7 datasets found
  1. Poverty rates in OECD countries 2022

    • statista.com
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    Statista, Poverty rates in OECD countries 2022 [Dataset]. https://www.statista.com/statistics/233910/poverty-rates-in-oecd-countries/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    Out of all OECD countries, Cost Rica had the highest poverty rate as of 2022, at over 20 percent. The country with the second highest poverty rate was the United States, with 18 percent. On the other end of the scale, Czechia had the lowest poverty rate at 6.4 percent, followed by Denmark.

    The significance of the OECD

    The OECD, or the Organisation for Economic Co-operation and Development, was founded in 1948 and is made up of 38 member countries. It seeks to improve the economic and social well-being of countries and their populations. The OECD looks at issues that impact people’s everyday lives and proposes policies that can help to improve the quality of life.

    Poverty in the United States

    In 2022, there were nearly 38 million people living below the poverty line in the U.S.. About one fourth of the Native American population lived in poverty in 2022, the most out of any ethnicity. In addition, the rate was higher among young women than young men. It is clear that poverty in the United States is a complex, multi-faceted issue that affects millions of people and is even more complex to solve.

  2. a

    2015 05: The Best and Worst Places to Grow Up

    • hub.arcgis.com
    • opendata.mtc.ca.gov
    Updated May 20, 2015
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    MTC/ABAG (2015). 2015 05: The Best and Worst Places to Grow Up [Dataset]. https://hub.arcgis.com/documents/bcc91b58522340f288527004a6ea56e5
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    Dataset updated
    May 20, 2015
    Dataset authored and provided by
    MTC/ABAG
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Children who grow up in some places go on to earn much more than they would if they grew up elsewhere. Location matters enormously. If you're poor and live in the San Francisco Bay Region, it is better to be in Contra Costa County than in San Francisco County or Alameda County. Not only that, the younger you are when you move to Contra Costa, the better you will do on average. Children who move at earlier ages are less likely to become single parents, more likely to go to college, and more likely to earn more.Every year a poor child spends in Contra Costa County adds about $160 to his or her annual household income at age 26, compared with a childhood spent in the average American county. Over the course of a full childhood, which is up to age 20 for the purposes of this analysis, the difference adds up to about $3,200, or 12 percent, more in average income as a young adult.These findings, particularly those that show how much each additional year matters, are from a new study by Raj Chetty and Nathaniel Hendren that has huge consequences on how we think about poverty and mobility in the United States.

  3. Most-to-Least Influential County-Level Economic Variables Contributing to...

    • plos.figshare.com
    xls
    Updated Jun 4, 2025
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    Michele L.F. Bolduc; Parya Saberi; Torsten B. Neilands; Carla I. Mercado; Shanice Battle Johnson; Zoe R. F. Freggens; Desmond Banks; Rashid Njai; Kai McKeever Bullard (2025). Most-to-Least Influential County-Level Economic Variables Contributing to County Prevalence of Poor Mental Health Based on Dominance Analysis Ranked by Standardized Dominance Weights, Overall and by Urban/Rural Classification, United States, 2019. [Dataset]. http://doi.org/10.1371/journal.pone.0300939.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 4, 2025
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Michele L.F. Bolduc; Parya Saberi; Torsten B. Neilands; Carla I. Mercado; Shanice Battle Johnson; Zoe R. F. Freggens; Desmond Banks; Rashid Njai; Kai McKeever Bullard
    License

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

    Area covered
    United States
    Description

    Most-to-Least Influential County-Level Economic Variables Contributing to County Prevalence of Poor Mental Health Based on Dominance Analysis Ranked by Standardized Dominance Weights, Overall and by Urban/Rural Classification, United States, 2019.

  4. FiveThirtyEight Police Killings Dataset

    • kaggle.com
    zip
    Updated Apr 26, 2019
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    FiveThirtyEight (2019). FiveThirtyEight Police Killings Dataset [Dataset]. https://www.kaggle.com/fivethirtyeight/fivethirtyeight-police-killings-dataset
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    zip(53916 bytes)Available download formats
    Dataset updated
    Apr 26, 2019
    Dataset authored and provided by
    FiveThirtyEighthttps://abcnews.go.com/538
    License

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

    Description

    Content

    Police Killings

    This directory contains the data behind the story Where Police Have Killed Americans In 2015.

