Tales from the Tails: Examining the Effect of Inequality at the Extremes of the Mortality Distribution

Vivian Chen, Tamkang University
Tse-Chuan Yang, Pennsylvania State University
Stephen A. Matthews, Pennsylvania State University

Quantile Regression (QR) is increasingly used in economics, but uptake by demographers, including mortality researchers, is limited. A goal of this paper is to provide an empirical application of QR to mortality research, specifically exploring county-level associations between inequality and mortality in the US. The inequality/mortality association is well documented, but QR is appropriate when the research question is whether inequality has a greater influence in the counties with high mortality compared to those with lower mortality. QR reveals the associations between predictors of mortality vary across counties depending on where they are located in the mortality distribution and a non-linear relationship between inequality and mortality; underscoring the fact that differentials associated with inequality are more important at upper quantiles than implied by OLS-based findings. This has implications for public policy designed to reduce health disparities including the need for targeting not one model fits all (one tale fits all).

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Presented in Poster Session 5