100+ datasets found
  1. The World Bank DataBank

    • pacific-data.sprep.org
    • rmi-data.sprep.org
    xlsx
    Updated Oct 17, 2023
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    Secretariat of the Pacific Regional Environment Programme (2023). The World Bank DataBank [Dataset]. https://pacific-data.sprep.org/dataset/world-bank-databank
    Explore at:
    xlsx(10563), xlsx(13016), xlsx(62144), xlsx(202624), xlsx(174718), xlsx(18003), xlsx(213238), xlsx(15240)Available download formats
    Dataset updated
    Oct 17, 2023
    Dataset provided by
    Pacific Regional Environment Programmehttps://www.sprep.org/
    License

    Public Domain Mark 1.0https://creativecommons.org/publicdomain/mark/1.0/
    License information was derived automatically

    Area covered
    Marshall Islands, 174.79248 15.813396, 161.608887 3.754634)), 161.608887 15.813396, POLYGON ((161.608887 3.754634, 174.79248 3.754634
    Description

    A collection of datasets for economic, demographic, and population metrics for the Marshall Islands derived from the World Bank DataBank interface. DataBank is an analysis and visualisation tool that contains collections of time series data on a variety of topics. Data are derived from a series of databases such as: World Development Indicators; Statistical Capacity Indicators, Education Statistics, Gender Statistics, Health Nutrition and Population Statistics, and others

  2. w

    Health Nutrition and Population Statistics

    • data360.worldbank.org
    • datacatalog1.worldbank.org
    Updated Apr 18, 2025
    + more versions
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    (2025). Health Nutrition and Population Statistics [Dataset]. https://data360.worldbank.org/en/dataset/WB_HNP
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    Dataset updated
    Apr 18, 2025
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    1960 - 2023
    Description

    Health Nutrition and Population Statistics database provides key health, nutrition and population statistics gathered from a variety of international and national sources. Themes include global surgery, health financing, HIV/AIDS, immunization, infectious diseases, medical resources and usage, noncommunicable diseases, nutrition, population dynamics, reproductive health, universal health coverage, and water and sanitation.

  3. Health Nutrition and Population Data Package

    • johnsnowlabs.com
    csv
    Updated Jan 20, 2021
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    John Snow Labs (2021). Health Nutrition and Population Data Package [Dataset]. https://www.johnsnowlabs.com/marketplace/health-nutrition-and-population-data-package/
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jan 20, 2021
    Dataset authored and provided by
    John Snow Labs
    Description

    This data package contains data on key health, education, nutrition, and population statistics gathered from different international sources.

  4. k

    Health Nutrition and Population Statistics

    • datasource.kapsarc.org
    • kapsarc.opendatasoft.com
    Updated Jul 11, 2025
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    (2025). Health Nutrition and Population Statistics [Dataset]. https://datasource.kapsarc.org/explore/dataset/worldbank-health-nutrition-and-population-statistics/
    Explore at:
    Dataset updated
    Jul 11, 2025
    Description

    Explore World Bank Health, Nutrition and Population Statistics dataset featuring a wide range of indicators such as School enrollment, UHC service coverage index, Fertility rate, and more from countries like Bahrain, China, India, Kuwait, Oman, Qatar, and Saudi Arabia.

    School enrollment, tertiary, UHC service coverage index, Wanted fertility rate, People with basic handwashing facilities, urban population, Rural population, AIDS estimated deaths, Domestic private health expenditure, Fertility rate, Domestic general government health expenditure, Age dependency ratio, Postnatal care coverage, People using safely managed drinking water services, Unemployment, Lifetime risk of maternal death, External health expenditure, Population growth, Completeness of birth registration, Urban poverty headcount ratio, Prevalence of undernourishment, People using at least basic sanitation services, Prevalence of current tobacco use, Urban poverty headcount ratio, Tuberculosis treatment success rate, Low-birthweight babies, Female headed households, Completeness of birth registration, Urban population growth, Antiretroviral therapy coverage, Labor force, and more.

    Bahrain, China, India, Kuwait, Oman, Qatar, Saudi Arabia

    Follow data.kapsarc.org for timely data to advance energy economics research.

