Nonparametric Panel Estimation of Labor Supply

A-Tier
Journal: Journal of Business & Economic Statistics
Year: 2019
Volume: 37
Issue: 2
Pages: 260-274

Authors (3)

Gaosheng Ju (not in RePEc) Li Gan (Texas A&M University) Qi Li (not in RePEc)

Score contribution per author:

1.341 = (α=2.01 / 3 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

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.

Technical Details

RePEc Handle
repec:taf:jnlbes:v:37:y:2019:i:2:p:260-274
Journal Field
Econometrics
Author Count
3
Added to Database
2026-01-25