The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolongued development arc in Sub-Saharan Africa.

The United Nations Population Division is a part of the United Nations Department of Economic and Social Affairs (UNDESA). Its primary mission is to provide timely and accurate demographic information and analysis to assist countries in making informed policy decisions related to population and development. The division produces a wide range of demographic data, reports, and publications, and it serves as a key source of information on global population trends.

Some of the main functions and activities of the United Nations Population Division include:

1. Data Collection and Analysis: The division collects and compiles data on population, fertility, mortality, migration, and other demographic variables from member states and other international sources. It analyzes this data to track global demographic trends and provides population estimates and projections.

2. World Population Prospects: The division publishes the "World Population Prospects," which is a comprehensive set of demographic data and projections for countries around the world. This report is regularly updated and is widely used by governments, researchers, and policymakers.

3. Demographic Research: The division conducts research on a wide range of demographic issues, including aging populations, urbanization, family planning, and more. This research helps to inform policies and programs aimed at addressing demographic challenges.

4. Technical Assistance: The division provides technical assistance to countries in areas related to population and development, including capacity building, data collection, and analysis.

5. Reports and Publications: The division produces a variety of reports, publications, and working papers on demographic topics. These resources are made available to the public and serve as valuable references for researchers and policymakers.

6. Population Conferences: The United Nations Population Division plays a role in organizing and supporting international conferences and events related to population and development issues. These conferences provide a platform for countries to discuss and coordinate actions to address demographic challenges.

Overall, the United Nations Population Division plays a crucial role in monitoring and understanding global demographic trends and supporting countries in their efforts to develop policies and programs that promote sustainable development and address population-related challenges.

General Information

The Pop-AUT database was developed for the DISCC-AT project, which required subnational population projections for Austria consistent with the updated Shared Socio-Economic Pathways (SSPs). For this database, the most recent version of the nationwide SSP population projections (IIASA-WiC POP 2023) are spatially downscaled, offering a detailed perspective at the subnational level in Austria. Recognizing the relevance of this information for a wider audience, the data has been made publicly accessible through an interactive dashboard. There, users are invited to explore how the Austrian population is projected to evolve under different SSP scenarios until the end of this century.

Methodology

The downscaling process of the nationwide Shared Socioeconomic Pathways (SSP) population projections is a four-step procedure developed to obtain subnational demographic projections for Austria. In the first step, population potential surfaces for Austria are derived. These indicate the attractiveness of a location in terms of habitability and are obtained using machine learning techniques, specifically random forest models, along with geospatial information such as land use, roads, elevation, distance to cities, and elevation (see, e.g., Wang et al. 2023).

The population potential surfaces play a crucial role in distributing the Austrian population effectively across the country. Calculations are based on the 1×1 km spatial resolution database provided by Wang et al. (2023), covering all SSPs in 5-year intervals from 2020 to 2100.

Moving to the second step, the updated nationwide SSP population projections for Austria (IIASA-WiC POP 2023) are distributed to all 1×1 km grid cells within the country. This distribution is guided by the previously computed grid cell-level population potential surfaces, ensuring a more granular representation of demographic trends.

The base year for all scenarios is 2015, obtained by downscaling the UN World Population Prospects 2015 count for Austria using the WorldPop (2015) 1×1 km population count raster.

In the third step, the 1×1 km population projections are temporally interpolated to obtain yearly projections for all SSP scenarios spanning the period from 2015 to 2100.

The final step involves the spatial aggregation of the gridded SSP-consistent population projections to the administrative levels of provinces (Bundesländer), districts (Bezirke), and municipalities (Gemeinden).

Dashboard

The data can be explored interactively through a dashboard.

Data Inputs

Updated nationwide SSP population projections: IIASA-WiC POP (2023) (https://zenodo.org/records/7921989)

Population potential surfaces: Wang, X., Meng, X., & Long, Y. (2022). Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways. Scientific Data, 9(1), 563.

Shapefiles: data.gv.at

WorldPop 2015: WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00647

Version

This is version 1.0, built upon the Review-Phase 2 version of the updated nationwide SSP population projections (IIASA-WiC POP 2023). Once these projections are revised, this dataset will be accordingly updated.

File Organization

The SSP-consistent population projections for Austria are accessible in two formats: .csv files for administrative units (provinces = Bundesländer, districts = Politische Bezirke, municipalities = Gemeinden) and 1×1 km raster files in GeoTIFF and NetCDF formats. All files encompass annual population counts spanning from 2015 to 2100.