  5. k

    Population Ranking

    • datasource.kapsarc.org
    Updated Jun 29, 2025
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    (2025). Population Ranking [Dataset]. https://datasource.kapsarc.org/explore/dataset/worldbank-population/
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    Dataset updated
    Jun 29, 2025
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Explore the World Bank Population dataset to access rankings and insights on global population statistics. Click here for extensive data on various countries.

    Rankings

    Afghanistan, Albania, Algeria, Andorra, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo, Costa Rica, Croatia, Cuba, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Monaco, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, San Marino, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovenia, Solomon Islands, Somalia, South Africa, South Sudan, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkmenistan, Tuvalu, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, ZimbabweFollow data.kapsarc.org for timely data to advance energy economics research..

  6. World Development Indicators (WDI) Data

    • kaggle.com
    zip
    Updated Aug 27, 2018
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    Google BigQuery (2018). World Development Indicators (WDI) Data [Dataset]. https://www.kaggle.com/datasets/bigquery/worldbank-wdi
    Explore at:
    zip(0 bytes)Available download formats
    Dataset updated
    Aug 27, 2018
    Dataset provided by
    BigQueryhttps://cloud.google.com/bigquery
    Authors
    Google BigQuery
    License

    Attribution-ShareAlike 3.0 (CC BY-SA 3.0)https://creativecommons.org/licenses/by-sa/3.0/
    License information was derived automatically

    Description

    Fork this notebook to get started on accessing data in the BigQuery dataset by writing SQL queries using the BQhelper module.

    Context

    World Development Indicators (WDI) by World Bank includes data spanning up to 56 years—from 1960 to 2016. WDI frames global trends with indicators on population, population density, urbanization, GNI, and GDP. These indicators measure the world’s economy and progress toward improving lives, achieving sustainable development, providing support for vulnerable populations, and reducing gender disparities.

    Content

    World Development Indicators Data is the primary World Bank collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates.

    Acknowledgements

    “World Development Indicators” by the World Bank, used under CC BY 3.0 IGO.

    Data Origin: https://bigquery.cloud.google.com/dataset/patents-public-data:worldbank_wdi

    Banner photo by Joshua Rawson-Harris on Unsplash

  7. World Bank: GHNP Data

    • kaggle.com
    zip
    Updated Mar 20, 2019
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    World Bank (2019). World Bank: GHNP Data [Dataset]. https://www.kaggle.com/theworldbank/world-bank-health-population
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    zip(0 bytes)Available download formats
    Dataset updated
    Mar 20, 2019
    Dataset authored and provided by
    World Bankhttp://worldbank.org/
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

    Content

    This dataset combines key health statistics from a variety of sources to provide a look at global health and population trends. It includes information on nutrition, reproductive health, education, immunization, and diseases from over 200 countries.

    Update Frequency: Biannual

    For more information, see the World Bank website.

    Fork this kernel to get started with this dataset.

    Acknowledgements

    https://datacatalog.worldbank.org/dataset/health-nutrition-and-population-statistics

    https://cloud.google.com/bigquery/public-data/world-bank-hnp

    Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

    Citation: The World Bank: Health Nutrition and Population Statistics

    Banner Photo by @till_indeman from Unplash.

    Inspiration

    What’s the average age of first marriages for females around the world?

  8. F

    Population, Total for Middle Income Countries

    • fred.stlouisfed.org
    json
    Updated Jul 2, 2025
    + more versions
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    (2025). Population, Total for Middle Income Countries [Dataset]. https://fred.stlouisfed.org/series/SPPOPTOTLMIC
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 2, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Population, Total for Middle Income Countries (SPPOPTOTLMIC) from 1960 to 2024 about income and population.

  9. M

    Mexico Multidimensional Poverty Headcount Ratio: World Bank: % of total...