    We linked entries from the Guardian's database on police killings to census data from the American Community Survey. The Guardian data was downloaded on June 2, 2015. More information about its database is available here.

    Census data was calculated at the tract level from the 2015 5-year American Community Survey using the tables S0601 (demographics), S1901 (tract-level income and poverty), S1701 (employment and education) and DP03 (county-level income). Census tracts were determined by geocoding addresses to latitude/longitude using the Bing Maps and Google Maps APIs and then overlaying points onto 2014 census tracts. GEOIDs are census-standard and should be easily joinable to other ACS tables -- let us know if you find anything interesting.

    Field descriptions:

    HeaderDescriptionSource
    nameName of deceasedGuardian
    ageAge of deceasedGuardian
    genderGender of deceasedGuardian
    raceethnicityRace/ethnicity of deceasedGuardian
    monthMonth of killingGuardian
    dayDay of incidentGuardian
    yearYear of incidentGuardian
    streetaddressAddress/intersection where incident occurredGuardian
    cityCity where incident occurredGuardian
    stateState where incident occurredGuardian
    latitudeLatitude, geocoded from address
    longitudeLongitude, geocoded from address
    state_fpState FIPS codeCensus
    county_fpCounty FIPS codeCensus
    tract_ceTract ID codeCensus
    geo_idCombined tract ID code
    county_idCombined county ID code
    namelsadTract descriptionCensus
    lawenforcementagencyAgency involved in incidentGuardian
    causeCause of deathGuardian
    armedHow/whether deceased was armedGuardian
    popTract populationCensus
    share_whiteShare of pop that is non-Hispanic whiteCensus
    share_bloackShare of pop that is black (alone, not in combination)Census
    share_hispanicShare of pop that is Hispanic/Latino (any race)Census
    p_incomeTract-level median personal incomeCensus
    h_incomeTract-level median household incomeCensus
    county_incomeCounty-level median household incomeCensus
    comp_incomeh_income / county_incomeCalculated from Census
    county_bucketHousehold income, quintile within countyCalculated from Census
    nat_bucketHousehold income, quintile nationallyCalculated from Census
    povTract-level poverty rate (official)Census
    urateTract-level unemployment rateCalculated from Census
    collegeShare of 25+ pop with BA or higherCalculated from Census

    Note regarding income calculations:

    All income fields are in inflation-adjusted 2013 dollars.

    comp_income is simply tract-level median household income as a share of county-level median household income.

    county_bucket provides where the tract's median household income falls in the distribution (by quintile) of all tracts in the county. (1 indicates a tract falls in the poorest 20% of tracts within the county.) Distribution is not weighted by population.

    nat_bucket is the same but for all U.S. counties.

    Context

    This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

    • Update Frequency: This dataset is updated daily.

    Acknowledgements

    This dataset is maintained using GitHub's API and Kaggle's API.

    This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

  5. Beta Coefficients, 95% Confidence Interval, and Statistical Significance for...

    • plos.figshare.com
    xls
    Updated Jun 4, 2025
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    Michele L.F. Bolduc; Parya Saberi; Torsten B. Neilands; Carla I. Mercado; Shanice Battle Johnson; Zoe R. F. Freggens; Desmond Banks; Rashid Njai; Kai McKeever Bullard (2025). Beta Coefficients, 95% Confidence Interval, and Statistical Significance for County-Level Economic Variables Using Linear Regression with Prevalence of Poor Mental Health as the Dependent Variable, Overall and by Urban/Rural Classification, United States, 2019. Blue-filled cells indicate a positive association between the variable and the dependent variable; red-filled cells indicate a negative association; greyed out cells indicate the variable was not significant; blank cells indicate a variable that was not included in the model. [Dataset]. http://doi.org/10.1371/journal.pone.0300939.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 4, 2025
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Michele L.F. Bolduc; Parya Saberi; Torsten B. Neilands; Carla I. Mercado; Shanice Battle Johnson; Zoe R. F. Freggens; Desmond Banks; Rashid Njai; Kai McKeever Bullard
    License

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

    Description

    Beta Coefficients, 95% Confidence Interval, and Statistical Significance for County-Level Economic Variables Using Linear Regression with Prevalence of Poor Mental Health as the Dependent Variable, Overall and by Urban/Rural Classification, United States, 2019. Blue-filled cells indicate a positive association between the variable and the dependent variable; red-filled cells indicate a negative association; greyed out cells indicate the variable was not significant; blank cells indicate a variable that was not included in the model.

