Measuring Changes in Disparity Gaps: An Application to Health Insurance

Abstract: We propose a method for reporting how program evaluations reduce gaps between groups, such as the gender or Black-white gap. We first show that the reduction in disparities between groups can be written as the difference in conditional average treatment effects (CATE) for each group. Then, using a Kitagawa-Oaxaca-Blinder-style decomposition, we highlight how these CATE can be decomposed into unexplained differences in CATE in other observables versus differences in composition across other observables (e.g. the "endowment"). Finally, we apply this approach to study the impact of Medicare on American's access to health insurance.