Publication Type
Journal Article
Version
acceptedVersion
Publication Date
3-2018
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
Black-white gap, Extreme quantile regression, Intermediate quantile, sample selection models
Discipline
Econometrics
Research Areas
Econometrics
Publication
Journal of Econometrics
Volume
203
Issue
1
First Page
129
Last Page
142
ISSN
0304-4076
Identifier
10.1016/j.jeconom.2017.11.004
Publisher
Elsevier
Citation
D'HAULTFOEUILLE, Xavier; MAUREL, Arnaud; and ZHANG, Yichong.
Extremal quantile regressions for selection models and the black-white wage gap. (2018). Journal of Econometrics. 203, (1), 129-142.
Available at: https://ink.library.smu.edu.sg/soe_research/2030
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.jeconom.2017.11.004