and changes each year based upon a technological growth rate (YlGroTech). Its computation is described below.

 <math>Agtec_{r}= Agtec_{r,t-1}*(1+ YlGroTech_{r})</math>

*The saturation coefficient Satk is a multiplier of the Cobb-Douglas function and of the technological change element. It is the ratio of the gap between a maximum possible yield (YLLim) and a moving average of yields to the gap between a maximum possible yield and the initial yield, raised to an exogenous yield exponent ('''''ylexp'''''). With positive parameters the form produces decreasing marginal returns.

<math>Satk_{r+1}=(YLLim_{r}-Syl_{r}/YLLim_{r}-YL_{r,t=1})^ {ylexp}</math>

where,

Sylr is a moving average of byl, the historical component of which is weighted by 1 minus the user-controlled global parameter '''''ylhw'''''.

'''''ylexp '''''is a global parameter

The maximum possible yield (YLLim) is estimated for each country and can change over time.  It is calculated as the maximum of 1.5 times the initial yield (YLr,t=1) and the multiple of an external user-controlled parameter ('''''ylmax''''') and an adjustment factor (YLMaxM).

<math>YLLim_{r} = max (ylmax_{r}* YLMaxM_{r}, 1.5* YL_{r,t=1})</math>

where,

'''''ylmax''''' is a country-specific parameter

The adjustment factor YLMaxM allows for some additional growth in the yields for poorer countries

<math>YLMaxM_{r} = 1*((1-DevWeight_{r})+(YL_{r}/YlMaxFound)^ {0.35* DevWeight_{r}})</math>

where,

DevWeightr is GDPPCPr/30, with a maximum value of 1

YlMaxFound is the maximum value of YL found in the first year

{| border="1" cellspacing="0" cellpadding="0"
|-
| style="width:575px;" |
'''Box1: Computation of technological growth rate for yield'''

The algorithmic structure for computing the annual values of YlGroTech involves four elements: 
<ol style="list-style-type:lower-alpha;">
<li>The difference between a targeted yield growth calculated the first year and the portion of that growth not initially related to growth of capital and labor (hence the underlying initial technology element of agricultural production growth); call it AgTechInit.</li>
<li>The gap between desired global crop stock levels and actual stocks (hence the global pressure for technological advance in agriculture); call it AgTechPress. This contribution is introduced by way of the ADJUSTR function of IFs.<ref>The ADJSTR function, used throughout the model, is a PID controller that builds in some anticipatory and smoothing behavior to equilibrium processes by calculating an adjustment factor. It considers both the gap between the current value of the specific variable of interest, here crop stocks, and a target value, as well as change in the gap since the last time step. Two parameters control the degree to which these two "differences" affect the calculation of the adjustment factor. In this case, these are the global, user-controllable parameters elfdpr1 and elfdpr2.</ref></li>
<li>The difference between the productivity of the agricultural sector calculated in the economic model and the initial year's value of that (hence reflecting changes in the contributions of human, social, physical, and knowledge capital to technological advance of the society generally); call if AgMfpLt.</li>
<li>The degree to which crop production is approaching upper limits of potential; this again involves the saturation coefficient (Satk).</li>
</ol>

The algorithmic structure of this is:

<math>YlgroTech_{r} = F(AgTechInit_{r},AgTechPress_{r},AgMfpLt_{r},Satk_{r})</math>

|}

'''''Second stage of yield calculation (short term factors)'''''

Before moving to the next stage, a check is made to see if the growth in byl is within reason.  Specifically, Byl is not allowed to exceed the moving average of Byl (Syl) times a given growth rate (YlGrbound).  This bound is the maximum of a user-controlled global parameter  '''''ylmaxgr''''' and an initial country specific target growth rate (Tgrylir).<ref>There is also an adjustment whereby ylmaxgr is reduced for countries with syl>5, falling to a value of 0.01 when syl>=8. Also, for countries with a yield greater than world yields, the additional growth rate in yields due to change in agricultural investment is restricted to a value that is equal to ylmaxgr.</ref>

At this point, the basic yield (Byl) is further adjusted by a number of factors.  The first of these is a simple country-specific user-controlled multiplier  '''''ylm'''''. This can be used to represent the effects of any number of exogenous factors, such as political/social management (e.g., collectivization of agriculture).

<math>YL_r= YL_r*ylm </math>

The basic yield represents the long-term tendency in yield but agricultural production levels are quite responsive to short-term factors such as fertilizer use levels and intensity of cultivation. Those short-term factors under farmer control (therefore excluding weather) depend in turn on prices, or more specifically on the profit (FPROFITR) that the farmer expects. Because of computational sequence, we use domestic food stocks as a proxy for profit level. Note that this adjustment is distinct from the adjustment above where global stocks affect the technological growth rate.

The stock adjustment factor uses the ADJSTR function to calculate an adjustment factor related to the current stocks, the recent change in stocks, and a desired stock level.  The desired stock level is given as a fraction (Agdstl) of the sum of crop demand (AGDEMf=1) and crop production (AGPf=1). Agdstl is set to be 1.5 times '''''dstl''''', which is a global parameter that can be adjusted by the user.

The focus in IFs on yield response to prices differs somewhat from the normal use of price elasticities of supply. For reference, Rosegrant, Agcaoili-Sombila, and Perez (1995: 5) report that price elasticities for crops are quite small, in the range of .05 to .4.<ref>Rosegrant, Mark W., Mercedita Agcaoili-Sombilla, and Nicostrato D. Perez. 1995. "Global Food Projections to 2020: Implications for Investment." Washington, D.C.: International Food Policy Research Institute. Food, Agriculture, and the Environment Discussion Paper 5.</ref>

'''''Third stage of yield calculation (Adjustment for a changing climate)'''''

In the third stage, IFs considers the potential effects of a changing climate on crop yields. This is introduced through the variable ENVYLCHG[[#_ftn4|[4]]] which is calculated in the environmental model. This variable consists of two parts: the direct effect of atmospheric carbon dioxide concentrations and the effects of changes in temperature and precipitation.

<math>ENVYLCHG_{r,f} =(((CO2Fert_{t}/100)+1)*((DeltaYClimate_{R,t}/100)+1)-1)*100</math>

The direct effect of atmospheric carbon dioxide assumes a linear relationship between changes in the atmospheric concentration from a base year of 1990 and the percentage change in crop yields.

<math>CO2Fert_{t+1} = envco2fert *((CO2PPM- CO2PPM_{t=1990})/CO2PPM_{t=1990} )</math>

where,

'''''envco2fert''''' is a global, user-controllable parameter

CO2PPMt=1990 is hard coded as 354.19 parts per million

The effect of changes in annual average temperature and precipitation are based upon two assumptions: 1) there is an optimal temperature (Topt) for crop growth, with yields falling both below and above this temperature and 2) there is a logarithmic relationship between precipitation and crop yields.  The choice of this functional form was informed by work reviewed in Cline (2007)<ref>Cline, William R. 2007. Global warming and agriculture: Impact estimates by country. Washington, DC: Peterson Institute for International Economics.</ref>.  Together, these result in the following equation:

<math>ClimateEffect_{t+1} = 100*{({e^{(-0.5*(T0_{r}+ DeltaT{r} - Topt)^2)/SigmaTsqd}*ln(P0_{r}*(DeltaP_{r}/100+1))/e^{(-0.5*(T0_{r}-Topt)^2/SigmaTsqd}*ln (P0_r))-1}} </math>

where,

T0 and P0 are country-specific annual average temperature (degrees C) and precipitation (mm/year) for the period 1980-99.

DeltaT and DeltaP are country specific changes in annual average temperature (degrees C) and precipitation (percent) compared to the period 1980-99.  These are tied to global average temperature changes and described in the documentation of the IFs environment model.

Topt is the average annual temperature at which yield is maximized.  It is hard coded with a value of 0.602 degrees C.

SigmaTsqd is a shape parameter determining how quickly yields decline when the temperature moves away from the optimum. It is hard coded with a value of 309.809.

CO2Fert and ClimateEffect are multiplied by each other to determine the effect on crop yields.

There are two final checks on crop yields.  They are not allowed to be less than one-fifth of the estimate of basic yield (Byl) and they cannot exceed the country-specific maximum ('''''ylmax''''') or 100 tons per hectare. Finally crop production is adjusted for production losses to arrive at post loss production (AGP). Losses are discussed in detail in the sections below

==== Meat Production ====

Meat production in IFs is the sum of animal meat production and non-meat animal products (AGPMILKEGGSR). Animal meat production in a particular country is a function of the herd size and the slaughter rate and non-animal meat products are calculated by applying a ratio MilkEggstoMeatIR which is calculated in the first year of the model as the ratio of non-meat animal production to the meat production. Meat production is then adjusted for production losses which are described in detail in the sections below.

<math>AGP_{r,f=2} =((LVHERD_{r}* slr)+ AGPMILKEGGS_r )- AGLOSSPROD_{r,f=2}</math>

where,

LVHERD is the size of livestock in a particular country in a particular year

'''''slr '''''is the slaughter rate which is a global parameter

AGLOSSPROD is the meat production loss.

===== Pre-processor and first year =====

In the pre-processor, meat production is initialized in the model using data from the FAO food balance sheets. Total meat production and animal meat production (which is the sum of bovine meat production, mutton and goat meat production, pig meat production, poultry meat production, and other meat production) are initialized separately. If data on all of the animal meat sub-categories is unavailable, then animal meat production is calculated as 30 percent of total meat production. Animal production is also not allowed to exceed 99% of the value of total meat production.

 

AGPMILKEGGSR, which is the non-meat animal production is then calculated as total meat production minus total animal meat production. The non-meat production ratio MilkEggstoMeatIR is calculated as the ratio of the initialized value of AGPMILKANDEGGSR and meat production in the first year. This is used in forecast years to calculate the value of non-meat animal production, and is held constant over time.

<math>MilkEggstoMeatI_{r} = AGPMILKEGGS_{r}/(AGP_{r,f=2}- AGPMILKEGGS_{r})</math>

The size of the livestock (LVHERDR) is also computed in the first year using the initialized value of pre-loss meat production. This value of LVHERDR is used in forecast years to compute meat production.

<math>LVHERD_{r} = (AGPppl_{r,f=2} - AGPMILKEGGS_{r} )/slr </math>

For a detailed discussion on the dynamics of livestock herd, refer to this [[Section|section]].

===== Forecast years =====

Pre-production loss values for meat production are calculated in IFs as meat production (AGPpplR,f=2) and production of non-meat animal products (AGPMILKANDEGGSR). Meat production, in metric tons, is given as the multiple of the herd size (LVHERDR) and the slaughter rate ('''''slr'''''). The latter is a global parameter. These values are then adjusted for production losses for meat (AGPRODLOSSR,f=2) to arrive at post production loss values (AGPR,f=2). The same meat production loss percentage is also applied to the non-meat production to arrive at post loss production values for the variable. The dynamics of production losses are discussed here.

<math>AGP_{r,f=2} = AGPppl_{r,f=2} - AGLOSSPROD_ {r,f=2}</math>

where,

<math>AGPppl_{r,f=2} = (AGPMILKANDEGGSppl_{r} +( LVHERD_{r}*slr)) </math>

Production of non-animal meat products is computed using the non-meat production ratio which is applied to the animal meat production.

<math>AGPMILKANDEGGSppl_{r} = MilkEggstoMeatI_{r} *( LVHERD_{r} * slr)</math>

The dynamics of the [[Livestock|livestock]] herd are described below.
<div></div>
==== Fish Production ====

The production of fish has two components, wild catch and aquaculture. Fish caught through aquaculture is treated as a stock in the model and is a function of a growth component.  Wild catch on the other hand is treated as a flow in the model.

===== Pre-processor and first year =====

Data for fish catch and aquaculture is derived through two main sources, namely the FAO food balance sheets and the FAO Fishstatj software. Data for fish production, imports and exports is initially extracted from the FAO Food Balance Sheets. However, no breakout is available for fish caught as wild catch and fish caught through aquaculture. This bifurcation is available in the dataset from the FAO Fishstatj database. The data from the FAO food balance sheets is broken down into fish catch (AGFISHCATCH) and aquaculture (AQUACUL) using data from the FAO fishstatj dataset.

In the first year, the values for pre-loss production of wild fish, AGFISHCATCHppl and aquaculture, AQUACULppl, are calculated by adding in a level of catch loss, which is not reflected in the FAO and Fishstatj data. Separate parameters, '''''aglossprodperc'''''f=3 ''and '''aglossprodperc'''f=4, ''are used for wild catch and aquaculture.

===== Forecast years =====

The amount of aquaculture (AQUACUL) in forecast years can be modified by the user. Production is assumed to grow over time. The default growth rate in the first year for all countries is 3.5 percent, but this value can be modified by the user, by country, with the parameter '''''aquaculgr'''''. This growth rate declines to 0 over a number of years given by the global parameter '''''aquaculconv'''''. Users can change the amount of aquaculture production, by country, with the multiplier '''''aquaculm<ref>In every year of the model, the effect of aquaculm is removed on the aquaculture variable. This is because the multiplier in this case is used on a stock rather than a flow due to which the effect of the multiplier needs to be removed in each time step.</ref>'''''. Finally, this is adjusted for production losses from aquaculture with Aquaculloss

<math>AQUACUL_{r} = (AQUACULppl_{r,t-1} * (1+ aquaculgr_{r,t} )* aquaculm_{r})- Aquaculloss_{r} </math>

where

''aquaculgrr,t declines from '''aquaculgr'''r,t=1'' to 0 over '''''aquaculconv''''' years

Wild catch is initialized in the pre-processor as the variable AGFISHCATCH. The pre- production loss of wild catch is computed after applying a multiplier '''''fishcatchm''''' and this is adjusted for losses (Catchloss) to arrive at post production loss wild fish catch.

<math>AGFISHCATCH_{r} = (AGFISHCATCHppl_{r,t-1} * fishcatchm_{r} )- Catchloss_{r} </math>

Total, post-production loss fish production (AGP) is then given as:

<math>AGP_{r,t=3} = AQUACUL_{r} + AGFISHCATCH_{r} </math>

==== Losses and waste ====

Losses can occur at several places along the chain from production. In earlier sections, we mentioned losses at the production stage. Losses can also occur in the process of transmission and distribution from the producer to the final consumer and at the consumer stage. The latter is sometimes referred to as food waste, but for our purposes, we will use the term loss for all three stages: production, transmission and distribution, and consumption.

The FAO Food Balance Sheets provide data on losses during transmission and distribution, but not at the production or consumption stages. Until we are able to find data showing a clear relationship between these losses and GDP per capita, or some other explanatory factor, we make an assumption of production losses and consumption losses of 10% for all countries. The user can make changes in these values with the parameters '''aglossprodperc''' and '''aglossconsperc''' respectively. The former can be set for crops, meat, wild catch, and aquaculture separately. The latter combines wild catch and aquaculture as fish, as we do not have separate data on the consumption of wild caught versus farmed fish. More details on the use of these parameters and the actual calculation of production and consumption losses are provided in sections 3.1.1-3.1.3 and 3.2.1, respectively.

Turning to transmission and distribution losses, some agricultural commodities will never make it from the producer to the final consumer because of pests, spoilage, etc.  The FAO food balance sheets provide data on food lost to waste for crops and meat , but not for fish. Thus, for now we assume that there are no losses in this stage for fish. For crops and meat, though we were able to establish relationships between transmission and distribution losses and GDP per capita. These are shown in the figures below:

----

===== Pre-processor and first year =====

The initial values for transmission and distribution losses are taken directly from the FAO Food balance sheets. For those countries without data, an assumed loss of 1 ton (0.000001 MMT) is used. These are given by the variable AGLOSSTRANS[[|r, f=1-3]]. As with consumption, wild catch and aquaculture are combined into a single category, fish, as we do not have separate data; also, for the moment the value of AGLOSSTRANSr, f=3 is set to 0 for all countries.

In the first year, a ratio of [[Transmission/distribution_loss_to_food_demand|transmission/distribution loss to food demand]], FDEM,  is computed as:

<math>AgLossTransToFoodRatI_{r,f=1to3} = AGLOSSTRANS_{r,f=1to3} / FDEM_{r,f=1to3} </math>

===== Forecast years =====

In future years, for crops and meat, the initial estimate for transmission and distribution losses are calculated as follows:

·         Predictions are made for the ratio of transmission/distribution loss to food demand as a function of GDP per capita (predaglosstrans) for the first year and the current year.

·         The ratio of the predicted values for the current year to the predicted value for the first year is multiplied by AgLossTransToFoodRatI.

·         That result is multiplied by FDEM for the current year to get losses in MMT.

·         That result is multipled by the parameter '''aglosstransm''', to get a final value.

 

This can be expressed as:

<math>AGLOSSTRANS_{r,f=1,2,3} = FDEM_{r,f=1,2,3,t=1} * predaglosstrans_{r,f=1,2,3,t}/predaglosstrans_{r,f=1,2,3,t=1}*AgLossTransToFoodRatioI_{r,f=1,2,3}*aglosstransm_{r,f=1,2,3}</math>

Some further adjustments may be made to AGLOSSTRANS in the process of balancing global trade and balancing domestic supply and demand. These are discussed later in this documentation.

== Agricultural Demand ==

IFs computes demand, or uses, for three agricultural categories—crops, meat, and fish.  These commodities are used for direct human consumption (FDEM), animal feed (FEDEM), industrial uses, e.g. biofuels (INDEM), and food processing and manufacturing (FMDEM). IFs also tracks the losses in transmission and distribution (AGLOSSTRANS). Total demand (AGDEM) is the sum of these five use categories and is given in MMT per year.

The sections above describe the calculation of AGLOSSTRANS, so that is not repeated here. The calculation of the demand for direct human consumption, FDEM begins with estimates of daily per capita calorie demand for crops, meat, and fish. Briefly, IFs first estimates total per capita calorie demand, which responds to GDP per capita (as a proxy for income). The division of total demand between demand for calories from crops and from meat and fish also changes in response to GDP per capita (more meat and fish demand with increasing income). Finally, the division of calories from meat and fish is calculated based on historic patterns. Using country and commodity specific factors, the daily per capita calorie demands are converted to grams per capita per day and protein per capita per day. The grams per capita per day are then multiplied by the size of the population, POP, and the number of days in a year, 365, to arrive at FDEM.

The other demands, FEDEM, INDEM, and FMDEM are driven by factors such as the size of the livestock herd, LVHERD, and the use of crops for fuel production. In cases where information is lacking, these demands are determined in relation to FDEM. Finally, there may be some modifications to all of the demand categories due to shortages or other factors, as described in the rest of this section.

==== Daily per capita demands – calories, grams, and protein ====

IFs tracks one set of variables for agricultural demands, or uses, on a daily per capita basis. These are. specifically, calories (CLPC), protein (PROTEINPC), and grams (GRAMSPC), for each category – crops, meat, and fish.

===== Pre-processor and first year =====

Daily calories per capita (CLPC), by category, are initialized in the IFs pre-processor using data from the FAO food balance sheets. Data on daily protein per capita and grams per capita are also read into the pre-processor.<ref>Note that although daily grams per capita are read in and used in the pre-processor, these are recalculated in the first year of the model</ref> If data are available for crops, meat, and fish, total values for calories, protein, and grams are calculated as sums of the three categories. For countries where no data are available for one or more of the categories, the model follows a set of procedures to fill in the missing data. These procedures uses, among other things, 1) equations that relate total calories per capita per day and the share of these calories from crops versus meat and fish to GDP per capita and 2) other ratios derived from global averages of those countries with data. Later in the pre-processor, CLAVAL, which represent the total calories (across all categories) per day for the population as a whole is also calculated.

The equation for total calories as a function of GDP per capita is stored as "GDP/Capita (PPP 2011) Versus Calorie Demand (fixed-effect)" and is illustrated below.<ref>Equation is CalPerCap = 2468.972+155.778*ln(GDPPCP). Because this equation was estimated using a fixed-effects model, the intercept does not have the same meaning as in a regular regression. Rather, it is the average of the fixed-effect across countries with data. This is not a problem for countries with data, as the shift factor in the first year will account for this. For countries without data, however, this can give a misleading estimate of initial daily calories per capita.</ref>

[[File:Calorie Demand vs GDP per capita.png|frame|center|200x250px|Calorie demand vs GDP per capita at PPP (fixed effect)]]

The equation for the share of calories from meat and fish as a function of GDP per capita is stored as " GDP/Capita (PPP 2011) Versus CLPC from MeatandFish (2010) Log"

Both of these are in a logarithmic form, indicating that both total calories and the share of calories from meat and fish increase with GDP per capita, but at a decreasing rate. As the data do not show a clear pattern for the breakdown between meat and fish, which is largely due to cultural patterns and geography, the model uses historical values rather than an estimated equation, as discussed below. In the pre-processor, an average global value is used for countries without data.

In the first year of the model, one of the first things that occurs is a recalculation of GRAMSPC as GRAMSPC = FDEM/(POP * 365) * 100000. This is to ensure the consistency between the daily per capita variable, GRAMSPC, and the annual national value, FDEM. This is necessary because FDEM may have been modified in the pre-processor as part of ensuring a balance between the initial year supply of agricultural produces and their use. This is described in more detail in Box 1.

In addition, a number of additional values related to calories to be used in the forecast period are calculated.

#CalActPredRat: the ratio between actual calories available and the predicted value.<ref>In the model this is currently calculated as CLAVAL/caldem, where caldem = the predicted value of total CLPC (after accounting for calmax) times the total population. It could just as easily be calculated as the predicted value of total CLPC (after accounting for calmax) divided by the actual value of total CLPC from the pre-processor.</ref> It is used as a multiplicative shift factor. The predicted level of is estimated using the equation for total calories per capita as a function of GDP per capita described above. This is bound from above by an assumed maximum value, given by the global parameter '''''calmax'''''. The value of calactpredrat gradually converges to 1 over a period given by the global parameter '''''agconv '''''and appears in future equations with the name AdjustForInitialDevc.
#MeatAndFishActPredRat: the ratio between actual share of calories from meat and fish to the predicted value. It is used as a multiplicative shift factor. The predicted level of is estimated using the equation for share of calories from meat and fish per capita as a function of GDP per capita described above.
#MeatToMeatFishRatI: the ratio between calories from meat and calories from meat and fish. It is used to separate the future estimates of calories from meat and fish into separate values for meat and fish.
#ProtToCalRatI: the ratio of daily per capita protein to daily per capita calories, by category. It is used to convert future estimates of calorie availability to protein availability. If for some reason the initial estimate of ProtToCalRatI is 0 for any category, the median value for that category based on 2010 is used.
#GramsToCalRatI: the ratio of daily per capita grams to daily per capita calories, by category. It is used to convert future estimates of calorie availability to a value in grams, which is then used to estimate aggregate demand for food for direct human consumption. If for some reason the initial estimate of GramsToCalRatI is 0 for any category, the median value for that category based on 2010 is used.

===== Forecast years =====

In the forecast years, daily per capita calorie demand begins with a prediction of a total demand, CalPerCap, as a function of average income using the equation above, with a maximum value given by '''''calmax'''''. Two other values are also calculated at this point. First, a base level of calories per capita, CalBase, is also calculated, which is given as the minimum of 3000 or '''''calmax'' '''minus 300. Second, because comparative cross sections show a growth of around 7.6 calories per capita per year independent of average income, a factor representing this increase (CaldGr) is calculated as:

<math>CaldGr_{r,t} = CaldGr_{r,t-1} +7.638*((calmax-MAX(CalBase,MIN(calmax, CalPerCap_{r} )))/(calmax-CalBase)</math>

Thus, depending on the exact values of '''''calmax''''', CalBase, and CalPerCap, CaldGr grows each year by a value that centers around 7.6 calories. This value is then added to the predicted value in calculating the total demand for calories.

The equation also takes into account '''''calmax'' '''and the multiplicative shift factor on calories per capita calculated in the first year of the model. The latter is named AdjustForinitialDevc, which, as noted previously, is calculate as the value of calactpredrat gradually converging to 1 over a period given by the global parameter '''agconv'''

<math>TotalCalPerCap_{r} = MIN('''''calmax''''',(CalPerCap_{r} + CaldGr_{r})* AdjustForInitialdevc_{r})* POP_r</math>

Finally, a value for the total calories per day, CalDem, is calculated by multiplying TotalCalPerCap times POP.

The next step is to divide the total calories between crops and meat plus fish. First, a predicted value of the share of total calories going to meat and fish, MeatAndFishPctPred, is calculated as a function of GDP per capita, using the equation described earlier. Second, the ratio of between actual share of calories from meat and fish to the predicted value, MeatAndFishActPredRat, calculated in the first year is potentially modified. Specifically, a new variable, AdjustForInitialDevm, is assigned either the intial value of MeatAndFishActPredRat, or a value that reflects convergence of MeatAndFishActPredRat to a value of 1 over a period given by the global parameter '''agconv'''. The countries for which convergence does not occur are the South Asian countries – India, Nepal and Mauritius –  which are traditionally low meat consuming countries. The actual share of calories from meat and fish is then calculated as:

<math>MeatAndFishPctAct_{r} = MeatAndFishPctPred_{r} * AdjustForInitialDevm_r</math>

A minimum value of 3.5 percent is also imposed.

With this value for MeatAndFishPctAct, the model can divide the total calories between crops and the combination of meat and fish. Using the value for MeatToMeatFishRatioI, calculated in the first year, the model can then estimate the calories from meat and fish separately. The values are stored in the variable CLPC(r,f)

At this point, these values are adjusted for changes in world food prices and elasticities to demand for these prices.

<math>CLPC_{r,f=1-3} = CLPC_{r,f=1-3} *(WAP_{f=1-3}/WAP_{f=1-3,t=1} )^{X}</math>

'''where,'''

WAPf=1-3 are the global food prices for crops, meat, and fish

X is the price elasticity of demand and takes on the value of '''''elascd''''', '''''elasm''''', and '''''elasfd'' '''for crops, meat, and fish, respectively

Given these adjustments, TotalCalPerCap is recalculated as the sum of CLPC for crops, meat, and fish.

Finally, a parameter '''''clpcm'' '''is applied to the final value of calories per capita that allows the user to manipulate demand for calories in addition to two parameters (that allow the user to eliminate hunger in a particular country over time) which are described below.

 The parameters '''''malnelimstartyr '''''and '''''malnelimtargetyr '''''allow the user to reduce hunger in any country over a specific period of time. The activation of these parameters by the user, calculates the required cumulative growth rate in calories to eliminate hunger (reduce the undernourished population to 5 percent of the total population) ClPCcum. This cumulative growth rate is calculated using a logarithmic function that computes the growth rate relative to the household income and unskilled labor in a country.<ref>The function used is as follows, Exp((Log(5) - 46.95226 + 0.18422 * Log(HHINC / labsups)) / -5.643)</ref>  Also, the user can activate a switch '''''malelimprecisesw''''', which calculates the specific number of calories required to eliminate hunger for the most undernourished part of the population. An individual who consumes less than 1000 calories per day but is still alive is assumed to be the most undernourished person in the population.

Therefore the final equation is as follows,

<math>CLPC_{r,f} = (CLPC_{r,f} *clpcm_{r,f} * ClPCcum_{r} )+ Caldef_{r,f} </math>

where,

'''''clpcm '''''is a multiplier that can be used to affect the demand for calories

ClPCcum is the cumulative growth rate required in calories per capita to eliminate hunger over a specific time period determined by malnelimstartyr and malnelimtargetyr

Caldef is the cumulative number of calories required to eliminate hunger for the most undernourished part of the population. This is calculated through the activation of '''''malelimprecisesw'''''.

At this point, i.e., after dealing with the hunger targets, the values for daily grams per capita (GRAMSPC) and daily protein per capita (PROTEINPC) are calculated by multiplying the values for CLPC by GramsToCalRatI and ProtToCalRatI, respectively. Recall that these values were computed in the first year.

A final adjustment to CLPC, PROTEINPC, and GRAMSPC can occur as a result of shortages. This begins with a reduction in FDEM, as described in the  Stocks section below, which is then translated into new values for GRAMSPC, which are then used to recalculate CLPC and PROTEINPC.

One final variable, CLAVAL, which represent the total calories (across all categories) per day for the population as a whole is then calculated as total calories per capita times the population.

==== Agricultural demand for direct human consumption (FDEM'')'' ====

FDEM represents the amount of agricultural commodities going directly to consumers, presumably for consumption.

===== Pre-processor and first year =====

The pre-processor reads in data from the FAO Food Balance Sheets and initializes values for the amount of agricultural commodities used for direct human consumption, FDEM. If these data are missing for any commodity, a value is calculated by multiplying the daily grams per capita by the size of the population (POP) and the numbers of days in a year (365), and then divided by 100000 to get the units correct. As noted in Box 1, certain adjustments may be made to ensure consistencies between supply and demand in individual countries, as well as between imports and exports across countries.

No adjustments are made to FDEM in the first year.

===== Forecast years =====

In the forecast years, FDEM is initially calculated based upon the calculation of daily grams per capita described in this section below:

<math>FDEM_{r,f=1-3} = GRAMSPC_{r,f=1-3} * POP_{r}* 365/100000</math>

There are two situations where the value of FDEM might be adjusted. The first case is where more than 85 percent of consumers’ expenditures are on food stuffs. If this is the case, the values of FDEM for crops and meat and fish are reduced proportionately, as described in this section below.

The second case is when a country faces absolute shortages, i.e., the total domestic supply, AGDEM, is not adequate to meet all of the demands, FDEM + FEDEM + INDEM + AGLOSSTRANS even after drawing down stocks to 0. Here, each of these demands/uses are reduced proportionately to restore the balance as described in Section 3.4: Stocks. In both cases, the decreases in FDEM are fed forward to reduce the actual calories available, as described here.

=== Feed demand for crops, meat and fish ===

Feed demand, FEDDEM, represents: 1) the amount of crops that are used to complement what livestock receive from grazing, and 2) an unspecified use of meat and fish, which appears in the FAO Food Balance Sheets.

===== Pre-processor and first year =====

The pre-processor reads in data from the FAO Food Balance Sheets and initializes values for the amount of agricultural commodities used as feed for other agricultural production, usually meat. If data are missing, a minimum value of 1 ton, or .000001 MMT is used.

An initial adjustment to feed demand for crops can occur in the pre-processor. This occurs when the production from grazing land is not being fully utilized. Specifically, this is when the amount of equivalent feed from grazing land, i.e. grazing land productivity, here named GLandCAP, implies a lower than assumed minimum value of 0.01 tons of crop equivalents per hectare, here named MinLDProd. The implied value of GLandCap is calculated as the difference between the total feed requirement for the number of livestock minus the feed demand divided by the amount of grazing land.

<math>GLandCAP_{r} = LiveHerd_r* fedreq_r-FEDDEM_{r,f=1}/ LDGraz_{r} </math>

'''where,'''

LiveHerd is the size of the livestock herd (discussed in this section )

LDGraz is the amount of grazing land (discussed in this section under Land Dynamics)

FEDDEMr,f=1 is the value for demand for crops for feed

''Fedreq is an estimate of the per animal feed requirements, which is a function of GDP per capita. The function is depicted in the figure below<ref>The specific equation is stored as “GDP/Capita (PPP) Versus Feed Requirements” and is defined by the two points (GDP/capita, fedreq) = (0, 2.5) and (30, 3.5).</ref>:''

[[File:Feed demand for crops 2.png|frame|center|Feed demand as a function of GDP per capita at PPP]] If the value of GLandCAP is less than the minimum, MinLDProd—currently hard coded as 0.01 tons of crop equivalents per hectare, based on values for the Saudi desert), then CFEDDEMr,f=1 is recalculated as the difference between the total feed requirement for the number of livestock minus the amount of feed equivalent produced by grazing using the minimum productivity.

<math>CFEDDEM_{r,f=1} = LiveHerd_{r} * fedreq_{r} - MinLDProd* LDGraz_{r}</math>

Note that this occurs when the feed from crops meets most, if not all, of the total feed requirements, implying little or no need for feed equivalents from grazing land. Also a minimum value of 0.01 MMT is set for CFEDDEM.

Finally, as noted in Box 1, certain adjustments may be made in the pre-processor to ensure consistencies between supply and demand in individual countries, as well as between imports and exports across countries.

In the first year, the model once again checks to make sure that the grazing land productivity exceeds a minimum value and this time stores this value for future use. A parallel equation to that in the pre-processor is used to get an initial estimate for grazing land productivity, now named GldCap:

<math>GLdCAP_{r} = (LVHERD_{r,t=1} * Fedreq_{r,t=1} -FEDDEM_{r,t=1})/LD_{r,l=2,t=1}</math>

'''where,'''

LVHERDr,t=1 replaces LiveHerd from the equation in the pre-processor

LDr,l=2,t=1 replaces LDGraz from the equation in the pre-processor

FEDDEMr,f=1 replaces CFEDDEMr,f=1 from the equation in the pre-processor

Fedreqr is the same as in the equation in the pre-processor

Now, if the model estimates that GldCAP is below the minimum level, still called MinLDProd and hard coded to a value of 0.01, a new value of GldCAP  calculated:

<math>GLdCAP_{r} = LVHERD_{r,t=1} * Fedreq_{r,t=1} -FEDDEM_{r,t=1}/LD_{r,l=2,t=1} </math>

'''where,'''

LVHERDr,t=1, LDr,l=2,t=1, FEDDEMr,f=1, and fedreqr are defined as above

'''''fedreqm'''r is a multiplier required to ensure that the grazing land productivity meets the difference between the total feed requirement and that provided by crops in the initial year. It is calculated as:''

<math>fedreqm_{r} = LD_{r,l=2,t=1} * MinLDProd + FEDDEM_{r,t=1} /(LVHERD_{r,t=1} * fedreq_{r,t=1} )</math>

Note that this value is always greater than or equal to 1 given the condition for making the adjustment. When no adjustment is made, fedreqm is set to 1. These values of GldCAP and fedreqm, calculated in the first year, are held constant for all forecast years

Finally, one other value is calculated in the first year – FeedToFoodRatI, which is the ratio between FEDDEM and FDEM. This is calculated for crops, meat, and fish, but is only used for the latter two categories in the forecast years, as described below.

==== Forecast years ====

In the forecast years, FEDDEM is calculated as a function of the size of the livestock herd (LVHERD), the feed requirements per unit livestock (fedreq), the amount of grazing land (LDl=2), and the productivity of grazing land (GldCAP), but adjustments are also made reflecting the effect of global crop prices on grazing intensity (WAPf=1), changes in the efficiency with which feed is converted into. meat, and the adjustment factor fedreqm calculated in the first year. There is also a parameter with which the user can cause a brute force increase or decrease in FEDDEM ('''feddemm''')

The model first calculates the amount of crop equivalent produced from grazing land using the following equation:

<math>GLFeedEq_{r} =(LD_{r,l=2} * GLdCAP_{r} )*( WAP_{f=1} / WAP_{f=1,t-1} )^{elglinpr} </math>

'''where''''',''

''LDr,l=2 is the amount of grazing land; the dynamics of this variable is discussed in section 3.10: Land Dynamics''

''GldCAPr is the country value for grazing land capacity initialized in the first year''

''WAPt,f=1 is global price for crops; and''

'''''elglinpr''' is a global parameter for the elasticity of livestock grazing intensity to annual changes in world crop prices; the basic assumption is that increasing prices should lead to increased grazing intensity and therefore greater productivity of grazing land<ref>The code, as written, ignores price effects that would reduce GLFeedEq. Since elglinpr is generally positive, this implies that decreases in world crop prices are ignored.</ref>''

''This production of crop equivalents from grazing land is then subtracted from total feed requirement in the following equation:''

''<math>FEDDEM_{r,f=1} =(LVHERD_{r} * Fedreq_{r} * fedreqm_{r}*max{0.5,(1-livhdpro/100)^(t-1) }-[GLFeedEq]_r )*feddemm</math>''

'''where,'''

LVHERD, fedreq, and fedreqm are as previously described. LVHERD and fedreq are updated each year as described in section 3.11: Livestock Dynamics and as a function of GDP per capita, respectively. fedreqm, determined in the first year, does not change over time.

