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Extremal quantile regressions for selection models and the black–white wage gap

D’Haultfœuille, Xavier, Arnaud Maurel () and Yichong Zhang
Authors registered in the RePEc Author Service: Xavier D'Haultfoeuille

Journal of Econometrics, 2018, vol. 203, issue 1, 129-142

Abstract: We consider the estimation of a semiparametric sample selection model without instrument or large support regressor. Identification relies on the independence between the covariates and selection, for arbitrarily large values of the outcome. We propose a simple estimator based on extremal quantile regression and establish its asymptotic normality 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 play a key role in explaining the black–white wage gap.

Keywords: Sample selection models; Extremal quantile regressions; Black–white wage gap (search for similar items in EconPapers)
JEL-codes: C21 C24 J31 (search for similar items in EconPapers)
Date: 2018
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Working Paper: Extremal Quantile Regressions for Selection Models and the Black-White Wage Gap (2014) Downloads
Working Paper: Extremal Quantile Regressions for Selection Models and the Black-White Wage Gap (2014) Downloads
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DOI: 10.1016/j.jeconom.2017.11.004

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