    • ceicdata.com
    Updated Jan 15, 2025
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    CEICdata.com (2025). Mexico Multidimensional Poverty Headcount Ratio: World Bank: % of total population [Dataset]. https://www.ceicdata.com/en/mexico/social-poverty-and-inequality/multidimensional-poverty-headcount-ratio-world-bank--of-total-population
    Explore at:
    Dataset updated
    Jan 15, 2025
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2010 - Dec 1, 2022
    Area covered
    Mexico
    Description

    Mexico Multidimensional Poverty Headcount Ratio: World Bank: % of total population data was reported at 1.700 % in 2022. This records a decrease from the previous number of 2.600 % for 2020. Mexico Multidimensional Poverty Headcount Ratio: World Bank: % of total population data is updated yearly, averaging 2.900 % from Dec 2010 (Median) to 2022, with 7 observations. The data reached an all-time high of 5.700 % in 2010 and a record low of 1.700 % in 2022. Mexico Multidimensional Poverty Headcount Ratio: World Bank: % of total population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Social: Poverty and Inequality. The multidimensional poverty headcount ratio (World Bank) is the percentage of a population living in poverty according to the World Bank's Multidimensional Poverty Measure. The Multidimensional Poverty Measure includes three dimensions – monetary poverty, education, and basic infrastructure services – to capture a more complete picture of poverty.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

  10. World Bank: International Debt Data

    • kaggle.com
    zip
    Updated Mar 20, 2019
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    World Bank (2019). World Bank: International Debt Data [Dataset]. https://www.kaggle.com/datasets/theworldbank/world-bank-intl-debt
    Explore at:
    zip(0 bytes)Available download formats
    Dataset updated
    Mar 20, 2019
    Dataset authored and provided by
    World Bankhttp://worldbank.org/
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

    Content

    This dataset contains both national and regional debt statistics captured by over 200 economic indicators. Time series data is available for those indicators from 1970 to 2015 for reporting countries.

    For more information, see the World Bank website.

    Fork this kernel to get started with this dataset.

    Acknowledgements

    https://bigquery.cloud.google.com/dataset/bigquery-public-data:world_bank_intl_debt

    https://cloud.google.com/bigquery/public-data/world-bank-international-debt

    Citation: The World Bank: International Debt Statistics

    Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

    Banner Photo by @till_indeman from Unplash.

    Inspiration

    What countries have the largest outstanding debt?

    https://cloud.google.com/bigquery/images/outstanding-debt.png" alt="enter image description here"> https://cloud.google.com/bigquery/images/outstanding-debt.png

  11. World Bank: Education Data

    • kaggle.com
    zip
    Updated Mar 20, 2019
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    World Bank (2019). World Bank: Education Data [Dataset]. https://www.kaggle.com/datasets/theworldbank/world-bank-intl-education
    Explore at:
    zip(0 bytes)Available download formats
    Dataset updated
    Mar 20, 2019
    Dataset authored and provided by
    World Bankhttp://worldbank.org/
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

    Content

    This dataset combines key education statistics from a variety of sources to provide a look at global literacy, spending, and access.

    For more information, see the World Bank website.

    Fork this kernel to get started with this dataset.

    Acknowledgements

    https://bigquery.cloud.google.com/dataset/bigquery-public-data:world_bank_health_population

    http://data.worldbank.org/data-catalog/ed-stats

    https://cloud.google.com/bigquery/public-data/world-bank-education

    Citation: The World Bank: Education Statistics

    Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

    Banner Photo by @till_indeman from Unplash.

    Inspiration

    Of total government spending, what percentage is spent on education?

  12. G

    Ghana Multidimensional Poverty Headcount Ratio: World Bank: % of total...

    • ceicdata.com
    Updated Feb 15, 2025
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    CEICdata.com (2025). Ghana Multidimensional Poverty Headcount Ratio: World Bank: % of total population [Dataset]. https://www.ceicdata.com/en/ghana/social-poverty-and-inequality/multidimensional-poverty-headcount-ratio-world-bank--of-total-population
    Explore at:
    Dataset updated
    Feb 15, 2025
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2012 - Dec 1, 2016
    Area covered
    Ghana
    Description

