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Using quantile regression to examine the effects of inequality across the mortality distribution in the U.S. counties

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  • Yang, Tse-Chuan
  • Chen, Vivian Yi-Ju
  • Shoff, Carla
  • Matthews, Stephen A.

Abstract

The U.S. has experienced a resurgence of income inequality in the past decades. The evidence regarding the mortality implications of this phenomenon has been mixed. This study employs a rarely used method in mortality research, quantile regression (QR), to provide insight into the ongoing debate of whether income inequality is a determinant of mortality and to investigate the varying relationship between inequality and mortality throughout the mortality distribution. Analyzing a U.S. dataset where the five-year (1998–2002) average mortality rates were combined with other county-level covariates, we found that the association between inequality and mortality was not constant throughout the mortality distribution and the impact of inequality on mortality steadily increased until the 80th percentile. When accounting for all potential confounders, inequality was significantly and positively related to mortality; however, this inequality–mortality relationship did not hold across the mortality distribution. A series of Wald tests confirmed this varying inequality–mortality relationship, especially between the lower and upper tails. The large variation in the estimated coefficients of the Gini index suggested that inequality had the greatest influence on those counties with a mortality rate of roughly 9.95 deaths per 1000 population (80th percentile) compared to any other counties. Furthermore, our results suggest that the traditional analytic methods that focus on mean or median value of the dependent variable can be, at most, applied to a narrow 20 percent of observations. This study demonstrates the value of QR. Our findings provide some insight as to why the existing evidence for the inequality–mortality relationship is mixed and suggest that analytical issues may play a role in clarifying whether inequality is a robust determinant of population health.

Suggested Citation

  • Yang, Tse-Chuan & Chen, Vivian Yi-Ju & Shoff, Carla & Matthews, Stephen A., 2012. "Using quantile regression to examine the effects of inequality across the mortality distribution in the U.S. counties," Social Science & Medicine, Elsevier, vol. 74(12), pages 1900-1910.
    • Handle: RePEc:eee:socmed:v:74:y:2012:i:12:p:1900-1910
    DOI: 10.1016/j.socscimed.2012.02.029
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    References listed on IDEAS

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    4. Owen O'Donnell & Eddy Van Doorslaer & Tom Van Ourti, 2013. "Health and Inequality," Tinbergen Institute Discussion Papers 13-170/V, Tinbergen Institute.
    5. Fiona Imlach Gunasekara & Kristie Carter & Peter Crampton & Tony Blakely, 2013. "Income and individual deprivation as predictors of health over time," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 58(4), pages 501-511, August.
    6. Margarita Carvalho & João Cerejeira, 2019. "Level Leverage decisions and manager characteristics," NIPE Working Papers 09/2019, NIPE - Universidade do Minho.
    7. Irena Antošová & Naďa Hazuchová & Jana Stávková, 2021. "Income situation of agricultural households of EU countries," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(4), pages 121-128.

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