'''''livhdpro''' is a global parameter related to the rate at which the productivity of crops in producing meat improves over time. This part of the equation implies that the amount of feed needed to produce a unit of meat declines over time to a minimum of half the original amount required''

'''''feddemm''' is a country-specific multiplier that can be used to increase or decrease crop demand for feed purposes''

For meat and fish, a simpler process is used. The feed to food ratio, FeedToFoodRatI, calculated in the initial years of the model is used to calculate the share of feed demand for meat and fish respectively.

<math>FEDDEM_{r,f} = FeedToFoodRatI_{r,f} * FDEM_{r,f}</math>

Note that there is no multiplier equivalent to '''feddemm'''for meat and fish.

Finally, as with FDEM, FEDDEM may be adjusted to account for excessive consumer spending on food, as described in Box 2 or due to shortages in crops, meat, or fish as described in Section 3.4: Stocks.

=== Industrial demand for crops, meat and fish ===

Industrial demand, INDEM, represents the amount of crops, meat, and fish that are used in industrial processes.

==== Pre-processor and first year ====

The pre-processor reads in data from the FAO Food Balance Sheets and initializes values for the amount of agricultural commodities used in industrial processes. If data are missing, a minimum value of 1 ton, or .000001 MMT is used.

Finally, as noted in Box 1, certain adjustments may be made in the pre-processor to ensure consistencies between supply and demand in individual countries, as well as between imports and exports across countries.

[[File:Industrial demand for crops.png|frame|center|Industrial demand for crops]] In the first year, two values related to industrial demand for crops are calculated. The first of these is a multiplicative shift factor (INDEMK), which is calculated as the ratio of actual to predicted industrial demand for crops.  The predicted value is given by a function that relates per capita industrial demand to GDP per capita, which is shown above.<ref>Equation is INDEM = 0.0376 + 0.000704 * GDPPCP</ref> This multiplicative shift factor remains constant over time. As with FEDDEM, one other value is calculated in the first year – IndToFoodRatI, which is the ratio between INDEM and FDEM. This is calculated for crops, meat, and fish, but is only used for the latter two categories in the forecast years, as described below.

==== Forecast years ====

In the forecast years, for crops, the initial value of industrial demand is updated using the table function above to get a predicted value for industrial demand per capita, which is then multiplied by population (POP) and the multiplicative shift factor (IndemK). At this point, a region-specific multiplier ('''indemm''') can either increase or decrease the initial estimate of INDEM.

A first adjustment to INDEM is related to the world energy price (WEP) and reflects the use of crops for fuel production. Specifically, as the world energy price increases relative to the price in the first year, the industrial demand for crops increases.

<math>INDEM_{r} = INDEM_{r} *(1+ WEP_{t}/WEP_{t=1}) *FoodforFuel)</math>

'''Where'''

WEP is world energy price

FoodforFuel is the elasticity of industrial use of crops to world energy prices. It starts at a value given by the global parameter '''elagind''', and declines to a value of 0 over 50 years.

The second adjustment relates to the world crop price (WAPf=1); as this increases relative to the price in the first year, industrial demand for crops declines.

<math>INDEM_{r} = INDEM_{r} *(WAP_{f=1,t}/WAP_{f=1,t=1} )^{elascd}</math>

''''Where''''

WAP is world crop price

'''''elascd''' is a global parameter specifying the elasticity of crop demand to global food prices''

A third adjustment is based on an assumed cap on per capita industrial demand for crops (IndemCapperPop—hard coded as 2. Specifically, INDEM is not allowed to exceed IndemCapperPop * POP.

For meat and fish, industrial demand is initially calculated by applying the Industrial demand to food ratio, IndToFoodRatI (calculated in the initial year of the model) to the value of food demand.

<math>INDEM_{r,f} = IndToFoodRatI_{r} * FDEM_{r,f} </math>

Note that there is no multiplier equivalent to '''indemm'''for meat and fish.

Finally, as with FDEM and FEDDEM, INDEM may be adjusted to account for excessive consumer spending on food, as described in section 3.2.5 or due to shortages in crops, meat, or fish as described in this Section below.

=== Food manufacturing demand ===

The final demand category, FMDEM, relates to the use of crops, meat, and fish in food manufacturing and processing.

==== Pre-processor and first year ====

The pre-processor reads in data from the FAO Food Balance Sheets and initializes values for the amount of agricultural commodities used in food manufacturing and processing.<ref>Note that the FAO Food Balance Sheets include data for agricultural commodities used for food manufacturing and as seed separately. We combine these into a single food manufacturing category.</ref> Note that If data are missing, a minimum value of 1 ton, or .000001 MMT is used.

As noted in Box 1, certain adjustments may be made in the pre-processor to ensure consistencies between supply and demand in individual countries, as well as between imports and exports across countries.

Paralleling the case for INDEM, FEDDEM, and AGLOSSTRANS, one other value is calculated in the first year –FManToFoodRatI, which is the ratio between INDEM and FDEM. This is calculated for crops, meat, and fish, and used for all three in the forecast years, as described below.

<math>FMDEM_{r,f} = FManToFoodRatI_{r,f} * FDEM_{r,f} </math>

==== Forecast years ====

In the forecast years, for all three categories, demand is calculated using the Food manufacturing to food demand ratio, FManToFoodRatI, calculated in the first year of the model and the value of food demand.

<math>FMDEM_{r,f} = FManToFoodRatI_{r,f} * FDEM_{r,f} </math>

As with FDEM, INDEM, and FEDDEM, FMDEM may be adjusted to account for any shortages in crops, meat, or fish as described in Section 3.4: Stocks. It is not currently affected by excessive consumer spending on food, as described in Box 2

=== Total agricultural demand and final adjustment to demand ===

==== Pre-processor and first year ====

AGDEM, which represents the sum of all uses. It is initialized in the first year of the model to ensure the balance with production, imports, and exports:

<math>AGDEM_{r,f=1-3,t=1} = AGP_{r,f=1-3,t=1} + AGM_{r,f=1-3,t=1} - AGX_{r,f=1-3,t=1} </math>

==== Forecast years ====

In the forecast years, AGDEM, is recalculated as the sum of the final values of feed, industry, and food demand and transmission losses:

<math>AGDEM_{r,f=1-3} = FEDDEM_{r,f=1-3} + INDEM_{r,f=1-3} + FDEM_{r,f=1-3} + FMDEM_{r,f=1-3} + AGLOSSTRANS_{r,f=1-3} </math>

Note that this occurs after any adjustments to the demand values as a result of excessive consumer spending on food, (described below), but before adjustments as a result of shortages, describe in Section 3.4: Stocks. Thus, it can be the case that the final value of AGDEM may exceed the sum of the individual demand values.

'''Final agricultural demand adjustment based on levels of consumer spending'''

One final adjustment is made to the agricultural demand variables in the forecast years.

If the preliminary estimate of total food demand in monetary terms (csprelim), is too large of a share of consumption, i.e., if

<math>CsPrelim_{r} = CSF_{r} *(FDEM_{r} * WAP_{f=1,t=1} + FDEM_{r,f=2} * WAP_{f=2,t=1} + FDEM_{r,f=3} * WAP_{f=3,t=1} )>0.85*C_{r,t=1}</math>

'''Where,'''

CSF is the ratio of consumer spending in the agricultural sector in the first year (CSr,s=1,t=1) to DemValr, a weighted sum of demands for agricultural products for food in the first year;

<math>DemVal_{r} = FDEM_{r,t-1} *WAP_{f=1,t-1} + FDEM_{r,f=2,t-1} * WAP_{f=2,t-1} + FDEM_{r,f=3,t-1} * WAP_{f=3,t-1} </math>

C is total household consumption in the first year

When this is the case, a series of steps are taken to bring these values back in line.

#The necessary reduction (NecReducr), which is in monetary terms, is calculated as CsPrelimr – 0.85*Cr
#A reduction factor (ReducFact) for meat and fish, assuming cuts would disproportionately be there,  is calculated as,

<math>ReducFact_(r,)=(NecReduc_{r}/csprelim)*2</math>

with a maximum value of 1 or full elimination

#The physical demands for crops for meat and fish in tons (FDEM, categories 2 and 3) are reduced by reducfact, and the values of the meat and fish reduction are saved for the next step

<math>Meatreduc_{r} = FDEM_{r,f=2} *ReducFact_{r}</math> <math> Fishreduc_{r} = FDEM_{r,f=3} *ReducFact_{r}</math>

<math>FDEM_{r,f=2,3} = FDEM_{r,f=2,3} *(1-Reducfact)_{r}</math>

#An estimate of the necessary reductions in crops for food, in monetary terms is estimated by subtracting the savings obtained through the reduction in meat demand

<math>FoodReduc_{r}= NecReduc_{r} - MeatReduc_{r}* CSF_{r} *WAP_{f=2,t=1} - FishReduc_{r} * CSF_{r} *WAP_{f=3,t=1} </math>

The physical demand for crops for food (FDEM) is then reduced as follows

<math>FDEM_{r,f=1} = Max(0.1*FDEM_{r,f=1} , FDEM_{r,f=1} - FoodReduc_{r}/(CSF_{r} *WAP_{f=1,t=1} ))</math>

Note that this ensures that FDEM is not reduced by more than ninety percent.

Finally, given the changes above, the total demand is recalculated as the sum of the final values of feed, industry, and food demand and transmission losses

<math>AGDEM_{r,f} = FEDDEM_{r,f} + INDEM_{r,f} + FDEM_{r,f} + FMDEM_{r,f} + AGLOSSTRANS_{r,f}</math>

{| cellpadding="0" cellspacing="0" width="100%" align="center" border="1"
|-
|
'''Box 1: Adjustments in the Pre-processor to Ensure Proper Balances'''

The pre-processor reads in data from the FAO Food Balance Sheets and initializes values for the amount of agricultural commodities used for direct human consumption, FDEM, feed (FEDEM), industry (INDEM), food manufacturing (FMDEM), as well as transmission losses (AGLOSSTRANS). All of these are measured in MMT per year. At the same time, it reads in data for production (AGP), imports (AGM), exports (AGX), and total domestic supply (AGDOMSUPP)[1].

A set of conditions should be meet for these variables for each category:

#AGDOMSUPP = AGP + AGM – AGX. This says that total domestic supply equals production plus imports minus exports. This equivalence can be broken if there are changes in stocks, which we will see in forecast years. Currently, however, we assume there are no such changes in the first year. Thus it may be necessary to make adjustment for the equivalence to hold in first year. This is done in the pre-processor, by keeping AGDOMSUPP the same and applying the following three rules:<ol style="list-style-type:lower-alpha;">

*If AGDOMSUPP > AGP + AGM – AGX, i.e., stocks were being drawn down, increase AGP and AGM while reducing AGX.
*If AGDOMSUPP < AGP + AGM – AGX, i.e., stocks were being added to, decrease AGP and AGM while increasing AGX.
*Make sure that AGP, AGM, and AGX do not fall below a minimum value.
*Sum of AGM across countries = Sum of AGX across countries. This says that imports and exports need to match. If they do not, the model calculates the average of the two sums and adjusts AGM and AGX in each country proportionately.
*AGP + AGM – AGX = FDEM + FEDEM + INDEM + FMDEM + AGLOSSTRANS. This says that the total domestic supply, which accounts for production losses, has to match the total uses (including losses in transmission and distribution).

The pre-processor includes procedures to ensure that these three conditions hold for the initial values in each country. This can lead to minor adjustments in the values for the supply and demand categories. These processes can also lead to changes in related variables, including the production of non-animal meat products (CAGPMILKEGGS), fish catch (AGFISHCATCH), aquaculture production (AQUACUL), the size of the livestock herd (LVHERD), and the breakdown of land areas (LD). The latter occurs because we do not want these processes to change crop yields (YL). 

|}

== Trade ==

Consistent with the approaches within both the economic model and the energy model, trade of agricultural products in IFs uses a pooled approach rather than a bilateral one.   That is, we can see the total exports and imports of each country/region, but not the specific volume of trade between any two.  Offered exports and demanded imports from each country/region are responsive to the past shares of export and import bases and are summed globally.  The average of the totals is taken as the actual level of global trade and the country exports and imports are normalized to that level. 

Price differentials across countries do not influence agricultural trade. Although the IFs project has experimented over time with making such trade responsive to prices, there is an increasing tendency globally for food prices to be more closely aligned across countries than was true historically.  Moreover, the use within IFs of local relative food surpluses or deficits (as indicated by stock levels) to adjust trade patterns is an effective proxy for the use of prices.

The initial year values of the imports (AGM) and exports (AGX) of the three agricultural commodities in physical quantities are determined in the pre-processor. Since we only have historical data on the imports and exports of fish in monetary terms, these need to be converted to physical terms. This is done by multiplying the monetary values, which are in $billion, by 1000*/2200 to get physical values in million tons. In addition, exports of fish are limited to be less than 70 percent of total fish available and imports less than 1 percent of total fish available. For each of the three agricultural commodity groupings, if there is an imbalance between global imports and global exports in the preprocessor, the latter takes precedence and national imports are adjusted to bring global imports into line with global exports.

In the first year, seven variables are set related to trade for each commodity: XKAVE, MKAVE, XKAVMAX, MKAVMAX at the country level and wxct=1, wmdt=1, and WAPt=1 at the global level.

XKAVE and MKAVE are moving average values of export and import propensity, respectively. They are specified as the ratio of agricultural exports and imports to a base value (xbase) for each commodity. For exports, this is basically the sum of production and demand for that commodity; for imports, it is just demand.

<math>XKAVE_{r,f=1-3,t=1} = AGX_{r,f=1-3,t=1}/(AGP_{r,f=1-3,t=1} + AGDEM_{r,f=1-3,t=1} )</math>

<math>MKAVE_{r,f=1-3,t=1} = AGM_{r,f=1-3,t=1}/ AGDEM_{r,f=1-3,t=1} </math>

XKAVMAX and MKAVMAX are maximum values of XKAVE and MKAVE. For crops and meat, XKAVMAX is set to 1.1 times XKAVE, but is not allowed to exceed a value of 0.7; MKAVMAX is set to 1.5 times XKAVE, but also is not allowed to exceed a value of 0.7. For fish, XKAVMAX is set to 1.1 times XKAVE, with a bound of 0.95; MKAVE is set to 1.5 times MKAVE, with a bound of 2. These values are held constant for all future years.

XPriceTermLag, and MPriceTermLag are set to 0 for all commodities. wxc and wmd are the total world agricultural exports and imports; these are set to a value of 1 in the first year. WAP is the initial world price index for each commodity, which is set to 100.

In the forecast years, the process for determining agricultural imports and exports involves the following steps:

#Estimating the agricultural export capacity and agricultural import demand for each country.
#Reconciling the differences between global agricultural export capacity and global agricultural import demand.
#Computing the actual levels of agricultural exports and agricultural imports for each country

The agricultural export capacity is estimated by multiplying the export propensity (XKAVE) by the current year’s production and demand. It is also limited by XKAVMAX:

<math>AGX_{r,f=1-3} = MIN(XKAVE_{r,f=1-3}, XKAVMAX_{r,f=1-3} )*(AGP_{r,f=1-3} + AGDEM_{r,f=1-3} )</math>

Similarly, the agricultural import demand is estimated by multiplying the import propensity (MKAVE) by the current year’s demand, with a limit set by MKAVMAX

<math>AGM_{r,f=1-3} =MIN(MKAVE_{r,f=1-3}, MKAVMAX_{r,f=1-3} )* AGDEM_{r,f=1-3} </math>

For each country, values are also estimated for its net surplus or deficit (surpdef) for each commodity. This is based on the following factors: 1) post-loss production, 2) domestic demand, 3) the difference between current and desired stocks, and 4) a trade term

<math>surpdef_{r,f=1-3} = AGP_{r,f=1-3} * (1-LOSS_{r,f=1-3}) -AGDEM_{r,f=1-3}
+ cumstk_{r,f=1-3} - agdstl*(AGP_{r,f=1-3} + AGDEM_{r,f=1-3})
+TradeTerm_{r,f=1-3}
)</math>

The first three factors are straightforward. Production minus demand reflects a basic net surplus, which is then adjusted by any net surplus in stocks. The TradeTerm is related the relative role a country plays in global imports and exports and is given as:

<math>TradeTerm_{r,f=1-3} =(AGM_{r,f=1-3}/wmd_{f=1-3,t-1} - AGX_{r,f=1-3}/wxc_{f=1-3,t-1} )*(wmd_{f=1-3,t-1}+ wxc_{f=1-3,t-1})/2</math>

The TradeTerm is positive (negative) when a country has a larger (smaller) share of the global imports than it does of the global exports of a particular commodity and vice versa. Since the TradeTerm is added to surpdef, it acts as a balancing mechanism; countries that appear as relatively larger (smaller) importers get a positive (negative) boost to their estimated net surplus, which tends to reduce (increase) imports as shown below.

At this point, the global sum of exports and imports across countries will likely differ. Therefore, a procedure is required to balance these. In preparation for this one more global variable and several country-level variables are calculated. The global variable is globalsurdefrate, which is the ratio of the sum across countries of net surplus divided by the sum across countries of demand and production, which is the stock base.

<math>globalsurdefrate_{f=1-3} =(\sum_r(surpdef_{r,f=1-3} )/(\sum_r(AGDEM_{r,f=1-3} + AGP_{r,f=1-3}))</math>

The country-level variables are as follows:

The first term modifies the country’s net surplus, increasing (decreasing) it when the global net surplus is negative (positive).

<math>countryextrasurdef_{r,f=1-3} = surpdef_{r,f=1-3} - globalsurdefrate_{f=1-3} *(AGDEM_{r,f=1-3}+ AGP_{r,f=1-3})</math>

The second term modifies how rapidly the net surplus is closed.

<math>countryextrasurdefadj_{r,f=1-3} = countryextrasurdef_{r,f=1-3}/5</math>

The third term is simply the ratio of exports to the sum of imports and exports.

<math>exportshare_{r,f=1-3} = AGX_{r,f=1-3}/(AGX_{r,f=1-3} + AGM_{r,f=1-3} )</math>

The next step is to calculate whether it is necessary to increase (decrease) imports and decrease (increase) exports for each country, and by how much. Whether a country needs to increase its initial estimates of imports and decrease its initial estimates of exports, or vice versa, is determined by the sign of countryextrasurdef. If this value is negative, i.e., the country has a net deficit, it will need to reduce exports and increase imports. The opposite holds for when countryextrasurdef is positive.

As for the amount by which imports and exports need to be increased or decreased, this is a function, in general, of the size of the necessary adjustment and the export share:

<math>AGX_{r,f=1-3} = AGX_{r,f=1-3} + countryextrasurfdefadj_{f=1-3} * exportshare_{r,f=1-3}</math> <math>AGM_{r,f=1-3} = AGM_{r,f=1-3}- countryextrasurfdefadj_{f=1-3} * (1-exportshare_{r,f=1-3})</math>

Note that the sign of countryextrasurdef and the fact that exportshare is a value between 0 and 1 ensure that when exports increases, imports fall, and vice versa.<ref>Two other variables, defadjmul and ImportBoost, are included in the calculations to make some finer adjustments to the changes in exports and imports; these relate to the observed behavior for specific countries and are not discussed in detail here.</ref>Finally, in this adjustment process, exports and imports are not allowed to fall by more than half or more than double.

This process may not fully reconcile global trade, so a final adjustment is made by setting world trade (WT) as the average of global exports and imports and then adjusting the country values accordingly:

<math>WT_{f=1-3} =(\sum_r(AGX_{r,f=1-3}) +\sum_r(AGM_{r,f=1-3}) )/2</math> <math> AGX_{r,f=1-3} = AGX_{r,f=1-3} * WT_{f=1-3}/(\sum_r(AGX_{r,f=1-3}) )</math> <math> AGM_{r,f=1-3} = AGM_{r,f=1-3} * WT_{f=1-3}/(\sum_r(AGM_{r,f=1-3}) )</math>

IFs can now update the moving average export (XKAVE) and import (MKAVE) propensities for the next time step. The weights given to history are set by the global parameters '''xhw'''and '''mhw'''. For small exporters, i.e., where exports are less than one tenth of the sum of production and demand, '''xhw'''is reduced by 40 percent, allowing for faster adjustment. XKAVE and MKAVE are updated as

<math>XKAVE_{r,f=1-3,t+1} = XKAVE_{r,f=1-3}+(1-xhw)* AGX_{r,f=1-3}/(AGP_{r,f=1-3} + AGDEM_{r,f=1-3} )</math>

<math>MKAVE_{r,f=1-3,t+1} = XMAVE_{r,f=1-3}+ {1-mhw} * AGM_{r,f=1-3}/AGDEM_{r,f=1-3} </math>

For crops, the import propensity is bound from below by a factor given by potential GDP (GDPPOT), demand (AGDEM), the conversion factor between agricultural imports in physical terms and dollar values (msf, see section on links to the economic model), and the initial world price for agriculture (WAP).

<math>XKAVE_{r,f=1-3,t+1} => (0.6*GDPPOT_{r})/(AGDEM_{r,f=1-3} * msf_{r}*WAP_{f,t=1})</math>

Finally, XKAVE and MKAVE are bound from above by XKAVMAX and MKAVMAX, respectively.

== Stocks ==

==== First year ====

Due to a lack of good historical data, in the first year, stocks for all three agricultural commodities are assumed to equal desired stocks. These are set to a fraction (agdstl) of total production (AGP) and demand (AGDEM) for each commodity.

<math>FSTOCK_{r,f=1-3} =(AGP_{r,f=1-3} + AGDEM_{r,f=1-3} )*Agdstl</math>

Where

Agdstl is a parameter used to set desired stock levels for agricultural commodities.  It is set to be 1.5 times '''dstl''', which is a global parameter that can be adjusted by the user

==== Forecast years ====

In future years, basic stock levels (CumStk) increase with production (AGP), decrease with demand or consumption (AGDEM), and adjust for net imports (AGM-AGX).

<math>CumStk_{r=1-3} = FSTOCK_{r,f=1-3,t-1} + StkAdj_{r,f=1-3}</math> <math>StkAdj_{r,f=1-3} = AGP_{r,f=1-3} - AGDEM_{r,f=1-3}+ (AGM_{r,f}- AGX_{r,f=1-3})</math>

Of course, the actual stock values (FSTOCK) are not allowed to go negative. If the basic stock level is negative, stocks are set at zero and a shortage (Sho) exists, which affects calorie availability. If the basic stock level is positive there is no shortage and stocks equal the basic level.

<math>if cumstk_{r,f=1-3}<0 then Sho_{r,f=1-3} =-StkAdj_{r,f=1-3} and FSTOCK_{r,f=1-3}= 0</math> <math>if cumstk_{r,f=1-3} > 0 then Sho_{r,f=1-3} = 0 and FSTOCK_{r,f=1-3} = cumstk_{r,f}</math>

Also, if shortages are greater than 0, a reduction factor (ReductionFactor''')'''is computed which is then used to adjust demand and losses.

<math>if SHO_{r,f}>0, ReductionFactor_{r,f} =(AGDEM_{r,f}- SHO_{r,f})/ AGDEM_{r,f}</math> <math> FDEM_{r,f}= FDEM_{r,f} * ReductionFactor_{r,f} </math> <math> FEDDEM_{r,f} = FEDDEM_{r,f} * ReductionFactor_{r,f} </math> <math> INDEM_{r,f} = INDEM_{r,f} * ReductionFactor_{r,f} </math> <math> FMDEM_{r,f} = FMDEM_{r,f} * ReductionFactor_{r,f} </math> <math> AGLOSSTRANS_{r,f} = AGLOSSTRANS_{r,f} * ReductionFactor_{r,f} </math>

== Calorie Availability ==

Daily per capita calorie availability (CLPC) is initialized in the pre-processor. Where available, data is taken from the FAO<ref>Note this occurs in DATAPOP, not DATAAGRI. The historic data series is SERIESCalPCap. Missing data are estimated based on access to water and sanitation or average income.</ref> It is multiplied by population (POP) to yield total daily calorie availability and brought into the model with the name CLAVAL. We already saw that this first year value is used in the calculation of two country-specific factors: 1) calactpredrat, which is a shift factor determined as the ratio of calorie availability to predicted calorie demand in the first year, and 2) sclavf, which is a conversion factor relating the total annual demand for food crops and crop equivalents from meat to daily calorie availability.

In the forecast years, CLAVAL is calculated using the final value of calories per capita.

<math>CLAVAL_r= CLPC_{r,f=4}* POP_{r} </math>

Calorie availability combines with regional calorie need in the population model for the calculation of possible starvation deaths (a seldom used variable because in official death statistics people do not die of starvation but rather of diseases associated with undernutrition); the population and health models therefore look instead to the impact of calorie availability on undernutrition and health.

== Prices ==

IFs keeps track of both national (FPRI) and world (WAP) price indices for each of the three agricultural commodities. All of these are set to an index value of 100 in the building of the base.

The national crop price indices (FPRI, category (1) respond to: 1) changes in global costs of crop production, the latter being expressed as the ratio of global accumulated capital investment in crops to global production and 2) changes in the level of domestic crop stocks. The first factor should provide a long-term basis for rising or falling prices tied to changing technology and other factors of production; the second factor generally should represent shorter-term market variations from that long-term level.

The impact of global costs is given by dividing the ratio of global investment in crops to global production (wkagagpr) in the current year to that same ratio in the first year.  The effect of stocks on crop prices (Mul) is calculated using the same ADJSTR function introduced in the description of crop supply, which considers the difference between both the current crop stocks and a desired vale and between current crop stocks and those in the previous year. Two parameters control the degree to which these two ‘differences’ affect the calculation of the adjustment factor. In this case, these are the global, user-controllable parameters '''fpricr1'''and '''fpricr2'''. All together the equation for domestic crop price indices in the coming year is given as

<math>FPRI_{r,f=1,t+1} = WAP_{f=1,t=1} * wkagagpr_{r,t}/wkagagpr_{r,t=1} * Mul_{r} </math>

The domestic crop price indices are also bound between 0.01 and 1000.

The national meat price indices are linked the global crop price. Specifically, they are given as a moving average of the global crop price index

<math>FPRI_{r,f=2,t+1} = fprihw* FPRI_{r,f=2,t} +(1-fprihw)* WAP_{f=1,t}</math>

Where

'''''fprihw''' is a global parameter used to control the speed at which the domestic meat price changes.''

The national fish price indices are all set equal to the global fish price index. The determination of the global fish price is similar to that for the national crop price, but here the stock of interest is the global stock and there is no effect related to costs. The ADJSTR function is used once again to calculate the adjustment factor (MUL), this time focusing on the desired global fish stock, the difference between this and the current global fish stock, and the change in the global fish stock in the past year. Again, two parameters control the degree to which these two "differences" affect the calculation of the adjustment factor. In this case, these are the global, user-controllable parameters '''fprim1'''and '''fprim2'''. The global and national fish prices are thus calculated as

The world price indices for crops and meat are computed, in the following year, as a weighted average of the domestic prices, with the weights given by crop and meat production:

<math>WAP_{r,f=1-2,t+1} =(\sum_r(FPRI_{r,f=1-2,t+1} * AGP_{r,f=1-2,t+1} ) )/(\sum_r(AGP_{r,f=1-2,t+1}) )</math>

== Returns and Profits ==

IFs estimates the net returns in agriculture (AGReturn) for each commodity as the ratio of gross returns (GReturn) to production costs (ProdCost and MProdCost). The agricultural profit ratios (FPROFITR) are then estimated as the ratio of AGReturn in the current year to its value in the initial year. At some points in the evolution of IFs we have used FPROFITR as a guide to rates of investment (see the calculation of mulrprof in All but First 2: Investment); the current formulation for investment does not do so. For completeness, however, we provide a description of these processes in the model, as they still exist as live code.

==== Pre-processor and first year ====

In the first year, values for FPROFITR, sfprofitr, and FPRofitR are all set to 1.

==== Forecast years ====

The production costs for crops are estimated as the cost of cropland, priced at the cost of new land development (CLD), plus the investment in agricultural capital (KAG). The net revenues are given as total yield times the domestic crop price index. This results in

<math>ProdCost_{r,f=1,t} = LD_{r,l=1,t} * CLD_{r,t}+ KAG_{r,t} </math>

<math>GReturn_{r,f=1,t} = (byl_{r,f}* LD_{r,f=1} * FPRI_{r,f=1} * (AGLOSSPROD_{r,f=1} /AGP_{r,f=1}))/ProdCost_{r,f=1,t}</math>

For meat, production costs are estimated by the value of the crop equivalents produced by grazing and the cost of feed, where the value is given by the domestic meat price index. The net revenues are based on the size of the herd and the domestic meat price index. This results in

<math>MProdCost_{r,f=2,t} =(LD_{r,l=2,t} * GLDCAP_{r,t} + FEDDEM_{r,t} )* FPRI_{r,f=2,t+1} </math> <math> GReturn_{r,f=2,t}=(LVHERD_{r,t} * FPRI_{r,f=2,t+1})/ProdCost_(r,f=2,t)</math>

For fish, the production costs are simply estimated by the total production of fish times the domestic meat price index. The net revenues are given as the total production of fish times the domestic fish price index. This implies

<math>MProdCost_{r,f=3,t}= FISH_{r,t} * FPRI_{r,f=2,t+1} </math> <math>GReturn_{r,f=3,t} =(AGP_{r,f=3}* FPRI_{r,f=3,t+1})/ProdCost_{r,f=3,t}</math>

The net returns for each commodity can then be calculated as

<math>AGReturn_{r,f=1-3,t} = GReturn_{r,f=1-3,t}/ProdCost_{r,f=1-3,t} </math>

These net returns are used to account for changes in profits over time, using the variable FPROFITR, which influences investment in agriculture. This variable is calculated for each commodity as

<math>FPROFIT_{r,f=1-3,t} = AGReturn_{r,f=1-3,t}/ARGeturn_{r,f=1-3,t=1} </math>

A similar variable (wfprofitr) is calculated at the global level as a production weighted average of country/region values, but only for crops.

== Investment ==

Investment in agriculture is relatively complex in IFs, because changes in investment are the key factor that allows us to clear the agricultural market in the long term. It is very similar to investment in energy, except that we do not need to compute type-specific investments—capital in agriculture is only used for the production function of crops.

We calculate a total agricultural investment need (INAG) to take to the economic model and place into the computation for investment among sectors. This calculation involves multiple factors.  These begin with an initial estimate or targeted level of investment (TInAg) that is the product of the ratio of investment to GDP in the previous year times the GDP in the current year.

Three factors modify that basic or target investment level.  Two of those are global and one is regional.  The first global factor is a multiplier linked to year-to-year change in the ratio of agricultural demand to GDP (WAgDemRMul); typically agricultural demand grows more slowly than GDP.  The second is a multiplier responsive to the level of global stocks (MulWSt); if those drop below target levels it would increase production globally and vice versa.  The model could use a global price average instead of stocks, but in the recursive structure stocks determine prices and therefore use of stocks accelerates responsiveness of investment.  Similarly, the regional factor represents a multiplier tied to regional stock levels (MulSt).

<math>TInAg_{r,t} = INAG_{r,t-1}/GDP_{r,t-1} * GDP_{r,t}* WAgDemRMul_{t} * MulWSt_{t}* MulSt_{r,t}</math>

where, <math> WAgDemRMul_{t} =((\sum_r(AGDEM_{r,t} )/WGDP_{t} )/((\sum_r(AGDEM_{r,t-1})/WGDP_{t-1})</math>

To elaborate, MulWSt and MulSt are adjustment factors related to global and domestic crop stocks, respectively. Both use the PID ADJSTR function described earlier, just as changes in prices use it in order to set prices that change year-to-year so as to chase supply-demand equilibration over time. For MulWSt, the controlling parameters in the PID function for stocks versus targets and changes in stocks are hard coded with values of -0.3 and -0.9, respectively. For MulSt, these parameters are hard coded with values of -0.2 and -0.4, respectively.

Experience with that initial estimate, however, shows that it can be overly responsive to one or more of the multiplicative adjustment factors, thereby setting up behavior that oscillates.  Therefore the next step is to compute a smoothed rate of investment as a share of GDP (SmInAgR).  That rate gives more weight (60 percent) to the final investment rate in the previous year than it does to the rate that results from the initial target investment calculation.  The overall result of this process is to smooth changes in the rate of investment over time.  Desired investment (INAG) is the product of that smoothed rate and GDP.

<math>INAG_{r,t}= SmInAgR_{r,t} * GDP_{r,t} </math>

where,

<math>SmInAgR_{r,t} = INAG_{r,t-1}/GDP_{r,t-1} *0.6+ TInAg_{r,t}/GDP_{r,t} *0.4)</math>

To further prevent too rapid of a shift in demand for agricultural investment, INAG is not allowed to increase by more than 30 percent or decrease by more than 25 percent from the actual investment in the current year. A second check ensures that the demand is no less than 0.5 percent and no greater than 40 percent of current agricultural capital (KAG).

At this point a user-controlled country-specific multiplier '''aginvm'''can boost or reduce INAG. One final check ensures that as long as GDP in the country is larger than it was in the first year, the demand for agricultural investment is not allowed to decline at an annual rate of more than 1 percent per year from the first year.

Investment need (INAG) then enters the economic model, which returns a value reconciled with all other investment needs and that feeds into further calculations in the agriculture model.

== Economic Linkages ==

Several variables, such as gross production, stocks, consumer spending, trade, prices and investment, are common to both the economic model and the two physical models. But hardly ever will the economic and physical models produce identical values, even during the first time step when both utilize "data." Thus, although we want the physical model value to override that of the economic model, it cannot simply replace it. Instead IFs extensively uses a procedure of computing an adjustment coefficient during the first time step. That coefficient is the ratio of the value in the economic model to the value in the physical model. In subsequent years IFs uses that coefficient to adjust the value from the physical model before its introduction into the economic model.

Gross production (ZS) in the agricultural sector illustrates this procedure. The value of gross production in the agricultural model is the sum of the products of agricultural production (AGP) and prices (WAP) in each agricultural category. Multiplying that times an adjustment factor (ZSF) computed in the first time stop to assure inter-model consistency produces gross production for the economic (ZS). World average prices (WAP) are used in all the economic/physical model conversions because they assure that global sums (e.g. of exports and imports) will balance.<ref>s in the subscript represents economic sector. s = 1 is defined as the agriculture sector.</ref>

<math>ZS_{r,s=1}=ZSF_{r}*\sum_f(WAP_{f,t=1}*AGP_{r,f,t} ) </math>

'''Where,'''

<math>ZSF_{r} =ZS_{r,s=1}/(\sum_f(WAP_{f,t=1}*AGP_{r,f,t=1} ) )</math>

Similarly, food stocks in each category (FSTOCK) and an adjustment factor (FSF) produce stocks (ST) for the economic model.

