Income Inequality and Health: New Methodology and an Application

Prior studies propose a way to express the Gini index of income inequality as a function of the ratio of mean to median household income under the assumption that individual income follows the lognormal distribution. This allows for easy and precise construction of annual US income inequality indices at different levels of geography. In this paper, we are the first to express the Atkinson index in a similar manner. We also contribute to the literature by expressing both indices under the assumption that individual income follows the Pareto distribution. We merge these indices into an individual level dataset consisting of the 2001-2012 annual editions of the U.S. Behavioral Risk Factor Surveillance System at the state and county level. In an application, we find preliminary evidence that greater income inequality negatively affects overall self-reported health.