The Blinder–Oaxaca Decomposition for Nonlinear Regression Models

Abstract

In this article, a general Blinder–Oaxaca decomposition for nonlinear models is derived, which allows the difference in an outcome variable between two groups to be decomposed into several components. We show how, using nldecompose, this general decomposition can be applied to different models with discrete and limited dependent variables. We further demonstrate how the standard errors of the estimated components can be calculated by using Stata's bootstrap command as a prefix.