<math>ST_{r,s=1}=FSF_{r}*\sum_{f}(FSTOCK_{r,f,t}* WAP_{f,t=1} ) </math>

'''Where,'''

<math>FSF_{r} = ST_{r,s=1}/(\sum_{f}(FSTOCK_{r,f,t=1}* WAP_{f,t=1} ) )</math>

A similar translation is made for consumer spending on agricultural commodities, recognizing that not all crop demand is directly by consumers.

<math>CS_{r,s=1} = CSF_{r} *(FDEM_{r,t} * WAP_{f=1,t=1} +\sum_{f=2,3}(AGDEM_{r,f,t}* WAP_{f,t=1} ) )</math>

'''Where'''

<math>CSF_{r} = CS_{r,s=1} /(FDEM_{r,t=1}* WAP_{f=1,t=1} +\sum_{f=2,3}(AGDEM_{r,f,t}* WAP_{f,t=1} ) )</math>

In the same fashion exports (AGX) and imports (AGM) from the agricultural model allow calculation of exports (XS) and imports (MS) for the economic model.

<math>XS_{r,s=1}= xsf_{r} *\sum_f(AGX_{r,f,t}* WAP_{f,t=1} ) </math>

'''Where'''

<math>xsf_{r}= XS_{r,s=1}/(\sum_{f}(AGX_{r,f,t=1}* WAP_{f,t=1} ) )</math>

and

<math>MS_{r,s=1} = msf_{r} *\sum_f(AGX_{r,f,t}* WAP_{f,t=1} ) </math>

'''where'''

<math>msf_{r}= MS_{r,s=1}/(\sum_f(AGN_{r,f,t}* WAP_{f,t=1} ) )</math>

A check and, if necessary, adjustment is made ensure that the monetary values of imports and exports match up at the global level.

<math>XS_{r,s=1} = XS_{r,s=1} * ((\sum_{r}(XS_{r,s=1} ) +\sum_{r}(MS_{r,s=1} ) )/2)/(\sum_{r}(XS_{r,s=1} ) )</math>

and

<math>MS_{r,s=1}= MS_{r,s=1} *((\sum_{r}(XS_{r,s=1} ) +\sum_r(MS_{r,s=1} ) )/2)/(\sum_{r}(MS_{r,s=1} ) )</math>

With respect to prices, the agriculture model passes to the economic model a value (PRI), which reflects the ratio of the current domestic crop price index to the initial world crop price index.

<math>PRI_{r,s=1} = FPRI_{r,f=1}/ WAP_{r,f=1,t=1} </math>

Finally, investment need (INAG) is passed to the economic model under the variable name IDS, category 1 (agriculture).

== Capital Dynamics ==

The economic model of IFs returns a (potentially) modified value of IDS, category 1, reflecting the total amount of capital available for agriculture. This value is assigned to the variable IAval, which overrides the value of INAG calculated earlier (earlier it was basically investment demand; after return from the economic model it becomes investment supply).<ref>Fs does have a global parameter agon that can be used to break the link between the agriculture and economic model, in which case INAG is not overwritten. This is done by setting agon to a value less than 0.5. Doing so treats the agriculture model as a partial equilibrium model rather than a general equilibrium model.</ref> The agriculture model divides the investment available for agriculture (IAval) into investment for cropland development and investment for other agriculture capital. The coefficient IALK indicates the portion going to cropland development.

IALK is set to a default value of 0.25 for all countries in the pre-processor. In forecast years, IALK changes from this initial value depending on change in the ratio of return on land (RETR) to return on capital (RETK).

IFs calculates the return rate on land as the crop yield (YL) in the first year divided by the current cost of developing a unit of cropland (CLD).

<math>RETLD_{r} = YL_{r,t=1}/ CLD_{r,t} </math>

The return on capital depends on the difference between the hypothetical level of crop yield (HYL) that could be obtained from an additional unit investment in agricultural capital and the crop yield without that increment (CompYl). Recalling how crop yield is estimated, the hypothetical crop yield is given as

<math>HypothYl_{r} = cD_{r} * agtec_{r} *(KAG_{r+1})^( ALPHA_{r} )*(labagi_{r} )^((1-ALPHA_{r} ) )* satk_{r}</math> <math> CompYl_{r} = cD_{r} * agtec_{r} *(KAG_{r} )^(ALPHA_{r} )*(labagi_{r} )^((1-ALPHA_{r} ) )* satk_{r}</math>

and the return on capital is given as

<math>RETCap_{r} = LD_{r,l=1} *(HypothYLl_{r}- CompYl_{r} )</math>

The ratio of the return to land to the return to capital (RETRAT) is given as

<math>RETRAT_{r} = RETLD_{r}/ RETCap_{r} </math>

The adjustment of IALK uses the same first and second order adjustment mechanism that we have seen before with the ADJSTR function. Here the ‘target’ level is the ratio of the return to land to the return to capital in the first year.

<math>IALK_{r,t+1} = IALK_{r,t=1} *(1+(RETRAT_{r}-RETRAT_{r,t=1}/1))^{eliasp1}*(1+(RETRAT_{r}-RETRAT_{r,t-1}/1))^{eliasp2}</math>

'''where,'''

'''''eliasp1''' and '''eliasp2''' are global parameters''

Two final checks are made on the value of IALK. First, it is not allowed to exceed a value related to the cost of replacing depreciated investment in land and bringing a portion of grazing or forested land into production.

<math>IALK_{r,t+1} =< ((0.04* LD_{r,l=3} +0.04* LD_{r,l=4} +dkl* LD_{r,l=1} )* CLD_{r})/ IAval_{r} </math>

Second, IALK is bound between 0.1 and 0.8.

Finally the model updates agricultural capital (KAG) for the next year by subtracting depreciation as represented by agricultural capital lifetime ('''lks'''), adding the residual (non-land) investment, and adjusting for any civilian damage from warfare (CIVDM – see international politics model documentation).

<math>KAG_{r,t+1} = KAG_{r,t}- KAG_{r,t} /lks_{s=1} + IAval_{r}*(1-IALK_{r,t+1} )*(1- CIVDM_{r})</math>

== Land Dynamics ==

 Land in IFs is divided into five categories—crop, grazing, forest, urban, and other land. Historical data on total land area (LDTot), crop land (LDl=1), grazing land (LDl=2), forest land (LDl=3), and other land (LDl=4) are taken from FAO data. Historical data on urban land (LDl=5) is taken from WRI.

==== Pre-processor and first year ====

A few adjustments to the historical data are made in the pre-processor.

*In the pre-processor total production of food is reconciled with the total trade. In cases where, demand is greater than domestic supply of crops, crop production is increased to reconcile demand with supply of food production. Crop land is also increased proportionately. 
*If urban land is more than three quarters the area of other land, land is shifted from urban to other land
*If no data is available for crop land, the same is set to 30 percent of total land area. If no data is available for grazing land, same is set to 5 percent of total land area. If no data is available for other land, same is set to 30 percent of total land area.

After these changes, total land area is recomputed as the sum of the area of the individual land categories.

The pre-processor also reads in a value for potentially arable land ('''landarablepot'''), which affects the amount of potential cropland in the model. The share of agricultural capital going to land (IALK) is set to 0.25 in the pre-processor.

One final parameter is estimated related to land in the pre-processor. This is the target rate of growth of cropland ('''tgrld'''). When data is available, this is currently estimated as the growth rate of cropland between the year 2015 and the year 2005.

<math>tgrld_{r} =(LD_{r,l=1,yr=2015}/LD_{r,l=1,yr=2005} )^{1/10}-1</math>

When no data are available for cropland in either 2015 or 2005, the target rate of growth of cropland is estimated as a function of average income

<math>tgrld_{r} =0.009-0.011*MIN(1,GDPPCP_{r}/30)</math>

with a maximum growth rate given as a function of cropland as a share of total land

<math>tgrld_{r} =< tmaxgrow_{r} =0.015-0.01*MIN(1,0.5* LD_{r,l=1}/LDTot_{r} )</math>

Finally, this target growth rate is restricted to fall between -0.003 and +0.01.

In the first year, IFs estimates an initial unit cost of cropland development (CLD) as

<math>CLD_{r,t=1}=(IDS_{r,s=1,t=1} * IALK_{r,t=1})/(LD_{r,l=1,t=1}*(dkl+tgrld_{r} ) )</math>

'''where,'''

IDS is the total investment in agriculture

IALK is the share of agricultural investment going to cropland development

'''''dkl''' is a global parameter indicating the depreciation rate of investment in cropland, essentially a maintenance cost for existing cropland''

'''''tgrld''' is the target growth rate for cropland''

A related factor (SCLdF), to be used in determining the cost of land development in future years, is also calculated in the first year

<math>SCLdF_{r} = CLD_{r,t=1}/LD_{r,l=1,t=1} </math>

==== Forecast years ====

IFs calculates changes in land use for the coming year as a result of four key dynamic processes. First, changes in urban land may result from income and population changes. Second, economic shifts related to investment, particularly in the agricultural sector, can affect the amount of cropland. Third, IFs there can be expansion or retirement of grazing land for undefined reasons. Finally, in certain scenarios, specific changes in forest land can result from policies related to issues such as conservation and environmental protection.

====== Changes in urban land from income and population changes ======

Changes in urban land result from changes in population and income. IFs first estimates a predicted level of urban land (LandUrbanPred), which is then compared to current urban land. Any changes are assumed to affect all other land types proportionately, unless this leads to not enough land in a particular category. The growth with income is based on an estimated relationship between income and urban land per capita (LandUrbanR)

The predicted level of urban land (LandUrbanPred) is then given as

<math>LandUrbanPred_{r} = LD_{r,l=4,t=1} *(POP_{r,t}/POP_{r,t=1} )*(LandUrbanR_{r,t}/LandUrbanR_{r,t=1} )</math>

The change in urban land (NUrbLD) is then calculated as

<math>NUrbLD_{r} = LandUrbanPred_{r} - LD_{r,l=4}</math>

Limits are placed on the change in urban land area. First, if urban land is growing, the amount of increase in a single year cannot exceed 1/100th of a variable that is related to the change in the non-urban share of all other land from the base year (NonUrbanShrR)

<math>NonUrbanShrR_{r} = (NonUrbanShr_{r,t}/NonUrbanShr_{r,t=1} )^{2}</math>

'''where,'''

<math>NonUrbanShr_{r,t=1,t} =(\sum_{l}LD_{r,l=1-4,t} )/(\sum_{l}LD_{r,l=1-5,t} )</math>

Second, if urban land is declining, it is not permitted to fall below 10,000 hectares. Third, the changes in Urban land are assumed to affect all other land categories proportionately

<math>Reduc_{r,l=1-4} = NUrbLD_{r} * LD_{r,l=1-4}/(\sum_{l}LD_{r,l=1-4} )</math>

However, this is not allowed to result in the area for a given land category falling below 1,000 hectares. Thus, there may be a slight reduction in the amount of new urban land in certain cases.

====== Changes in cropland due to investment and/or depreciation. ======

The changes in cropland are driven by the economics of land. Specifically, they are a function of the profitability of cropland. Also, they are assumed to affect, at least directly, only the forest and the other land categories.

A maximum amount of cropland expansion each year (MaxLandExpansion) is fixed by the amount of forest land, the amount of other lands, the amount of potential arable land, and the existing amount of cropland. The maximum amount of expansion must be at least 2/100th of the existing cropland, but beyond that it cannot exceed either the total amount of forest and other land or the difference between 110% of the potential arable land (landarablepot) and current cropland.

The change in the amount of cropland and the initially estimated share of agricultural investment going to cropland in the following year are computed differently depending upon the maximum amount of cropland expansion relative to the amount of existing cropland and the current level of average income in a country. Specifically, if the maximum amount of cropland expansion is less than 10 percent of existing cropland then it is assumed that there is no change in cropland (lddev = 0) and that no agricultural investment is targeted for cropland development (IALK = 0).

If the condition mentioned in the previous paragraph is met, i.e., there is an ‘adequate’ amount of land for expanding cropland, the amount of change in cropland (lddev) is initially calculated as

<math>LdDev_{r} =(((IAval_{r} * IALK_{r})/CLD_{r} )* ldcropm_{r})-(LD_{r,l=1}*dkl)</math>

'''where'''

IAval is the total amount of funds available for investment in agriculture which is equal to IDS

IALK is the share of agricultural investment going to cropland development

CLD is the unit cost of cropland development

'''''dkl''' is the depreciation rate of investment in cropland (essential a maintenance cost for existing cropland)''

'''''ldcropm''' is a country-specific multiplier that can be used to increase or decrease changes in cropland''

Note that this equation takes into account the need to maintain existing cropland. Also, at this point, the value of LdDev is bound from below to ensure that it does not imply a greater than 10 percent decrease in existing cropland. For relatively poor countries (GDPPCP < 10), the constraint is even stricter. Specifically, IFs calls for a shift in funds to ensure that no cropland is lost. The desired shift in funds is given as

<math>DesShift_{r} =-CLD_{r} * LdDev_{r} </math>

The actual shift in funds is limited to 90 percent of the available funds, however, where the available funds are the investment in agriculture not initially designated for cropland development

<math>Shift_r = MIN(0.9* IAval_{r} *(1-IALK_{r} ),DesShift_{r} )</math>

The value of lddev given the actual shift in funds is given as

<math>LdDev_{r} = LdDev_{r} + Shift_{r}/ CLD_{r} </math>

In addition, the share of investment in agriculture designated for cropland development is updated to be

<math>IALK_{r}= IALK_{r}+ Shift_{r}/IAval_{r} </math>

The changes in cropland are linked to changes in land in the forest and ‘other’ categories. The amount coming from/going to forests reflects the share of forest land relative to ‘other’ land, as well as the current level of development. For countries with a GDP per capita higher than 15,000 dollars and where LdDev is less than 0, more is given back to forest land and the ForShrPar is set to 0.25.

<math>LDDEVFor_{r}=LdDev_{r}* LD_{r,l=3}/(LD_{r,l=3} + LD_{r,l=4} )* ForShrPar_{r}</math>

'''where,'''

 ForShrPar is given by the function depicted below;

[[File:Changes in cropland due to Investment and or Depreciation.png|frame|center|Changes in crop land due to investment and or depreciation]]

The solid line holds when land is being converted from forests to cropland (lddev > 0) and the dotted line holds when land is being converted from cropland to forests (LdDev < 0). In either case, this implies that the less of the change is related to forest land than would be expected by its share.

Two other qualifiers are that the changes in forest land (LDDEVFor) and the changes in ‘other’ land cannot exceed 90 percent of existing land in these categories and the shifts cannot result in either land category falling below 1,000 hectares. These limits feedback to the change in cropland, finally resulting in the following

<math>LdDev_{r}= LDDEVFor_{r} + LDDEVOth_{r}</math>

<math>LD_{r,l=1}= LD_{r,l=1}+ LdDev_{r}</math>

<math>LD_{r,l=3}= LD_{r,l=3} - LDDEVFor_{r} </math>

<math>LD_{r,l=4}= LD_{r,l=4}- LDDEVOth_{r} </math>

Turning back to the future cost of cropland development, this is estimated differently based only on whether there is ‘adequate’ room for cropland land expansion, defined as when the maximum amount of cropland expansion is greater than 10 percent of existing cropland. If this is the case, the future price of cropland is estimated as

<math>CLD_{r,t+1}= CLD_{r,t=1}* LD_{r,l=1,t}/ LD_{r,l=1,t=1} * RemRat_{r}^{0.2}</math>

'''where'''

RemRat is the ratio of the maximum land for expansion in the first year to the maximum land for expansion in the current year, with a maximum value of 10

<math>RemRat_{r} = MaxLandExpansion_{r,t=1}/MAX(0.1* MaxLandExpansion_{r,t=1},MaxLandExpansion_{r,t} ) </math>

This basically states that the price of cropland development grows linearly with growth in cropland and exponentially with declines in available land for cropland expansion.

Alternatively, if the maximum amount of cropland expansion in a given year is less than or equal to10 percent of existing cropland, the cost of bringing new land under cultivation is assumed to grow at the maximum of either 2 percent per year from the cost in the first year or the growth of cropland from the first year. Furthermore, it is not allowed to decline. Thus

<math>[CLD]_{r,t+1}=MAX(CLD_{r,t}, CLD_{r,t=1} * LD_{r,l=1,t}/ LD_{r,l=1,t=1} , CLD_{r,t=1}*(1+2*(t-2015)/100))</math>

====== Changes in grazing land ======

IFs assumes that relatively poor countries (GDPPCP < 10) will continue to develop additional grazing land, whereas relatively rich countries (GDPPCP > 15) will retire grazing land. No change is expected in countries with average income between $10,000 and $15,000. The annual expansion of grazing land in poor countries is initially estimated as 0.5 percent of the amount of grazing land in the first year. The retirement of grazing land in richer countries is initially estimated as 0.2 percent of current grazing land.

As with cropland, any changes in grazing land will be compensated by changes in forest and ‘other’ land. Each category is initially assumed to be affected proportionately, e.g.,

<math>ForestShr_{r}= LD_{r,l=3}/(LD_{r,l=3} + LD_{r,l=4} )</math>

Unlike the case for changes in cropland, there is no adjustment to the forest share as a function of income or the direction of change in grazing land. As with the changes in cropland, however, the changes in forest and ‘other’ land cannot exceed 90 percent of existing land in these categories and the shifts cannot result in either land category falling below 1,000 hectares. Again, these limits feed back to the change in grazing land.

====== Change in forest land due to a policy choice ======

The model user can also force the land in forest area to increase or decrease at the expense of crop and grazing land via a forest multiplier '''forestm'''. The change in forestland, LDSHIFT, is bound. In the case of an increase, i.e., '''forestm'''> 1, the amount of added land is limited to 20 percent of crop and grazing land; in the case of a decrease, i.e., '''forestm'''< 1, the amount of forest land removed is limited to 20 percent of existing forest land.

<math>- LD_{r,l=3}/5< LANDSHIFT_{r} <(LD_{r,l=1}+ LD_{r,l=2})/5</math>

<math>LD_{r,l=3}= LD_{r,l=3}+ LANDSHIFT_{r}</math>

The amount of land taken from cropland and grazing land is proportional to the amount of each.

<math>CropShare_{r}=LD_{r,l=1}/(LD_{r,l=1}+ LD_{r,l=2} )</math>

<math>LD_{r,l=1}= LD_{r,l=1}+LANDSHIFT_{r}* CROPSHARE_{r}</math>

<math>LD_{r,l=2}= LD_{r,l=2}+ LANDSHIFT_{r} *(1-CROPSHARE_{r})</math>

====== Final checks and renormalization of land use ======

Two final adjustments are made to the land area values to clean up any quirks that might have be introduced in the previous processes. First, the values for each category are bound between one thousand and ten billion hectares. Second, the values are normalized so that the sum of the categories equals the total amount of land.

<math>[LD]_{r,l=1-5,t+1}=LD{r,l=1-5} * LD_{r,l=1-5}/(\sum_{l}LD_{r,l=1-5} )</math>

Finally, a value for world forest area (WFORST) is calculated at the end of this process by summing forestland area across all countries.

<math>WFORST_{t+1}=\sum_{l}LD_{r,l=3} </math>

== Livestock Dynamics ==

In addition to capital and land, the other "stock" or "level" variable with important temporal dynamics is the livestock herd (LVHERD).

==== Pre-processor and first year ====

In the pre-processor, as explained earlier, the values for total meat production and animal meat production are initialized. From these values, IFs calculates the value for livestock by dividing the total animal meat by the slaughter rate ('''slr''')

==== Forecast years ====

The value of LVHERD is calculated by using pre-production loss meat production (AGPppl), adjusting the same for animal products produced (AGPMILKEGGS). This gives total animal meat production. The animal meat production is then divided by the slaughter rate '''slr<ref>For details on the base year value of meat production, which is based on historical data related to production, imports, exports, and assumptions about expected meat consumption and production losses, see the description of agricultural data initialization in the pre-processor.</ref>'''

<math>LVHERD_{r}=(AGPppl_{r,f=2}- AGPMILKEGGS_{r})/slr</math>

== Water Dynamics ==

Water use begins with data on total water withdrawals from FAO Aquastat.  These are divided by the size of the population to get an estimate of water use per capita.

In future years, water use per capita is forecast to increase in parallel with crop production per capita.  Specifically, an expected level of water use per capita as a function of crop production per capita (see figure below) is calculated for crop production in the current year (CropPC) and crop production in the first year (CropPCI).  The ratio of these values is multiplied by the water use per capita in the first year (WatUsePCI) to get water use per capita in the current year (WatUsePC).  This is multiplied by population (POP) to get total water use (WATUSE)

<math>WatUsePC_{r}= WatUsePCI_{r}*f(CropPC_{r} )/f(CropPCI_{r} </math>

<math>WATUSE_{r}= WatUsePC_{r} * POP_{r}</math>

[[File:Water dynamics.png|Water Use per capita compared to GDP per capita]]

= Data Tables read in Agricultural Pre-Processor – DATAGRI.BAS =

{| border="1" cellspacing="0" cellpadding="0" width="0"
|-
| nowrap="nowrap" style="width:234px;height:35px;" |
'''Table'''

| nowrap="nowrap" style="width:142px;height:35px;" |
'''Definition'''

| nowrap="nowrap" style="width: 212px; height: 35px;" |
'''Original Source'''

| style="width: 406px; height: 35px;" |
'''Variable to which series relates in PP/How the series is used in the PP'''

|-
| style="width:234px;height:20px;" |
SeriesLandArea

| style="width:142px;height:20px;" |
Land Area

| style="width: 212px; height: 20px;" |
WDI

| style="width: 406px; height: 20px;" |
LandArea

|-
| style="width:234px;height:69px;" |
SeriesMalnChil%WeightWB

| style="width:142px;height:69px;" |
Percentage of children under 5 malnourished based on weight; US benchmark

| style="width: 212px; height: 69px;" |
World Health Organization.

| style="width: 406px; height: 69px;" |
Malnourished children

|-
| style="width:234px;height:35px;" |
SeriesMalnPop%WB

| style="width:142px;height:35px;" |
Percentage of population malnourished

| style="width: 212px; height: 35px;" |
FAO

| style="width: 406px; height: 35px;" |
Malnourished population

|-
| style="width:234px;height:20px;" |
SeriesLandCrop

| style="width:142px;height:20px;" |
Land, crop

| style="width: 212px; height: 20px;" |
FAO

| style="width: 406px; height: 20px;" |
LDCrop

|-
| style="width:234px;height:20px;" |
SeriesLandForest

| style="width:142px;height:20px;" |
Land, forest

| style="width: 212px; height: 20px;" |
FAO

| style="width: 406px; height: 20px;" |
LdFor

|-
| style="width:234px;height:20px;" |
SeriesLandGrazing

| style="width:142px;height:20px;" |
Land, grazing

| style="width: 212px; height: 20px;" |
FAO

| style="width: 406px; height: 20px;" |
LdGraz

|-
| style="width:234px;height:69px;" |
SeriesAgFishAquaProdAqAnimalsFSJ

| style="width:142px;height:69px;" |
Total Aquatic Animals Aquaculture Production (tonnes) from FishstatJ

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Break down data from FAO into aquaculture and catch stat using data from fish stat j

|-
| style="width:234px;height:69px;" |
SeriesAgFishAquaProdAqPlantsFSJ

| style="width:142px;height:69px;" |
Total Aquatic Plants Aquaculture Production (tonnes) from FishstatJ

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Break down data from FAO into aquaculture and catch stat using data from fish stat j

|-
| style="width:234px;height:69px;" |
SeriesAgFishAquaProdCephalopodsFSJ

| style="width:142px;height:69px;" |
Total Cephalopods Aquaculture Production (tonnes) from FishstatJ

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Break down data from FAO into aquaculture and catch stat using data from fish stat j

|-
| style="width:234px;height:69px;" |
SeriesAgFishAquaProdCrustaceansFSJ

| style="width:142px;height:69px;" |
Total Crustaceans Aquaculture Production (tonnes) from FishstatJ

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Break down data from FAO into aquaculture and catch stat using data from fish stat j

|-
| style="width:234px;height:69px;" |
SeriesAgFishAquaProdDemersalFSJ

| style="width:142px;height:69px;" |
Total  Demersal Aquaculture Production (tonnes) from FishstatJ

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Break down data from FAO into aquaculture and catch stat using data from fish stat j

|-
| style="width:234px;height:69px;" |
SeriesAgFishAquaProdFreshwaterFSJ

| style="width:142px;height:69px;" |
Total Freshwater Aquaculture Production (tonnes) from FishstatJ

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Break down data from FAO into aquaculture and catch stat using data from fish stat j

|-
| style="width:234px;height:69px;" |
SeriesAgFishAquaProdMarineFSJ

| style="width:142px;height:69px;" |
Total Marine Aquaculture Production (tonnes) from FishstatJ

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Break down data from FAO into aquaculture and catch stat using data from fish stat j

|-
| style="width:234px;height:69px;" |
SeriesAgFishAquaProdMolluscsFSJ

| style="width:142px;height:69px;" |
Total Molluscs Aquaculture Production (tonnes) from FishstatJ

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Break down data from FAO into aquaculture and catch stat using data from fish stat j

|-
| style="width:234px;height:69px;" |
SeriesAgFishAquaProdPelagicFSJ

| style="width:142px;height:69px;" |
Total Pelagic Aquaculture Production (tonnes) from FishstatJ

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Break down data from FAO into aquaculture and catch stat using data from fish stat j

|-
| style="width:234px;height:52px;" |
SeriesAgFishCalPerCapPerDayAqAnimalsFAO

| style="width:142px;height:52px;" |
Total Aquatic Animals used for Calories/capita/day (kcal/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishCalPerCapPerDayAqPlantsFAO

| style="width:142px;height:52px;" |
Total Aquatic Plants used for Calories/capita/day (kcal/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishCalPerCapPerDayBodyOilFAO

| style="width:142px;height:52px;" |
Total Body Oil used for Calories/capita/day (kcal/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishCalPerCapPerDayLiverOilFAO

| style="width:142px;height:52px;" |
Total Fish Liver Oil used for Calories/capita/day (kcal/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:69px;" |
SeriesAgFishCatchProdAqAnimalsFSJ

| style="width:142px;height:69px;" |
Total Aquatic Animals Catch Production (tonnes) from FishstatJ

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Break down data from FAO into aquaculture and catch stat using data from fish stat j

|-
| style="width:234px;height:69px;" |
SeriesAgFishCatchProdAqPlantsFSJ

| style="width:142px;height:69px;" |
Total Aquatic Plants Catch Production (tonnes) from FishstatJ

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Break down data from FAO into aquaculture and catch stat using data from fish stat j

|-
| style="width:234px;height:69px;" |
SeriesAgFishCatchProdCephalopodsFSJ

| style="width:142px;height:69px;" |
Total Cephalopods Capture Production from FishstatJ

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Break down data from FAO into aquaculture and catch stat using data from fish stat j

|-
| style="width:234px;height:69px;" |
SeriesAgFishCatchProdCrustaceansFSJ

| style="width:142px;height:69px;" |
Total Crustaceans Capture Production from FishstatJ

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Break down data from FAO into aquaculture and catch stat using data from fish stat j

|-
| style="width:234px;height:69px;" |
SeriesAgFishCatchProdDemersalFSJ

| style="width:142px;height:69px;" |
Total Demersal Capture Production from FishstatJ

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Break down data from FAO into aquaculture and catch stat using data from fish stat j

|-
| style="width:234px;height:69px;" |
SeriesAgFishCatchProdFreshwaterFSJ

| style="width:142px;height:69px;" |
Total Freshwater Capture Production from FishstatJ

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Break down data from FAO into aquaculture and catch stat using data from fish stat j

|-
| style="width:234px;height:69px;" |
SeriesAgFishCatchProdMarineFSJ

| style="width:142px;height:69px;" |
Total Marine Capture Production from FishstatJ

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Break down data from FAO into aquaculture and catch stat using data from fish stat j

|-
| style="width:234px;height:69px;" |
SeriesAgFishCatchProdMolluscsFSJ

| style="width:142px;height:69px;" |
Total Molluscs Capture Production from FishstatJ

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Break down data from FAO into aquaculture and catch stat using data from fish stat j

|-
| style="width:234px;height:69px;" |
SeriesAgFishCatchProdPelagicFSJ

| style="width:142px;height:69px;" |
Total Pelagic Capture Production (tonnes) from Fishstatj

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Break down data from FAO into aquaculture and catch stat using data from fish stat j

|-
| style="width:234px;height:52px;" |
SeriesAgFishDomesticSupplyAqPlantsFAO

| style="width:142px;height:52px;" |
Domestic Supply Quantity of Aquatic Plants (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishDomesticSupplyBodyOilFAO

| style="width:142px;height:52px;" |
Domestic Supply Quantity ofl Body Oil (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishDomesticSupplyLiverOilFAO

| style="width:142px;height:52px;" |
Domestic Supply Quantity of Fish Liver Oil (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishDomesticSupplyMealFAO

| style="width:142px;height:52px;" |
Domestic Supply Production of Fish Meal (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishExportsAqPlantsFAO

| style="width:142px;height:52px;" |
Total Aquatic Plants Exports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishExportsBodyOilFAO

| style="width:142px;height:52px;" |
Total Body Oil Exports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishExportsLiverOilFAO

| style="width:142px;height:52px;" |
Total Fish Liver Oil EXports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishExportsMealFAO

| style="width:142px;height:52px;" |
Total Fish Meal Exports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishFoodSupplyPerCapPerDayAqAnimalsFAO

| style="width:142px;height:52px;" |
Total Aquatic Animals used for Food Supply/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishFoodSupplyPerCapPerDayAqPlantsFAO

| style="width:142px;height:52px;" |
Total Aquatic Plants used for Food Supply/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishFoodSupplyPerCapPerDayBodyOilFAO

| style="width:142px;height:52px;" |
Total Body Oil used for Food Supply/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishFoodSupplyPerCapPerDayLiverOilFAO

| style="width:142px;height:52px;" |
Total Fish Liver Oil used for Food Supply/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishImportsAqPlantsFAO

| style="width:142px;height:52px;" |
Total Aquatic Plants Imports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishImportsBodyOilFAO

| style="width:142px;height:52px;" |
Total Body Oil Imports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishImportsLiverOilFAO

| style="width:142px;height:52px;" |
Total Fish Liver Oil Imports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishImportsMealFAO

| style="width:142px;height:52px;" |
Total Fish Meal Imports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProdAqPlantsFAO

| style="width:142px;height:52px;" |
Production Quantity of Aquatic Plants (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProdBodyOilFAO

| style="width:142px;height:52px;" |
Production Quantity of (Fish) Body Oil (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProdLiverOilFAO

| style="width:142px;height:52px;" |
Production Quantityt of Fish Liver Oil (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProdMealFAO

| style="width:142px;height:52px;" |
Production Quantity of Fish Meal (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProteinPerCapPerDayAqAnimalsFAO

| style="width:142px;height:52px;" |
Total Aquatic Animals used for Protein/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProteinPerCapPerDayAqPlantsFAO

| style="width:142px;height:52px;" |
Total Aquatic Plants used for Protein/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProteinPerCapPerDayBodyOilFAO

| style="width:142px;height:52px;" |
Total Body Oil used for Protein/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProteinPerCapPerDayLiverOilFAO

| style="width:142px;height:52px;" |
Total Fish Liver Oil used for Protein/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFeedAqPlantsFAO

| style="width:142px;height:52px;" |
Total Aquatic Plants used for Feed (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFeedBodyOilFAO

| style="width:142px;height:52px;" |
Total Body Oil used for Feed (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFeedLiverOilFAO

| style="width:142px;height:52px;" |
Total Fish Liver Oil used for Feed (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFeedMealFAO

| style="width:142px;height:52px;" |
Total Fish Meal used for Feed (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFoodAqAnimalsFAO

| style="width:142px;height:52px;" |
Total Aquatic Animals used for Food (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFoodAqPlantsFAO

| style="width:142px;height:52px;" |
Total Aquatic Plants used for Food (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFoodBodyOilFAO

| style="width:142px;height:52px;" |
Total Body Oil for Food (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFoodLiverOilFAO

| style="width:142px;height:52px;" |
Total Fish Liver Oil used for Food (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoOtherUtilAqAnimalsFAO

| style="width:142px;height:52px;" |
Total Aquatic Animals used for Other Utilities (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoOtherUtilAqPlantsFAO

| style="width:142px;height:52px;" |
Total Aquatic Plants used for Other Utilities (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoOtherUtilBodyOilFAO

| style="width:142px;height:52px;" |
Total Body Oil used for Other Utilities (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoOtherUtilLiverOilFAO

| style="width:142px;height:52px;" |
Total Fish Liver Oil used for Other Utilities (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoOtherUtilMealFAO

| style="width:142px;height:52px;" |
Total Fish Meal used for Other Utilities (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoSeedAqAnimalsFAO

| style="width:142px;height:52px;" |
Total Aquatic Animals used for Seed (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:69px;" |
SeriesAgFishCalPerCapPerDayCephalopodsFAO

| style="width:142px;height:69px;" |
Total Cephalopods Fish Quantity used for Calories/capita/day (kcal/capita/day)

| style="width: 212px; height: 69px;" |
FAO

| style="width: 406px; height: 69px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:69px;" |
SeriesAgFishCalPerCapPerDayCrustaceansFAO

| style="width:142px;height:69px;" |
Total Crustaceans Fish used for Calories/capita/day (kcal/capita/day)

| style="width: 212px; height: 69px;" |
FAO

| style="width: 406px; height: 69px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:69px;" |
SeriesAgFishCalPerCapPerDayDemersalFAO

| style="width:142px;height:69px;" |
Total Demersal Fish consumed for Calories/cap/day (kcal/capita/day)

| style="width: 212px; height: 69px;" |
FAO

| style="width: 406px; height: 69px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:69px;" |
SeriesAgFishCalPerCapPerDayFreshwaterFAO

| style="width:142px;height:69px;" |
Total Freshwater Fish consumed for calories/cap/day (kcal/capita/day)

| style="width: 212px; height: 69px;" |
FAO

| style="width: 406px; height: 69px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishCalPerCapPerDayMarineFAO

| style="width:142px;height:52px;" |
Total Marine Fish used for Calories/capita/day (kcal/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishCalPerCapPerDayMolluscsFAO

| style="width:142px;height:52px;" |
Total Molluscs Fish used for Calories/capita/day (kcal/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:69px;" |
SeriesAgFishCalPerCapPerDayPelagicFAO

| style="width:142px;height:69px;" |
Total Pelagic Fish consumed for Calories/capita/day (Tonnes)

| style="width: 212px; height: 69px;" |
FAO

| style="width: 406px; height: 69px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishDomesticSupplyAqAnimalsFAO

| style="width:142px;height:52px;" |
Domestic Supply Quantity ofAquatic Animals (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishDomesticSupplyCephalopodsFAO

| style="width:142px;height:52px;" |
Domestic Supply Quantity of Cephalopods Fish (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishDomesticSupplyCrustaceansFAO

| style="width:142px;height:52px;" |
Domestic Supply of Crustaceans Fish (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishDomesticSupplyDemersalFAO

| style="width:142px;height:52px;" |
Total Demersal Fish used for Food(Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishDomesticSupplyFreshwaterFAO

| style="width:142px;height:52px;" |
Total Domestic Supply of Freshwater Fish (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishDomesticSupplyMarineFAO

