Modeling Area-Level Health Rankings

39 Pages Posted: 20 Sep 2013 Last revised: 22 Feb 2021

See all articles by Charles Courtemanche

Charles Courtemanche

Georgia State University - Andrew Young School of Policy Studies

Samir Soneji

Geisel School of Medicine at Dartmouth

Rusty Tchernis

Georgia State University - Department of Economics; National Bureau of Economic Research (NBER); IZA Institute of Labor Economics

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Date Written: September 2013

Abstract

We propose a Bayesian factor analysis model to rank the health of localities. Mortality and morbidity variables empirically contribute to the resulting rank, and population and spatial correlation are incorporated into a measure of uncertainty. We use county-level data from Texas and Wisconsin to compare our approach to conventional rankings that assign deterministic factor weights and ignore uncertainty. Greater discrepancies in rankings emerge for Texas than Wisconsin since the differences between the empirically-derived and deterministic weights are more substantial. Uncertainty is evident in both states but becomes especially large in Texas after incorporating noise from imputing its considerable missing data.

Suggested Citation

Courtemanche, Charles and Soneji, Samir and Tchernis, Rusty, Modeling Area-Level Health Rankings (September 2013). NBER Working Paper No. w19450, Available at SSRN: https://ssrn.com/abstract=2328497

Charles Courtemanche (Contact Author)

Georgia State University - Andrew Young School of Policy Studies ( email )

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Samir Soneji

Geisel School of Medicine at Dartmouth ( email )

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Rusty Tchernis

Georgia State University - Department of Economics ( email )

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Atlanta, GA 30302-3992
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