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Measuring the Spatial Distribution of Health Rankings in the United States


  • Will Davis
  • Alexander D. Gordan
  • Rusty Tchernis


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
    Note: HE

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    References listed on IDEAS

    1. Raj Chetty & Nathaniel Hendren, 2018. "The Impacts of Neighborhoods on Intergenerational Mobility II: County-Level Estimates," The Quarterly Journal of Economics, Oxford University Press, vol. 133(3), pages 1163-1228.
    2. 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.
    3. Raj Chetty & Nathaniel Hendren, 2018. "The Impacts of Neighborhoods on Intergenerational Mobility I: Childhood Exposure Effects," The Quarterly Journal of Economics, Oxford University Press, vol. 133(3), pages 1107-1162.
    4. 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.
    5. 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.
    6. 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.
    7. 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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    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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