| style="width:142px;height:52px;" |
Domestic Supply Quantity of Marine Fish (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishDomesticSupplyMolluscsFAO

| style="width:142px;height:52px;" |
Total Domestic Supply of Molluscs Fish (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishDomesticSupplyPelagicFAO

| style="width:142px;height:52px;" |
Domestic Supply Quantity of Pelagic Fish (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishExportsAqAnimalsFAO

| style="width:142px;height:52px;" |
Total Aquatic Animals Exports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishExportsCephalopodsFAO

| style="width:142px;height:52px;" |
Total Exports of Cephalopods Fish (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishExportsCrustaceansFAO

| style="width:142px;height:52px;" |
Total Crustaceans Fish Exports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishExportsDemersalFAO

| style="width:142px;height:52px;" |
Total Demersal Fish Exports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishExportsFreshwaterFAO

| style="width:142px;height:52px;" |
Total Freshwater Fish Exports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishExportsMarineFAO

| style="width:142px;height:52px;" |
Total Marine Fish Exports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishExportsMolluscsFAO

| style="width:142px;height:52px;" |
Total Molluscs Fish Exports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishExportsPelagicFAO

| style="width:142px;height:52px;" |
Total Pelagic Fish Exports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:69px;" |
SeriesAgFishFoodSupplyPerCapPerDayCephalopodsFAO

| style="width:142px;height:69px;" |
Total Cephalopods Fish Quantity used for Food/capita/day (g/capita/day)

| style="width: 212px; height: 69px;" |
FAO

| style="width: 406px; height: 69px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:69px;" |
SeriesAgFishFoodSupplyPerCapPerDayCrustaceansFAO

| style="width:142px;height:69px;" |
Total Crustaceans Fish used for Food Supply/capita/day (g/capita/day)

| style="width: 212px; height: 69px;" |
FAO

| style="width: 406px; height: 69px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishFoodSupplyPerCapPerDayDemersalFAO

| style="width:142px;height:52px;" |
Total Demersal Fish for Food Supply/cap/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishFoodSupplyPerCapPerDayFreshwaterFAO

| style="width:142px;height:52px;" |
Total Freshwater Fish used for Food Supply/cap/day  (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishFoodSupplyPerCapPerDayMarineFAO

| style="width:142px;height:52px;" |
Total Marine Fish used for Food Suply/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishFoodSupplyPerCapPerDayMolluscsFAO

| style="width:142px;height:52px;" |
Total Molluscs Fish used for Food Supply/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishFoodSupplyPerCapPerDayPelagicFAO

| style="width:142px;height:52px;" |
Total Pelagic Fish used for Food Supply/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishImportsAqAnimalsFAO

| style="width:142px;height:52px;" |
Total Aquatic Animals Imports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishImportsCephalopodsFAO

| style="width:142px;height:52px;" |
Total Imports of Cephalopods Fish (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishImportsCrustaceansFAO

| style="width:142px;height:52px;" |
Total Crustaceans Fish Imports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishImportsDemersalFAO

| style="width:142px;height:52px;" |
Total Demersal Fish Imports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishImportsFreshwaterFAO

| style="width:142px;height:52px;" |
Total Freshwater Fish Imports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishImportsMarineFAO

| style="width:142px;height:52px;" |
Total Marine Fish Imports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishImportsMolluscsFAO

| style="width:142px;height:52px;" |
Total Molluscs Fish Imports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishImportsPelagicFAO

| style="width:142px;height:52px;" |
Total Pelagic Fish Imports (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProdAqAnimalsFAO

| style="width:142px;height:52px;" |
Production Quantity Aquatic Animals (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProdCephalopodsFAO

| style="width:142px;height:52px;" |
Production Quantity of Cephalopods Fish (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProdCrustaceansFAO

| style="width:142px;height:52px;" |
Production Quantity of Crustaceans Fish (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProdDemersalFAO

| style="width:142px;height:52px;" |
Production quantity of Demersal Fish (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProdFreshwaterFAO

| style="width:142px;height:52px;" |
Total Domestic Freshwater Fish Production (tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProdMarineFAO

| style="width:142px;height:52px;" |
Production Quantity of Marine Fish (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProdMolluscsFAO

| style="width:142px;height:52px;" |
Production Quantity of Molluscs Fish (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProdPelagicFAO

| style="width:142px;height:52px;" |
Production quantity of Pelagic Fish (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:69px;" |
SeriesAgFishProteinPerCapPerDayCephalopodsFAO

| style="width:142px;height:69px;" |
Total Cephalopods Fish Quantity used for Protein/capita/day (g/capita/day)

| style="width: 212px; height: 69px;" |
FAO

| style="width: 406px; height: 69px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProteinPerCapPerDayCrustaceansFAO

| style="width:142px;height:52px;" |
Total Crustaceans Fish used for Protein/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:69px;" |
SeriesAgFishProteinPerCapPerDayDemersalFAO

| style="width:142px;height:69px;" |
Total Demersal Fish consumed for Protein/cap/day (g/capita/day)

| style="width: 212px; height: 69px;" |
FAO

| style="width: 406px; height: 69px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:69px;" |
SeriesAgFishProteinPerCapPerDayFreshwaterFAO

| style="width:142px;height:69px;" |
Total Freshwater Fish consumed for protein/cap/day (g/capita/day)

| style="width: 212px; height: 69px;" |
FAO

| style="width: 406px; height: 69px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProteinPerCapPerDayMarineFAO

| style="width:142px;height:52px;" |
Total Marine Fish used for Protein/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProteinPerCapPerDayMolluscsFAO

| style="width:142px;height:52px;" |
Total Molluscs Fish used for Protein/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:69px;" |
SeriesAgFishProteinPerCapPerDayPelagicFAO

| style="width:142px;height:69px;" |
Total Pelagic Fish consumed for Protein/capita/day (g/capita/day)

| style="width: 212px; height: 69px;" |
FAO

| style="width: 406px; height: 69px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFeedCephalopodsFAO

| style="width:142px;height:52px;" |
Total Cephalopods Fish Quantity used for Feed (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFeedCrustaceansFAO

| style="width:142px;height:52px;" |
Total Crustaceans Fish used for Feed (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFeedDemersalFAO

| style="width:142px;height:52px;" |
Total Demersal Fish used for Feed(Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFeedFreshwaterFAO

| style="width:142px;height:52px;" |
Total Freshwater Fish used for feed  (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFeedMarineFAO

| style="width:142px;height:52px;" |
Total Marine Fish used for Feed (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFeedMolluscsFAO

| style="width:142px;height:52px;" |
Total Molluscs Fish used for Feed (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFeedPelagicFAO

| style="width:142px;height:52px;" |
Total Pelagic Fish used for Feed (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFoodCephalopodsFAO

| style="width:142px;height:52px;" |
Total Cephalopods Fish Quantity used for Food (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFoodCrustaceansFAO

| style="width:142px;height:52px;" |
Total Crustaceans Fish used for Food (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFoodDemersalFAO

| style="width:142px;height:52px;" |
Total Demersal Fish used for Food(Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFoodFreshwaterFAO

| style="width:142px;height:52px;" |
Total Freshwater Fish for food  (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFoodMarineFAO

| style="width:142px;height:52px;" |
Total Marine Fish used for Food (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFoodMolluscsFAO

| style="width:142px;height:52px;" |
Total Molluscs Fish used for Food (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFoodPelagicFAO

| style="width:142px;height:52px;" |
Total Pelagic Fish used for Food (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoOtherUtilCephalopodsFAO

| style="width:142px;height:52px;" |
Total Cephalopods Fish Quantity used for Other Utilities (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoOtherUtilCrustaceansFAO

| style="width:142px;height:52px;" |
Total Crustaceans Fish used for Other Utilities (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoOtherUtilDemersalFAO

| style="width:142px;height:52px;" |
Total Demersal Fish used for other Utilities(Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoOtherUtilFreshwaterFAO

| style="width:142px;height:52px;" |
Total Freshwater Fish used for other Utilities (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoOtherUtilMarineFAO

| style="width:142px;height:52px;" |
Total Marine Fish used for Other Utilities (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoOtherUtilMolluscsFAO

| style="width:142px;height:52px;" |
Total Molluscs Fish used for Other Utilities (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoOtherUtilPelagicFAO

| style="width:142px;height:52px;" |
Total Pelagic Fish used for Other Utilities (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoSeedCephalopodsFAO

| style="width:142px;height:52px;" |
Total Cephalopods Fish Quantity used for Seed (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoSeedCrustaceansFAO

| style="width:142px;height:52px;" |
Total Crustaceans Fish used for Seed (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoSeedDemersalFAO

| style="width:142px;height:52px;" |
Total Demersal Fish used for Feed(Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoSeedFreshwaterFAO

| style="width:142px;height:52px;" |
Total Freshwater Fish used for Seed  (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoSeedMarineFAO

| style="width:142px;height:52px;" |
Total Marine Fish used for Seed (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoSeedMolluscsFAO

| style="width:142px;height:52px;" |
Total Molluscs Fish used for Seed (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoSeedPelagicFAO

| style="width:142px;height:52px;" |
Total Pelagic Fish used for Seed (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishExportQuantityFAOTrade

| style="width:142px;height:52px;" |
Quantity of fish exported(Tonnes) from FishstatJ

| style="width: 212px; height: 52px;" |
FAO, FishstatJ

| style="width: 406px; height: 52px;" |
AGXFishQuantTradetbl

|-
| style="width:234px;height:35px;" |
SeriesAgFishExportValueFAOTrade

| style="width:142px;height:35px;" |
Export value of fish ($1000 US)  from FishstatJ software

| style="width: 212px; height: 35px;" |
FAO, FishstatJ

| style="width: 406px; height: 35px;" |
Fish imports and exports

|-
| style="width:234px;height:35px;" |
SeriesAgFishImportQuantityFAOTrade

| style="width:142px;height:35px;" |
Quantity of fish imported (Tonnes) from FishstatJ

| style="width: 212px; height: 35px;" |
FAO, FishstatJ

| style="width: 406px; height: 35px;" |
Fish imports and exports

|-
| style="width:234px;height:35px;" |
SeriesAgFishImportValueFAOTrade

| style="width:142px;height:35px;" |
Import value of fish ($1000 US)  from FishstatJ software

| style="width: 212px; height: 35px;" |
FAO, FishstatJ

| style="width: 406px; height: 35px;" |
Fish imports and exports

|-
| style="width:234px;height:52px;" |
SeriesAgCropExportQuantityFAOTrade

| style="width:142px;height:52px;" |
Quantity of Crops exported (Tonnes) from FAO Trade Domain

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Crop trade

|-
| style="width:234px;height:52px;" |
SeriesAgCropExportValueFAOTrade

| style="width:142px;height:52px;" |
Value of Crops exported (1000$ US) from FAO Trade Domain

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Crop trade

|-
| style="width:234px;height:52px;" |
SeriesAgCropImportQuantityFAOTrade

| style="width:142px;height:52px;" |
Quantity of Crops Imported (Tonnes) from FAO Trade Domain

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Crop trade

|-
| style="width:234px;height:52px;" |
SeriesAgCropImportValueFAOTrade

| style="width:142px;height:52px;" |
Value of Crops Imported(1000$ USD) from FAO Trade Domain

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Crop trade

|-
| style="width:234px;height:52px;" |
SeriesAgMeatExportQuantityFAOTrade

| style="width:142px;height:52px;" |
Quantity of meat exported (Tonnes) from FAO Trade Domain

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Meat trade

|-
| style="width:234px;height:52px;" |
SeriesAgMeatExportValueFAOTrade

| style="width:142px;height:52px;" |
Value of meat exported (1000$ US) from FAO Trade Domain

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Meat trade

|-
| style="width:234px;height:52px;" |
SeriesAgMeatImportQuantityFAOTrade

| style="width:142px;height:52px;" |
Quantity of Meat Imported (Tonnes) from FAO Trade Domain

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Meat trade

|-
| style="width:234px;height:52px;" |
SeriesAgMeatImportValueFAOTrade

| style="width:142px;height:52px;" |
Value of Meat Imported (1000$ US) from FAO Trade Domain

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Meat trade

|-
| style="width:234px;height:20px;" |
SeriesAgProdCereals

| style="width:142px;height:20px;" |
Cereal production

| style="width: 212px; height: 20px;" |
FAO

| style="width: 406px; height: 20px;" |
Crop production (AGP)

|-
| style="width:234px;height:35px;" |
SeriesAgProdFruitsExclMelons

| style="width:142px;height:35px;" |
Production of fruit, excluding melons

| style="width: 212px; height: 35px;" |
FAO

| style="width: 406px; height: 35px;" |
Crop production (AGP)

|-
| style="width:234px;height:20px;" |
SeriesAgProdPulses

| style="width:142px;height:20px;" |
Pulses production

| style="width: 212px; height: 20px;" |
FAO

| style="width: 406px; height: 20px;" |
Crop production (AGP)

|-
| style="width:234px;height:35px;" |
SeriesAgProdRootsTub

| style="width:142px;height:35px;" |
Root and tuber production

| style="width: 212px; height: 35px;" |
FAO

| style="width: 406px; height: 35px;" |
Crop production (AGP)

|-
| style="width:234px;height:35px;" |
SeriesAgProdVegMel

| style="width:142px;height:35px;" |
Vegetable, melon production

| style="width: 212px; height: 35px;" |
FAO

| style="width: 406px; height: 35px;" |
Crop production (AGP)

|-
| style="width:234px;height:20px;" |
SeriesLandOther

| style="width:142px;height:20px;" |
Land, other

| style="width: 212px; height: 20px;" |
FAO

| style="width: 406px; height: 20px;" |
LdOth

|-
| style="width:234px;height:35px;" |
SeriesLandBuiltGFNcorine

| style="width:142px;height:35px;" |
Land Area, Artificial Land

| style="width: 212px; height: 35px;" |
CORINE Land Cover

| style="width: 406px; height: 35px;" |
LdUrbTbl

|-
| style="width:234px;height:52px;" |
SeriesLandBuiltGFNgaez

| style="width:142px;height:52px;" |
Land Area, Settlement and Infrastructure

| style="width: 212px; height: 52px;" |
Global Agro-Ecological Zones (GAEZ) Model

| style="width: 406px; height: 52px;" |
LdUrbTbl

|-
| style="width:234px;height:35px;" |
SeriesLandBuiltGFNglc

| style="width:142px;height:35px;" |
Land Area, Infrastructure aggregated

| style="width: 212px; height: 35px;" |
Global Land Cover (GLC) 2000

| style="width: 406px; height: 35px;" |
LdUrbTbl

|-
| style="width:234px;height:103px;" |
SeriesLandBuiltGFNsage

| style="width:142px;height:103px;" |
Land Area, Buit area

| style="width: 212px; height: 103px;" |
Sustainability and the Global Environment (SAGE) at University of Wisconsin

| style="width: 406px; height: 103px;" |
LdUrbTbl

|-
| style="width:234px;height:20px;" |
SeriesAgProdMeat

| style="width:142px;height:20px;" |
Meat production

| style="width: 212px; height: 20px;" |
FAO

| style="width: 406px; height: 20px;" |
Meat production (AGP)

|-
| style="width:234px;height:20px;" |
SeriesAgFruVegEx

| style="width:142px;height:20px;" |
Fruit, vegetable exports

| style="width: 212px; height: 20px;" |
FAO

| style="width: 406px; height: 20px;" |
Food imports

|-
| style="width:234px;height:20px;" |
SeriesAgFruVegIm

| style="width:142px;height:20px;" |
Fruit, vegetable imports

| style="width: 212px; height: 20px;" |
FAO

| style="width: 406px; height: 20px;" |
Food imports

|-
| style="width:234px;height:35px;" |
SeriesLandPotentialArable

| style="width:142px;height:35px;" |
total potential arable land

| style="width: 212px; height: 35px;" |
FAOTERRASTAT

| style="width: 406px; height: 35px;" |
LandArablePot

|-
| style="width:234px;height:341px;" |
SeriesWaterAnRenResources

| style="width:142px;height:341px;" |
Annually renewable water resources

| style="width: 212px; height: 341px;" |
FAO: Water Resources, Development and Management Service. AQUASTAT Information System on Water in Agriculture: Review of Water Resource Statistics by Country.  [http://www.fao.org/waicent/faoinfo/agricult/agl/aglw/aquastat/water_res/index.htm http://www.fao.org/waicent/faoinfo/agricult/agl/aglw/aquastat/water_res/index.htm].

| style="width: 406px; height: 341px;" |
Water resources

|-
| style="width:234px;height:341px;" |
SeriesWaterAnWithdrawals

| style="width:142px;height:341px;" |
Annual water withdrawals/use (1990=70-99;2000=update, mostly 2000)

| style="width: 212px; height: 341px;" |
FAO: Water Resources, Development and Management Service. AQUASTAT Information System on Water in Agriculture: Review of Water Resource Statistics by Country.  [http://www.fao.org/waicent/faoinfo/agricult/agl/aglw/aquastat/water_res/index.htm http://www.fao.org/waicent/faoinfo/agricult/agl/aglw/aquastat/water_res/index.htm].

| style="width: 406px; height: 341px;" |
Water use

|-
| style="width:234px;height:341px;" |
SeriesWaterAnRenResourcesOld

| style="width:142px;height:341px;" |
Annually renewable water resources

| style="width: 212px; height: 341px;" |
FAO: Water Resources, Development and Management Service. AQUASTAT Information System on Water in Agriculture: Review of Water Resource Statistics by Country.  [http://www.fao.org/waicent/faoinfo/agricult/agl/aglw/aquastat/water_res/index.htm http://www.fao.org/waicent/faoinfo/agricult/agl/aglw/aquastat/water_res/index.htm].

| style="width: 406px; height: 341px;" |
Water resources

|-
| style="width:234px;height:222px;" |
SeriesLandUrban&Built

| style="width:142px;height:222px;" |
Land, urban and built-up areas

| style="width: 212px; height: 222px;" |
Loveland, T.R., Reed, B.C., J.F., Brown, J.F., Ohlen, D.O., Zhu, Z., Yang, L.  Merchant. J. 2000. Global Land Cover Characteristics Database  V 2.0. [http://edcdaac.usgs.gov/glcc/globdoc2_0.html http://edcdaac.usgs.gov/glcc/globdoc2_0.html]

| style="width: 406px; height: 222px;" |
LdUrbTbl

|-
| style="width:234px;height:40px;" |
SeriesAgBovineMeatProductionFAO

| style="width:142px;height:40px;" |
Total Domestic Bovine Meat Production (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Meat production (AGP)

|-
| style="width:234px;height:40px;" |
SeriesAgCerealsEx

| style="width:142px;height:40px;" |
Cereal exports

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Trade data

|-
| style="width:234px;height:40px;" |
SeriesAgCerealsIm

| style="width:142px;height:40px;" |
Cereal imports

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Trade data

|-
| style="width:234px;height:40px;" |
SeriesAgCerealSupply

| style="width:142px;height:40px;" |
Cereal, domestic supply quantity

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Trade data

|-
| style="width:234px;height:40px;" |
SeriesAgCerealWaste

| style="width:142px;height:40px;" |
FAO Cereal Waste

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Trade data

|-
| style="width:234px;height:40px;" |
SeriesAgMeatEx

| style="width:142px;height:40px;" |
Meat exports

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Trade data

|-
| style="width:234px;height:40px;" |
SeriesAgMeatIm

| style="width:142px;height:40px;" |
Meat imports

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Trade data

|-
| style="width:234px;height:40px;" |
SeriesAgMeatOtherProductionFAO

| style="width:142px;height:40px;" |
Total Domestic Meat (Other) Production (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Meat production (AGP)

|-
| style="width:234px;height:52px;" |
SeriesAgMuttonandGoatMeatProductionFAO

| style="width:142px;height:52px;" |
Total Domestic Mutton and Goat Meat Production (million metric tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Meat production (AGP)

|-
| style="width:234px;height:40px;" |
SeriesAgPigMeatProductionFAO

| style="width:142px;height:40px;" |
Total Domestic Pigmeat Production (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Meat production (AGP)

|-
| style="width:234px;height:40px;" |
SeriesAgPoultryMeatProductionFAO

| style="width:142px;height:40px;" |
Total Domestic Poultry Meat Production (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Meat production (AGP)

|-
| style="width:234px;height:40px;" |
SeriesAgPulsesEx

| style="width:142px;height:40px;" |
Pulse exports

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Trade data

|-
| style="width:234px;height:40px;" |
SeriesAgPulsesIm

| style="width:142px;height:40px;" |
Pulseimports

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Trade data

|-
| style="width:234px;height:40px;" |
SeriesAgVegetableSupply

| style="width:142px;height:40px;" |
FAO Vegetable Supply

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Trade data

|-
| style="width:234px;height:40px;" |
SeriesAgVegetableWaste

| style="width:142px;height:40px;" |
FAO Vegetable Waste

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Trade data

|-
| style="width:234px;height:52px;" |
SeriesAGCropCalPerCapPerDayFAO

| style="width:142px;height:52px;" |
Total calories consumed from crops per capita per day (kcal/capita/day).

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
CLPC

|-
| style="width:234px;height:40px;" |
SeriesAGCropDomesticSupplyFAO

| style="width:142px;height:40px;" |
Domestic Supply Quantity of Crops (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Trade data

|-
| style="width:234px;height:40px;" |
SeriesAGCropExportsFAO

| style="width:142px;height:40px;" |
Total Crop Exports (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Trade data

|-
| style="width:234px;height:69px;" |
SeriesAGCropFatPerCapPerDayFAO

| style="width:142px;height:69px;" |
Total grams of fat consumed from crops per capita per day (g/capita/day).

| style="width: 212px; height: 69px;" |
FAO

| style="width: 406px; height: 69px;" |
GRAMSPC

|-
| style="width:234px;height:52px;" |
SeriesAGCropFoodSupplyPerCapPerDayFAO

| style="width:142px;height:52px;" |
Total food supply per capita per day from crops (g/capita/day).

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
GRAMSPC

|-
| style="width:234px;height:40px;" |
SeriesAGCropImportsFAO

| style="width:142px;height:40px;" |
Total Crop Imports (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Trade data

|-
| style="width:234px;height:40px;" |
SeriesAGCropProductionFAO

| style="width:142px;height:40px;" |
Total Domestic Crop Production (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Crop production (AGP)

|-
| style="width:234px;height:52px;" |
SeriesAGCropProteinPerCapPerDayFAO

| style="width:142px;height:52px;" |
Total protein consumption per capita per day from crops (g/capita/day).

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
PROTEINPC

|-
| style="width:234px;height:40px;" |
SeriesAGCroptoFeedFAO

| style="width:142px;height:40px;" |
Total crop quantity used for feed (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
FEDDEM

|-
| style="width:234px;height:40px;" |
SeriesAGCroptoFoodFAO

| style="width:142px;height:40px;" |
Total crop quantity used for food (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
FDEM

|-
| style="width:234px;height:40px;" |
SeriesAGCroptoFoodManuFAO

| style="width:142px;height:40px;" |
Total crop quantity used for food manufacture (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
FDEM

|-
| style="width:234px;height:40px;" |
SeriesAGCroptoOtherUtilFAO

| style="width:142px;height:40px;" |
Total crop quantity used for other utilities (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
INDEM

|-
| style="width:234px;height:40px;" |
SeriesAGCroptoSeedFAO

| style="width:142px;height:40px;" |
Total crops used for seeds (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
FMDEM

|-
| style="width:234px;height:40px;" |
SeriesAGCroptoWasteFAO

| style="width:142px;height:40px;" |
Total crops that go to waste(tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
AGLOSSTRANS

|-
| style="width:234px;height:86px;" |
SeriesAGFishCalPerCapPerDayFAO

| style="width:142px;height:86px;" |
Total per capita per day caloric supplies derived from fish for human consumption (kcal/capita/day).

| style="width: 212px; height: 86px;" |
FAO

| style="width: 406px; height: 86px;" |
CLPC

|-
| style="width:234px;height:40px;" |
SeriesAGFishDomesticSupplyFAO

| style="width:142px;height:40px;" |
Domestic Supply Quantity of Fish  (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Trade data

|-
| style="width:234px;height:40px;" |
SeriesAGFishExportsFAO

| style="width:142px;height:40px;" |
Total Fish Exports (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Trade data

|-
| style="width:234px;height:69px;" |
SeriesAGFishFatPerCapPerDayFAO

| style="width:142px;height:69px;" |
Total grams of fat consumed from fish per capita per day (g/capita/day).

| style="width: 212px; height: 69px;" |
FAO

| style="width: 406px; height: 69px;" |
GRAMSPC

|-
| style="width:234px;height:52px;" |
SeriesAGFishFoodSupplyPerCapPerDayFAO

| style="width:142px;height:52px;" |
Total food supply per capita per day from fish (g/capita/day).

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
GRAMSPC

|-
| style="width:234px;height:40px;" |
SeriesAGFishImportsFAO

| style="width:142px;height:40px;" |
Total Fish Imports (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Trade data

|-
| style="width:234px;height:52px;" |
SeriesAGFishProteinPerCapPerDayFAO

| style="width:142px;height:52px;" |
Total protein consumption per capita per day from fish (g/capita/day).

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
PROTEINPC

|-
| style="width:234px;height:40px;" |
SeriesAGFishtoFeedFAO

| style="width:142px;height:40px;" |
Total fish quantity used for feed (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
FEDDEM

|-
| style="width:234px;height:40px;" |
SeriesAGFishtoFoodFAO

| style="width:142px;height:40px;" |
Total fish quantity used for food (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
FDEM

|-
| style="width:234px;height:40px;" |
SeriesAGFishtoOtherUtilFAO

| style="width:142px;height:40px;" |
Total fish quantity used for other utilities (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
INDEM

|-
| style="width:234px;height:40px;" |
SeriesAGFishtoSeedFAO

| style="width:142px;height:40px;" |
Total fish used for seeds i.e. reproduction (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
FMDEM

|-
| style="width:234px;height:52px;" |
SeriesAGMeatCalPerCapPerDayFAO

| style="width:142px;height:52px;" |
Total calories consumed from meat per capita per day (kcal/capita/day).

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
CLPC

|-
| style="width:234px;height:40px;" |
SeriesAGMeatDomesticSupplyFAO

| style="width:142px;height:40px;" |
Domestic Supply Quantity of Meat (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Trade data

|-
| style="width:234px;height:40px;" |
SeriesAGMeatExportsFAO

| style="width:142px;height:40px;" |
Total Meat Exports (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Trade data

|-
| style="width:234px;height:69px;" |
SeriesAGMeatFatPerCapPerDayFAO

| style="width:142px;height:69px;" |
Total grams of fat consumed from meat per capita per day (g/capita/day).

| style="width: 212px; height: 69px;" |
FAO

| style="width: 406px; height: 69px;" |
GRAMSPC

|-
| style="width:234px;height:52px;" |
SeriesAGMeatFoodSupplyPerCapPerDayFAO

| style="width:142px;height:52px;" |
Total food supply per capita per day from meat (g/capita/day).

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
GRAMSPC

|-
| style="width:234px;height:40px;" |
SeriesAGMeatImportsFAO

| style="width:142px;height:40px;" |
Total Meat Imports (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Trade data

|-
| style="width:234px;height:40px;" |
SeriesAGMeatProductionFAO

| style="width:142px;height:40px;" |
Total Domestic Meat Production (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Meat production (AGP)

|-
| style="width:234px;height:52px;" |
SeriesAGMeatProteinPerCapPerDayFAO

| style="width:142px;height:52px;" |
Total protein consumption per capita per day from meat (g/capita/day).

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
PROTEINPC

|-
| style="width:234px;height:40px;" |
SeriesAGMeattoFeedFAO

| style="width:142px;height:40px;" |
Total meat quantity used for feed (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
FEDDEM

|-
| style="width:234px;height:40px;" |
SeriesAGMeattoFoodFAO

| style="width:142px;height:40px;" |
Total meat quantity used for food (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
FDEM

|-
| style="width:234px;height:52px;" |
SeriesAGMeattoFoodManuFAO

| style="width:142px;height:52px;" |
Total meat quantity used for food manufacture (tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
FDEM

|-
| style="width:234px;height:40px;" |
SeriesAGMeattoOtherUtilFAO

| style="width:142px;height:40px;" |
Total meat quantity used for other utilities (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
INDEM

|-
| style="width:234px;height:40px;" |
SeriesAGMeattoSeedFAO

| style="width:142px;height:40px;" |
Total meat used for seeds i.e. reproduction (tonnes)

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
FMDEM

|-
| style="width:234px;height:40px;" |
SeriesAGMeattoWasteFAO

| style="width:142px;height:40px;" |
Total meat that goes to waste (tonnes).

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
AGLOSSTRANS

|-
| style="width:234px;height:69px;" |
SeriesAgFishAquaProdOthersFSJ

| style="width:142px;height:69px;" |
Total Others Aquaculture Production (tonnes) from FishstatJ

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Data from Fish stat j

|-
| style="width:234px;height:69px;" |
SeriesAgFishCatchProdAqMammalsFSJ

| style="width:142px;height:69px;" |
Total Aquatic Mammals Catch Production (tonnes) from FishstatJ

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Data from Fish stat j

|-
| style="width:234px;height:69px;" |
SeriesAgFishCatchProdOthersFSJ

| style="width:142px;height:69px;" |
Total Others Capture Production from FishstatJ

| style="width: 212px; height: 69px;" |
FAO  Global Aquaculture Production Quantity data

| style="width: 406px; height: 69px;" |
Data from Fish stat j

|-
| style="width:234px;height:52px;" |
SeriesAgFishFatPerCapPerDayAqAnimalsFAO

| style="width:142px;height:52px;" |
Total Aquatic Animals used for Fat/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishFatPerCapPerDayAqPlantsFAO

| style="width:142px;height:52px;" |
Total Aquatic Plants used for Fat/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishFatPerCapPerDayBodyOilFAO

| style="width:142px;height:52px;" |
Total Body Oil used for Fat/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishFatPerCapPerDayLiverOilFAO

| style="width:142px;height:52px;" |
Total Fish Liver Oil used for Fat/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishStockVarAqPlantsFAO

| style="width:142px;height:52px;" |
Total Aquatic Plants Stock Variations (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishStockVarBodyOilFAO

| style="width:142px;height:52px;" |
Total Body Oil Stock Variations (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishStockVarLiverOilFAO

| style="width:142px;height:52px;" |
Total Fish Liver Oil Stock Variations (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishStockVarMealFAO

| style="width:142px;height:52px;" |
Total Fish Meal Stock Variations (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:69px;" |
SeriesAgFishCalPerCapPerDayAqMammalsFAO

| style="width:142px;height:69px;" |
Total Aquatic Mammals used for Calories/capita/day (kcal/capita/day)

| style="width: 212px; height: 69px;" |
FAO

| style="width: 406px; height: 69px;" |
CLPC

|-
| style="width:234px;height:52px;" |
SeriesAgFishDomesticSupplyAqMammalsFAO

| style="width:142px;height:52px;" |
Domestic Supply of Aquatic Mammals (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishExportsAqMammalsFAO

| style="width:142px;height:52px;" |
Total Aquatic Mammals Exports(Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishFatPerCapPerDayAqMammalsFAO

| style="width:142px;height:52px;" |
Total Aquatic Mammals used for Fat/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:69px;" |
SeriesAgFishFatPerCapPerDayCephalopodsFAO

| style="width:142px;height:69px;" |
Total Cephalopods Fish Quantity used for Fat/capita/day (g/capita/day)

| style="width: 212px; height: 69px;" |
FAO

| style="width: 406px; height: 69px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishFatPerCapPerDayCrustaceansFAO

| style="width:142px;height:52px;" |
Total Crustaceans Fish used for Fat/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishFatPerCapPerDayDemersalFAO

| style="width:142px;height:52px;" |
Total Demersal Fish consumed for Fat/cap/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishFatPerCapPerDayFreshwaterFAO

| style="width:142px;height:52px;" |
Total Freshwater Fish consumed for Fat/cap/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishFatPerCapPerDayMarineFAO

| style="width:142px;height:52px;" |
Total Marine Fish used for Fat/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishFatPerCapPerDayMolluscsFAO

| style="width:142px;height:52px;" |
Total Molluscs Fish used for Fat/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:69px;" |
SeriesAgFishFatPerCapPerDayPelagicFAO

| style="width:142px;height:69px;" |
Total Pelagic Fish consumed for Fat/capita/day (g/capita/day)

| style="width: 212px; height: 69px;" |
FAO

| style="width: 406px; height: 69px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:69px;" |
SeriesAgFishFoodSupplyPerCapPerDayAqMammalsFAO

| style="width:142px;height:69px;" |
Total Aquatic Mammals used for Food Supply/capita/day (g/capita/day)

| style="width: 212px; height: 69px;" |
FAO

| style="width: 406px; height: 69px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProdAqMammalsFAO

| style="width:142px;height:52px;" |
Production Quantity of Aquatic Mammals (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishProteinPerCapPerDayAqMammalsFAO

| style="width:142px;height:52px;" |
Total Aquatic Mammals used for Protein/capita/day (g/capita/day)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishStockVarAqAnimalsFAO

| style="width:142px;height:52px;" |
Total Aquatic Animals Stock Variation (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishStockVarCephalopodsFAO

| style="width:142px;height:52px;" |
Total Cephalopods Fish Stock Variations (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishStockVarCrustaceansFAO

| style="width:142px;height:52px;" |
Total Crustaceans Fish Stock Variations (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishStockVarDemersalFAO

| style="width:142px;height:52px;" |
Total Demersal Fish Stock VariationTonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishStockVarFreshwaterFAO

| style="width:142px;height:52px;" |
Total Freshwater Stock Variation (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishStockVarMarineFAO

| style="width:142px;height:52px;" |
Total Marine Fish Stock Variations (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishStockVarMolluscsFAO

| style="width:142px;height:52px;" |
Total Molluscs Fish Stock Variations (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishStockVarPelagicFAO

| style="width:142px;height:52px;" |
Total Pelagic Fish Stock Variations (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoFoodAqMammalsFAO

| style="width:142px;height:52px;" |
Total Aquatic Mammals used for Food (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:52px;" |
SeriesAgFishtoOtherUtilAqMammalsFAO

| style="width:142px;height:52px;" |
Total Aquatic Mammals used for Other Utilities (Tonnes)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Read in all fish series from FAO Food Balance Sheets

|-
| style="width:234px;height:40px;" |
SeriesAgFishProdAquaInland

| style="width:142px;height:40px;" |
Fish,Production, Inland Aquaculture

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Data from Fish stat j

|-
| style="width:234px;height:40px;" |
SeriesAgFishProdAquaMarine

| style="width:142px;height:40px;" |
Fish, production, Marine Aquaculture

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Data from Fish stat j

|-
| style="width:234px;height:40px;" |
SeriesAgFishProdCatchInland

| style="width:142px;height:40px;" |
Fish, Production, Inland Catch

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Data from Fish stat j

|-
| style="width:234px;height:40px;" |
SeriesAgFishProdCatchMarine

| style="width:142px;height:40px;" |
Fish, Production, Marine Catch

| style="width: 212px; height: 40px;" |
FAO

| style="width: 406px; height: 40px;" |
Data from Fish stat j

|-
| style="width:234px;height:52px;" |
SeriesAgFishExportVal

| style="width:142px;height:52px;" |
Global Commodities Production and Trade Value of Fish Exports (USD)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Trade data

