Decomposition of Gender or Racial Inequality with Endogenous Intervening Covariates: An Extension of the DiNardo-Fortin-Lemieux Method

In: Sociological methodology, Band 45, Heft 1, S. 388-428

Abstract

This paper begins by clarifying that propensity-score weighting in the DiNardo-Fortin-Lemieux (DFL) decomposition analysis—unlike propensity-score weighting in Rubin's causal model, in which confounding covariates can be endogenous—may generate biased estimates for the decomposition of inequality into "direct" and "indirect" components when intervening variables are endogenous. The paper also clarifies that the Blinder-Oaxaca method confounds the modeling of two distinct counterfactual situations: one in which the covariate effects of the first group become equal to those of the second group and the other in which the covariate distribution of the second group becomes equal to that of the first group. The paper shows that the DFL method requires a distinct condition to provide an unbiased decomposition of inequality that remains under each counterfactual situation. The paper then introduces a combination of the DFL method with Heckman's two-step method as a way of testing and eliminating bias in the DFL estimate when some intervening covariates are endogenous. The paper also intends to bring gender and race back to the center of statistical causal analysis. An application focuses on the decomposition of gender inequality in earned income among white-collar regular employees in Japan.