Modeling Endogenous Mobility in Wage Determiniation

John Abowd (), Kevin McKinney and Ian M. Schmutte

Working Papers from U.S. Census Bureau, Center for Economic Studies

Abstract: We evaluate the bias from endogenous job mobility in fixed-effects estimates of worker- and firm-specific earnings heterogeneity using longitudinally linked employer-employee data from the LEHD infrastructure file system of the U.S. Census Bureau. First, we propose two new residual diagnostic tests of the assumption that mobility is exogenous to unmodeled determinants of earnings. Both tests reject exogenous mobility. We relax the exogenous mobility assumptions by modeling the evolution of the matched data as an evolving bipartite graph using a Bayesian latent class framework. Our results suggest that endogenous mobility biases estimated firm effects toward zero. To assess validity, we match our estimates of the wage components to out-of-sample estimates of revenue per worker. The corrected estimates attribute much more of the variation in revenue per worker to variation in match quality and worker quality than the uncorrected estimates.

Keywords: Earnings heterogeneity; Mobility Bias; Latent Class Model; Markov Chain Monte Carlo (search for similar items in EconPapers)
Pages: 49 pages
Date: 2015-06
New Economics Papers: this item is included in nep-dcm, nep-lab and nep-ure
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Citations: View citations in EconPapers (8)

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https://www2.census.gov/ces/wp/2015/CES-WP-15-18.pdf First version, 2015 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:cen:wpaper:15-18

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