Data contains 2022 world population data publised by the UN DESA for six most populous countries of the world. File also contains the analysis of decomposition of demographic indicators on population growth.

Before 2025, the world's total population is expected to reach eight billion. Furthermore, it is predicted to reach over 10 billion in 2060, before slowing again as global birth rates are expected to decrease. Moreover, it is still unclear to what extent global warming will have an impact on population development. A high share of the population increase is expected to happen on the African continent.

The spatial raster dataset depicts the distribution of population, expressed as the number of people per cell. Residential population estimates between 1975 and 2020 in 5 years intervals and projections to 2025 and 2030 derived from CIESIN GPWv4.11 were disaggregated from census or administrative units to grid cells, informed by the distribution, density, and classification of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch. This dataset is an update of the product released in 2022. Major improvements are the following: use of built-up volume maps (GHS-BUILT-V R2022A); use of more recent and detailed population estimates derived from GPWv4.11 integrating both UN World Population Prospects 2022 country population data and World Urbanisation Prospects 2018 data on Cities; revision of GPWv4.11 population growthrates by convergence to upper administrative level growthrates; systematic improvement of census coastlines; systematic revision of census units declared as unpopulated; integration of non-residential built-up volume information (GHS-BUILT-V_NRES R2023A); spatial resolution of 100m Mollweide (and 3 arcseconds in WGS84); projections to 2030.

These data include gridded estimates of population at approximately 1km for 2021. These datasets results were produced based on using the spatial distribution of unconstrained and constrained population datasets for individual countries for 2020. Country totals were adjusted to match the corresponding official United Nations population estimates, prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (World Population Prospects 2022).

Countries used for estimates of bilateral international migration flows based on methods presented in Abel & Cohen (2019) and Abel & Cohen (2022). The countries in the list correspond to both the estimates in the Figshare collection for the total bilateral international migration flow estimates and the Figshare collection for the sex-specifc bilateral international migration flow estimates.Version DetailsThe countries in the list are for the update of estimates of international migration flows based on the most recent published UN DESA International Migrant Stock (IMS2024) and World Population Prospects (WPP2024) data inputs. Refer to the version history for previous country list files based on older versions of the IMS and WPP data.

The demographic indicators of the People’s Republic of China, Hong Kong, Macao, and Taiwan were compiled from (1) the World Bank United Nations (UN) Population Division, World Population Prospects: 2022 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) UN Statistical Division. Population and Vital Statistics Report (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Program. The dataset consists of descriptive demographic statistics of the People’s Republic of China, Hong Kong, Macao, and Taiwan and includes the following indicators: (1) total population, (2) population by broad age groups, (3) annual rate of population change, (4) crude birth rate and crude death rate, (5) annual number of births and deaths, (6) total fertility, (7) mortality under age 5, (8) life expectancy at birth by sex, (9) life expectancy at birth (both sexes combined), (10) annual natural change and net migration, (11) population by age and sex: 2101, (12) annual number of deaths per 1,000 population, and (13) annual number of deaths.

Total current population for Madagascar in 2025 is 31,797,306, a 2.38% increase from 2024.

<ul style='margin-top:20px;'>

<li>Total population for Madagascar in 2024 was <strong>31,056,610</strong>, a <strong>0.45% decline</strong> from 2023.</li>
<li>Total population for Madagascar in 2023 was <strong>31,195,932</strong>, a <strong>2.49% increase</strong> from 2022.</li>
<li>Total population for Madagascar in 2022 was <strong>30,437,261</strong>, a <strong>2.51% increase</strong> from 2021.</li>
</ul>Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.

We assess the potential impact of international migration on population ageing in Asian countries by estimating replacement migration for the period 2022-2050.

This open data deposit contains the code (R-scripts) and the datasets (csv-files) for the replacement migration scenarios and a zero-migration scenario:

• Constant chronological old-age dependency ratio (Constant OADR scenario)
• Constant prospective old-age dependency ratio (Constant POADR scenario)
• Constant chronological working-age population (Constant WA scenario)
• Constant prospective working-age population (Constant PWA scenario)
• Zero-migration (ZM scenario)

Countries included in the analysis: Armenia, China, Georgia, Hong Kong, Japan, Macao, North Korea, Singapore, South Korea, Taiwan, Thailand.