|-
| style="width:234px;height:52px;" |
SeriesAgFishImportVal

| style="width:142px;height:52px;" |
Global Commodities Production and Trade Value of Fish Imports (USD)

| style="width: 212px; height: 52px;" |
FAO

| style="width: 406px; height: 52px;" |
Trade data

|-
| style="width:234px;height:35px;" |
SeriesAgFishAquaOther

| style="width:142px;height:35px;" |
Aquaculture, other (plants, frogs, crocodiles, turtles)

| style="width: 212px; height: 35px;" |
FAO

| style="width: 406px; height: 35px;" |
Data from Fish stat j

|-
| style="width:234px;height:20px;" |
SeriesAgFishImpt

| style="width:142px;height:20px;" |
Fish, import value

| style="width: 212px; height: 20px;" |
FAO

| style="width: 406px; height: 20px;" |
Trade data

|-
| style="width:234px;height:20px;" |
SeriesAidCerealDon

| style="width:142px;height:20px;" |
Cereal donations

| style="width: 212px; height: 20px;" |
FAO

| style="width: 406px; height: 20px;" |
Trade data

|-
| style="width:234px;height:20px;" |
SeriesAidCerealRec

| style="width:142px;height:20px;" |
Cereal gifts/aid received

| style="width: 212px; height: 20px;" |
FAO

| style="width: 406px; height: 20px;" |
Trade data

|-
| style="width:234px;height:256px;" |
SeriesAgFishAquaInland

| style="width:142px;height:256px;" |
Aquaculture, inland

| style="width: 212px; height: 256px;" |
FAO, Aquaculture Quantities Dataset 1984-1997, Fishery Statistics Database downloadable with Fishstat-Plus software at: ([http://www.fao.org/WAICENT/FAOINFO/FISHERY/statist/FISOFT/FISHPLUS.HTM http://www.fao.org/WAICENT/FAOINFO/FISHERY/statist/FISOFT/FISHPLUS.HTM]

| style="width: 406px; height: 256px;" |
Data from Fish stat j

|-
| style="width:234px;height:256px;" |
SeriesAgFishAquaMarine

| style="width:142px;height:256px;" |
Aquaculture, marine fish catch

| style="width: 212px; height: 256px;" |
FAO, Aquaculture Quantities Dataset 1984-1997, Fishery Statistics Database downloadable with Fishstat-Plus software at: ([http://www.fao.org/WAICENT/FAOINFO/FISHERY/statist/FISOFT/FISHPLUS.HTM http://www.fao.org/WAICENT/FAOINFO/FISHERY/statist/FISOFT/FISHPLUS.HTM]

| style="width: 406px; height: 256px;" |
Data from Fish stat j

|-
| style="width:234px;height:256px;" |
SeriesAgFishAquaCatchTot

| style="width:142px;height:256px;" |
Fish production totals, aquaculture and capture

| style="width: 212px; height: 256px;" |
FAO, Aquaculture Quantities Dataset 1984-1997, Fishery Statistics Database downloadable with Fishstat-Plus software at: ([http://www.fao.org/WAICENT/FAOINFO/FISHERY/statist/FISOFT/FISHPLUS.HTM http://www.fao.org/WAICENT/FAOINFO/FISHERY/statist/FISOFT/FISHPLUS.HTM]

| style="width: 406px; height: 256px;" |
Data from Fish stat j

|-
| style="width:234px;height:273px;" |
SeriesAgFishAquaTotal

| style="width:142px;height:273px;" |
Aquaculture, coastal and marine total fish production

| style="width: 212px; height: 273px;" |
Fishery Information, Data and Statistics Unit,  FAO. 2004. FISHSTAT Plus:  Version 2.3 (available on-line at [http://www.fao.org/fi/statist/FISOFT/FISHPLUS.asp http://www.fao.org/fi/statist/FISOFT/FISHPLUS.asp]); Capture production 1950-2002 dataset. Rome: FAO

| style="width: 406px; height: 273px;" |
Data from Fish stat j

|-
| style="width:234px;height:256px;" |
SeriesAgFishExpt

| style="width:142px;height:256px;" |
Fish, export value

| style="width: 212px; height: 256px;" |
FAO, Aquaculture Quantities Dataset 1984-1997, Fishery Statistics Database downloadable with Fishstat-Plus software at: ([http://www.fao.org/WAICENT/FAOINFO/FISHERY/statist/FISOFT/FISHPLUS.HTM http://www.fao.org/WAICENT/FAOINFO/FISHERY/statist/FISOFT/FISHPLUS.HTM]

| style="width: 406px; height: 256px;" |
Trade data

|-
| style="width:234px;height:273px;" |
SeriesAgFishInlandProd

| style="width:142px;height:273px;" |
Fish capture, inland

| style="width: 212px; height: 273px;" |
Fishery Information, Data and Statistics Unit,  FAO. 2004. FISHSTAT Plus:  Version 2.3 (available on-line at [http://www.fao.org/fi/statist/FISOFT/FISHPLUS.asp http://www.fao.org/fi/statist/FISOFT/FISHPLUS.asp]); Capture production 1950-2002 dataset. Rome: FAO

| style="width: 406px; height: 273px;" |
Data from Fish stat j

|-
| style="width:234px;height:256px;" |
SeriesAgFish%Protein

| style="width:142px;height:256px;" |
Fish protein as percent of total supply

| style="width: 212px; height: 256px;" |
FAO, Aquaculture Quantities Dataset 1984-1997, Fishery Statistics Database downloadable with Fishstat-Plus software at: ([http://www.fao.org/WAICENT/FAOINFO/FISHERY/statist/FISOFT/FISHPLUS.HTM http://www.fao.org/WAICENT/FAOINFO/FISHERY/statist/FISOFT/FISHPLUS.HTM])

| style="width: 406px; height: 256px;" |
PROTEINPC

|-
| style="width:234px;height:273px;" |
SeriesAgFishFreshwaterCatch

| style="width:142px;height:273px;" |
Freshwater fish catch

| style="width: 212px; height: 273px;" |
Fishery Information, Data and Statistics Unit,  FAO. 2004. FISHSTAT Plus:  Version 2.3 (available on-line at [http://www.fao.org/fi/statist/FISOFT/FISHPLUS.asp http://www.fao.org/fi/statist/FISOFT/FISHPLUS.asp]); Capture production 1950-2002 dataset. Rome: FAO

| style="width: 406px; height: 273px;" |
Data from Fish stat j

|-
| style="width:234px;height:273px;" |
SeriesAgFishMarineCatch

| style="width:142px;height:273px;" |
Marine fish catch

| style="width: 212px; height: 273px;" |
Fishery Information, Data and Statistics Unit,  FAO. 2004. FISHSTAT Plus:  Version 2.3 (available on-line at [http://www.fao.org/fi/statist/FISOFT/FISHPLUS.asp http://www.fao.org/fi/statist/FISOFT/FISHPLUS.asp]); Capture production 1950-2002 dataset. Rome: FAO

| style="width: 406px; height: 273px;" |
Data from Fish stat j

|}

= Key variables in the agricultural model =

{| border="1" cellspacing="0" cellpadding="0" width="0" style="width:528px;"
|-
| style="width:110px;height:52px;" |
'''Name'''

| style="width:90px;height:52px;" |
'''Unit'''

| style="width:100px;height:52px;" |
'''Dimensionality'''

| style="width:106px;height:52px;" |
'''Description'''

| style="width:121px;height:52px;" |
'''Where Initialized*'''

|-
| style="width:110px;height:137px;" |
AGDEM

| style="width:90px;height:137px;" |
10^6 tons

| style="width:100px;height:137px;" |
country, food type

| style="width:106px;height:137px;" |
total agricultural demand/apparent consumption by food type

| style="width:121px;height:137px;" |
FY

|-
| style="width:110px;height:137px;" |
AGLOSSCONS

| style="width:90px;height:137px;" |
10^6 tons

| style="width:100px;height:137px;" |
country, food type

| style="width:106px;height:137px;" |
Consumption losses

| style="width:121px;height:137px;" |
FY

|-
| style="width:110px;height:137px;" |
AGLOSSPROD

| style="width:90px;height:137px;" |
10^6 tons

| style="width:100px;height:137px;" |
country, food type

| style="width:106px;height:137px;" |
Production losses

| style="width:121px;height:137px;" |
FY

|-
| style="width:110px;height:137px;" |
AGLOSSTRANS

| style="width:90px;height:137px;" |
10^6 tons

| style="width:100px;height:137px;" |
country, food type

| style="width:106px;height:137px;" |
Transmission and distribution losses

| style="width:121px;height:137px;" |
PP

|-
| style="width:110px;height:35px;" |
AGM

| style="width:90px;height:35px;" |
10^6 tons

| style="width:100px;height:35px;" |
country, food type

| style="width:106px;height:35px;" |
food imports

| style="width:121px;height:35px;" |
PP

|-
| style="width:110px;height:69px;" |
AGP

| style="width:90px;height:69px;" |
10^6 tons

| style="width:100px;height:69px;" |
food type

| style="width:106px;height:69px;" |
total food production by food type

| style="width:121px;height:69px;" |
PP for crops, FY for meat and fish

|-
| style="width:110px;height:69px;" |
AGPMILKAND EGGS

| style="width:90px;height:69px;" |
10^6 tons

| style="width:100px;height:69px;" |
country

| style="width:106px;height:69px;" |
Total non-meat animal products

| style="width:121px;height:69px;" |
PP

|-
| style="width:110px;height:35px;" |
AGX

| style="width:90px;height:35px;" |
10^6 tons

| style="width:100px;height:35px;" |
country, food type

| style="width:106px;height:35px;" |
food exports

| style="width:121px;height:35px;" |
PP

|-
| style="width:110px;height:86px;" |
AQUACUL

| style="width:90px;height:86px;" |
10^6 tons fish

| style="width:100px;height:86px;" |
country

| style="width:106px;height:86px;" |
total fish production in aquaculture

| style="width:121px;height:86px;" |
PP

|-
| style="width:110px;height:86px;" |
CDALF

| style="width:90px;height:86px;" |
dimensionless (0-1)

| style="width:100px;height:86px;" |
country

| style="width:106px;height:86px;" |
Cobb-Douglas alpha

| style="width:121px;height:86px;" |
FY (ECONOMY)

|-
| style="width:110px;height:52px;" |
CIVDM

| style="width:90px;height:52px;" |
dimensionless

| style="width:100px;height:52px;" |
country

| style="width:106px;height:52px;" |
civilian damage from war

| style="width:121px;height:52px;" |
FY (SOCIOPOL)

|-
| style="width:110px;height:52px;" |
CLAVAL

| style="width:90px;height:52px;" |
10^6 Calories/day

| style="width:100px;height:52px;" |
country

| style="width:106px;height:52px;" |
actual calorie availability

| style="width:121px;height:52px;" |
PP (DATAAGRI)

|-
| style="width:110px;height:69px;" |
CLD

| style="width:90px;height:69px;" |
thousand $/hectare

| style="width:100px;height:69px;" |
country

| style="width:106px;height:69px;" |
cost of land development

| style="width:121px;height:69px;" |
FY

|-
| style="width:110px;height:69px;" |
CLPC

| style="width:90px;height:69px;" |
calories

| style="width:100px;height:69px;" |
country, food type

| style="width:106px;height:69px;" |
Calories per capita per day

| style="width:121px;height:69px;" |
PP

|-
| style="width:110px;height:137px;" |
CO2PER

| style="width:90px;height:137px;" |
Percent

| style="width:100px;height:137px;" |
none

| style="width:106px;height:137px;" |
CO2, percentage increase in atmosphere, pre-industrial base

| style="width:121px;height:137px;" |
FY (ENVIRONMENT)

|-
| style="width:110px;height:69px;" |
CS

| style="width:90px;height:69px;" |
billion $

| style="width:100px;height:69px;" |
country, economic sector

| style="width:106px;height:69px;" |
value of HH consumption

| style="width:121px;height:69px;" |
PP (DATAECON)

|-
| style="width:110px;height:120px;" |
CULTREG

| style="width:90px;height:120px;" |
Index

| style="width:100px;height:120px;" |
country

| style="width:106px;height:120px;" |
Culutrual region: CULTREG 6 includes India, Nepal, Mauritius

| style="width:121px;height:120px;" |
PP (DataValues)

|-
| style="width:110px;height:103px;" |
ENVYLCHG

| style="width:90px;height:103px;" |
Percent

| style="width:100px;height:103px;" |
country

| style="width:106px;height:103px;" |
annual change in agricultural yield due to climate change

| style="width:121px;height:103px;" |
FY (ENVIRONMENT)

|-
| style="width:110px;height:86px;" |
FDEM

| style="width:90px;height:86px;" |
10^6 tons

| style="width:100px;height:86px;" |
country

| style="width:106px;height:86px;" |
food production going directly to food

| style="width:121px;height:86px;" |
PP

|-
| style="width:110px;height:69px;" |
FEDDEM

| style="width:90px;height:69px;" |
10^6 tons

| style="width:100px;height:69px;" |
country

| style="width:106px;height:69px;" |
food production going to livestock

| style="width:121px;height:69px;" |
PP

|-
| style="width:110px;height:69px;" |
FISH

| style="width:90px;height:69px;" |
10^6 tons fish

| style="width:100px;height:69px;" |
country

| style="width:106px;height:69px;" |
total fish production

| style="width:121px;height:69px;" |
FY

|-
| style="width:110px;height:52px;" |
FMDEM

| style="width:90px;height:52px;" |
10^6 tons

| style="width:100px;height:52px;" |
country,food type

| style="width:106px;height:52px;" |
food production going to food manufacturing

| style="width:121px;height:52px;" |
PP

|-
| style="width:110px;height:120px;" |
FPRI

| style="width:90px;height:120px;" |
Base 100

| style="width:100px;height:120px;" |
country, food type

| style="width:106px;height:120px;" |
country specific food price by food type (all 100 in base year)

| style="width:121px;height:120px;" |
FY

|-
| style="width:110px;height:52px;" |
FPROFITR

| style="width:90px;height:52px;" |
dimensionless

| style="width:100px;height:52px;" |
country, food type

| style="width:106px;height:52px;" |
food profit ratio to initial year

| style="width:121px;height:52px;" |
FY

|-
| style="width:110px;height:52px;" |
FSTOCK

| style="width:90px;height:52px;" |
10^6 tons

| style="width:100px;height:52px;" |
country, food type

| style="width:106px;height:52px;" |
food stocks, by food type

| style="width:121px;height:52px;" |
FY

|-
| style="width:110px;height:52px;" |
GRAMSPC

| style="width:90px;height:52px;" |
grams

| style="width:100px;height:52px;" |
country, food type

| style="width:106px;height:52px;" |
Food supply per capita per day in grams

| style="width:121px;height:52px;" |
PP

|-
| style="width:110px;height:52px;" |
I

| style="width:90px;height:52px;" |
billion$

| style="width:100px;height:52px;" |
country

| style="width:106px;height:52px;" |
total investment

| style="width:121px;height:52px;" |
FY (ECONOMY)

|-
| style="width:110px;height:86px;" |
IALK

| style="width:90px;height:86px;" |
ratio (fraction)

| style="width:100px;height:86px;" |
country

| style="width:106px;height:86px;" |
investment in agriculture, land share

| style="width:121px;height:86px;" |
PP

|-
| style="width:110px;height:69px;" |
IDS

| style="width:90px;height:69px;" |
billion$

| style="width:100px;height:69px;" |
country, economic sector

| style="width:106px;height:69px;" |
investment by economic sector

| style="width:121px;height:69px;" |
FY (ECONOMY)

|-
| style="width:110px;height:137px;" |
INDEM

| style="width:90px;height:137px;" |
10^6 tons crops

| style="width:100px;height:137px;" |
country

| style="width:106px;height:137px;" |
industrial crop demand (crop production going directly to industry)

| style="width:121px;height:137px;" |
PP

|-
| style="width:110px;height:52px;" |
KAG

| style="width:90px;height:52px;" |
billion$

| style="width:100px;height:52px;" |
country

| style="width:106px;height:52px;" |
value of agricultural capital

| style="width:121px;height:52px;" |
FY

|-
| style="width:110px;height:52px;" |
LABS

| style="width:90px;height:52px;" |
million people

| style="width:100px;height:52px;" |
country, economic sector

| style="width:106px;height:52px;" |
labor supply

| style="width:121px;height:52px;" |
FY (ECONOMY)

|-
| style="width:110px;height:52px;" |
landarablepot

| style="width:90px;height:52px;" |
10^6 ha

| style="width:100px;height:52px;" |
country

| style="width:106px;height:52px;" |
potential arable land

| style="width:121px;height:52px;" |
PP

|-
| style="width:110px;height:35px;" |
LD

| style="width:90px;height:35px;" |
10^6 ha

| style="width:100px;height:35px;" |
country, land type

| style="width:106px;height:35px;" |
Amount of land

| style="width:121px;height:35px;" |
PP

|-
| style="width:110px;height:52px;" |
LVHERD

| style="width:90px;height:52px;" |
10^6 tons meat

| style="width:100px;height:52px;" |
country

| style="width:106px;height:52px;" |
Size of livestock herd

| style="width:121px;height:52px;" |
PP

|-
| style="width:110px;height:52px;" |
MFPRATE

| style="width:90px;height:52px;" |
dimensionless

| style="width:100px;height:52px;" |
country, economic sector

| style="width:106px;height:52px;" |
multifactor productivity rate

| style="width:121px;height:52px;" |
FY (ECONOMY)

|-
| style="width:110px;height:52px;" |
MS

| style="width:90px;height:52px;" |
billion $

| style="width:100px;height:52px;" |
country, economic sector

| style="width:106px;height:52px;" |
value of imports

| style="width:121px;height:52px;" |
PP (DATAECON)

|-
| style="width:110px;height:52px;" |
PROTEINPC

| style="width:90px;height:52px;" |
per capita per day

| style="width:100px;height:52px;" |
country, food type

| style="width:106px;height:52px;" |
Proteins per capita per day

| style="width:121px;height:52px;" |
PP

|-
| style="width:110px;height:69px;" |
TGRYL

| style="width:90px;height:69px;" |
growth rate in decimal form

| style="width:100px;height:69px;" |
country

| style="width:106px;height:69px;" |
target growth rate in yield

| style="width:121px;height:69px;" |
FY

|-
| style="width:110px;height:103px;" |
WAP

| style="width:90px;height:103px;" |
Base 100

| style="width:100px;height:103px;" |
food type

| style="width:106px;height:103px;" |
global food price by food type (all 100 in base year)

| style="width:121px;height:103px;" |
?

|-
| style="width:110px;height:86px;" |
WAPRO

| style="width:90px;height:86px;" |
10^6 tons

| style="width:100px;height:86px;" |
food type

| style="width:106px;height:86px;" |
world agricultural production

| style="width:121px;height:86px;" |
FY

|-
| style="width:110px;height:52px;" |
WATUSE

| style="width:90px;height:52px;" |
cubic km

| style="width:100px;height:52px;" |
country

| style="width:106px;height:52px;" |
water usage, annual

| style="width:121px;height:52px;" |
FY

|-
| style="width:110px;height:69px;" |
WATUSEPC

| style="width:90px;height:69px;" |
cubic km/10^6 persons

| style="width:100px;height:69px;" |
country

| style="width:106px;height:69px;" |
water usage per capita, annual

| style="width:121px;height:69px;" |
PP

|-
| style="width:110px;height:103px;" |
WEP

| style="width:90px;height:103px;" |
Base 100

| style="width:100px;height:103px;" |
none

| style="width:106px;height:103px;" |
World Energy Price per Barrel Oil Equivalent (Base 100)

| style="width:121px;height:103px;" |
FY (ENERGY)

|-
| style="width:110px;height:35px;" |
WFORST

| style="width:90px;height:35px;" |
10^6 ha forest land

| style="width:100px;height:35px;" |
none

| style="width:106px;height:35px;" |
world forest area

| style="width:121px;height:35px;" |
FY

|-
| style="width:110px;height:35px;" |
WGDP

| style="width:90px;height:35px;" |
billion $

| style="width:100px;height:35px;" |
none

| style="width:106px;height:35px;" |
global GDP

| style="width:121px;height:35px;" |
FY

|-
| style="width:110px;height:52px;" |
WSTK

| style="width:90px;height:52px;" |
10^6 tons

| style="width:100px;height:52px;" |
food type

| style="width:106px;height:52px;" |
world agricultural stocks

| style="width:121px;height:52px;" |
FY

|-
| style="width:110px;height:52px;" |
XS

| style="width:90px;height:52px;" |
billion $

| style="width:100px;height:52px;" |
country, economic sector

| style="width:106px;height:52px;" |
value of exports

| style="width:121px;height:52px;" |
PP (DATAECON)

|-
| style="width:110px;height:86px;" |
YL

| style="width:90px;height:86px;" |
10^6 tons crops/10^6 ha crop land

| style="width:100px;height:86px;" |
country

| style="width:106px;height:86px;" |
productivity of crop land in terms of crops

| style="width:121px;height:86px;" |
FY

|-
| style="width:110px;height:69px;" |
ZS

| style="width:90px;height:69px;" |
billion $

| style="width:100px;height:69px;" |
country, economic sector

| style="width:106px;height:69px;" |
value of gross production

| style="width:121px;height:69px;" |
PP (DATAECON)

|}

= Key User controllable parameters in the IFs agricultural model =

{| border="1" cellspacing="0" cellpadding="0" width="0" style="width:388px;"
|-
| style="width:132px;height:34px;" |
'''Name'''

| style="width:64px;height:34px;" |
'''Unit'''

| style="width:64px;height:34px;" |
'''Dimensionality'''

| style="width:64px;height:34px;" |
'''Description'''

| style="width:64px;height:34px;" |
'''Default Value'''

|-
| style="width:132px;height:85px;" |
Agconv

| style="width:64px;height:85px;" |
years

| style="width:64px;height:85px;" |
year

| style="width:64px;height:85px;" |
agricultural demand convergence time to function

| style="width:64px;height:85px;" |
75

|-
| style="width:132px;height:85px;" |
Aginvm

| style="width:64px;height:85px;" |
Multiplier Base 1

| style="width:64px;height:85px;" |
year, country

| style="width:64px;height:85px;" |
multiplier on investment in agriculture

| style="width:64px;height:85px;" |
1

|-
| style="width:132px;height:102px;" |
aglossconsperc

| style="width:64px;height:102px;" |
percentage

| style="width:64px;height:102px;" |
year, country, food type (crop, meat, fish)

| style="width:64px;height:102px;" |
waste rate of agricultural consumption

| style="width:64px;height:102px;" |
'''10'''

|-
| style="width:132px;height:136px;" |
aglossprodperc

| style="width:64px;height:136px;" |
percentage

| style="width:64px;height:136px;" |
year, country, food type (crop, meat, aquaculture, fish catch)

| style="width:64px;height:136px;" |
loss rate at point of production

| style="width:64px;height:136px;" |
'''10'''

|-
| style="width:132px;height:102px;" |
aglosstransm

| style="width:64px;height:102px;" |
Multiplier Base 1

| style="width:64px;height:102px;" |
year, country, food type (crop, meat, fish)

| style="width:64px;height:102px;" |
loss rate from producer to consumer, multiplier

| style="width:64px;height:102px;" |
'''1'''

|-
| style="width:132px;height:170px;" |
Agon

| style="width:64px;height:170px;" |
switch (0-1)

| style="width:64px;height:170px;" |
year

| style="width:64px;height:170px;" |
switch to turn off or on linkages between ag module and other modules; default is on

| style="width:64px;height:170px;" |
1

|-
| style="width:132px;height:85px;" |
aquaculconv

| style="width:64px;height:85px;" |
years

| style="width:64px;height:85px;" |
year

| style="width:64px;height:85px;" |
time over which aquaculture growth falls to 0

| style="width:64px;height:85px;" |
50

|-
| style="width:132px;height:51px;" |
Aquaculgr

| style="width:64px;height:51px;" |
growth rate in percent

| style="width:64px;height:51px;" |
year, country

| style="width:64px;height:51px;" |
aquaculture growth rate, initial

| style="width:64px;height:51px;" |
3.5

|-
| style="width:132px;height:102px;" |
Aquaculm

| style="width:64px;height:102px;" |
dimensionless

| style="width:64px;height:102px;" |
year, country

| style="width:64px;height:102px;" |
multiplier on aquaculture production

| style="width:64px;height:102px;" |
1

|-
| style="width:132px;height:203px;" |
Calmax

| style="width:64px;height:203px;" |
Calories/person/day

| style="width:64px;height:203px;" |
year

| style="width:64px;height:203px;" |
maximum kilocalories needed per day per person.  This value should be a biologically-determined number that you will not normally change over time.

| style="width:64px;height:203px;" |
3800

|-
| style="width:132px;height:374px;" |
Calmeatm

| style="width:64px;height:374px;" |
dimensionless (0-1)

| style="width:64px;height:374px;" |
year

| style="width:64px;height:374px;" |
the maximum portion of calories that will come from meat.  The model increases the portion of calories taken in the form of meat with income up to this level (a value between 0 and 1). 

| style="width:64px;height:374px;" |
0.7

|-
| style="width:132px;height:51px;" |
clpcm

| style="width:64px;height:51px;" |
Multiplier Base1

| style="width:64px;height:51px;" |
year,country

| style="width:64px;height:51px;" |
Per capita calorie multiplier

| style="width:64px;height:51px;" |
1

|-
| style="width:132px;height:68px;" |
Dkl

| style="width:64px;height:68px;" |
depreciation rate in decimal form

| style="width:64px;height:68px;" |
year

| style="width:64px;height:68px;" |
depreciation rate of investment in land

| style="width:64px;height:68px;" |
0.01

|-
| style="width:132px;height:546px;" |
Dstl

| style="width:64px;height:546px;" |
dimensionless (0-1)

| style="width:64px;height:546px;" |
year

| style="width:64px;height:546px;" |
Desired stock (inventory) level in the economy.  It is in proportional terms so that 0.1 represents a 10% target stock level (of a base that usually includes annual production and may include demand, exports, or imports).  There is little reason for most users to want to change this parameter.

| style="width:64px;height:546px;" |
0.1

|-
| style="width:132px;height:153px;" |
Elagind

| style="width:64px;height:153px;" |
dimensionless

| style="width:64px;height:153px;" |
year

| style="width:64px;height:153px;" |
elasticity of industrial (incl. energy) use of crops with energy price

| style="width:64px;height:153px;" |
0.2

|-
| style="width:132px;height:85px;" |
elagmpr1

| style="width:64px;height:85px;" |
 

| style="width:64px;height:85px;" |
 

| style="width:64px;height:85px;" |
elasticity of agricultural imports to prices

| style="width:64px;height:85px;" |
 

|-
| style="width:132px;height:102px;" |
elagmpr2

| style="width:64px;height:102px;" |
 

| style="width:64px;height:102px;" |
 

| style="width:64px;height:102px;" |
elasticity of agricultural imports to change in prices

| style="width:64px;height:102px;" |
 

|-
| style="width:132px;height:85px;" |
elagxpr1

| style="width:64px;height:85px;" |
 

| style="width:64px;height:85px;" |
 

| style="width:64px;height:85px;" |
elasticity of agricultural exports to prices

| style="width:64px;height:85px;" |
 

|-
| style="width:132px;height:102px;" |
elagxpri2

| style="width:64px;height:102px;" |
 

| style="width:64px;height:102px;" |
 

| style="width:64px;height:102px;" |
elasticity of agricultural exports to change in prices

| style="width:64px;height:102px;" |
 

|-
| style="width:132px;height:85px;" |
Elascd

| style="width:64px;height:85px;" |
dimensionless

| style="width:64px;height:85px;" |
year

| style="width:64px;height:85px;" |
elasticity of crop food demand to prices

| style="width:64px;height:85px;" |
-0.15

|-
| style="width:132px;height:68px;" |
Elasfd

| style="width:64px;height:68px;" |
dimensionless

| style="width:64px;height:68px;" |
year

| style="width:64px;height:68px;" |
elasticity of fish demand to prices

| style="width:64px;height:68px;" |
-0.3

|-
| style="width:132px;height:68px;" |
Elasmd

| style="width:64px;height:68px;" |
dimensionless

| style="width:64px;height:68px;" |
year

| style="width:64px;height:68px;" |
elasticity of meat demand to prices

| style="width:64px;height:68px;" |
-0.3

|-
| style="width:132px;height:68px;" |
elfdpr1

| style="width:64px;height:68px;" |
dimensionless

| style="width:64px;height:68px;" |
year

| style="width:64px;height:68px;" |
elasticity of yield to stocks/inventories

| style="width:64px;height:68px;" |
-0.5

|-
| style="width:132px;height:102px;" |
elfdpr2

| style="width:64px;height:102px;" |
dimensionless

| style="width:64px;height:102px;" |
year

| style="width:64px;height:102px;" |
elasticity of yield to changes in stocks/inventories

| style="width:64px;height:102px;" |
-1

|-
| style="width:132px;height:102px;" |
Elglinpr

| style="width:64px;height:102px;" |
dimensionless

| style="width:64px;height:102px;" |
year

| style="width:64px;height:102px;" |
elasticity of livestock grazing intensity to prices

| style="width:64px;height:102px;" |
0.5

|-
| style="width:132px;height:85px;" |
eliasp1

| style="width:64px;height:85px;" |
dimensionless

| style="width:64px;height:85px;" |
year

| style="width:64px;height:85px;" |
elasticity of ag investment in land to return

| style="width:64px;height:85px;" |
0.2

|-
| style="width:132px;height:102px;" |
eliasp2

| style="width:64px;height:102px;" |
dimensionless

| style="width:64px;height:102px;" |
year

| style="width:64px;height:102px;" |
elasticity of ag investment in land to change in return

| style="width:64px;height:102px;" |
0.4

|-
| style="width:132px;height:68px;" |
elinag1

| style="width:64px;height:68px;" |
dimensionless

| style="width:64px;height:68px;" |
year

| style="width:64px;height:68px;" |
elasticity of ag investment to profit

| style="width:64px;height:68px;" |
0.15

|-
| style="width:132px;height:102px;" |
elinag2

| style="width:64px;height:102px;" |
dimensionless

| style="width:64px;height:102px;" |
year

| style="width:64px;height:102px;" |
elasticity of ag investment to change in profit

| style="width:64px;height:102px;" |
0.3

|-
| style="width:132px;height:102px;" |
ellvhpr1

| style="width:64px;height:102px;" |
 

| style="width:64px;height:102px;" |
 

| style="width:64px;height:102px;" |
elasticity of livestock herd size to stock level

| style="width:64px;height:102px;" |
 

|-
| style="width:132px;height:136px;" |
ellvhpr2

| style="width:64px;height:136px;" |
 

| style="width:64px;height:136px;" |
 

| style="width:64px;height:136px;" |
elasticity of livestock herd size to changes in stock level

| style="width:64px;height:136px;" |
 

|-
| style="width:132px;height:34px;" |
envco2fert

| style="width:64px;height:34px;" |
No unit

| style="width:64px;height:34px;" |
No unit

| style="width:64px;height:34px;" |
Crop CO2 sensitivity

| style="width:64px;height:34px;" |
0.1365

|-
| style="width:132px;height:85px;" |
envylchgadd

| style="width:64px;height:85px;" |
percentage

| style="width:64px;height:85px;" |
year

| style="width:64px;height:85px;" |
additive factor for effect of climate on yield

| style="width:64px;height:85px;" |
0

|-
| style="width:132px;height:68px;" |
envylchgm

| style="width:64px;height:68px;" |
Multiplier Base 1

| style="width:64px;height:68px;" |
year, country

| style="width:64px;height:68px;" |
multiplier on effect of climate on yield

| style="width:64px;height:68px;" |
1

|-
| style="width:132px;height:68px;" |
feddemm

| style="width:64px;height:68px;" |
Multiplier Base 1

| style="width:64px;height:68px;" |
year,country

| style="width:64px;height:68px;" |
Livestock feed demand multiplier

| style="width:64px;height:68px;" |
1

|-
| style="width:132px;height:34px;" |
fishcatchm

| style="width:64px;height:34px;" |
Multiplier Base 1

| style="width:64px;height:34px;" |
year, country

| style="width:64px;height:34px;" |
fish catch multiplier

| style="width:64px;height:34px;" |
1

|-
| style="width:132px;height:34px;" |
Forest

| style="width:64px;height:34px;" |
Multiplier Base 1

| style="width:64px;height:34px;" |
year, country

| style="width:64px;height:34px;" |
forest land multiplier

| style="width:64px;height:34px;" |
1

|-
| style="width:132px;height:85px;" |
fpricr1

| style="width:64px;height:85px;" |
dimensionless

| style="width:64px;height:85px;" |
year

| style="width:64px;height:85px;" |
food prices, response to stock level

| style="width:64px;height:85px;" |
-0.3

|-
| style="width:132px;height:102px;" |
fpricr2

| style="width:64px;height:102px;" |
dimensionless

| style="width:64px;height:102px;" |
year

| style="width:64px;height:102px;" |
food prices, response to change in stock level

| style="width:64px;height:102px;" |
-0.6

|-
| style="width:132px;height:85px;" |
Fprihw

| style="width:64px;height:85px;" |
ratio

| style="width:64px;height:85px;" |
year

| style="width:64px;height:85px;" |
food prices (inertial delay) in change

| style="width:64px;height:85px;" |
0.8

|-
| style="width:132px;height:85px;" |
fprimt1

| style="width:64px;height:85px;" |
dimensionless

| style="width:64px;height:85px;" |
year

| style="width:64px;height:85px;" |
fish prices, response to stock level

| style="width:64px;height:85px;" |
-0.3

|-
| style="width:132px;height:102px;" |
fprimt2

| style="width:64px;height:102px;" |
dimensionless

| style="width:64px;height:102px;" |
year

| style="width:64px;height:102px;" |
fish prices, response to change in stock level

| style="width:64px;height:102px;" |
-0.6

|-
| style="width:132px;height:68px;" |
indemm

| style="width:64px;height:68px;" |
Multiplier Base 1

| style="width:64px;height:68px;" |
year,country

| style="width:64px;height:68px;" |
Industrial agricultural demand multiplier

| style="width:64px;height:68px;" |
1

|-
| style="width:132px;height:102px;" |
Ldcropm

| style="width:64px;height:102px;" |
dimensionless

| style="width:64px;height:102px;" |
year, country

| style="width:64px;height:102px;" |
multiplier on land to be developed for cropland

| style="width:64px;height:102px;" |
1

|-
| style="width:132px;height:102px;" |
Ldwf

| style="width:64px;height:102px;" |
hectares/person

| style="width:64px;height:102px;" |
year

| style="width:64px;height:102px;" |
land withdrawal factor with population growth

| style="width:64px;height:102px;" |
0.05

|-
| style="width:132px;height:102px;" |
Livhdpro

| style="width:64px;height:102px;" |
dimensionless

| style="width:64px;height:102px;" |
year

| style="width:64px;height:102px;" |
livestock herd productivity with grain feeding

| style="width:64px;height:102px;" |
0.5

|-
| style="width:132px;height:51px;" |
Lks

| style="width:64px;height:51px;" |
years

| style="width:64px;height:51px;" |
country, economic sector

| style="width:64px;height:51px;" |
lifetime of capital

| style="width:64px;height:51px;" |
30

|-
| style="width:132px;height:119px;" |
Lvcf

| style="width:64px;height:119px;" |
tons crops/tons meat

| style="width:64px;height:119px;" |
year

| style="width:64px;height:119px;" |
global livestock to calorie conversion factor, compared to crops

| style="width:64px;height:119px;" |
2

|-
| style="width:132px;height:136px;" |
malelimprecisesw

| style="width:64px;height:136px;" |
switch (0-1)

| style="width:64px;height:136px;" |
year,country

| style="width:64px;height:136px;" |
elimination of hunger for only the undernoursihed population

| style="width:64px;height:136px;" |
0

|-
| style="width:132px;height:102px;" |
malnelimstartyr

| style="width:64px;height:102px;" |
year

| style="width:64px;height:102px;" |
year,country

| style="width:64px;height:102px;" |
start year for an elimination of hunger scenario

| style="width:64px;height:102px;" |
0

|-
| style="width:132px;height:102px;" |
malnelimtargetyr

| style="width:64px;height:102px;" |
year

| style="width:64px;height:102px;" |
year,country

| style="width:64px;height:102px;" |
Target year for an elimination of hunger scenario

| style="width:64px;height:102px;" |
0

|-
| style="width:132px;height:546px;" |
Meatmax

| style="width:64px;height:546px;" |
tons meat/person/yr

| style="width:64px;height:546px;" |
year

| style="width:64px;height:546px;" |
The maximum meat consumption per person, in tons per person per year.  This parameter is only used to restrict meat consumption calculations in the initial year, in case of unreasonable data.  If you wish to introduce scenarios around dietary patterns (for instance, to reduce meat consumption), use the parameter calmeatm.