  6. Child poverty in OECD countries 2022

    • statista.com
    Updated Jun 27, 2025
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    Statista (2025). Child poverty in OECD countries 2022 [Dataset]. https://www.statista.com/statistics/264424/child-poverty-in-oecd-countries/
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    Dataset updated
    Jun 27, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    Among the OECD countries, Costa Rica had the highest share of children living in poverty, reaching **** percent in 2022. Türkiye followed with a share of ***percent of children living in poverty, while **** percent of children in Spain, Chile, and the United States did the same. On the other hand, only ***** percent of children in Finland were living in poverty.

  7. Countries with the lowest estimated GDP per capita 2024

    • statista.com
    Updated Nov 28, 2025
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    Statista (2025). Countries with the lowest estimated GDP per capita 2024 [Dataset]. https://www.statista.com/statistics/256547/the-20-countries-with-the-lowest-gdp-per-capita/
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    Dataset updated
    Nov 28, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2024
    Area covered
    Worldwide
    Description

    19 of the 20 countries with the lowest estimated GDP per capita in the world in 2024 are located in Sub-Saharan Africa. South Sudan is believed to have a GDP per capita of just 351.02 U.S. dollars - for reference, Luxembourg has the highest GDP per capita in the world, at almost 130,000 U.S. dollars, which is around 400 times larger than that of Burundi (U.S. GDP per capita is over 250 times higher than Burundi's). Poverty in Sub-Saharan Africa Many parts of Sub-Saharan Africa have been among the most impoverished in the world for over a century, due to lacking nutritional and sanitation infrastructures, persistent conflict, and political instability. These issues are also being exacerbated by climate change, where African nations are some of the most vulnerable in the world, as well as the population boom that will place over the 21st century. Of course, the entire population of Sub-Saharan Africa does not live in poverty, and countries in the southern part of the continent, as well as oil-producing states around the Gulf of Guinea, do have some pockets of significant wealth (especially in urban areas). However, while GDP per capita may be higher in these countries, wealth distribution is often very skewed, and GDP per capita figures are not representative of average living standards across the population. Outside of Africa Yemen is the only country outside of Africa to feature on the list, due to decades of civil war and instability. Yemen lags very far behind some of its neighboring Arab states, some of whom rank among the richest in the world due to their much larger energy sectors. Additionally, the IMF does not make estimates for Afghanistan, which would also likely feature on this list.

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    Learn how you can add new datasets to our index.

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Statista, Poverty rates in OECD countries 2022 [Dataset]. https://www.statista.com/statistics/233910/poverty-rates-in-oecd-countries/
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Poverty rates in OECD countries 2022

Explore at:
15 scholarly articles cite this dataset (View in Google Scholar)
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
United States
Description

Out of all OECD countries, Cost Rica had the highest poverty rate as of 2022, at over 20 percent. The country with the second highest poverty rate was the United States, with 18 percent. On the other end of the scale, Czechia had the lowest poverty rate at 6.4 percent, followed by Denmark.

The significance of the OECD

The OECD, or the Organisation for Economic Co-operation and Development, was founded in 1948 and is made up of 38 member countries. It seeks to improve the economic and social well-being of countries and their populations. The OECD looks at issues that impact people’s everyday lives and proposes policies that can help to improve the quality of life.

Poverty in the United States

In 2022, there were nearly 38 million people living below the poverty line in the U.S.. About one fourth of the Native American population lived in poverty in 2022, the most out of any ethnicity. In addition, the rate was higher among young women than young men. It is clear that poverty in the United States is a complex, multi-faceted issue that affects millions of people and is even more complex to solve.

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