Distribution regression with sample selection, with an application to wage decompositions in the UK - Victor Chernozhukov; Iván Fernández-Val; Siyi Luo (Centre for Microdata Methods and Practice (cemmap), 2018)
We develop a distribution regression model under endogenous sample selection. This model is a semiparametric generalization of the Heckman selection model that accommodates much rich patterns of heterogeneity in the selection process and effect of the covariates. The model applies to continuous, discrete and mixed outcomes. We study the identi fication of the model, and develop a computationally attractive two-step method to estimate the model parameters, where the fi rst step is a probit regression for the selection equation and the second step consists of multiple distribution regressions with selection corrections for the outcome equation. We construct estimators of functionals of interest such as actual and counterfactual distributions of latent and observed outcomes via plug-in rule. We derive functional central limit theorems for all the estimators and show the validity of multiplier bootstrap to carry out functional inference. We apply the methods to wage decompositions in the UK using new data. Here we decompose the difference between the male and female wage distributions into four effects: composition, wage structure, selection structure and selection sorting. We uncover positive sorting for single men and negative sorting for married women that accounts for a substantial fraction of the gender wage gap at the top of the distribution. These fi ndings can be interpreted as evidence of assortative matching in the marriage market and glass-ceiling in the labor market.
Keywords: 330; Sample selection; distribution regression; quantile; heterogeneity; uniform inference; gender wage gap; assortative matching; glass ceiling