| style="width:64px;height:546px;" |
0.12

|-
| style="width:132px;height:459px;" |
Mhw

| style="width:64px;height:459px;" |
dimensionless

| style="width:64px;height:459px;" |
year

| style="width:64px;height:459px;" |
iMport propensity, historical (inertial) delay in change.  Values near 1.0 imply very rapid adjustment and values near 0 imply little or no adjustment.  Significant changes in this parameter could destabilize model behavior.

| style="width:64px;height:459px;" |
0.5

|-
| style="width:132px;height:102px;" |
Ofscth

| style="width:64px;height:102px;" |
10^6 tons fish

| style="width:64px;height:102px;" |
year

| style="width:64px;height:102px;" |
total global non-aquaculture fish production

| style="width:64px;height:102px;" |
80

|-
| style="width:132px;height:493px;" |
Protecm

| style="width:64px;height:493px;" |
Multiplier Base 1

| style="width:64px;height:493px;" |
year, country

| style="width:64px;height:493px;" |
Trade protection multiplier.  A multiplier on the price of imported goods, unit-less, by region.  A value of 1 implies no change, while higher values proportionately increase the prices of imported goods and lower values decrease them.

| style="width:64px;height:493px;" |
1

|-
| style="width:132px;height:34px;" |
Slr

| style="width:64px;height:34px;" |
fraction (0-1)

| style="width:64px;height:34px;" |
year

| style="width:64px;height:34px;" |
slaughter rate

| style="width:64px;height:34px;" |
0.7

|-
| style="width:132px;height:68px;" |
Tgrld

| style="width:64px;height:68px;" |
growth rate in decimal form

| style="width:64px;height:68px;" |
country

| style="width:64px;height:68px;" |
target growth in cultivated land

| style="width:64px;height:68px;" |
0.1

|-
| style="width:132px;height:408px;" |
Xhw

| style="width:64px;height:408px;" |
dimensionless

| style="width:64px;height:408px;" |
year

| style="width:64px;height:408px;" |
eXport propensity, inertial delay in change.  This parameter computes a moving average of export propensity.  A value of 0.7 would weight the historical or moving average by 0.7 and the newly computed value by 1-0.7 or 0.3.

| style="width:64px;height:408px;" |
0.7

|-
| style="width:132px;height:102px;" |
Ylexp

| style="width:64px;height:102px;" |
dimensionless

| style="width:64px;height:102px;" |
year

| style="width:64px;height:102px;" |
yield, exponent controlling saturation speed

| style="width:64px;height:102px;" |
0.5

|-
| style="width:132px;height:68px;" |
Ylhw

| style="width:64px;height:68px;" |
ratio

| style="width:64px;height:68px;" |
year

| style="width:64px;height:68px;" |
yield, inertial delay in change

| style="width:64px;height:68px;" |
0.2

|-
| style="width:132px;height:34px;" |
Ylm

| style="width:64px;height:34px;" |
Multiplier Base 1

| style="width:64px;height:34px;" |
year, country

| style="width:64px;height:34px;" |
yield multiplier

| style="width:64px;height:34px;" |
1

|-
| style="width:132px;height:68px;" |
Ylmax

| style="width:64px;height:68px;" |
10^6 tons crops/10^6 ha crop land

| style="width:64px;height:68px;" |
year, country

| style="width:64px;height:68px;" |
crop yield, maximum

| style="width:64px;height:68px;" |
15

|-
| style="width:132px;height:68px;" |
Ylmaxgr

| style="width:64px;height:68px;" |
growth rate in decimal form

| style="width:64px;height:68px;" |
year

| style="width:64px;height:68px;" |
maximum growth in yield

| style="width:64px;height:68px;" |
0.075

|}

= References and Bibliography =

<references />

Version 8.57 (December 12th, 2025)

2026-01-06T22:35:40Z

Yutang.Xiong:

= Version 8.57 (December 12th, 2025) =
[[Version 8.56 (November 20th, 2025)|PREVIOUS VERSION]] | [[Index Page For All Other Version Notes|INDEX PAGE]]

You can download IFs Version 8.57 at [https://ifsfiles.du.edu/IFs%20with%20Pardee%208_57%20December%2012%202025.zip IFs 8.57].

== Interface Updates ==

*Fix to calculate the starting year value correctly where only forecasted values are available, when the "Use all available history data" is turned on.
*Increased font size for labels in Pyramids.
*In graphical display, base-year vertical line now appears by default when the start year in graph is less than the base year.

== Model Updates ==
*Added new variable to display intersectoral flows in Flex Displays: INTSFLOWS.
*Fixed problem with rebasing to 1995 in function estimateHistory.
*We have changed the adjustments made to the Agriculture (and Energy) Column in the SAM, were previosly any change introduced into Gross Production by changing ylm (or enmpm) was being proportionally distributed to All the sectors in the Intersectoral Flows, but now we are NOT passing that change to the Sectors, so all of it goes to VADD, as if it was purely comming from an improvement in technology. This helps solve a problem where we were seeing reductions in Gross Production of other sectors by this intervention alone, caused by reducing the relative weight of other sectors relative to the new total, specifically for Iraq. Also added parameters outputagprotect and outputenprotect, when using a multiplier on Yield or Energy Production, and this parameter is 0 (default) the sectoral flows are protected and 0 effect is transfered to it, but if it is 1, the full effect is passed to the sectoral flows, values between 0 and 1 can also be used for proportional effect.
*Added Monadic Leader Travel (LDRTRIP, LDRTRIPOUT, ldrtripm).

== Data Updates ==
*49 Tables inserted & updated in IFsHistSeries
*7 preprocessors updated, poverty data from PIP
*14 tables updated, Diplometrics
*7 new tables, civil war impact from JDM, can be searched by SFCivilWarPardee
*10 preprocessors updated, FAO land use
*1 preprocessor updated, FreedomHouse
*6 preprocessors updated, GDP hist+foreast from WDI+IMF (2025Oct release)
*3 tables updated, life expectancy from IHME
*1 preprocessor updated, CO2 emission from CDIAC
*7 Rows updated in DataDict
*Agg rules added for 5 rows according to GitHub ticket 355
*Agg rules changed from SUM to GDP for 2 diplo tables (arms trade % GDP)

[[Category:Versions]]

Version 8.57 (December 12th, 2025)

2026-01-06T22:35:30Z

Yutang.Xiong:

= Version 8.57 (December 12th, 2025) =
[[Version 8.56 (November 20th, 2025)|PREVIOUS VERSION]] | [[Index Page For All Other Version Notes|INDEX PAGE]]

Test. You can download IFs Version 8.57 at [https://ifsfiles.du.edu/IFs%20with%20Pardee%208_57%20December%2012%202025.zip IFs 8.57].

== Interface Updates ==

*Fix to calculate the starting year value correctly where only forecasted values are available, when the "Use all available history data" is turned on.
*Increased font size for labels in Pyramids.
*In graphical display, base-year vertical line now appears by default when the start year in graph is less than the base year.

== Model Updates ==
*Added new variable to display intersectoral flows in Flex Displays: INTSFLOWS.
*Fixed problem with rebasing to 1995 in function estimateHistory.
*We have changed the adjustments made to the Agriculture (and Energy) Column in the SAM, were previosly any change introduced into Gross Production by changing ylm (or enmpm) was being proportionally distributed to All the sectors in the Intersectoral Flows, but now we are NOT passing that change to the Sectors, so all of it goes to VADD, as if it was purely comming from an improvement in technology. This helps solve a problem where we were seeing reductions in Gross Production of other sectors by this intervention alone, caused by reducing the relative weight of other sectors relative to the new total, specifically for Iraq. Also added parameters outputagprotect and outputenprotect, when using a multiplier on Yield or Energy Production, and this parameter is 0 (default) the sectoral flows are protected and 0 effect is transfered to it, but if it is 1, the full effect is passed to the sectoral flows, values between 0 and 1 can also be used for proportional effect.
*Added Monadic Leader Travel (LDRTRIP, LDRTRIPOUT, ldrtripm).

== Data Updates ==
*49 Tables inserted & updated in IFsHistSeries
*7 preprocessors updated, poverty data from PIP
*14 tables updated, Diplometrics
*7 new tables, civil war impact from JDM, can be searched by SFCivilWarPardee
*10 preprocessors updated, FAO land use
*1 preprocessor updated, FreedomHouse
*6 preprocessors updated, GDP hist+foreast from WDI+IMF (2025Oct release)
*3 tables updated, life expectancy from IHME
*1 preprocessor updated, CO2 emission from CDIAC
*7 Rows updated in DataDict
*Agg rules added for 5 rows according to GitHub ticket 355
*Agg rules changed from SUM to GDP for 2 diplo tables (arms trade % GDP)

[[Category:Versions]]

World Development Indicators, World Bank

2025-09-30T16:50:56Z

Yutang.Xiong: /* Tables in IFs */ removed 6 tables related to poverty as they are now being updated from WorldBank PIP directly

__FORCETOC__

= Summary =
The World Bank's Open Data is an initiative from the the World Bank Group to produce reliable data about its member countries, including a number of macro, financial, and sector databases. Their datasets include ones focused on development, aid effectiveness, climate change, economy, education, debt, infrastructure, etc. The World Bank also has a World Development Indicators (WDI) database, which is the premier compilation of cross country comparable data on development and is the primary collection of development indicators. WDI specifically contains 1,400 time series indicators from 217 economies, with much of that data going back more than 50 years. Data for Open Data and WDI is compiled from statistical systems of member countries and officially recognized international sources. Most social and economic data are updated annually or less often and some economic and financial data are updated quarterly. The WDI database is updated quarterly, sometimes more often.

The data team uses World Bank Open Data and WDI for a number of series about education, agriculture, aid, gender issues, environment, finances, economics, and development. Most importantly, WB databases are used for GDP and GDPPC. Example series from the World Bank include but are not limited to, SeriesAgFoodProductIndex, SeriesAidRec%GNI, SeriesCompanyValue%GDP, SeriesExportImport%GDP.

For series GovWBDoingBusinessCostofStarting, the source (Doing Business) has been discontinued now.

The last update was in July 2025.

= Tables in IFs =
{| class="wikitable"
|Variable
|Definition
|Units
|UsedInPreprocessor
|UsedInPreprocessorFileName
|Code in Source
|Decimal Places
|Formula
|-
|ArmsImp%TotImp
|Arms imports (SIPRI trend indicator values)
|Percent
|1
|ECONOMY
|MS.MIL.MPRT.KD
|3
|%'NE.IMP.GNFS.CD'
|-
|XDebtPNG%GDP
|External debt, private non-guaranteed, as percentage of gross domestic product
|Percent
|1
|ECONOMY
|DT.DOD.DPNG.CD
|4
|%'NY.GDP.MKTP.CD'
|-
|XDebtPPG%GDP
|External debt, public and publicly guaranteed, as percentage of gross domestic product
|Percent
|1
|ECONOMY
|DT.DOD.DPPG.CD
|4
|%'NY.GDP.MKTP.CD'
|-
|XFlowsIDA%GDP
|Net flows from IDA as % of GDP
|Percent
|1
|ECONOMY
|DT.NFL.MIDA.CD
|4
|%'NY.GDP.MKTP.CD'
|-
|XFlowsIMFNonCon%GDP
|Net nonconcessional flows from IMF as % of GDP
|Percent
|1
|ECONOMY
|DT.NFL.IMFN.CD
|4
|%'NY.GDP.MKTP.CD'
|-
|XIMFCredit%GDP
|IMF credits as % of GDP
|Percent
|1
|ECONOMY
|DT.DOD.DIMF.CD
|4
|%'NY.GDP.MKTP.CD'
|-
|XIncPayments%GDP
|Income payments as % of GDP
|Percent
|1
|ECONOMY
|BM.GSR.FCTY.CD
|4
|%'NY.GDP.MKTP.CD'
|-
|XPortBonds%GDP
|Portfolio investment in bonds (PPG and PNG) as % of GDP
|Percent
|1
|ECONOMY
|DT.NFL.BOND.CD
|4
|%'NY.GDP.MKTP.CD'
|-
|XPortEquity%GDP
|Portfolio investment in equity as % of GDP
|Percent
|1
|ECONOMY
|BX.PEF.TOTL.CD.WD
|4
|%'NY.GDP.MKTP.CD'
|-
|XReserves%GDP
|Gross international reserves as % of GDP
|Percent
|1
|ECONOMY
|FI.RES.TOTL.CD
|4
|%'NY.GDP.MKTP.CD'
|-
|XFlowsIMFCon%GDP
|Net concessional flows from IMF as % of GDP
|Percent
|1
|ECONOMY
|DT.NFL.IMFC.CD
|4
|%'NY.GDP.MKTP.CD'
|-
|XIncReceipts%GDP
|Income receipts as % of GDP
|Percent
|1
|ECONOMY
|BX.GSR.FCTY.CD
|3
|%'NY.GDP.MKTP.CD'
|-
|XFlowsIBRD%GDP
|Net flows from IBRD as % of GDP
|Percent
|1
|ECONOMY
|DT.NFL.MIBR.CD
|4
|%'NY.GDP.MKTP.CD'
|-
|XWBLoans%GDP
|IBRD loans and IDA credits as % of GDP
|Percent
|1
|ECONOMY
|DT.DOD.MWBG.CD
|4
|%'NY.GDP.MKTP.CD'
|-
|GovtCurRev%GDP
|Current government revenue as % of GDP
|Percent
|1
|ECONOMY
|GC.REV.XGRT.GD.ZS
|3
|%'NY.GDP.MKTP.CD'
|-
|ExportServices%GDP
|Exports of services as % of GDP
|Percent
|1
|ECONOMY
|BX.GSR.NFSV.CD
|4
|%'NY.GDP.MKTP.CD'
|-
|ExportsMerchandise%GDP
|Exports of merchandise as % of GDP
|Percent
|1
|ECONOMY
|TX.VAL.MRCH.CD.WT
|4
|%'NY.GDP.MKTP.CD'
|-
|ImportServices%GDP
|Imports of Services as % of GDP
|Percent
|1
|ECONOMY
|BM.GSR.NFSV.CD
|4
|%'NY.GDP.MKTP.CD'
|-
|ImportsMerchandise%GDP
|Imports of merchandiseas % of GDP
|Percent
|1
|ECONOMY
|TM.VAL.MRCH.CD.WT
|4
|%'NY.GDP.MKTP.CD'
|-
|XWorkerRemitPaid%GDP
|Worker remittances by country where paid as % of GDP
|Percent
|1
|ECONOMY
|BM.TRF.PWKR.CD.DT
|4
|%'NY.GDP.MKTP.CD'
|-
|XWorkerRemitReceived%GDP
|Worker remittances received by country as % of GDP
|Percent
|1
|ECONOMY
|BX.TRF.PWKR.CD.DT
|4
|%'NY.GDP.MKTP.CD'
|-
|XDebt%GDP
|External debt as percentage of gross domestic product
|Percent
|1
|ECONOMY
|DT.DOD.DECT.CD
|4
|%'NY.GDP.MKTP.CD'
|-
|NetForeignAssets%GDP
|Net Foreign Assets (current LCU) as a percent of GDP.
|Percent
|0
|
|FM.AST.NFRG.CN
|4
|%'NY.GDP.MKTP.CN'
|-
|Labor
|Labor force size
|Millions
|1
|POPULATION
|SL.TLF.TOTL.IN
|4
|/1000000
|-
|PopulationUrban
|Urban population
|Millions
|1
|POPULATION
|SP.URB.TOTL
|4
|/1000000
|-
|GDPCurDol
|Gross Domestic Product in Current US$
|Billion $
|1
|ENERGY
|NY.GDP.MKTP.CD
|4
|/1000000000
|-
|Xdebt
|External long-term (more than 1 year) debt: public, publically guaranteed and priv nonguaranteed
|Billion $
|1
|ECONOMY
|DT.DOD.DLXF.CD
|4
|/1000000000
|-
|LaborFemale%
|Portion of labor force made up by women
|Percent
|1
|POPULATION
|SL.TLF.TOTL.FE.ZS
|4
|
|-
|LaborSecInd%Tot
|Labor in industry as % of total
|Percent
|0
|
|SL.IND.EMPL.ZS
|4
|
|-
|LaborSecSer%Tot
|Labor in services as % of total
|Percent
|0
|
|SL.SRV.EMPL.ZS
|4
|
|-
|VaddInd%
|Value added in industry as percent of GDP
|Percent
|1
|ECONOMY
|NV.IND.TOTL.ZS
|3
|
|-
|OresMetsIm%MerchIm
|Ores and Metals imports as % of merchandise imports
|Percent
|1
|ECONOMY
|TM.VAL.MMTL.ZS.UN
|4
|
|-
|GovtDebt%GDP
|Central government debt as % of GDP
|Percent
|1
|ECONOMY
|GC.DOD.TOTL.GD.ZS
|4
|
|-
|VaddMan%
|Value added in manufacturing as percent of GDP
|Percent
|1
|ECONOMY
|NV.IND.MANF.ZS
|3
|
|-
|PopForeign%
|Foreign population as % of total population
|Percent
|0
|
|SM.POP.TOTL.ZS
|4
|
|-
|LaborAgriSector
|Agricultural labor force as % of total employment, ISIC multiple revisions
|Percent
|1
|ECONOMY
|SL.AGR.EMPL.ZS
|3
|
|-
|GenderUnpaidDomesticWorkTimeFemale
|Proportion of time spent on unpaid domestic and care work, female (% of 24 hour day).
|Percent
|0
|
|SG.TIM.UWRK.FE
|2
|
|-
|GenderUnpaidDomesticWorkTimeMale
|Proportion of time spent on unpaid domestic and care work, male (% of 24 hour day).
|Percent
|1
|POPULATION
|SG.TIM.UWRK.MA
|2
|
|-
|ExportGoodSer%
|Exports of goods and services as % of GDP
|Percent
|1
|ECONOMY
|NE.EXP.GNFS.ZS
|4
|
|-
|EnElecTransLoss%
|Electric power transmission and distribution losses (% of output)
|Percent
|1
|INFRASTRUCTURE
|EG.ELC.LOSS.ZS
|4
|
|-
|GovtEdPub%GDP
|Educational expenditures (public) as percent of GDP
|Percent
|1
|ECONOMY
|SE.XPD.TOTL.GD.ZS
|4
|
|-
|GovWBDoingBusinessCostofStarting
|Cost of registring a business as a percent of per capita income
|Percent
|1
|SOCIOPOL
|IC.REG.COST.PC.ZS
|4
|
|-
|GovtMil%GDPWDI
|Military expenditures (as percent of GDP). Taiwan, China values filled by SIPRI.
|Percent
|1
|ECONOMY
|MS.MIL.XPND.GD.ZS
|4
|
|-
|GovCon%GDP
|Government (general) final consumption as % of GDP
|Percent
|1
|ECONOMY
|NE.CON.GOVT.ZS
|3
|
|-
|GovExpense%GDP
|Government expense as % of GDP (compare with govt expenditures)
|Percent
|1
|ECONOMY
|GC.XPN.TOTL.GD.ZS
|3
|
|-
|ImportGoodSer%
|Imports of goods and services as % of GDP
|Percent
|1
|ECONOMY
|NE.IMP.GNFS.ZS
|4
|
|-
|EnElecConsPerCap
|Electricity consumption per capita
|KWH/Cap
|1
|INFRASTRUCTURE
|EG.USE.ELEC.KH.PC
|4
|
|-
|InvestGrCapForm%GDP
|Gross capital formation (Investment), percent of GDP
|Percent
|1
|ECONOMY
|NE.GDI.TOTL.ZS
|3
|
|-
|XFDIInflows%GDP
|Foreign direct investment net inflow as % of GDP
|Percent
|1
|ECONOMY
|BX.KLT.DINV.WD.GD.ZS
|4
|
|-
|XFDIOutflows%GDP
|Foreign direct investment net outflow as % of GDP
|Percent
|1
|ECONOMY
|BM.KLT.DINV.WD.GD.ZS
|3
|
|-
|IncShareL20%
|Income share of poorest 20% of population
|Percent
|1
|ECONOMY
|SI.DST.FRST.20
|3
|
|-
|LaborUnemploy%
|Labor unemployment, % of labor force
|Percent
|1
|POPULATION
|SL.UEM.TOTL.ZS
|3
|
|-
|TaxImportDuties%Rev
|Customs and other import duties (% of tax revenue)
|Percent
|1
|ECONOMY
|GC.TAX.IMPT.ZS
|4
|
|-
|OresMetsEx%MerchEx
|Ores and Metals exports as % of merchandise exports
|Percent
|1
|ECONOMY
|TX.VAL.MMTL.ZS.UN
|4
|
|-
|PopContrUse%WDI
|Prevalence of contraceptive use
|Percent
|0
|
|SP.DYN.CONU.ZS
|4
|
|-
|EnElecAccess%National
|Access to electric energy National (% of population)
|percentage
|1
|INFRASTRUCTURE
|EG.ELC.ACCS.ZS
|4
|
|-
|EnElecAccess%Rural
|Access to electricity in Rural areas (% of population)
|percentage
|1
|INFRASTRUCTURE
|EG.ELC.ACCS.RU.ZS
|4
|
|-
|EnElecAccess%Urban
|Access to electricity in Urban areas (% of population)
|percentage
|1
|INFRASTRUCTURE
|EG.ELC.ACCS.UR.ZS
|4
|
|-
|MalnChil%WeightWB
|Percentage of children under 5 malnourished based on weight; US benchmark
|Percent
|1
|AGRICULTURE
|SH.STA.MALN.ZS
|4
|
|-
|TaxGoodSer%CurRev
|Taxes on goods and services as % of total govt revenue
|Percent
|1
|ECONOMY
|GC.TAX.GSRV.RV.ZS
|3
|
|-
|TaxSocSec%CurRev
|Social security taxes as % of total govt revenue
|Percent
|1
|ECONOMY
|GC.REV.SOCL.ZS
|3
|
|-
|GiniExtended
|Gini index of income inequality (lower = more equal)
|Index
|1
|ECONOMY
|SI.POV.GINI
|3
|
|-
|ICTInternet%Pop
|Percent of population on-line
|Percent
|1
|SOCIOPOL
|IT.NET.USER.ZS
|4
|
|-
|LaborAgr%TotFemale
|Employees, agriculture, female as % of female employment
|Percent
|1
|POPULATION
|SL.AGR.EMPL.FE.ZS
|4
|
|-
|VaddAg%
|Value added in agriculture as percent of GDP
|Percent
|1
|ECONOMY
|NV.AGR.TOTL.ZS
|3
|
|-
|VaddSer%
|Value added in services as percent of GDP
|Percent
|1
|ECONOMY
|NV.SRV.TOTL.ZS
|3
|
|-
|LaborAgr%TotMale
|Employees, agriculture, males as % of male employment
|Percent
|1
|POPULATION
|SL.AGR.EMPL.MA.ZS
|4
|
|-
|XCurActBal%GDP
|Current account balance (% of GDP)
|Percent
|1
|ECONOMY
|BN.CAB.XOKA.GD.ZS
|3
|
|-
|MaternalMortalityRatio
|Maternal deaths per 100,000 live births
|Ratio
|1
|POPULATION
|SH.STA.MMRT
|2
|
|-
|EnvPMWDI
|PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)
|micrograms per cubic meter
|1
|ENVIRONMENT
|EN.ATM.PM25.MC.M3
|5
|
|-
|ChildStuntingPercentWDI
|Stunting (Height for age) amongst children aged under the age of 5
|Percent
|1
|HEALTH
|SH.STA.STNT.ZS
|5
|
|-
|HealthMalarBedNetsWDI
|Use of insecticide-treated bed nets (% of under-5 population)
|Percent
|1
|HEALTH
|SH.MLR.NETS.ZS
|3
|
|-
|AidRecGrant%TotRev
|Grants and other revenue include grants from other foreign governments, international organizations, and other government units;
|Percent
|1
|ECONOMY
|GC.REV.GOTR.ZS
|3
|
|-
|IncBelowNPL%WDI
|Population below national poverty line
|Percent
|1
|ECONOMY
|SI.POV.NAHC
|1
|
|-
|ICTImport%Imp
|Imports of ICT goods as % of total goods imports
|Percent
|0
|
|TM.VAL.ICTG.ZS.UN
|2
|
|-
|ICTExport%Exp
|Exports of ICT goods as % of total goods exports
|Percent
|0
|
|TX.VAL.ICTG.ZS.UN
|2
|
|}

= Data Pulling Instructions: World Bank Individual Indicators =
Step 1. To pull data from the world bank, navigate to [https://data.worldbank.org/ data.worldbank.org]

Step 2. To find the series, you can type in the search bar, click <Country> or click <nowiki><Indicator>.</nowiki>

Step 3. Go to step 4 if clicked on <Country> or to step 7 if clicked on <nowiki><Indicator>.</nowiki>
[[File:WBscreenshot10.jpg|center|World Bank Data homepage|thumb|499x499px]]

Step 4. If clicked on <Country>, you can select an individual country or options under various categories, such as <Region> and <Income levels>. This example will use the <High Income> category.
[[File:WB IMG STEP 04.jpg|center|thumb|544x544px|Countries and Economies main page]]

Step 5. If a category is selected, the following example screen of <High Income> will formulate. From here, select the relevant topic/theme/SDG Goal, then click on the desired indicator. Then skip to steps 10-12, which show how to download an indicator.
[[File:WB IMG STEP 05.jpg|center|thumb|504x504px|High Income example page]]
Step 6. If an individual country is selected, the following example screen will formulate. From here, select the relevant topic/theme/SDG Goal, then click on the desired indicator. Then skip to steps 10-12, which show how to download an indicator.

[[File:WBscreenshot15.jpg|center|Afghanistan example page|thumb|507x507px]]
Step 7. If clicked on <nowiki><Indicator>, navigate to the relevant category of the desired indicator or type in the search bar. For this example, click on the <Climate Change> category.</nowiki>
[[File:WB IMG STEP 07.jpg|center|Indicators main page|thumb|499x499px]]

Step 8. The following page will populate. Click on <Access to electricity (% of population)> as an indicator example.
[[File:WB IMG STEP 08.jpg|center|Example category from indicators page|thumb|508x508px]]

Step 9. Clicking on <Access to electricity (% of population)> will lead to this page.

[[File:WBscreenshot5.jpg|center|Access to electricity indicator|thumb|502x502px]]

Step 10. If a more specific variable is desired, select one of the options of the right side. As an example, <Access to electricity, urban (% of urban population)> is showed here.

[[File:WBscreenshot12.jpg|center|Access to electricity indicator|thumb|504x504px]]

Step 11. Before downloading, select desired time range on the sliding scale beneath the graph.

Step 12. On the right side of the graph, under <Download>, click <EXCEL>. An excel data set will immediately download. You may also click <WDI Tables> to see the data in a slightly different format.
[[File:WB IMG STEP 12.jpg|center|Download Indicator|thumb|493x493px]]

= Data Pulling Instructions: World Development Indicators =
Step 1. To pull data from the World Bank's World Development Indicators database, navigate to https://databank.worldbank.org/source/world-development-indicators

Step 2. In order to pull data, you'll need to first make selections regarding the database to pull from, countries, series, and time. These variables are on the left hand side.
[[File:WBwdiScreenshot9.jpg|center|World Bank WDI main page|thumb|505x505px]]

Step 3. First, select a database. This example will stay with WDI. [[File:WBwdiScreenshot.jpg|center|WDI page with database options|thumb|502x502px]]

Step 4. Second, select the countries to include in the data. To choose all countries, click the checkbox above the list of country names. [[File:WBwdiScreenshot10.jpg|center|WDI page with country options|thumb|509x509px]]

Step 5. Third, select series from keywords or alphabetical list. This example will choose <Access to electricity, (% of population)>.
[[File:WB WDI IMG STEP 05.jpg|center|thumb|508x508px|WDI page with series options]]
Step 6. Finally, select time as range of years. To choose all years, click the checkbox above the list of years.
. [[File:WBwdiScreenshot11.jpg|center|WDI page with year options|thumb|501x501px]]

Step 7. When all variables have been selected, click <Apply Changes>. [[File:WBwdiScreenshot12.jpg|center|WDI page with "Apply Changes"|thumb|499x499px]]

Step 8. The following preview page will formulate. You can also adjust the layout on the left side under the <Layout> tab, and you can preview data in different formats by clicking on <nowiki><Table>, <Chart>, and <Map>. </nowiki>

To download, click <Download Options> in right hand corner. Then click <Excel> and an Excel dataset will immediately download.
[[File:WB WDI IMG STEP 08(2).jpg|center|thumb|501x501px|WDI page with download options]]

= Data Notes =
We updated 82 series from WDI, 71 of them are preprocessors and 11 non preprocessors.
# Download CSV file from https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators
# Open the WDICSV.csv file.
# Use the "'''Code in Source'''" from the DataDict and "'''Indicator Code'''" from WDICSV file to preprare the series.
# Be care of the "Formula", if there is a "%'''XXXX'''"(the XXXX is the "Indicator Code"), then you need to use the series divide the series after "%". The series after "%" also in the WDICSV.
## Please keep in mind *100 after you finish the division to make it in "'''Percent'''" instead of decimal.
## Always applying "'''round'''" function after you applied multiplying 100.
# The "'''Years'''" for some series are discountinued, please remove the years with all "Nulls" before importing.
# Blending with old data in IFsHistSeries:
## A country that has data in the previous release but not anymore. For this case, we need to bring the data in unless we have a solid proof to say they delete a country's data on purpose. For example, in the table GovCon%GDP, Nigeria used to have values, but the new table has all nulls for it because WDI somehow removes values for Nigeria.
## Some historical years of data are missing completely for all countries. WDI does this every now and then. For example, for certain tables, it used to have coverage back to 1960, then along the updates WDI only offers data back to 1980 or even 2000.
# If you want to try to use python scripts to pull the data, you may access the scripts from [https://github.com/n1shamin/World-Development-Indicators-Update-2025 here].