    Ghana Multidimensional Poverty Headcount Ratio: World Bank: % of total population data was reported at 32.800 % in 2016. This records a decrease from the previous number of 33.200 % for 2012. Ghana Multidimensional Poverty Headcount Ratio: World Bank: % of total population data is updated yearly, averaging 33.000 % from Dec 2012 (Median) to 2016, with 2 observations. The data reached an all-time high of 33.200 % in 2012 and a record low of 32.800 % in 2016. Ghana Multidimensional Poverty Headcount Ratio: World Bank: % of total population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ghana – Table GH.World Bank.WDI: Social: Poverty and Inequality. The multidimensional poverty headcount ratio (World Bank) is the percentage of a population living in poverty according to the World Bank's Multidimensional Poverty Measure. The Multidimensional Poverty Measure includes three dimensions – monetary poverty, education, and basic infrastructure services – to capture a more complete picture of poverty.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

  13. T

    World - Rural Population

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Jul 20, 2013
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    TRADING ECONOMICS (2013). World - Rural Population [Dataset]. https://tradingeconomics.com/world/rural-population-wb-data.html
    Explore at:
    xml, excel, csv, jsonAvailable download formats
    Dataset updated
    Jul 20, 2013
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1976 - Dec 31, 2025
    Area covered
    World, World
    Description

    Rural population in World was reported at 3445666944 in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Rural population - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

  14. w

    Education Statistics

    • data360.worldbank.org
    • data.opendata.am
    Updated Apr 18, 2025
    + more versions
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    (2025). Education Statistics [Dataset]. https://data360.worldbank.org/en/dataset/WB_EDSTATS
    Explore at:
    Dataset updated
    Apr 18, 2025
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    1970 - 2023
    Description

    The World Bank EdStats All Indicator Query holds over 4,000 internationally comparable indicators that describe education access, progression, completion, literacy, teachers, population, and expenditures. The indicators cover the education cycle from pre-primary to vocational and tertiary education. The query also holds learning outcome data from international and regional learning assessments (e.g. PISA, TIMSS, PIRLS), equity data from household surveys, and projection/attainment data to 2050. For further information, please visit the EdStats website.

    For further details, please refer to https://datatopics.worldbank.org/education/wRsc/about

  15. Health Nutrition and Population Statistics

    • johnsnowlabs.com
    csv
    Updated Jan 20, 2021
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    John Snow Labs (2021). Health Nutrition and Population Statistics [Dataset]. https://www.johnsnowlabs.com/marketplace/health-nutrition-and-population-statistics/
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jan 20, 2021
    Dataset authored and provided by
    John Snow Labs
    Area covered
    World
    Description

    This time-series dataset includes data on countries worldwide and information on indicators pertaining to health and population by each country from 1960 to 2021.

  16. Spain ES: Population: as % of Total: Aged 0-14

    • ceicdata.com
    Updated Feb 15, 2025
    + more versions
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    CEICdata.com (2025). Spain ES: Population: as % of Total: Aged 0-14 [Dataset]. https://www.ceicdata.com/en/spain/population-and-urbanization-statistics/es-population-as--of-total-aged-014
    Explore at:
    Dataset updated
    Feb 15, 2025
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2006 - Dec 1, 2017
    Area covered
    Spain
    Variables measured
    Population
    Description

    Spain ES: Population: as % of Total: Aged 0-14 data was reported at 14.687 % in 2017. This records a decrease from the previous number of 14.778 % for 2016. Spain ES: Population: as % of Total: Aged 0-14 data is updated yearly, averaging 21.063 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 28.114 % in 1970 and a record low of 14.298 % in 2005. Spain ES: Population: as % of Total: Aged 0-14 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Spain – Table ES.World Bank.WDI: Population and Urbanization Statistics. Population between the ages 0 to 14 as a percentage of the total population. Population is based on the de facto definition of population.; ; World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; Weighted average;

  17. F

    Population Growth for Mozambique

    • fred.stlouisfed.org
    json
    Updated Jul 2, 2025
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    (2025). Population Growth for Mozambique [Dataset]. https://fred.stlouisfed.org/series/SPPOPGROWMOZ
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 2, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    Mozambique
    Description

    Graph and download economic data for Population Growth for Mozambique (SPPOPGROWMOZ) from 1961 to 2024 about Mozambique, population, and rate.

