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Nonparametric Panel Estimation of Labor Supply

Bibliographic Data

ID19419482
AuthorsGaosheng Ju (Fudan University), Li Gan (0009-0009-3869-569X, Research Institute of Economics and Management, Southwestern University of Finance and Economics, China; and Department of Economics, Texas A&M University, College Station, TX 77843-4228), Qi Li (0000-0003-3042-3584, International School of Economics and Management, Capital University of Economics and Business, China; Department of Economics, Texas A&M University, College Station, TX 77843-4228)
Year2019
Volume37
Issue2
Pages260-274
Publication date2019-04-03
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.2017.1321546
OpenAlexW2663508797
LanguageEN
References cited33

In this article, we estimate structural labor supply with piecewise-linear budgets and nonseparable endogenous unobserved heterogeneity. We propose a two-stage method to address the endogeneity issue that comes from the correlation between the covariates and unobserved heterogeneity. In the first stage, Evdokimov’s nonparametric de-convolution method serves to identify the conditional distribution of unobserved heterogeneity from the quasi-reduced model that uses panel data. In the second stage, the conditional distribution is plugged into the original structural model to estimate labor supply. We apply this methodology to estimate the labor supply of U.S. married men in 2004 and 2005. Our empirical work demonstrates that ignoring the correlation between the covariates and unobserved heterogeneity will bias the estimates of wage elasticities upward. The labor elasticity estimated from a fixed effects model is less than half of that obtained from a random effects model

Covariate · Econometrics · Economics · Endogeneity · Instrumental variable · Labour economics · Nonparametric statistics · Panel data · Statistics · Wage · Fiscal Policy and Economic Growth · Gender, Labor, and Family Dynamics · Labor market dynamics and wage inequality · Mathematics

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Highly citedNo

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