Please note that for Armenia and Hong Kong (2023) and Georgia (2024) later baseline years are applied due to the UN country-specific assumptions on post-Covid-19 mortality.

For detailed information about the scenarios and parameters:

Dörflinger, M., Potancokova, M., Marois, G. (2024): The potential impact of international migration on prospective population ageing in Asian countries. Asian Population Studies. https://doi.org/10.1080/17441730.2024.2436201

All underlying data (UN World Population Prospects 2022) are openly available at:

https://population.un.org/wpp/Download/Archive

Code

1_Data.R:

• Load and merge data from UN World Population Prospects 2022
• Define sample
• Prepare data (prospective old-age thresholds, model sex and age pattern of migrants)

2_Scenarios.R:

• Replacement migration scenarios:
  • Constant chronological old-age dependency
  • Constant prospective old-age dependency
  • Constant chronological working-age population
  • Constant prospective working-age population
• Zero-migration scenario

3_Robustness_checks.R:

• Run replacement migrations scenarios with different model sex and age patterns for net migration

Program version used: RStudio "Chocolate Cosmos" (e4392fc9, 2024-06-05). Files may not be compatible with other versions.

Datasets

The datasets contain the key information on population size, the relevant indicators (OADR, POADR, WA, PWA) and replacement migration volumes and rates by country and year. Please see readme_datasets.txt for detailed information.

Acknowledgements

Part of the research was developed in the Young Scientists Summer Program at the International Institute for Applied Systems Analysis, Laxenburg (Austria) with financial support from the German National Member Organization.

The MacroDemography Database is an ongoing project aimed combining long-run macroeconomic and demographic data in a readily usable format for researchers aiming to explore questions relating to the relationship between economies and their underlying age structure. By enriching the set of demographic variables used in long run economic analysis while taking advantage of the contribution of the contribution of the Jordà-Schularick-Taylor Macrohistory Database in providing a rich set of macroeconomic variables for a panel of countries it is possible to revisit many questions in the literature on the economic implications of population aging that were not possible in the past. My aim is to continue to update this database with various sources of demographic variable to improve the completeness of the panel over time. At present the data contains an unbalanced panel of 18 countries spanning the time period from 1870 to 2018. The dataset "MacroDemography.dta" is the main data, while "MacroDemography_wProjections" appends median variant projections for population data from 2020 to 2100. This dataset uses the following sources that should be cited when using the relevant statistics. Please visit the source websites for more information and https://www.josephkopecky.com/ where updates will be regularly posted. For Macroeconomic Data:Òscar Jordà, Moritz Schularick, and Alan M. Taylor. 2017. “Macrofinancial History and the New Business Cycle Facts.” in NBER Macroeconomics Annual 2016, volume 31, edited by Martin Eichenbaum and Jonathan A. Parker. Chicago: University of Chicago Press. For rates of return data: Òscar Jordà, Katharina Knoll, Dmitry Kuvshinov, Moritz Schularick, and Alan M. Taylor. 2019. “The Rate of Return on Everything, 1870–2015.” Quarterly Journal of Economics, 134(3), 1225-1298. For data on bank balance sheet ratios:Òscar Jordà, Björn Richter, Moritz Schularick, and Alan M. Taylor. 2021. "Bank capital redux: solvency, liquidity, and crisis." The Review of Economic Studies, 88(1), 260-286. Much of the demographic data comes from the Human Mortality database:HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org (data downloaded on 15/03/2023). US Data Population data pre-1933 comes from the US census:https://www.census.gov/data/tables/time-series/demo/popest/pre-1980-national.htmlData on projections comes from the UN Population Prospects data:United Nations, Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022: Methodology of the United Nations population estimates and projections. UN DESA/POP/2022/TR/NO. 4.

Monaco Population: Total: Aged 15-64 data was reported at 18,491.000 Person in 2023. This records a decrease from the previous number of 18,617.000 Person for 2022. Monaco Population: Total: Aged 15-64 data is updated yearly, averaging 19,229.000 Person from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 20,914.000 Person in 2000 and a record low of 14,530.000 Person in 1960. Monaco Population: Total: Aged 15-64 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Monaco – Table MC.World Bank.WDI: Population and Urbanization Statistics. Total population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.;World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2022 Revision.;Sum;

A flexible model to reconstruct education-specific fertility rates: Sub-saharan Africa case study

The fertility rates are consistent with the United Nation World Population Prospects (UN WPP) 2022 fertility rates.