  18. w

    Integrated Living Conditions Survey 2015 - Armenia

    • microdata.worldbank.org
    • microdata.armstat.am
    • +1more
    Updated Apr 24, 2018
    + more versions
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    National Statistical Service of the Republic of Armenia (NSS RA) (2018). Integrated Living Conditions Survey 2015 - Armenia [Dataset]. https://microdata.worldbank.org/index.php/catalog/2964
    Explore at:
    Dataset updated
    Apr 24, 2018
    Dataset authored and provided by
    National Statistical Service of the Republic of Armenia (NSS RA)
    Time period covered
    2015
    Area covered
    Armenia
    Description

    Abstract

    The Integrated Living Conditions Survey (ILCS), conducted annually by the NSS National Statistical Service of the Republic of Armenia, formed the basis for monitoring living conditions in Armenia. The ILCS is a universally recognized best-practice survey for collecting data to inform about the living standards of households. The ILCS comprises comprehensive and valuable data on the welfare of households and separate individuals which gives the NSS an opportunity to provide the public with up to date information on the population’s income, expenditures, the level of poverty and the other changes in living standards on an annual basis.

    Geographic coverage

    Urban and rural communities

    Analysis unit

    • Households;
    • Individuals.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    During the 2001-2003 surveys two-stage random sample was used; the first stage covered the selection of settlements - cities and villages, while the second stage was focused on the selection of households in these settlements. The surveys were conducted on the principle of monthly rotation of households by clusters (sample units). In 2002 and 2003 the number of households was 387 with the sample covering 14 cities and 30 villages in 2002 and 17 cities and 20 villages in 2003.

    During the 2004-2006 surveys the sampling frame for the ILCS was built using the database of addresses for the 2001 Population Census; the database was developed with the World Bank technical assistance. The database of addresses of all households in Armenia was divided into 48 strata including 12 communities of Yerevan city. The households from other regions (marzes) were grouped according to the following three categories: big towns with 15,000 and more population; villages, and other towns. Big towns formed 16 strata (the only exception was the Vayots Dzor marz where there are no big towns). The villages and other towns formed 10 strata each. According to this division, a random, two-step sample stratified at marz level was developed. All marzes, as well as all urban and rural settlements were included in the sample population according to the share of population residing in those settlements as percent to the total population in the country. In the first step, the settlements, i.e. primary sample units, were selected: 43 towns out of 48 or 90 percent of all towns in Armenia were surveyed during the year; also 216 villages out of 951 or 23 percent of all villages in the country were covered by the survey. In the second step, the respondent households were selected: 6,816 households (5,088 from urban and 1,728 from rural settlements). As a result, for the first time since 1996 survey data were representative at the marz level.

    During the 2007-2012 surveys the sampling frame for ILCS was designed according to the database of addresses for the 2001 Population Census, which was developed with the World Bank technical assistance. The sample consisted of two parts: core sample and oversample.

    1) For the creation of core sample, the sample frame (database of addresses of all households in Armenia) was divided into 48 strata including 12 communities of Yerevan city. The households from other regions (marzes) were grouped according to three categories: large towns (with population of 15000 and higher), villages and other towns. Large towns formed by 16 groups (strata), while the villages and towns formed by 10 strata each. According to that division, a random, two-step sample stratified at the marz level was developed. All marzes, as well as all urban and rural settlements were included in the sample population according to the share of households residing in those settlements as percent to the total households in the country. In the first step, using the PPS method the enumeration units (i.e., primary sample units to be surveyed during the year) were selected. 2007 sample includes 48 urban and 18 rural enumeration areas per month. 2) The oversample was drawn from the list of villages included in MCA-Armenia Rural Roads Rehabilitation Project. The enumeration areas of villages that were already in the core sample were excluded from that list. From the remaining enumeration areas 18 enumeration areas were selected per month. Thus, the rural sample size was doubled. 3) After merging the core sample and oversample, the survey households were selected in the second step. 656 households were surveyed per month, from which 368 from urban and 288 from rural settlements. Each month 82 interviewers had conducted field work, and their workload included 8 households per month. In 2007 number of surveyed households was 7,872 (4,416 from urban and 3,456 from rural areas).

