Extremal Quantile Regressions for Selection Models and the Black-White Wage Gap

Xavier D'Haultfoeuille, Arnaud Maurel and Yichong Zhang

No 20257, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C21 C24 J31 (search for similar items in EconPapers)
Date: 2014-06
New Economics Papers: this item is included in nep-ecm, nep-lab and nep-lma
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Published as Xavier D’Haultfœuille & Arnaud Maurel & Yichong Zhang, 2017. "Extremal quantile regressions for selection models and the black–white wage gap," Journal of Econometrics, .

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Journal Article: Extremal quantile regressions for selection models and the black–white wage gap (2018)
Working Paper: Extremal Quantile Regressions for Selection Models and the Black-White Wage Gap (2014)
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