The Bayesian model developed to reconstruct the fertility rates using Demographic and Health Surveys and the UN WPP is published in a working paper.

Abstract

The future world population growth and size will be largely determined by the pace of fertility decline in sub-Saharan Africa. Correct estimates of education-specific fertility rates are crucial for projecting the future population. Yet, consistent cross-country comparable estimates of education-specific fertility for sub-Saharan African countries are still lacking. We propose a flexible Bayesian hierarchical model to reconstruct education-specific fertility rates by using the patchy Demographic and Health Surveys (DHS) data and the United Nations’ (UN) reliable estimates of total fertility rates (TFR). Our model produces estimates that match the UN TFR to different extents (in other words, estimates of varying levels of consistency with the UN). We present three model specifications: consistent but not identical with the UN, fully-consistent (nearly identical) with the UN, and consistent with the DHS. Further, we provide a full time series of education-specific TFR estimates covering five-year periods between 1980 and 2014 for 36 sub-Saharan African countries. The results show that the DHS-consistent estimates are usually higher than the UN-fully-consistent ones. The differences between the three model estimates vary substantially in size across countries, yielding 1980-2014 fertility trends that differ from each other mostly in level only but in some cases also in direction.

Funding

The data set are part of the BayesEdu Project at Wittgenstein Centre for Demography and Global Human Capital (IIASA, OeAW, University of Vienna) funded from the “Innovation Fund Research, Science and Society” by the Austrian Academy of Sciences (ÖAW).

We provide education-specific total fertility rates (ESTFR) from three model specifications: (1) estimated TFR consistent but not identical with the TFR estimated by the UN (“Main model (UN-consistent)”; (2) estimated TFR fully consistent (nearly identical) with the TFR estimated by the UN ( “UN-fully -consistent”, and (3) estimated TFR consistent only with the TFR estimated by the DHS ( “DHS-consistent”).

For education- and age-specific fertility rates that are UN-fully consistent, please see https://doi.org/10.5281/zenodo.8182960

Variables

Country: Country names

Education: Four education levels, No Education, Primary Education, Secondary Education and Higher Education.

Year: Five-year periods between 1980 and 2015.

ESTFR: Median education-specific total fertility rate estimate

sd: Standard deviation

Upp50: 50% Upper Credible Interval

Lwr50: 50% Lower Credible Interval

Upp80: 80% Upper Credible Interval

Lwr80: 80% Lower Credible Interval

Model: Three model specifications as explained above and in the working paper. DHS-consistent, Main model (UN-consistent) and UN-fully consistent.

List of countries:

Angola, Benin, Burkina Faso, Burundi, Cote D'Ivoire, Cameroon, Central African Republic, Chad, Comoros, Congo, Democratic Republic of Congo, Eswatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, South Africa, Tanzania, Togo, Uganda, Zambia, Zimbabwe

R scripts (numbered from 1 to 9) to prepare data, perform calculations, and run analyses for the projection of speaker numbers for 27 Indigenous languages of Canada between the years 2001 and 2101. Contains data on first language collected during the censuses of 2001, 2006, 2011, 2016, and 2021 provided by Statistics Canada.Contains fertility and mortality schedules taken from the 2022 World Population Prospects (UN). Contains other data files that were produced from the data and calculations described above.

Education- and age-specific fertility rates for 50 African and Latin American countries between 1970 and 2020.

The fertility rates are consistent with the United Nation's World Population Prospects (UN WPP) 2022 fertility rates.

The Bayesian model developed to reconstruct the fertility rates using Demographic and Health Surveys and the UN WPP is published in a working paper.

Abstract:

Consistent and reliable time series of education- and age-specific fertility rates for the past are difficult to obtain in developing countries, although they are needed to evaluate the impact of women’s education on fertility along periods and cohorts. In this paper, we propose a Bayesian framework to reconstruct age-specific fertility rates by level of education using prior information from the birth history module of the Demographic and Health Surveys (DHS) and the UN World Population Prospects. In our case study regions, we reconstruct age- and education-specific fertility rates which are consistent with the UN age specific fertility rates by four levels of education for 50 African and Latin American countries from 1970 to 2020 in five-year steps. Our results show that the Bayesian approach allows for estimating reliable education- and age-specific fertility rates using multiple rounds of the DHS surveys. The time series obtained confirm the main findings of the literature on fertility trends, and age and education specific differentials.