    For the survey 2013 the sample frame for ILCS was designed in accordance with the database of addresses of all private households in the country developed on basis of the 2001 Population Census results, with the technical assistance of the World Bank. The method of systematic representative probability sampling was used to frame the sample. For the purpose of drawing the sample, the sample frame was divided into 32 strata including 12 communities of Yerevan City (currently, the administrative districts). According to this division, a two-tier sample was drawn stratified by regions and by Yerevan. All regions and Yerevan, as well as all urban and rural communities were included in the sample in accordance to the shares of their resident households within the total number of households in the country. In the first round, enumeration areas - that is primary sample units to be surveyed during the year - were selected. The ILCS 2013 sample included 32 enumeration areas in urban and 16 enumeration areas in rural communities per month. The households to be surveyed were selected in the second round. A total of 432 households were surveyed per month, of which 279 and 153 households from urban and rural communities, respectively. Every month 48 interviewers went on field work with a workload of 9 households per month.

    The sample frame for 2014-2016 was designed in accordance with the database of addresses of all private households in the country developed on basis of the 2011 Population Census results, with the technical assistance of the World Bank. The method of systematic representative probability sampling was used to frame the sample.
    For drawing the sample, the sample frame was divided into 32 strata including 12 communities of Yerevan City (currently, the administrative districts). According to this division, a two-tier sample was drawn stratified by regions and by Yerevan. All regions and Yerevan, as well as all urban and rural communities were included in the sample in accordance to the shares of their resident households within the total number of households in the country. In the first round, enumeration areas - that is primary sample units to be surveyed during the year - were selected. The ILCS 2014 sample included 30 enumeration areas in urban and 18 enumeration areas in rural communities per month. The method of representative probability sampling was used to frame the sample. At regional level, all communities were grouped into two categories - towns and villages. According to this division, a two-tier sample was drawn stratified by regions and by Yerevan. All regions and Yerevan, as well as all rural and urban communities were included in the sample in accordance to the shares of their resident households within the total number of households in the country. In the first round, enumeration districts - that is primary sample units to be surveyed during the year - were selected. The ILCS 2015 sample included 30 enumeration districts in urban and 18 enumeration districts in rural communities per month.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The Questionnaire is filled in by the interviewer during the least five visits to households per month. During face-to-face interviews with the household head or another knowledgeable adult member, the interviewer collects information on the composition and housing conditions of the household, the employment status, educational level and health condition of the members, availability and use of land, livestock, and agricultural machinery, monetary and commodity flows between households, and other information.

    The 2015 survey questionnaire had the following sections: (1) "List of Household Members", (2) "Migration", (3) "Housing and Dwelling Conditions", (4) "Employment", (5) "Education", (6) "Agriculture", (7) "Food Production", (8) "Monetary and Commodity Flows between Households", (9) "Health (General) and Healthcare", (10) "Debts", (11) "Subjective Assessment of Living Conditions", (12) "Provision of Services", (13) "Social Assistance", (14) "Households as Employers for Service Personnel", and (15) "Household Monthly Consumption of Energy Resources".

    The Diary is completed directly by the household for one month. Every day the household would record all its expenditures on food, non-food products and services, also giving a detailed description of such purchases; e.g. for food products the name, quantity, cost, and place of purchase of the product is recorded. Besides, the household records its consumption of food products received and used from its own land and livestock, as well as from other sources (e.g. gifts, humanitarian aid). Non-food products and services purchased or received for free are also recorded in the diary. Then, the household records its income received during the month. At the end of the month, information on rarely used food products, durable goods and ceremonies is recorded, as well. The records in the diary are verified by the interviewer in the course of 5

  19. k

    Worldbank - Gender Statistics

    • datasource.kapsarc.org
    Updated Jul 11, 2025
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    (2025). Worldbank - Gender Statistics [Dataset]. https://datasource.kapsarc.org/explore/dataset/worldbank-gender-statistics-gcc/
    Explore at:
    Dataset updated
    Jul 11, 2025
    Description

    Explore gender statistics data focusing on academic staff, employment, fertility rates, GDP, poverty, and more in the GCC region. Access comprehensive information on key indicators for Bahrain, China, India, Kuwait, Oman, Qatar, and Saudi Arabia.

