Extremal Quantile Regressions for Selection Models and the Black-White Wage Gap
Arnaud Maurel () and
Yichong Zhang ()
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Yichong Zhang: Singapore Management University
No 8256, IZA Discussion Papers from Institute of Labor Economics (IZA)
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: 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)
Pages: 67 pages
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Published in: Journal of Econometrics, 2018, 203 (1), 129-142
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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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