Advancing Distribution Decomposition Methods Beyond Common Supports: Applications to Racial Wealth Disparities

TL;DR

This paper introduces a new distribution decomposition method that relaxes the common support assumption, enabling more comprehensive analysis of racial wealth disparities by including all observations.

Contribution

The proposed method extends existing decomposition techniques by allowing for non-overlapping supports, providing a more complete understanding of wealth gaps.

Findings

Including all observations reveals additional contributions to the wealth gap (3%-19%).

The method offers a more accurate decomposition of racial wealth disparities.

Application to U.S. data illustrates the importance of non-overlapping supports.

Abstract

I generalize state-of-the-art approaches that decompose differences in the distribution of a variable of interest between two groups into a portion explained by covariates and a residual portion. The method that I propose relaxes the overlapping supports assumption, allowing the groups being compared to not necessarily share exactly the same covariate support. I illustrate my method revisiting the black-white wealth gap in the U.S. as a function of labor income and other variables. Traditionally used decomposition methods would trim (or assign zero weight to) observations that lie outside the common covariate support region. On the other hand, by allowing all observations to contribute to the existing wealth gap, I find that otherwise trimmed observations contribute from 3% to 19% to the overall wealth gap, at different portions of the wealth distribution.