Modeling Endogenous Mobility in Earnings Determination

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. Replication code can be found at DOI:10.5281/zenodo.zenodo.376600 and our Github repository endogenous-mobility-replication .Abowd acknowledges direct support from NSF Grants SES-0339191, CNS-0627680, SES-0922005, TC-1012593, and SES-1131848. This research uses data from the Census Bureau's Longitudinal EmployerHousehold Dynamics Program, which was partially supported by National Science Foundation Grants SES-9978093, SES-0339191 and ITR-0427889; National Institute on Aging Grant AG018854; and grants from the Alfred P. Sloan Foundation. AbstractWe 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 exogenous mobility by modeling the matched data as an evolving bipartite graph using a Bayesian latent-type framework. Our results suggest that allowing endogenous mobility increases the variation in earnings explained by individual heterogeneity and reduces the proportion due to employer and match effects. To assess external validity, we match our estimates of the wage components to out-ofsample estimates of revenue per worker. The mobility-bias corrected estimates attribute much more of the variation in revenue per worker to variation in match quality and worker quality than the uncorrected estimates.

A Distributional Framework for Matched Employer Employee Data

“…This suggests that workers are more likely to move when paid less. Such evidence of endogenous mobility is in line with estimates in Abowd, McKinney, and Schmutte (2018), and it is consistent with the predictions of wage posting models with match-specific heterogeneity. In contrast, our estimates in the bottom panel suggest that high earnings realizations do not strongly affect mobility.…”

“…Also related, Abowd, McKinney, and Schmutte () proposed a Bayesian approach where both firm and worker heterogeneity are discrete. Their setup allows for latent match effects to drive job mobility, in a way that is related to—but different from—our dynamic model.…”

Human Capital, Firm Capabilities, and Innovation

“…One possible explanation is that our estimates suffer from a downward bias, either caused by the presence of match quality and endogenous inventor mobility or as the result of limited mobility among moving inventors in the connected worker-firm network (see Andrews et al 2008). We implement a battery of empirical checks to examine the implications of endogenous mobility or limited mobility bias (Abowd et al 2019;Lazear et al 2015 andJochmans andWeidner 2019). We find that these potential downward biases are unlikely to explain the negative assortative matching between inventors and firms we uncover.…”

“…A potential channel for the error term to influence mobility is through the existence of "match effects" between firms and workers. Both Lazear et al (2015) and Abowd et al (2019) examine the correlation between the average residual of a "match" and the fixed effects of future employers. The test of Abowd et al (2019) partitions moving workers by the decile of their origin firm fixed effect and their destination firm fixed effect.…”

“…However, the bottom panel of Figure 6 depicts the number of moving inventors in each decile to decile cell. It is clear that some cells are not sufficiently populated in our sample to draw firm conclusions and conduct the chi-square test described in Abowd et al (2019). < Insert Figure 6 and Table 6 around here > An alternative test, suggested by Lazear et al (2015), takes the subsample of mobile inventors and regresses the destination firm's fixed effect on the average residual during employment at the origin firm.…”