Data Sources

2025-08-27T22:24:30Z

Yutang.Xiong: /* Centre d'Etudes Prospectives et d'Informations Internationales */

===== Bulletin of the Atomic Scientists =====

* [[Bulletin of the Atomic Scientists Data]]

===== Barro-Lee =====
* [[Educational Attainment Dataset, Barro-Lee|Education Attainment Dataset]]

===== Bertelsmann Stiftung =====
* [[The Transformation Index (BTI)]]

===== Carbon Dioxide Information and Analysis Center (CDIAC) =====

* [[Carbon Dioxide Information and Analysis Center (CDIAC)|Carbon Emissions Data]]

===== Center for Systemic Peace =====

* [[Polity5]]
* [[Major Episodes of Political Violence (MEPV)]]
* [[State Fragility Index, Center for Systemic Peace|State Fragility Index]]
* [[Political Instability Task Force (PITF), Center for Systematic Peace|Political Instability Task Force (PITF)]]

===== Centre d'Etudes Prospectives et d'Informations Internationales (CEPII) =====

* [[BACI]]
* [[GeoDist, CEPII|GeoDist]]

===== CIA =====

* [[CIA World Factbook]]

===== Climate Watch =====
* [[Emissions Data, Climate Watch|Emissions Data]]

===== Correlates of War =====
* [[The Correlates of War Project, Correlates of War|The Correlates of War Project]]

===== Demographic and Health Surveys (DHS) =====

* [[STATcompiler, Demographic and Health Surveys (DHS)|STATcompiler]]

===== Economist Intelligence Unit (EIU) =====

* [[Economist Intelligence Unit (EIU)|Economist Intelligence Unit (EIU) Data]]
* [[The Political Instability Index, Economist Intelligence Unit (EIU)|The Political Instability Index]]

===== Emissions Database for Global Atmospheric Research =====

* [[Emissions Database for Global Atmospheric Research Data]]

===== Instituut voor Milieuvraagstukken (IVM) =====

* [[Globally potentially available cropland data, Environmental Geography Instituut voor Milieuvraagstukken (IVM)|Globally potentially available cropland data]]

===== EMBER =====
* [[Electricity Data, EMBER|Electricity Data]]

===== Energy Institute =====

* [[Statistical Review of World Energy, Energy Institute|Statistical Review of World Energy]]

===== Europa World Yearbook =====

* [[Europa World Yearbook Data]]

===== European Commission =====

* [[Eurostat European Commission|Eurostat, European Commission]]

===== Federation of American Scientists =====

* [[Federation of American Scientists Data]]

===== Federal Institute for Geosciences and Natural Resources (BGR) =====

* [[Energy Data, BGR|Energy Data]]

===== Food and Agriculture Organization (FAO) =====

* [[AQUASTAT, FAO|AQUASTAT - FAO's Global Information System on Water and Agriculture]]
* [[FishstatJ, FAO|FishStatJ]]
* [[FAOSTAT Food Balance Sheet]]
* [[FAOSTAT Food Security and Nutrition]]
* [[FAOSTAT Land Use|FAOSTAT Land]]
* [[FAOSTAT SDG Indicators]]
* [[FAOSTAT Trade Crops and livestock products]]

===== Fraser Institute =====

* [https://pardeewiki.du.edu//index.php?title=Fraser_International Economic Data]

===== Freedom House =====

* [[Countries at the Crossroads, Freedom House|Countries at the Crossroads]]
* [[FreedomHouse|Freedom in the World]]
* [[Freedom on the Net, Freedom House|Freedom on the Net]]

===== Fund for Peace =====
* [[Fragile States Index, Fund for Peace|Fragile States Index]]

===== Global Carbon Budget (GCB) =====

* [[Carbon Data, Global Carbon Budget (GCB)|Carbon Data]]

===== Global Footprint Network =====

* [[Global Footprint Network|Global Footprint Network Data]]

===== Global Trade Analysis Project (GTAP) =====

* [[Global Trade Analysis Project (GTAP)|Global Trade Analysis Project (GTAP) Data]]

===== Heritage Foundation =====

* [[Heritage Foundation Data]]

===== International Groundwater Resources Assessment Centre (IGRAC) =====

* [[Global Depletion of Groundwater Resources, International Groundwater Resources Assessment Centre (IGRAC)|Global Depletion of Groundwater Resources]]

===== Institute for Employment Research (IAB) =====

* [[Brain Drain Data, Institute for Employment Research (IAB)|Brain Drain Data]]

===== International Centre for Tax & Development (ICTD) =====

* [[International Centre for Tax & Development (ICTD)|International Centre for Tax & Development (ICTD) Data]]

===== International Diabetes Federation =====

* [[Diabetes Atlas, International Diabetes Federation|Diabetes Atlas]]

===== [[International Energy Agency (IEA)]] =====
* [[End-Use Prices Data Explorer, International Energy Agency (IEA)|End-Use Prices Data Explorer]]
* [[World Energy Balances, International Energy Agency (IEA)|World Energy Balances]]
* [[World Energy Outlook, International Energy Agency (IEA)|World Energy Outlook]]
* [[World Energy Statistics, International Energy Agency (IEA)|World Energy Statistics]]

===== International Food Policy Research Institute =====

* [[International Food Policy Research Institute Data]]

===== Institute for Health Metrics and Evaluation (IHME) =====

* [[IHME|Global Burdens of Disease (GBD)]]

===== International Institute for Applied Systems Analysis (IIASA) =====

* [[Shared Socioeconomic Pathway (SSP) Extensions Explorer, International Institute for Applied Systems Analysis (IIASA)|Shared Socioeconomic Pathway (SSP) Extensions Explorer]]
* [[RCP Representative Concentration Pathways Database|RCP Database]]

===== International Institute for Management Development (IMD) =====

* [[International Institute for Management Development (IMD) Data]]

===== International Labor Organization (ILO) =====
*[[ILOstat]]
*[[ILO-WIEGO Statistical Reports]]

=====International Monetary Fund (IMF)=====

* [[Balance of Payments and International Investment Position statistics (BoP/IIP), IMF|Balance of Payments and International Investment Position statistics (BoP/IIP)]]
* [[Fiscal Monitor, IMF|Fiscal Monitor]]
* [[International Financial Statistics, IMF|International Financial Statistics]]
* [[World Economic Outlook, IMF|World Economic Outlook (WEO)]]
* [[IMF GFS Government Finance Statistics|Government Finance Statistics (GFS)]]
* [[IMF COFOG Classifications of Functions of Government|Classification of the Functions of Government (COFOG)]]

===== Inter-Parliamentary Union =====

* [[Women in Parliaments, Inter-Parliamentary Union|Women in Parliaments]]

===== International Renewable Energy Agency (IRENA) =====

* [[Energy Data, International Renewable Energy Agency (IRENA)|Energy Data]]

===== International Research & Exchanges Board (IREX) =====

* [[International Research & Exchanges Board (IREX) Data]]

===== International Study Center, Boston College =====

* [[TIMMS & PIRLS, International Study Center, Boston College|TIMSS & PIRLS]]

===== International Telecommunications Union (ITU) =====

* [[World Telecommunication Indicators Database, ITU|World Telecommunication Indicators Database]] = database has been discontinued
* [[International Telecommunication Union DataHub]]
* [[Global Cybersecurity Index (GCI), ITU|Global Cybersecurity Index (GCI)]]

===== International Organization of Motor Vehicle Manufacturers (OICA) =====

* [[International Organization of Motor Vehicle Manufacturers (OICA) Data]]

===== International Road Federation =====

* [[IRF World Road Statistics|World Road Statistics]]

===== Kellogg Institute For International Studies =====

* [[Kellogg Institute Data]]

===== LIS Cross-National Data Center =====

* [[LIS Cross-National Data Center Data]]

===== Mo Ibrahim Foundation =====

* [[Ibrahim Index of African Governance, Mo Ibrahim Foundation|Ibrahim Index of African Governance]]

===== MyGeoHub =====

* [[MY GEO HUB|Historical Irrigation Dataset (HID)]]

===== National Science Foundation (NSF) =====

* [[Science and Engineering Indicators, National Science Foundation (NSF)|Science and Engineering Indicators]]
* [[Science and Technology Studies (STS), National Science Foundation (NSF)|Science and Technology Studies (STS)]]

===== Organisation of Economic Cooperation and Development (OECD) =====
* International Transport Forum (ITF)
** [[Transport Data and Statistics, International Transport Forum (ITF)|Transport Data and Statistics]]
*[[OECD The Organisation for Economic Co-operation and Development|OECD Data Explorer]]
*[[OECD Factbook]]
*[[OECD Information Technology Outlook (ITO)|OECD Information Technology Outlook]]
* [[Measuring the Internet Economy, Organisation for Economic Co-operation and Development (OECD)|Measuring the Internet Economy]]
* [[Pensions at a Glance, Organisation for Economic Co-operation and Development (OECD)|Pensions at a Glance]]
* [[OECD Science & Technology|Science & Technology]]
* [[Social Institutions and Gender Index, OECD|Social Institutions and Gender Index]]
*[[Structural Analysis Database (STAN), OECD|Structural Analysis Database (STAN)]]

===== Political Terror Scale (PTS) =====

* [[Political Terror Scale (PTS) Data]]

===== Population Reference Bureau (PRB) =====

* [[Population Reference Bureau (PRB)|Assorted Annual Population Data Sheets]]

===== Potsdam Institute for Climate Impact Research (PIK) =====

* [[Climate Data, Potsdam Institute for Climate Impact Research (PIK)|Climate Data]]

===== PRIS Project =====

* [[IRIS, PRIS Project|IRIS]]

===== Reporters Without Borders =====

* [[Worldwide Press Freedom Index, Reporters Without Borders|Worldwide Press Freedom Index]]

===== Royal Netherlands Meteorological Institute (WMO KNMI) =====

* [[World Meteorological Organization Climate Explorer Reports]]

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

* [[Stockholm International Peace Research Institute (SIPRI)|Military Expenditure Data]]

===== SpeedTest =====

* [[SpeedTest Data]]

===== The Conference Board =====

* [[Total Economy Database, The Conference Board|Total Economy Database]]

===== Transparency International (TI) =====

* [[Transparency International|Corruption Perceptions Index]]

===== UBS Group AG =====

* [[Global Wealth Report, UBS Group AG|Global Wealth Report]]

===== Uppsala Conflict Data Program (UCDP) =====

* [[Uppsala Conflict Data Program (UCDP) Datasets]]
* [[UCDP/PRIO Armed Conflict Database|Uppsala Conflict Data Program (UCDP)/Peace Research Institute Oslo (PRIO) Armed Conflict Dataset]]

===== United Nations Economic Commission for Europe (UNECE) =====

* [[United Nations Economic Commission for Europe (UNECE) Data|United Nations Economic Commission for Europe Data]]

===== United Nations =====

* [[General Assembly of the United Nations (UNGA)]]
* [[Joint United Nations Programme on HIV/AIDS (UNAIDS)]]
* [[Millennium Development Goals Database, UN|Millennium Development Goals Database]]
* United Nations Department of Economic and Social Affairs
** [[United Nation World Marriage Data, United Nations Department of Economic and Social Affairs|United Nation World Marriage Data]]
* United Nations Educational, Scientific and Cultural Organization (UNESCO)
** [[Institute for Statistics, UNESCO (UIS)]]
* [[United Nations Industrial Development Organization (UNIDO)]]
* United Nations International Children's Emergency Fund (UNICEF)
** [[Child protection and development, UNICEF|Child protection and development]]
*[[United Nations Office of Drug and Crime (UNODC)]]
**[[United Nations Surveys on Crime Trends and the Operations of Criminal Justice Systems (UN-CTS), UNODC]]
*United Nations Population Division (UNPD)
** [[World Population Prospects (WPP), United Nations Population Division (UNPD)|World Population Prospects (WPP)]]
** [[UNDP Human Development Reports]]
*[[United Nation SDG Indicators Global Database (UNSDG)]]
*[[United Nations Statistics Division (UNSD)]]
**[[Environmental Indicators, UNSD|Environmental Indicators]]
*[[UN Trade and Development (UNCTAD)|United Nations Trade and Development (UNCTAD)]]
*[[United Nations Treaty Collection]]
*[[World Tourism Organization (UN Tourism)]]

===== University of Maryland =====

* [[Global Terrorism Database, University of Maryland|Global Terrorism Database]]
* [[Peace and Conflict Instability Ledger, University of Maryland/Center for International Development and Conflict Management (CIDCM)|Peace and Conflict Instability Ledger]]
* [[Minorities at risk|All Minorities at Risk (AMAR) Project]]

===== University of Pennsylvania =====

* [[The Political Constraint Index (POLCON), University of Pennsylvania|The Political Constraint Index (POLCON)]]

===== UN University World Institute for Development Economics Research (UNU WIDER) =====

* [[World Income Inequality Database, UN University World Institute for Development Economics Research (UNU WIDER)|World Income Inequality Database]]

===== US Energy Information Administration (EIA) =====

* [[Energy Information Administration (EIA)|Energy Information Administration Data]]

===== US Geological Survey =====

* [[World Petroleum Assessment, U.S. Geological Survey|World Petroleum Assessment]]

===== Vision of Humanity =====

* [[Vision of Humanity Data]]

===== Wittgenstein Centre =====

* [[Wittgenstein Centre Human Capital Data Explorer]]

===== World Bank =====

* [[The Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE), World Bank|Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE)]]
* [[Business Ready (B-READY), World Bank|Business Ready (B-READY)]]
* [[Climate Change Action Plan (CCAP), World Bank|Climate Change Action Plan (CCAP)]]
* [[Climate Change Knowledge Portal, World Bank|Climate Change Knowledge Portal]]
* [[Country Policy and Institutional Assessment (CPIA), World Bank|Country Policy and Institutional Assessment (CPIA)]]
* [[Database of Political Institutions, World Bank|Database of Political Institutions]]
* [[Education Statistics (EdStats), World Bank|Education Statistics (EdStats)]]
*[[Gender Statistics, World Bank|Gender Statistics]]
*[[Global Financial Development, World Bank|Global Financial Development]]
*[[Global Integrity Index]]
*[[Informal Economy Database]]
*[[International Development Association, World Bank|International Development Association]]
*[[Knowledge Economy Index, World Bank|Knowledge Economy Index]]
*[[Poverty and Inequality Platform, World Bank|Poverty and Inequality Platform]]
*[[Private Participation in Infrastructure (PPI) Project Database, World Bank|Private Participation in Infrastructure (PPI) Project Database]]
*[[Road Costs Knowledge System (ROCKS), World Bank|Road Costs Knowledge System (ROCKS)]]
*[[World Development Indicators, World Bank|World Development Indicators (WDI)]]
*[[World Development Report, World Bank|World Development Report]]
*[[Worldwide Governance Indicators|World Governance Indicators (WGI)]]

===== World Democratic Forum (WDF) =====

* [[World Democratic Forum (WDF) Data]]

===== World Economic Forum =====

*[[Global Competitiveness Index, World Economic Forum|Global Competitiveness Index]]

===== [[World Energy Council (WEC)]]=====

* [[Oil & Gas Journal, WEC]]

===== World Health Organization (WHO)=====

*[[Global Health Expenditure Database, WHO|Global Health Expenditure Database]]
*[[Joint Monitoring Programme, WHO/UNICEF|Joint Monitoring Programme (JMP)]]
*[[WHO Tobacco Free Initiative (TFI)]]
*[[Surveillance of chronic disease Risk Factors (SuRF), WHO|Surveillance of chronic disease Risk Factors (SuRF)]]
*[[World Malaria Report, WHO|World Malaria Report]]

* [[Global Health Observatory (GHO)]]
** [[Obesity, adult (18+) and child and adolescent (5-19)|Obesity]]

===== World Intellectual Property Organization (WIPO) =====
* [[The Global Innovation Index, World Intellectual Property Organization (WIPO)|The Global Innovation Index]]

===== World Justice Project =====

*[[World Justice Project Rule of Law Index|Rule of Law Index]]

===== World Poverty Clock =====

* [[World Poverty Clock|World Poverty Clock Data]]

=====World Military Expenditures and Arms Transfers (WMEAT)=====

* [[World Military Expenditures and Arms Transfers (WMEAT)|World Military Expenditures and Arms Transfers (WMEAT) Reports]]

=====World Trade Organization (WTO)=====

* [[Dispute Statistics, WTO|Dispute Statistics]]

=====World Value Survey (WVS)=====

* [[World Value Survey|World Value Survey Data]]

==== [[Research Papers/Projects]] ====

==== [[Preprocessor|Preprocessors]] ====
[[Template]]

Data Sources

2025-08-27T22:24:09Z

Yutang.Xiong:

===== Bulletin of the Atomic Scientists =====

* [[Bulletin of the Atomic Scientists Data]]

===== Barro-Lee =====
* [[Educational Attainment Dataset, Barro-Lee|Education Attainment Dataset]]

===== Bertelsmann Stiftung =====
* [[The Transformation Index (BTI)]]

===== Carbon Dioxide Information and Analysis Center (CDIAC) =====

* [[Carbon Dioxide Information and Analysis Center (CDIAC)|Carbon Emissions Data]]

===== Center for Systemic Peace =====

* [[Polity5]]
* [[Major Episodes of Political Violence (MEPV)]]
* [[State Fragility Index, Center for Systemic Peace|State Fragility Index]]
* [[Political Instability Task Force (PITF), Center for Systematic Peace|Political Instability Task Force (PITF)]]

===== Centre d'Etudes Prospectives et d'Informations Internationales =====

* [[BACI]]
* [[GeoDist, CEPII|GeoDist]]

===== CIA =====

* [[CIA World Factbook]]

===== Climate Watch =====
* [[Emissions Data, Climate Watch|Emissions Data]]

===== Correlates of War =====
* [[The Correlates of War Project, Correlates of War|The Correlates of War Project]]

===== Demographic and Health Surveys (DHS) =====

* [[STATcompiler, Demographic and Health Surveys (DHS)|STATcompiler]]

===== Economist Intelligence Unit (EIU) =====

* [[Economist Intelligence Unit (EIU)|Economist Intelligence Unit (EIU) Data]]
* [[The Political Instability Index, Economist Intelligence Unit (EIU)|The Political Instability Index]]

===== Emissions Database for Global Atmospheric Research =====

* [[Emissions Database for Global Atmospheric Research Data]]

===== Instituut voor Milieuvraagstukken (IVM) =====

* [[Globally potentially available cropland data, Environmental Geography Instituut voor Milieuvraagstukken (IVM)|Globally potentially available cropland data]]

===== EMBER =====
* [[Electricity Data, EMBER|Electricity Data]]

===== Energy Institute =====

* [[Statistical Review of World Energy, Energy Institute|Statistical Review of World Energy]]

===== Europa World Yearbook =====

* [[Europa World Yearbook Data]]

===== European Commission =====

* [[Eurostat European Commission|Eurostat, European Commission]]

===== Federation of American Scientists =====

* [[Federation of American Scientists Data]]

===== Federal Institute for Geosciences and Natural Resources (BGR) =====

* [[Energy Data, BGR|Energy Data]]

===== Food and Agriculture Organization (FAO) =====

* [[AQUASTAT, FAO|AQUASTAT - FAO's Global Information System on Water and Agriculture]]
* [[FishstatJ, FAO|FishStatJ]]
* [[FAOSTAT Food Balance Sheet]]
* [[FAOSTAT Food Security and Nutrition]]
* [[FAOSTAT Land Use|FAOSTAT Land]]
* [[FAOSTAT SDG Indicators]]
* [[FAOSTAT Trade Crops and livestock products]]

===== Fraser Institute =====

* [https://pardeewiki.du.edu//index.php?title=Fraser_International Economic Data]

===== Freedom House =====

* [[Countries at the Crossroads, Freedom House|Countries at the Crossroads]]
* [[FreedomHouse|Freedom in the World]]
* [[Freedom on the Net, Freedom House|Freedom on the Net]]

===== Fund for Peace =====
* [[Fragile States Index, Fund for Peace|Fragile States Index]]

===== Global Carbon Budget (GCB) =====

* [[Carbon Data, Global Carbon Budget (GCB)|Carbon Data]]

===== Global Footprint Network =====

* [[Global Footprint Network|Global Footprint Network Data]]

===== Global Trade Analysis Project (GTAP) =====

* [[Global Trade Analysis Project (GTAP)|Global Trade Analysis Project (GTAP) Data]]

===== Heritage Foundation =====

* [[Heritage Foundation Data]]

===== International Groundwater Resources Assessment Centre (IGRAC) =====

* [[Global Depletion of Groundwater Resources, International Groundwater Resources Assessment Centre (IGRAC)|Global Depletion of Groundwater Resources]]

===== Institute for Employment Research (IAB) =====

* [[Brain Drain Data, Institute for Employment Research (IAB)|Brain Drain Data]]

===== International Centre for Tax & Development (ICTD) =====

* [[International Centre for Tax & Development (ICTD)|International Centre for Tax & Development (ICTD) Data]]

===== International Diabetes Federation =====

* [[Diabetes Atlas, International Diabetes Federation|Diabetes Atlas]]

===== [[International Energy Agency (IEA)]] =====
* [[End-Use Prices Data Explorer, International Energy Agency (IEA)|End-Use Prices Data Explorer]]
* [[World Energy Balances, International Energy Agency (IEA)|World Energy Balances]]
* [[World Energy Outlook, International Energy Agency (IEA)|World Energy Outlook]]
* [[World Energy Statistics, International Energy Agency (IEA)|World Energy Statistics]]

===== International Food Policy Research Institute =====

* [[International Food Policy Research Institute Data]]

===== Institute for Health Metrics and Evaluation (IHME) =====

* [[IHME|Global Burdens of Disease (GBD)]]

===== International Institute for Applied Systems Analysis (IIASA) =====

* [[Shared Socioeconomic Pathway (SSP) Extensions Explorer, International Institute for Applied Systems Analysis (IIASA)|Shared Socioeconomic Pathway (SSP) Extensions Explorer]]
* [[RCP Representative Concentration Pathways Database|RCP Database]]

===== International Institute for Management Development (IMD) =====

* [[International Institute for Management Development (IMD) Data]]

===== International Labor Organization (ILO) =====
*[[ILOstat]]
*[[ILO-WIEGO Statistical Reports]]

=====International Monetary Fund (IMF)=====

* [[Balance of Payments and International Investment Position statistics (BoP/IIP), IMF|Balance of Payments and International Investment Position statistics (BoP/IIP)]]
* [[Fiscal Monitor, IMF|Fiscal Monitor]]
* [[International Financial Statistics, IMF|International Financial Statistics]]
* [[World Economic Outlook, IMF|World Economic Outlook (WEO)]]
* [[IMF GFS Government Finance Statistics|Government Finance Statistics (GFS)]]
* [[IMF COFOG Classifications of Functions of Government|Classification of the Functions of Government (COFOG)]]

===== Inter-Parliamentary Union =====

* [[Women in Parliaments, Inter-Parliamentary Union|Women in Parliaments]]

===== International Renewable Energy Agency (IRENA) =====

* [[Energy Data, International Renewable Energy Agency (IRENA)|Energy Data]]

===== International Research & Exchanges Board (IREX) =====

* [[International Research & Exchanges Board (IREX) Data]]

===== International Study Center, Boston College =====

* [[TIMMS & PIRLS, International Study Center, Boston College|TIMSS & PIRLS]]

===== International Telecommunications Union (ITU) =====

* [[World Telecommunication Indicators Database, ITU|World Telecommunication Indicators Database]] = database has been discontinued
* [[International Telecommunication Union DataHub]]
* [[Global Cybersecurity Index (GCI), ITU|Global Cybersecurity Index (GCI)]]

===== International Organization of Motor Vehicle Manufacturers (OICA) =====

* [[International Organization of Motor Vehicle Manufacturers (OICA) Data]]

===== International Road Federation =====

* [[IRF World Road Statistics|World Road Statistics]]

===== Kellogg Institute For International Studies =====

* [[Kellogg Institute Data]]

===== LIS Cross-National Data Center =====

* [[LIS Cross-National Data Center Data]]

===== Mo Ibrahim Foundation =====

* [[Ibrahim Index of African Governance, Mo Ibrahim Foundation|Ibrahim Index of African Governance]]

===== MyGeoHub =====

* [[MY GEO HUB|Historical Irrigation Dataset (HID)]]

===== National Science Foundation (NSF) =====

* [[Science and Engineering Indicators, National Science Foundation (NSF)|Science and Engineering Indicators]]
* [[Science and Technology Studies (STS), National Science Foundation (NSF)|Science and Technology Studies (STS)]]

===== Organisation of Economic Cooperation and Development (OECD) =====
* International Transport Forum (ITF)
** [[Transport Data and Statistics, International Transport Forum (ITF)|Transport Data and Statistics]]
*[[OECD The Organisation for Economic Co-operation and Development|OECD Data Explorer]]
*[[OECD Factbook]]
*[[OECD Information Technology Outlook (ITO)|OECD Information Technology Outlook]]
* [[Measuring the Internet Economy, Organisation for Economic Co-operation and Development (OECD)|Measuring the Internet Economy]]
* [[Pensions at a Glance, Organisation for Economic Co-operation and Development (OECD)|Pensions at a Glance]]
* [[OECD Science & Technology|Science & Technology]]
* [[Social Institutions and Gender Index, OECD|Social Institutions and Gender Index]]
*[[Structural Analysis Database (STAN), OECD|Structural Analysis Database (STAN)]]

===== Political Terror Scale (PTS) =====

* [[Political Terror Scale (PTS) Data]]

===== Population Reference Bureau (PRB) =====

* [[Population Reference Bureau (PRB)|Assorted Annual Population Data Sheets]]

===== Potsdam Institute for Climate Impact Research (PIK) =====

* [[Climate Data, Potsdam Institute for Climate Impact Research (PIK)|Climate Data]]

===== PRIS Project =====

* [[IRIS, PRIS Project|IRIS]]

===== Reporters Without Borders =====

* [[Worldwide Press Freedom Index, Reporters Without Borders|Worldwide Press Freedom Index]]

===== Royal Netherlands Meteorological Institute (WMO KNMI) =====

* [[World Meteorological Organization Climate Explorer Reports]]

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

* [[Stockholm International Peace Research Institute (SIPRI)|Military Expenditure Data]]

===== SpeedTest =====

* [[SpeedTest Data]]

===== The Conference Board =====

* [[Total Economy Database, The Conference Board|Total Economy Database]]

===== Transparency International (TI) =====

* [[Transparency International|Corruption Perceptions Index]]

===== UBS Group AG =====

* [[Global Wealth Report, UBS Group AG|Global Wealth Report]]

===== Uppsala Conflict Data Program (UCDP) =====

* [[Uppsala Conflict Data Program (UCDP) Datasets]]
* [[UCDP/PRIO Armed Conflict Database|Uppsala Conflict Data Program (UCDP)/Peace Research Institute Oslo (PRIO) Armed Conflict Dataset]]

===== United Nations Economic Commission for Europe (UNECE) =====

* [[United Nations Economic Commission for Europe (UNECE) Data|United Nations Economic Commission for Europe Data]]

===== United Nations =====

* [[General Assembly of the United Nations (UNGA)]]
* [[Joint United Nations Programme on HIV/AIDS (UNAIDS)]]
* [[Millennium Development Goals Database, UN|Millennium Development Goals Database]]
* United Nations Department of Economic and Social Affairs
** [[United Nation World Marriage Data, United Nations Department of Economic and Social Affairs|United Nation World Marriage Data]]
* United Nations Educational, Scientific and Cultural Organization (UNESCO)
** [[Institute for Statistics, UNESCO (UIS)]]
* [[United Nations Industrial Development Organization (UNIDO)]]
* United Nations International Children's Emergency Fund (UNICEF)
** [[Child protection and development, UNICEF|Child protection and development]]
*[[United Nations Office of Drug and Crime (UNODC)]]
**[[United Nations Surveys on Crime Trends and the Operations of Criminal Justice Systems (UN-CTS), UNODC]]
*United Nations Population Division (UNPD)
** [[World Population Prospects (WPP), United Nations Population Division (UNPD)|World Population Prospects (WPP)]]
** [[UNDP Human Development Reports]]
*[[United Nation SDG Indicators Global Database (UNSDG)]]
*[[United Nations Statistics Division (UNSD)]]
**[[Environmental Indicators, UNSD|Environmental Indicators]]
*[[UN Trade and Development (UNCTAD)|United Nations Trade and Development (UNCTAD)]]
*[[United Nations Treaty Collection]]
*[[World Tourism Organization (UN Tourism)]]

===== University of Maryland =====

* [[Global Terrorism Database, University of Maryland|Global Terrorism Database]]
* [[Peace and Conflict Instability Ledger, University of Maryland/Center for International Development and Conflict Management (CIDCM)|Peace and Conflict Instability Ledger]]
* [[Minorities at risk|All Minorities at Risk (AMAR) Project]]

===== University of Pennsylvania =====

* [[The Political Constraint Index (POLCON), University of Pennsylvania|The Political Constraint Index (POLCON)]]

===== UN University World Institute for Development Economics Research (UNU WIDER) =====

* [[World Income Inequality Database, UN University World Institute for Development Economics Research (UNU WIDER)|World Income Inequality Database]]

===== US Energy Information Administration (EIA) =====

* [[Energy Information Administration (EIA)|Energy Information Administration Data]]

===== US Geological Survey =====

* [[World Petroleum Assessment, U.S. Geological Survey|World Petroleum Assessment]]

===== Vision of Humanity =====

* [[Vision of Humanity Data]]

===== Wittgenstein Centre =====

* [[Wittgenstein Centre Human Capital Data Explorer]]

===== World Bank =====

* [[The Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE), World Bank|Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE)]]
* [[Business Ready (B-READY), World Bank|Business Ready (B-READY)]]
* [[Climate Change Action Plan (CCAP), World Bank|Climate Change Action Plan (CCAP)]]
* [[Climate Change Knowledge Portal, World Bank|Climate Change Knowledge Portal]]
* [[Country Policy and Institutional Assessment (CPIA), World Bank|Country Policy and Institutional Assessment (CPIA)]]
* [[Database of Political Institutions, World Bank|Database of Political Institutions]]
* [[Education Statistics (EdStats), World Bank|Education Statistics (EdStats)]]
*[[Gender Statistics, World Bank|Gender Statistics]]
*[[Global Financial Development, World Bank|Global Financial Development]]
*[[Global Integrity Index]]
*[[Informal Economy Database]]
*[[International Development Association, World Bank|International Development Association]]
*[[Knowledge Economy Index, World Bank|Knowledge Economy Index]]
*[[Poverty and Inequality Platform, World Bank|Poverty and Inequality Platform]]
*[[Private Participation in Infrastructure (PPI) Project Database, World Bank|Private Participation in Infrastructure (PPI) Project Database]]
*[[Road Costs Knowledge System (ROCKS), World Bank|Road Costs Knowledge System (ROCKS)]]
*[[World Development Indicators, World Bank|World Development Indicators (WDI)]]
*[[World Development Report, World Bank|World Development Report]]
*[[Worldwide Governance Indicators|World Governance Indicators (WGI)]]

===== World Democratic Forum (WDF) =====

* [[World Democratic Forum (WDF) Data]]

===== World Economic Forum =====

*[[Global Competitiveness Index, World Economic Forum|Global Competitiveness Index]]

===== [[World Energy Council (WEC)]]=====

* [[Oil & Gas Journal, WEC]]

===== World Health Organization (WHO)=====

*[[Global Health Expenditure Database, WHO|Global Health Expenditure Database]]
*[[Joint Monitoring Programme, WHO/UNICEF|Joint Monitoring Programme (JMP)]]
*[[WHO Tobacco Free Initiative (TFI)]]
*[[Surveillance of chronic disease Risk Factors (SuRF), WHO|Surveillance of chronic disease Risk Factors (SuRF)]]
*[[World Malaria Report, WHO|World Malaria Report]]

* [[Global Health Observatory (GHO)]]
** [[Obesity, adult (18+) and child and adolescent (5-19)|Obesity]]

===== World Intellectual Property Organization (WIPO) =====
* [[The Global Innovation Index, World Intellectual Property Organization (WIPO)|The Global Innovation Index]]

===== World Justice Project =====

*[[World Justice Project Rule of Law Index|Rule of Law Index]]

===== World Poverty Clock =====

* [[World Poverty Clock|World Poverty Clock Data]]

=====World Military Expenditures and Arms Transfers (WMEAT)=====

* [[World Military Expenditures and Arms Transfers (WMEAT)|World Military Expenditures and Arms Transfers (WMEAT) Reports]]

=====World Trade Organization (WTO)=====

* [[Dispute Statistics, WTO|Dispute Statistics]]

=====World Value Survey (WVS)=====

* [[World Value Survey|World Value Survey Data]]

==== [[Research Papers/Projects]] ====

==== [[Preprocessor|Preprocessors]] ====
[[Template]]

GeoDist, CEPII

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Pending Edits

Data Sources

2025-08-27T22:22:51Z

Yutang.Xiong:

===== Bulletin of the Atomic Scientists =====

* [[Bulletin of the Atomic Scientists Data]]

===== Barro-Lee =====
* [[Educational Attainment Dataset, Barro-Lee|Education Attainment Dataset]]

===== Bertelsmann Stiftung =====
* [[The Transformation Index (BTI)]]

===== Carbon Dioxide Information and Analysis Center (CDIAC) =====

* [[Carbon Dioxide Information and Analysis Center (CDIAC)|Carbon Emissions Data]]

===== Center for Systemic Peace =====

* [[Polity5]]
* [[Major Episodes of Political Violence (MEPV)]]
* [[State Fragility Index, Center for Systemic Peace|State Fragility Index]]
* [[Political Instability Task Force (PITF), Center for Systematic Peace|Political Instability Task Force (PITF)]]

===== Centre d'Etudes Prospectives et d'Informations Internationales =====

* [[BACI]]
* [[GeoDist]]

===== CIA =====

* [[CIA World Factbook]]

===== Climate Watch =====
* [[Emissions Data, Climate Watch|Emissions Data]]

===== Correlates of War =====
* [[The Correlates of War Project, Correlates of War|The Correlates of War Project]]

===== Demographic and Health Surveys (DHS) =====

* [[STATcompiler, Demographic and Health Surveys (DHS)|STATcompiler]]

===== Economist Intelligence Unit (EIU) =====

* [[Economist Intelligence Unit (EIU)|Economist Intelligence Unit (EIU) Data]]
* [[The Political Instability Index, Economist Intelligence Unit (EIU)|The Political Instability Index]]

===== Emissions Database for Global Atmospheric Research =====

* [[Emissions Database for Global Atmospheric Research Data]]

===== Instituut voor Milieuvraagstukken (IVM) =====

* [[Globally potentially available cropland data, Environmental Geography Instituut voor Milieuvraagstukken (IVM)|Globally potentially available cropland data]]

===== EMBER =====
* [[Electricity Data, EMBER|Electricity Data]]

===== Energy Institute =====

* [[Statistical Review of World Energy, Energy Institute|Statistical Review of World Energy]]

===== Europa World Yearbook =====

* [[Europa World Yearbook Data]]

===== European Commission =====

* [[Eurostat European Commission|Eurostat, European Commission]]

===== Federation of American Scientists =====

* [[Federation of American Scientists Data]]

===== Federal Institute for Geosciences and Natural Resources (BGR) =====

* [[Energy Data, BGR|Energy Data]]

===== Food and Agriculture Organization (FAO) =====

* [[AQUASTAT, FAO|AQUASTAT - FAO's Global Information System on Water and Agriculture]]
* [[FishstatJ, FAO|FishStatJ]]
* [[FAOSTAT Food Balance Sheet]]
* [[FAOSTAT Food Security and Nutrition]]
* [[FAOSTAT Land Use|FAOSTAT Land]]
* [[FAOSTAT SDG Indicators]]
* [[FAOSTAT Trade Crops and livestock products]]

===== Fraser Institute =====

* [https://pardeewiki.du.edu//index.php?title=Fraser_International Economic Data]

===== Freedom House =====

* [[Countries at the Crossroads, Freedom House|Countries at the Crossroads]]
* [[FreedomHouse|Freedom in the World]]
* [[Freedom on the Net, Freedom House|Freedom on the Net]]

===== Fund for Peace =====
* [[Fragile States Index, Fund for Peace|Fragile States Index]]

===== Global Carbon Budget (GCB) =====

* [[Carbon Data, Global Carbon Budget (GCB)|Carbon Data]]

===== Global Footprint Network =====

* [[Global Footprint Network|Global Footprint Network Data]]

===== Global Trade Analysis Project (GTAP) =====

* [[Global Trade Analysis Project (GTAP)|Global Trade Analysis Project (GTAP) Data]]

===== Heritage Foundation =====

* [[Heritage Foundation Data]]

===== International Groundwater Resources Assessment Centre (IGRAC) =====

* [[Global Depletion of Groundwater Resources, International Groundwater Resources Assessment Centre (IGRAC)|Global Depletion of Groundwater Resources]]

===== Institute for Employment Research (IAB) =====

* [[Brain Drain Data, Institute for Employment Research (IAB)|Brain Drain Data]]

===== International Centre for Tax & Development (ICTD) =====

* [[International Centre for Tax & Development (ICTD)|International Centre for Tax & Development (ICTD) Data]]

===== International Diabetes Federation =====

* [[Diabetes Atlas, International Diabetes Federation|Diabetes Atlas]]

===== [[International Energy Agency (IEA)]] =====
* [[End-Use Prices Data Explorer, International Energy Agency (IEA)|End-Use Prices Data Explorer]]
* [[World Energy Balances, International Energy Agency (IEA)|World Energy Balances]]
* [[World Energy Outlook, International Energy Agency (IEA)|World Energy Outlook]]
* [[World Energy Statistics, International Energy Agency (IEA)|World Energy Statistics]]

===== International Food Policy Research Institute =====

* [[International Food Policy Research Institute Data]]

===== Institute for Health Metrics and Evaluation (IHME) =====

* [[IHME|Global Burdens of Disease (GBD)]]

===== International Institute for Applied Systems Analysis (IIASA) =====

* [[Shared Socioeconomic Pathway (SSP) Extensions Explorer, International Institute for Applied Systems Analysis (IIASA)|Shared Socioeconomic Pathway (SSP) Extensions Explorer]]
* [[RCP Representative Concentration Pathways Database|RCP Database]]

===== International Institute for Management Development (IMD) =====

* [[International Institute for Management Development (IMD) Data]]

===== International Labor Organization (ILO) =====
*[[ILOstat]]
*[[ILO-WIEGO Statistical Reports]]

=====International Monetary Fund (IMF)=====

* [[Balance of Payments and International Investment Position statistics (BoP/IIP), IMF|Balance of Payments and International Investment Position statistics (BoP/IIP)]]
* [[Fiscal Monitor, IMF|Fiscal Monitor]]
* [[International Financial Statistics, IMF|International Financial Statistics]]
* [[World Economic Outlook, IMF|World Economic Outlook (WEO)]]
* [[IMF GFS Government Finance Statistics|Government Finance Statistics (GFS)]]
* [[IMF COFOG Classifications of Functions of Government|Classification of the Functions of Government (COFOG)]]

===== Inter-Parliamentary Union =====

* [[Women in Parliaments, Inter-Parliamentary Union|Women in Parliaments]]

===== International Renewable Energy Agency (IRENA) =====

* [[Energy Data, International Renewable Energy Agency (IRENA)|Energy Data]]

===== International Research & Exchanges Board (IREX) =====

* [[International Research & Exchanges Board (IREX) Data]]

