Primary Submission Category: Causal Inference Education
Disparity Analysis: A Tale of Two Approaches
Authors: Aleksei Opacic, Lai Wei, Xiang Zhou,
Presenting Author: Aleksei Opacic*
To understand the patterns and trends of various forms of inequality, quantitative social science research has typically relied on statistical models linking the conditional mean of an outcome variable to a set of explanatory factors, a prime example of which is the widely used Kitagawa-Oaxaca-Blinder (KOB) method. In this paper, we explicate, contrast, and extend two distinct approaches to studying group disparities, which we term the descriptive approach, as epitomized by the KOB method and its variants, and the prescriptive approach, which focuses on how a disparity of interest would change under a hypothetical intervention to one or more manipulable treatments. For the descriptive approach, we propose a generalized nonparametric KOB decomposition that considers multiple (sets of) explanatory variables sequentially. For the prescriptive approach, we introduce a variety of stylized interventions, such as lottery-type and affirmative-action-type interventions that close between-group gaps in treatment. We illustrate the two approaches by assessing the Black-White gap in college completion, how it is statistically explained by racial differences in socioeconomic background, academic performance, and college selectivity, and the extent to which it would be reduced under hypothetical reallocations of college-goers from different racial and economic backgrounds into different tiers of college – reallocations that could be targeted by race- or class-conscious admissions policies.