    academic staff, Access to anti-retroviral drugs, Adjusted net enrollment rate, Administration and Law programmes, Age at first marriage, Age dependency ratio, Cause of death, Children out of school, Completeness of birth registration, consumer prices, Cost of business start-up procedures, Employers, Employment in agriculture, Employment in industry, Employment in services, employment or training, Engineering and Mathematics programmes, Female headed households, Female migrants, Fertility planning status: mistimed pregnancy, Fertility planning status: planned pregnancy, Fertility rate, Firms with female participation in ownership, Fisheries and Veterinary programmes, Forestry, GDP, GDP growth, GDP per capita, gender parity index, Gini index, GNI, GNI per capita, Government expenditure on education, Government expenditure per student, Gross graduation ratio, Households with water on the premises, Inflation, Informal employment, Labor force, Labor force with advanced education, Labor force with basic education, Labor force with intermediate education, Learning poverty, Length of paid maternity leave, Life expectancy at birth, Mandatory retirement age, Manufacturing and Construction programmes, Mathematics and Statistics programmes, Number of under-five deaths, Part time employment, Population, Poverty headcount ratio at national poverty lines, PPP, Primary completion rate, Retirement age with full benefits, Retirement age with partial benefits, Rural population, Sex ratio at birth, Unemployment, Unemployment with advanced education, Urban population

    Bahrain, China, India, Kuwait, Oman, Qatar, Saudi Arabia

    Follow data.kapsarc.org for timely data to advance energy economics research.

  20. C

    Chad Multidimensional Poverty Headcount Ratio: World Bank: % of total...

    • ceicdata.com
    Updated Mar 1, 2025
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    CEICdata.com (2025). Chad Multidimensional Poverty Headcount Ratio: World Bank: % of total population [Dataset]. https://www.ceicdata.com/en/chad/social-poverty-and-inequality/multidimensional-poverty-headcount-ratio-world-bank--of-total-population
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    Dataset updated
    Mar 1, 2025
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2011 - Dec 1, 2022
    Area covered
    Chad
    Description

    Chad Multidimensional Poverty Headcount Ratio: World Bank: % of total population data was reported at 80.900 % in 2022. This records an increase from the previous number of 79.300 % for 2018. Chad Multidimensional Poverty Headcount Ratio: World Bank: % of total population data is updated yearly, averaging 80.900 % from Dec 2011 (Median) to 2022, with 3 observations. The data reached an all-time high of 85.700 % in 2011 and a record low of 79.300 % in 2018. Chad Multidimensional Poverty Headcount Ratio: World Bank: % of total population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chad – Table TD.World Bank.WDI: Social: Poverty and Inequality. The multidimensional poverty headcount ratio (World Bank) is the percentage of a population living in poverty according to the World Bank's Multidimensional Poverty Measure. The Multidimensional Poverty Measure includes three dimensions – monetary poverty, education, and basic infrastructure services – to capture a more complete picture of poverty.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

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Close
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Secretariat of the Pacific Regional Environment Programme (2023). The World Bank DataBank [Dataset]. https://pacific-data.sprep.org/dataset/world-bank-databank
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The World Bank DataBank

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xlsx(10563), xlsx(13016), xlsx(62144), xlsx(202624), xlsx(174718), xlsx(18003), xlsx(213238), xlsx(15240)Available download formats
Dataset updated
Oct 17, 2023
Dataset provided by
Pacific Regional Environment Programmehttps://www.sprep.org/
License

Public Domain Mark 1.0https://creativecommons.org/publicdomain/mark/1.0/
License information was derived automatically

Area covered
Marshall Islands, 174.79248 15.813396, 161.608887 3.754634)), 161.608887 15.813396, POLYGON ((161.608887 3.754634, 174.79248 3.754634
Description

A collection of datasets for economic, demographic, and population metrics for the Marshall Islands derived from the World Bank DataBank interface. DataBank is an analysis and visualisation tool that contains collections of time series data on a variety of topics. Data are derived from a series of databases such as: World Development Indicators; Statistical Capacity Indicators, Education Statistics, Gender Statistics, Health Nutrition and Population Statistics, and others

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