4 datasets found
  1. 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
    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

    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.

  2. 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.

  3. 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.

  4. 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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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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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.

Related Article
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

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.

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