Working Paper: NBER ID: w20257
Authors: Xavier Dhaultfoeuille; Arnaud Maurel; Yichong Zhang
Abstract: We consider the estimation of a semiparametric location-scale model subject to endogenous selection, in the absence of an instrument or a large support regressor. Identification relies on the independence between the covariates and selection, for arbitrarily large values of the outcome. In this context, we propose a simple estimator, which combines extremal quantile regressions with minimum distance. We establish the asymptotic normality of this estimator by extending previous results on extremal quantile regressions to allow for selection. Finally, we apply our method to estimate the black-white wage gap among males from the NLSY79 and NLSY97. We find that premarket factors such as AFQT and family background characteristics play a key role in explaining the level and evolution of the black-white wage gap.
Keywords: quantile regression; selection models; black-white wage gap
JEL Codes: C21; C24; J31
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| premarket factors (G14) | wage gap (J31) |
| selection bias (C24) | wage gap (J31) |
| wage gap (J31) | residual wage gap (J31) |
| wage gap (J31) | convergence over time (F62) |