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Measuring the Spatial Distribution of Health Rankings in the United States

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  • Will Davis
  • Alexander D. Gordan
  • Rusty Tchernis

Abstract

We rank counties in the United States of America with respect to population health. We utilize the five observable county health variables used to construct the University of Wisconsin Population Health Institute’s County Health Rankings (CHRs). Our method relies on a factor analysis model to directly compute weights for our rankings, incorporate county population sizes into the variances, and allow for spillovers of health stock across county lines. We find that demographic and economic variation explain a large portion of the variation in health rankings. We address the importance of uncertainty caused by imputation of missing data and show that the use of rankings leads to inherently greater uncertainty. Analyzing the health of counties both within and across state lines shows substantial degrees of disparity. We find some disagreement between our ranks and the CHRs, but we show that much can be learned by combining results from both methods.

Suggested Citation

  • Will Davis & Alexander D. Gordan & Rusty Tchernis, 2020. "Measuring the Spatial Distribution of Health Rankings in the United States," NBER Working Papers 27259, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:27259
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    1. Magne Mogstad & Joseph P. Romano & Azeem Shaikh & Daniel Wilhelm, 2020. "Inference for Ranks with Applications to Mobility across Neighborhoods and Academic Achievement across Countries," NBER Working Papers 26883, National Bureau of Economic Research, Inc.
    2. Raj Chetty & Nathaniel Hendren, 2018. "The Impacts of Neighborhoods on Intergenerational Mobility II: County-Level Estimates," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(3), pages 1163-1228.
    3. Isaiah Andrews & Toru Kitagawa & Adam McCloskey, 2018. "Inference on winners," CeMMAP working papers CWP31/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Chib, Siddhartha & Greenberg, Edward, 1996. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometric Theory, Cambridge University Press, vol. 12(3), pages 409-431, August.
    5. Raj Chetty & Nathaniel Hendren, 2018. "The Impacts of Neighborhoods on Intergenerational Mobility I: Childhood Exposure Effects," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(3), pages 1107-1162.
    6. Nicolas R. Ziebarth, 2009. "Measurement of Health, the Sensitivity of the Concentration Index, and Reporting Heterogeneity," SOEPpapers on Multidisciplinary Panel Data Research 211, DIW Berlin, The German Socio-Economic Panel (SOEP).
    7. Qiu, Qihua & Sung, Jaesang & Davis, Will & Tchernis, Rusty, 2018. "Using spatial factor analysis to measure human development," Journal of Development Economics, Elsevier, vol. 132(C), pages 130-149.
    8. Martin Klein & Tommy Wright & Jerzy Wieczorek, 2020. "A joint confidence region for an overall ranking of populations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(3), pages 589-606, June.
    9. Ziebarth, Nicolas, 2010. "Measurement of health, health inequality, and reporting heterogeneity," Social Science & Medicine, Elsevier, vol. 71(1), pages 116-124, July.
    10. Hogan J.W. & Tchernis R., 2004. "Bayesian Factor Analysis for Spatially Correlated Data, With Application to Summarizing Area-Level Material Deprivation From Census Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 314-324, January.
    11. Ncube, Collette N. & Enquobahrie, Daniel A. & Albert, Steven M. & Herrick, Amy L. & Burke, Jessica G., 2016. "Association of neighborhood context with offspring risk of preterm birth and low birthweight: A systematic review and meta-analysis of population-based studies," Social Science & Medicine, Elsevier, vol. 153(C), pages 156-164.
    12. Xie, Minge & Singh, Kesar & Zhang, Cun-Hui, 2009. "Confidence Intervals for Population Ranks in the Presence of Ties and Near Ties," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 775-788.
    13. Udo Schneider & Christian Pfarr & Brit Schneider & Volker Ulrich, 2012. "I feel good! Gender differences and reporting heterogeneity in self-assessed health," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 13(3), pages 251-265, June.
    14. Bleichrodt, Han & van Doorslaer, Eddy, 2006. "A welfare economics foundation for health inequality measurement," Journal of Health Economics, Elsevier, vol. 25(5), pages 945-957, September.
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    Cited by:

    1. Jaesang Sung & Qihua Qiu & Will Davis & Rusty Tchernis, 2022. "Design and Application of an Area-Level Suicide Risk Index with Spatial Correlation," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(1), pages 77-104, May.
    2. Johannes S. Kunz & Carol Propper & Kevin E. Staub & Rainer Winkelmann, 2023. "Assessing the Quality of Public Services: For-profits, Chains, and Concentration in the Hospital Market," Papers 2023-01, Centre for Health Economics, Monash University.

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    More about this item

    JEL classification:

    • I1 - Health, Education, and Welfare - - Health
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality

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