Funding:

These data sets are part of the BayesEdu Project at Wittgenstein Centre for Demography and Global Human Capital (IIASA, OeAW, University of Vienna) funded from the “Innovation Fund Research, Science and Society” by the Austrian Academy of Sciences (ÖAW).

Variables:

Country: Country names

Education: Four education levels, No Education, Primary Education, Secondary Education and Higher Education.

Age group: Five-year age groups between 15-19 and 45-49.

Year: Five-year periods between 1970 and 2020.

Median: Median education and age-specific fertility rate estimate

Upper_CI: 95% Upper Credible Interval

Lower_CI: 95% Lower Credible Interval

List of countries:

Angola

Benin

Brazil

Burkina Faso

Burundi

Cameroon

Central African Republic

Chad

Colombia

Comoros

Congo

Côte D'Ivoire

DR Congo

Ecuador

Egypt

Eswatini

Ethiopia

Gabon

Gambia

Ghana

Guatemala

Guinea

Honduras

Kenya

Lesotho

Liberia

Madagascar

Malawi

Mali

Mexico

Morocco

Mozambique

Namibia

Nicaragua

Niger

Nigeria

Paraguay

Peru

Rwanda

Sao Tome and Principe

Senegal

Sierra Leone

South Africa

Sudan

Tanzania

Togo

Tunisia

Uganda

Zambia

Zimbabwe

Liechtenstein Population: Total: Aged 15-64 data was reported at 25,969.000 Person in 2023. This records a decrease from the previous number of 26,024.000 Person for 2022. Liechtenstein Population: Total: Aged 15-64 data is updated yearly, averaging 20,850.500 Person from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 26,024.000 Person in 2022 and a record low of 10,402.000 Person in 1960. Liechtenstein Population: Total: Aged 15-64 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Liechtenstein – Table LI.World Bank.WDI: Population and Urbanization Statistics. Total population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.;World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2022 Revision.;Sum;

This data describes the average life expectancy at birth for various nations from 1543-2021 . Data Variable description: The average number of years that a newborn could expect to live, if he or she were to pass through life exposed to the sex- and age-specific death rates prevailing at the time of his or her birth, for a specific year, in a given country, territory, or geographic area. (Definition from the WHO) Data Variable time span: 1543 – 2021 Data published by : United Nations, Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022, Online Edition; Zijdeman et al. (2015) (via clio-infra.eu); Riley, J. C. (2005). Estimates of Regional and Global Life Expectancy, 1800-2001. Population and Development Review, 31(3), 537–543. http://www.jstor.org/stable/3401478 Link https://population.un.org/wpp/Download/ ; https://clioinfra.eu/Indicators/LifeExpectancyatBirthTotal.html ; https://doi.org/10.1111/j.1728-4457.2005.00083.x;https://ourworldindata.org/health-meta License: Copyright © 2022 by United Nations, made available under a Creative Commons license CC BY 3.0 IGO: http://creativecommons.org/licenses/by/3.0/igo/

Analysis of ‘Pakistan dataset 1960 to 2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yasirarfat/pakistan-dataset-1960-to-2020 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Pakistan Dataset

SOURCE_ORGANIZATION International Monetary Fund, Balance of Payments Statistics Yearbook and data files. World Bank staff estimates based data from International Monetary Fund's Direction of Trade database. International Monetary Fund, Balance of Payments Statistics Yearbook and data files. World Bank staff estimates through the WITS platform from the Comtrade database maintained by the United Nations Statistics Division. World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database and the World Trade Organization’s (WTO) Integrated Data Base (IDB) and Consolidated Tariff Schedules (CTS) database. United Nations Conference on Trade and Development, Handbook of Statistics and data files, and International Monetary Fund, International Financial Statistics. World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision. Derived from total population. Population source: (1) United Nations Population Division. World Population Prospects: 2019 Revision, (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme. World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision. World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.

--- Original source retains full ownership of the source dataset ---

Costa Rica CR: Population: Total: Aged 15-64 data was reported at 3,596,937.000 Person in 2023. This records an increase from the previous number of 3,573,975.000 Person for 2022. Costa Rica CR: Population: Total: Aged 15-64 data is updated yearly, averaging 1,959,207.500 Person from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 3,596,937.000 Person in 2023 and a record low of 688,790.000 Person in 1960. Costa Rica CR: Population: Total: Aged 15-64 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Population and Urbanization Statistics. Total population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.;World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2022 Revision.;Sum;