===== International Study Center, Boston College =====

* [[TIMMS & PIRLS, International Study Center, Boston College|TIMSS & PIRLS]]

===== International Telecommunications Union (ITU) =====

* [[World Telecommunication Indicators Database, ITU|World Telecommunication Indicators Database]] = database has been discontinued
* [[International Telecommunication Union DataHub]]
* [[Global Cybersecurity Index (GCI), ITU|Global Cybersecurity Index (GCI)]]

===== International Organization of Motor Vehicle Manufacturers (OICA) =====

* [[International Organization of Motor Vehicle Manufacturers (OICA) Data]]

===== International Road Federation =====

* [[IRF World Road Statistics|World Road Statistics]]

===== Kellogg Institute For International Studies =====

* [[Kellogg Institute Data]]

===== LIS Cross-National Data Center =====

* [[LIS Cross-National Data Center Data]]

===== Mo Ibrahim Foundation =====

* [[Ibrahim Index of African Governance, Mo Ibrahim Foundation|Ibrahim Index of African Governance]]

===== MyGeoHub =====

* [[MY GEO HUB|Historical Irrigation Dataset (HID)]]

===== National Science Foundation (NSF) =====

* [[Science and Engineering Indicators, National Science Foundation (NSF)|Science and Engineering Indicators]]
* [[Science and Technology Studies (STS), National Science Foundation (NSF)|Science and Technology Studies (STS)]]

===== Organisation of Economic Cooperation and Development (OECD) =====
* International Transport Forum (ITF)
** [[Transport Data and Statistics, International Transport Forum (ITF)|Transport Data and Statistics]]
*[[OECD The Organisation for Economic Co-operation and Development|OECD Data Explorer]]
*[[OECD Factbook]]
*[[OECD Information Technology Outlook (ITO)|OECD Information Technology Outlook]]
* [[Measuring the Internet Economy, Organisation for Economic Co-operation and Development (OECD)|Measuring the Internet Economy]]
* [[Pensions at a Glance, Organisation for Economic Co-operation and Development (OECD)|Pensions at a Glance]]
* [[OECD Science & Technology|Science & Technology]]
* [[Social Institutions and Gender Index, OECD|Social Institutions and Gender Index]]
*[[Structural Analysis Database (STAN), OECD|Structural Analysis Database (STAN)]]

===== Political Terror Scale (PTS) =====

* [[Political Terror Scale (PTS) Data]]

===== Population Reference Bureau (PRB) =====

* [[Population Reference Bureau (PRB)|Assorted Annual Population Data Sheets]]

===== Potsdam Institute for Climate Impact Research (PIK) =====

* [[Climate Data, Potsdam Institute for Climate Impact Research (PIK)|Climate Data]]

===== PRIS Project =====

* [[IRIS, PRIS Project|IRIS]]

===== Reporters Without Borders =====

* [[Worldwide Press Freedom Index, Reporters Without Borders|Worldwide Press Freedom Index]]

===== Royal Netherlands Meteorological Institute (WMO KNMI) =====

* [[World Meteorological Organization Climate Explorer Reports]]

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

* [[Stockholm International Peace Research Institute (SIPRI)|Military Expenditure Data]]

===== SpeedTest =====

* [[SpeedTest Data]]

===== The Conference Board =====

* [[Total Economy Database, The Conference Board|Total Economy Database]]

===== Transparency International (TI) =====

* [[Transparency International|Corruption Perceptions Index]]

===== UBS Group AG =====

* [[Global Wealth Report, UBS Group AG|Global Wealth Report]]

===== Uppsala Conflict Data Program (UCDP) =====

* [[Uppsala Conflict Data Program (UCDP) Datasets]]
* [[UCDP/PRIO Armed Conflict Database|Uppsala Conflict Data Program (UCDP)/Peace Research Institute Oslo (PRIO) Armed Conflict Dataset]]

===== United Nations Economic Commission for Europe (UNECE) =====

* [[United Nations Economic Commission for Europe (UNECE) Data|United Nations Economic Commission for Europe Data]]

===== United Nations =====

* [[General Assembly of the United Nations (UNGA)]]
* [[Joint United Nations Programme on HIV/AIDS (UNAIDS)]]
* [[Millennium Development Goals Database, UN|Millennium Development Goals Database]]
* United Nations Department of Economic and Social Affairs
** [[United Nation World Marriage Data, United Nations Department of Economic and Social Affairs|United Nation World Marriage Data]]
* United Nations Educational, Scientific and Cultural Organization (UNESCO)
** [[Institute for Statistics, UNESCO (UIS)]]
* [[United Nations Industrial Development Organization (UNIDO)]]
* United Nations International Children's Emergency Fund (UNICEF)
** [[Child protection and development, UNICEF|Child protection and development]]
*[[United Nations Office of Drug and Crime (UNODC)]]
**[[United Nations Surveys on Crime Trends and the Operations of Criminal Justice Systems (UN-CTS), UNODC]]
*United Nations Population Division (UNPD)
** [[World Population Prospects (WPP), United Nations Population Division (UNPD)|World Population Prospects (WPP)]]
** [[UNDP Human Development Reports]]
*[[United Nation SDG Indicators Global Database (UNSDG)]]
*[[United Nations Statistics Division (UNSD)]]
**[[Environmental Indicators, UNSD|Environmental Indicators]]
*[[UN Trade and Development (UNCTAD)|United Nations Trade and Development (UNCTAD)]]
*[[United Nations Treaty Collection]]
*[[World Tourism Organization (UN Tourism)]]

===== University of Maryland =====

* [[Global Terrorism Database, University of Maryland|Global Terrorism Database]]
* [[Peace and Conflict Instability Ledger, University of Maryland/Center for International Development and Conflict Management (CIDCM)|Peace and Conflict Instability Ledger]]
* [[Minorities at risk|All Minorities at Risk (AMAR) Project]]

===== University of Pennsylvania =====

* [[The Political Constraint Index (POLCON), University of Pennsylvania|The Political Constraint Index (POLCON)]]

===== UN University World Institute for Development Economics Research (UNU WIDER) =====

* [[World Income Inequality Database, UN University World Institute for Development Economics Research (UNU WIDER)|World Income Inequality Database]]

===== US Energy Information Administration (EIA) =====

* [[Energy Information Administration (EIA)|Energy Information Administration Data]]

===== US Geological Survey =====

* [[World Petroleum Assessment, U.S. Geological Survey|World Petroleum Assessment]]

===== Vision of Humanity =====

* [[Vision of Humanity Data]]

===== Wittgenstein Centre =====

* [[Wittgenstein Centre Human Capital Data Explorer]]

===== World Bank =====

* [[The Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE), World Bank|Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE)]]
* [[Business Ready (B-READY), World Bank|Business Ready (B-READY)]]
* [[Climate Change Action Plan (CCAP), World Bank|Climate Change Action Plan (CCAP)]]
* [[Climate Change Knowledge Portal, World Bank|Climate Change Knowledge Portal]]
* [[Country Policy and Institutional Assessment (CPIA), World Bank|Country Policy and Institutional Assessment (CPIA)]]
* [[Database of Political Institutions, World Bank|Database of Political Institutions]]
* [[Education Statistics (EdStats), World Bank|Education Statistics (EdStats)]]
*[[Gender Statistics, World Bank|Gender Statistics]]
*[[Global Financial Development, World Bank|Global Financial Development]]
*[[Global Integrity Index]]
*[[Informal Economy Database]]
*[[International Development Association, World Bank|International Development Association]]
*[[Knowledge Economy Index, World Bank|Knowledge Economy Index]]
*[[Poverty and Inequality Platform, World Bank|Poverty and Inequality Platform]]
*[[Private Participation in Infrastructure (PPI) Project Database, World Bank|Private Participation in Infrastructure (PPI) Project Database]]
*[[Road Costs Knowledge System (ROCKS), World Bank|Road Costs Knowledge System (ROCKS)]]
*[[World Development Indicators, World Bank|World Development Indicators (WDI)]]
*[[World Development Report, World Bank|World Development Report]]
*[[Worldwide Governance Indicators|World Governance Indicators (WGI)]]

===== World Democratic Forum (WDF) =====

* [[World Democratic Forum (WDF) Data]]

===== World Economic Forum =====

*[[Global Competitiveness Index, World Economic Forum|Global Competitiveness Index]]

===== [[World Energy Council (WEC)]]=====

* [[Oil & Gas Journal, WEC]]

===== World Health Organization (WHO)=====

*[[Global Health Expenditure Database, WHO|Global Health Expenditure Database]]
*[[Joint Monitoring Programme, WHO/UNICEF|Joint Monitoring Programme (JMP)]]
*[[WHO Tobacco Free Initiative (TFI)]]
*[[Surveillance of chronic disease Risk Factors (SuRF), WHO|Surveillance of chronic disease Risk Factors (SuRF)]]
*[[World Malaria Report, WHO|World Malaria Report]]

* [[Global Health Observatory (GHO)]]
** [[Obesity, adult (18+) and child and adolescent (5-19)|Obesity]]

===== World Intellectual Property Organization (WIPO) =====
* [[The Global Innovation Index, World Intellectual Property Organization (WIPO)|The Global Innovation Index]]

===== World Justice Project =====

*[[World Justice Project Rule of Law Index|Rule of Law Index]]

===== World Poverty Clock =====

* [[World Poverty Clock|World Poverty Clock Data]]

=====World Military Expenditures and Arms Transfers (WMEAT)=====

* [[World Military Expenditures and Arms Transfers (WMEAT)|World Military Expenditures and Arms Transfers (WMEAT) Reports]]

=====World Trade Organization (WTO)=====

* [[Dispute Statistics, WTO|Dispute Statistics]]

=====World Value Survey (WVS)=====

* [[World Value Survey|World Value Survey Data]]

==== [[Research Papers/Projects]] ====

==== [[Preprocessor|Preprocessors]] ====
[[Template]]

Membership in the International System Database, Pardee Institute of Internationa Futures

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Pending edits

Civil War Database, Pardee Institue of International Futures

2025-08-27T22:14:39Z

Yutang.Xiong: Created page with "Pending edits"

Pending edits

Character of Bilateral Interaction in the International System (CBIIS)

2025-08-27T22:11:50Z

Yutang.Xiong: Created page with "Pending edits"

Pending edits

Clustering of Development Patterns

2025-08-27T22:11:08Z

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Pending edits.

Emissions Database for Global Atmospheric Research (EDGAR)

2025-08-21T19:58:32Z

Yutang.Xiong: Yutang.Xiong moved page Emissions Database for Global Atmospheric Research (EDGAR)) to Emissions Database for Global Atmospheric Research (EDGAR) without leaving a redirect

= Summary =
EDGAR, the Electronic Data Gathering, Analysis, and Retrieval system, is the primary system for companies and others submitting documents under the Securities Act of 1933, the Securities Exchange Act of 1934, the Trust Indenture Act of 1939, and the Investment Company Act of 1940.

It includes millions of company and individual filings, EDGAR benefits investors, corporations, and the U.S. economy overall by increasing the efficiency, transparency, and fairness of the securities markets.

= Tables In IFs =
{| class="wikitable"
|+
!Variable
!Definition
!Last IFs Update
!UsedInPreprocessor
|-
|EmissionsCH4EntericFermentation
|Methane emissions from enteric fermentation in gigagrams
|7/29/2019
|0
|-
|EmissionsCH4Oil&Gas
|Methane emissions from Oil and Gas production
|7/29/2019
|0
|-
|EmissionsCH4SolidFuels
|Methane emissions from solid fuels in gigagrams
|7/29/2019
|0
|-
|EmissionsCH4Other
|Methane emissions from other sources in gigagrams
|7/29/2019
|0
|-
|EmissionsCH4RiceCultivation
|Methane emissions from rice cultivation
|7/29/2019
|0
|-
|EmissionsCH4WasteWater
|Methane emissions from waste water handling
|7/29/2019
|0
|}

= Data Pulling Instructions =

#Go to https://www.sec.gov/edgar/search/
#Search

= Data Notes =

Emissions Database for Global Atmospheric Research (EDGAR)

2025-08-21T19:58:11Z

Yutang.Xiong: Yutang.Xiong moved page Electronic Data Gathering, Analysis, and Retrieval (EDGAR) Data to Emissions Database for Global Atmospheric Research (EDGAR)) without leaving a redirect: correction of data source name

Carbon Data, Global Carbon Budget (GCB)

2025-08-05T18:41:07Z

Yutang.Xiong:

= Summary =
The Global Carbon Budget was founded by the Global Carbon Project international science team to track the trends in global carbon emissions and sinks and is a key measure of progress towards the goals of the Paris Agreement. The Global Carbon Budget Office is led by Professor Pierre Friedlingstein from the University of Exeter’s Global Systems Institute with the support of more than 100 people from 70 organizations in 18 countries. The budget is updated annually and published at the COP meetings every year. The source includes data for CO^2 emissions from coal, oil, gas, cement, flaring, bunkers, and the sum of all of those.

In July of 2025, the table SeriesEmissionsCarbonGCP was updated to replace EmissionsCarbonCDIAC as this series only provides data up to 2021. The Global Carbon Budget is updated annually ensuring data for more recent years. However, the Global Carbon Budget data only goes back to 1850 whereas EmissionsCarbonCDIAC has data up until 1751. Therefore, the research aide must blend the years 1751-1849 into SeriesEmissionsCarbonGCP.

==== Methodology: ====
The GCB uses CDIAC-FF’s data extended by 2–3 years using energy growth rates derived from data published by the Energy Institute as the starting point of the dataset. The GCB does do refinements if: there are official estimates available from developed countries as there is an assumption that those are of higher quality, if estimates from CDIAC-FF are in clear disagreement with those from other sources (like the IEA for example), if there is a final-year data available that provide higher quality estimates than by using growth rates derived from EI, and if the CDIAC-FF’s data contain implausible values.

= Table In IFs =
{| class="wikitable"
|+
!Variable
!Definition
!Extended Source Definition
!UsedInPreprocessor
!UsedInPreprocessorFileName
|-
|SeriesEmissionsCarbonGCP
|Fossil CO2 emissions by country (territorial) without land use.
|National estimates include emissions from fossil fuel combustion and oxidation and cement production and excludes emissions from bunker fuels.
|1
|ENVIRONMENT
|}

= Data Pulling =

# Navigate to the site: https://globalcarbonbudget.org/
# The data is located in the boxed section. As the data is updated the year will change (next edition will be GCB 2025).[[File:GCB Home Page.png|none|thumb|600x600px]]
# Once you are in the page, scroll down until you get to the Datasets section. Click on the National Fossil Carbon Emissions Excel. The data will be in the sheet "Territorial Emissions." The data must be divided by 1,000 since the unit in the sheet is million tonnes of carbon and the unit in IFs is billion tonnes of carbon.[[File:Dataset Needed from GCP .png|none|thumb|600x600px]]
# If you would like to use a script to pull the data and calculate there is one linked [https://github.com/n1shamin/Global-Carbon-Budget- here].

Data Sources

2025-06-04T23:16:19Z

Yutang.Xiong:

===== Barro-Lee =====
* [[Educational Attainment Dataset, Barro-Lee|Education Attainment Dataset]]

===== Bertelsmann Stiftung =====
* [[The Transformation Index (BTI)]]

===== Carbon Dioxide Information and Analysis Center (CDIAC) =====

* [[Carbon Dioxide Information and Analysis Center (CDIAC)|Carbon Emissions Data]]

===== Center for Systemic Peace =====

* [[Polity5]]

===== Energy Institute =====

* [[Statistical Review of World Energy, Energy Institute|Statistical Review of World Energy]]

===== Federal Institute for Geosciences and Natural Resources (BGR) =====

* [[Energy Data, BGR|Energy Data]]

===== Food and Agriculture Organization (FAO) =====

* [[AQUASTAT, FAO|AQUASTAT - FAO's Global Information System on Water and Agriculture]]
* [[FishstatJ, FAO|FishStatJ]]
* [[FAOSTAT Food Balance Sheet]]
* [[FAOSTAT Food Security and Nutrition]]
* [[FAOSTAT Land Use|FAOSTAT Land]]
* [[FAOSTAT SDG Indicators]]
* [[FAOSTAT Trade Crops and livestock products]]

===== International Diabetes Federation =====

* [[International Diabetes Federation|Prevalence of Diabetes]]

=====International Monetary Fund (IMF)=====

* [[Balance of Payments and International Investment Position statistics (BoP/IIP), IMF|Balance of Payments and International Investment Position statistics (BoP/IIP)]]
* [[Fiscal Monitor, IMF|Fiscal Monitor]]
* [[World Economic Outlook, IMF|World Economic Outlook (WEO)]]

===== International Telecommunications Union (ITU) =====

* [[World Telecommunication Indicators Database, ITU|World Telecommunication Indicators Database]]

===== Transparency International (TI) =====

* [[Transparency International|Corruption Perceptions Index]]

===== United Nations =====

* [[Joint United Nations Programme on HIV/AIDS (UNAIDS)]]
* [[World Population Prospects (WPP), United Nations Population Division (UNPD)]]
* United Nations Educational, Scientific and Cultural Organization (UNESCO)
** [[Institute for Statistics, UNESCO (UIS)]]
* United Nations International Children's Emergency Fund (UNICEF)
** [[Child protection and development, UNICEF|Child protection and development]]

===== World Bank =====

* [[The Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE), World Bank|The Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE)]]
* [[Business Ready (B-READY), World Bank|Business Ready (B-READY)]]
* [[Education Statistics (EdStats), World Bank|Education Statistics (EdStats)]]
*[[Gender Statistics, World Bank|Gender Statistics]]
*[[Informal Economy Database]]
*[[Poverty and Inequality Platform, World Bank|Poverty and Inequality Platform]]
*[[World Development Indicators, World Bank|World Development Indicators (WDI)]]

===== World Health Organization (WHO)=====

*[[Global Health Expenditure Database, WHO|Global Health Expenditure Database]]
*[[Joint Monitoring Programme, WHO/UNICEF|Joint Monitoring Programme (JMP)]]

=====World Value Survey (WVS)=====

Statistical Review of World Energy, Energy Institute

2025-06-04T22:32:14Z

Yutang.Xiong: Yutang.Xiong moved page Statistical Review of World Energy, Energy Institute (EI) to Statistical Review of World Energy, Energy Institute without leaving a redirect

= Series pulled from Statistical Review of World Energy =

{| cellspacing="0" border="1" bgcolor="#ffffff"
|+ '''Copy Of DataDict'''
|-
! bgcolor="#c0c0c0" | Table
! bgcolor="#c0c0c0" | Group
! bgcolor="#c0c0c0" | Definition
! bgcolor="#c0c0c0" | Years
! bgcolor="#c0c0c0" | Source
! bgcolor="#c0c0c0" | Last IFs Update
! bgcolor="#c0c0c0" | UsedInPreprocessor
|-
!SeriesEnReserOilBP
!Energy, Infrastructure
!Oil reserves
!1980-2020
!BP‘s Statistical Review of World Energy
!2024/02/12
!Yes
|-
!SeriesEnProdOilBP
!Energy, Infrastructure
!Oil production
!1980-2020
!BP‘s Statistical Review of World Energy
!2024/02/12
!Yes
|-
!SeriesEnReserGasBP
!Energy, Infrastructure
!Gas (natural) reserves
!1980-2020
!BP‘s Statistical Review of World Energy
!2024/02/12
!Yes
|-
!SeriesEnProdGasBP
!Energy, Infrastructure
!Gas (natural) production
!1980-2020
!BP‘s Statistical Review of World Energy
!2024/02/12
!Yes
|-
!SeriesEnProdCoalBP
!Energy, Infrastructure
!Coal production
!1980-2020
!BP‘s Statistical Review of World Energy
!2024/02/12
!Yes
|-
!SeriesEnConNucBP
!Energy, Infrastructure
!Nuclear-electricity consumption
!1980-2020
!BP‘s Statistical Review of World Energy
!2024/02/12
!Yes
|-
!SeriesEnConHydroBP
!Energy, Infrastructure
!Hydro-electricity consumption
!1980-2020
!BP‘s Statistical Review of World Energy
!2024/02/12
!Yes
|- valign="top"
| '''SeriesEnReserCoalBP'''
| '''Energy, Infrastructure'''
| '''Coal reserves'''
| '''2007-2014'''
| '''BP’s Statistical Review of World Energy 2013'''
| '''2016/01/27'''
| align="right" | '''Yes'''
|- valign="top"
| SeriesEnConCoalBP
| Energy, Infrastructure
| Coal consumption
| 1965-2014
| BP’s Statistical Review of World Energy 2013
| 2016/01/27
| align="right" | No
|- valign="top"
| SeriesEnConGeothBioBP
| Energy, Infrastructure
| Geothermal, biomass and other renewable energy consumption
| 1965-2014
| BP’s Statistical Review of World Energy 2013
| 2016/01/27
| align="right" | No
|- valign="top"
| SeriesEnConSolarBP
| Energy, Infrastructure
| Solar energy consumption
| 1965-2014
| BP’s Statistical Review of World Energy 2013
| 2016/01/27
| align="right" | No
|- valign="top"
| SeriesEnInstGeothermalBP
| Energy, Infrastructure
| Cumulative installed geothermal power capacity
| 1975,1980,1985,1990,1995,2000-2014
| BP’s Statistical Review of World Energy 2013
| 2016/01/27
| align="right" | No
|- valign="top"
| SeriesEnInstSolarBP
| Energy, Infrastructure
| Cumulative installed photovoltaic (PV) power
| 1996-2014
| BP’s Statistical Review of World Energy 2013
| 2016/01/27
| align="right" | No
|- valign="top"
| SeriesEnInstWindBP
| Energy, Infrastructure
| Cumulative installed wind turbine capacity
| 1995-2014
| BP’s Statistical Review of World Energy 2013
| 2016/01/27
| align="right" | No
|- valign="top"
| SeriesEnProdBioBP
| Energy, Infrastructure
| Biofuels production
| 1990-2014
| BP’s Statistical Review of World Energy 2013
| 2016/01/27
| align="right" | No
|- valign="top"
| SeriesEnConWindBP
| Energy, Infrastructure
| Wind energy consumption
| 1965-2014
| BP’s Statistical Review of World Energy 2013
| 2016/01/27
| align="right" | No
|- valign="top"
| SeriesEnConGasBP
| Energy, Infrastructure
| Natural gas consumption
| 1965-2014
| BP’s Statistical Review of World Energy 2013
| 2016/01/27
| align="right" | No
|- valign="top"
| SeriesEnConOilBP
| Energy, Infrastructure
| Oil consumption
| 1965-2014
| BP’s Statistical Review of World Energy 2013
| 2016/01/27
| align="right" | No
|}

= Instructions on importing data =

*For the BP energy data, it is fairly straight forward. I pulled it all from the BP website [https://www.energyinst.org/statistical-review/resources-and-data-downloads Resources and data downloads | Statistical Review of World Energy (energyinst.org)]
*I used all of the conversions that are listed in the DataDict and each one works well. When preparing the data, please compare the units from the original source and the units in the DataDict. Use the BBOE_UnitConversions.xlsx to transform the data to unified units (BBOE) in IFs. The latest update date for the BBOE_UnitConversion was 2024/02/12.
**For example, the unit from BP for oil production is in MTOE (Million tonnes of oil equivalent). We need to convert that to BBOE (Billion Barrels of Oil Equivalent), which is why we multiply by those numbers in the Formula.
**The conversion factors can be found here: [https://www.iea.org/data-and-statistics/data-tools/unit-converter Unit Converter – Data Tools - IEA] or [Https://www.convert-me.com/en/convert/energy/?u=bboe&v=1 https://www.convert-me.com/en/convert/energy/?u=bboe&v=1]
*''Note: Russian Federation, it was called the USSR before 1991 and we need to consolidate it into one country when pulling the data.''

Data Sources

2025-06-04T22:32:05Z

Yutang.Xiong:

===== Barro-Lee =====
* [[Educational Attainment Dataset, Barro-Lee|Education Attainment Dataset]]

===== Bertelsmann Stiftung =====
* [[The Transformation Index (BTI)]]

===== Carbon Dioxide Information and Analysis Center (CDIAC) =====

* [[Carbon Dioxide Information and Analysis Center (CDIAC)|Carbon Emissions Data]]

===== Energy Institute =====

* [[Statistical Review of World Energy, Energy Institute|Statistical Review of World Energy]]

===== Federal Institute for Geosciences and Natural Resources (BGR) =====

* [[Energy Data, BGR|Energy Data]]

===== Food and Agriculture Organization (FAO) =====

* [[AQUASTAT, FAO|AQUASTAT - FAO's Global Information System on Water and Agriculture]]
* [[FishstatJ, FAO|FishStatJ]]
* [[FAOSTAT Food Balance Sheet]]
* [[FAOSTAT Food Security and Nutrition]]
* [[FAOSTAT Land Use|FAOSTAT Land]]
* [[FAOSTAT SDG Indicators]]
* [[FAOSTAT Trade Crops and livestock products]]

===== International Diabetes Federation =====

* [[International Diabetes Federation|Prevalence of Diabetes]]

=====International Monetary Fund (IMF)=====

* [[Balance of Payments and International Investment Position statistics (BoP/IIP), IMF|Balance of Payments and International Investment Position statistics (BoP/IIP)]]
* [[Fiscal Monitor, IMF|Fiscal Monitor]]
* [[World Economic Outlook, IMF|World Economic Outlook (WEO)]]

===== International Telecommunications Union (ITU) =====

* [[World Telecommunication Indicators Database, ITU|World Telecommunication Indicators Database]]

===== Transparency International (TI) =====

* [[Transparency International|Corruption Perceptions Index]]

===== United Nations =====

* [[Joint United Nations Programme on HIV/AIDS (UNAIDS)]]
* [[World Population Prospects (WPP), United Nations Population Division (UNPD)]]
* United Nations Educational, Scientific and Cultural Organization (UNESCO)
** [[Institute for Statistics, UNESCO (UIS)]]
* United Nations International Children's Emergency Fund (UNICEF)
** [[Child protection and development, UNICEF|Child protection and development]]

===== World Bank =====

* [[The Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE), World Bank|The Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE)]]
* [[Business Ready (B-READY), World Bank|Business Ready (B-READY)]]
* [[Education Statistics (EdStats), World Bank|Education Statistics (EdStats)]]
*[[Gender Statistics, World Bank|Gender Statistics]]
*[[Informal Economy Database]]
*[[Poverty and Inequality Platform, World Bank|Poverty and Inequality Platform]]
*[[World Development Indicators, World Bank|World Development Indicators (WDI)]]

===== World Health Organization (WHO)=====

*[[Global Health Expenditure Database, WHO|Global Health Expenditure Database]]
*[[Joint Monitoring Programme, WHO/UNICEF|Joint Monitoring Programme (JMP)]]

=====World Value Survey (WVS)=====

Statistical Review of World Energy, Energy Institute

2025-06-04T22:30:56Z

Yutang.Xiong: /* Series pulled from BP */

Statistical Review of World Energy, Energy Institute

2025-06-04T22:30:22Z

Yutang.Xiong: Yutang.Xiong moved page BP data to Statistical Review of World Energy, Energy Institute (EI) without leaving a redirect

= Series pulled from BP =

{| cellspacing="0" border="1" bgcolor="#ffffff"
|+ '''Copy Of DataDict'''
|-
! bgcolor="#c0c0c0" | Table
! bgcolor="#c0c0c0" | Group
! bgcolor="#c0c0c0" | Definition
! bgcolor="#c0c0c0" | Years
! bgcolor="#c0c0c0" | Source
! bgcolor="#c0c0c0" | Last IFs Update
! bgcolor="#c0c0c0" | UsedInPreprocessor
|-
!SeriesEnReserOilBP
!Energy, Infrastructure
!Oil reserves
!1980-2020
!BP‘s Statistical Review of World Energy
!2024/02/12
!Yes
|-
!SeriesEnProdOilBP
!Energy, Infrastructure
!Oil production
!1980-2020
!BP‘s Statistical Review of World Energy
!2024/02/12
!Yes
|-
!SeriesEnReserGasBP
!Energy, Infrastructure
!Gas (natural) reserves
!1980-2020
!BP‘s Statistical Review of World Energy
!2024/02/12
!Yes
|-
!SeriesEnProdGasBP
!Energy, Infrastructure
!Gas (natural) production
!1980-2020
!BP‘s Statistical Review of World Energy
!2024/02/12
!Yes
|-
!SeriesEnProdCoalBP
!Energy, Infrastructure
!Coal production
!1980-2020
!BP‘s Statistical Review of World Energy
!2024/02/12
!Yes
|-
!SeriesEnConNucBP
!Energy, Infrastructure
!Nuclear-electricity consumption
!1980-2020
!BP‘s Statistical Review of World Energy
!2024/02/12
!Yes
|-
!SeriesEnConHydroBP
!Energy, Infrastructure
!Hydro-electricity consumption
!1980-2020
!BP‘s Statistical Review of World Energy
!2024/02/12
!Yes
|- valign="top"
| '''SeriesEnReserCoalBP'''
| '''Energy, Infrastructure'''
| '''Coal reserves'''
| '''2007-2014'''
| '''BP’s Statistical Review of World Energy 2013'''
| '''2016/01/27'''
| align="right" | '''Yes'''
|- valign="top"
| SeriesEnConCoalBP
| Energy, Infrastructure
| Coal consumption
| 1965-2014
| BP’s Statistical Review of World Energy 2013
| 2016/01/27
| align="right" | No
|- valign="top"
| SeriesEnConGeothBioBP
| Energy, Infrastructure
| Geothermal, biomass and other renewable energy consumption
| 1965-2014
| BP’s Statistical Review of World Energy 2013
| 2016/01/27
| align="right" | No
|- valign="top"
| SeriesEnConSolarBP
| Energy, Infrastructure
| Solar energy consumption
| 1965-2014
| BP’s Statistical Review of World Energy 2013
| 2016/01/27
| align="right" | No
|- valign="top"
| SeriesEnInstGeothermalBP
| Energy, Infrastructure
| Cumulative installed geothermal power capacity
| 1975,1980,1985,1990,1995,2000-2014
| BP’s Statistical Review of World Energy 2013
| 2016/01/27
| align="right" | No
|- valign="top"
| SeriesEnInstSolarBP
| Energy, Infrastructure
| Cumulative installed photovoltaic (PV) power
| 1996-2014
| BP’s Statistical Review of World Energy 2013
| 2016/01/27
| align="right" | No
|- valign="top"
| SeriesEnInstWindBP
| Energy, Infrastructure
| Cumulative installed wind turbine capacity
| 1995-2014
| BP’s Statistical Review of World Energy 2013
| 2016/01/27
| align="right" | No
|- valign="top"
| SeriesEnProdBioBP
| Energy, Infrastructure
| Biofuels production
| 1990-2014
| BP’s Statistical Review of World Energy 2013
| 2016/01/27
| align="right" | No
|- valign="top"
| SeriesEnConWindBP
| Energy, Infrastructure
| Wind energy consumption
| 1965-2014
| BP’s Statistical Review of World Energy 2013
| 2016/01/27
| align="right" | No
|- valign="top"
| SeriesEnConGasBP
| Energy, Infrastructure
| Natural gas consumption
| 1965-2014
| BP’s Statistical Review of World Energy 2013
| 2016/01/27
| align="right" | No
|- valign="top"
| SeriesEnConOilBP
| Energy, Infrastructure
| Oil consumption
| 1965-2014
| BP’s Statistical Review of World Energy 2013
| 2016/01/27
| align="right" | No
|}

= Instructions on importing data =

*For the BP energy data, it is fairly straight forward. I pulled it all from the BP website [https://www.energyinst.org/statistical-review/resources-and-data-downloads Resources and data downloads | Statistical Review of World Energy (energyinst.org)]
*I used all of the conversions that are listed in the DataDict and each one works well. When preparing the data, please compare the units from the original source and the units in the DataDict. Use the BBOE_UnitConversions.xlsx to transform the data to unified units (BBOE) in IFs. The latest update date for the BBOE_UnitConversion was 2024/02/12.
**For example, the unit from BP for oil production is in MTOE (Million tonnes of oil equivalent). We need to convert that to BBOE (Billion Barrels of Oil Equivalent), which is why we multiply by those numbers in the Formula.
**The conversion factors can be found here: [https://www.iea.org/data-and-statistics/data-tools/unit-converter Unit Converter – Data Tools - IEA] or [Https://www.convert-me.com/en/convert/energy/?u=bboe&v=1 https://www.convert-me.com/en/convert/energy/?u=bboe&v=1]
*''Note: Russian Federation, it was called the USSR before 1991 and we need to consolidate it into one country when pulling the data.''

Data Sources

2025-06-04T22:22:12Z

Yutang.Xiong:

===== Barro-Lee =====
* [[Educational Attainment Dataset, Barro-Lee|Education Attainment Dataset]]

===== Bertelsmann Stiftung =====
* [[The Transformation Index (BTI)]]

===== Carbon Dioxide Information and Analysis Center (CDIAC) =====

* [[Carbon Dioxide Information and Analysis Center (CDIAC)|Carbon Emissions Data]]

===== Federal Institute for Geosciences and Natural Resources (BGR) =====

* [[Energy Data, BGR|Energy Data]]

===== Food and Agriculture Organization (FAO) =====

* [[AQUASTAT, FAO|AQUASTAT - FAO's Global Information System on Water and Agriculture]]
* [[FishstatJ, FAO|FishStatJ]]
* [[FAOSTAT Food Balance Sheet]]
* [[FAOSTAT Food Security and Nutrition]]
* [[FAOSTAT Land Use|FAOSTAT Land]]
* [[FAOSTAT SDG Indicators]]
* [[FAOSTAT Trade Crops and livestock products]]

===== International Diabetes Federation =====

* [[International Diabetes Federation|Prevalence of Diabetes]]

=====International Monetary Fund (IMF)=====

* [[Balance of Payments and International Investment Position statistics (BoP/IIP), IMF|Balance of Payments and International Investment Position statistics (BoP/IIP)]]
* [[Fiscal Monitor, IMF|Fiscal Monitor]]
* [[World Economic Outlook, IMF|World Economic Outlook (WEO)]]

===== International Telecommunications Union (ITU) =====

* [[World Telecommunication Indicators Database, ITU|World Telecommunication Indicators Database]]

===== Transparency International (TI) =====

* [[Transparency International|Corruption Perceptions Index]]

===== United Nations =====

* [[Joint United Nations Programme on HIV/AIDS (UNAIDS)]]
* [[World Population Prospects (WPP), United Nations Population Division (UNPD)]]
* United Nations Educational, Scientific and Cultural Organization (UNESCO)
** [[Institute for Statistics, UNESCO (UIS)]]
* United Nations International Children's Emergency Fund (UNICEF)
** [[Child protection and development, UNICEF|Child protection and development]]

===== World Bank =====

* [[The Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE), World Bank|The Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE)]]
* [[Business Ready (B-READY), World Bank|Business Ready (B-READY)]]
* [[Education Statistics (EdStats), World Bank|Education Statistics (EdStats)]]
*[[Gender Statistics, World Bank|Gender Statistics]]
*[[Informal Economy Database]]
*[[Poverty and Inequality Platform, World Bank|Poverty and Inequality Platform]]
*[[World Development Indicators, World Bank|World Development Indicators (WDI)]]

===== World Health Organization (WHO)=====

*[[Global Health Expenditure Database, WHO|Global Health Expenditure Database]]
*[[Joint Monitoring Programme, WHO/UNICEF|Joint Monitoring Programme (JMP)]]

=====World Value Survey (WVS)=====