Ian M. Schmutte

Job Referral Networks and the Determination of Earnings in Local Labor Markets

Despite their documented importance in the labor market, little is known about how workers use social networks to find jobs and their resulting effect on earnings. I use geographically detailed US employer-employee data to infer the role of social networks in connecting workers to jobs in high-paying firms. To identify social interactions in job search, I exploit variation in social network quality within small neighborhoods. Workers are more likely to change jobs, and more likely to move to a higher-paying firm, when their neighbors are employed in high-paying firms. Furthermore, local referral networks help match high-ability workers to high-paying firms.

A Formal Test of Assortative Matching in the Labor Market

We estimate a structural model of job assignment in the presence of coordination frictions due to Shimer (2005). The coordination friction model places restrictions on the joint distribution of worker and firm effects from a linear decomposition of log labor earnings. These restrictions permit estimation of the unobservable ability and productivity differences between workers and their employers as well as the way workers sort into jobs on the basis of these unobservable factors. The estimation is performed on matched employer-employee data from the LEHD program of the U.S. Census Bureau. The estimated correlation between worker and firm effects from the earnings decomposition is close to zero, a finding that is often interpreted as evidence that there is no sorting by comparative advantage in the labor market. Our estimates suggest that this finding actually results from a lack of sufficient heterogeneity in the workforce and available jobs. Workers do sort into jobs on the basis of productive differences, but the effects of sorting are not visible because of the composition of workers and employers.

Getting Handcuffs on an Octopus

Theoretical work on minimum wage policy emphasizes labor market dynamics, but the resulting implications for worker mobility remain largely untested. The authors show that in the teenage labor market, higher minimum wage standards reduce worker flows and increase job stability. Furthermore, they find that the employment effects of a relatively higher minimum wage vary considerably across markets with different levels of turnover and labor market tightness. Results help to explain the small effects of minimum wage standards on employment commonly found in the aggregate data and are consistent with labor market models that involve search frictions.

Labor markets with endogenous job referral networks: Theory and empirical evidence

This paper develops a model of frictional job search in which job referral networks evolve endogenously in response to local labor market conditions. An intuitive “Network Balance” condition characterizes the equilibrium density of the job referral network. The model helps explain observed counter-cyclical movements in referral-based search, and shows that endogenous referral networks may amplify labor market shocks. It also implies that the use of referrals by others limits the effectiveness of referral-based search. I find support for this prediction using data from the Cornell National Social Survey. The data show workers are less likely to find jobs through referral in markets where referrals are more widely used.

Economic Analysis and Statistical Disclosure Limitation

This paper explores the consequences for economic research of methods used by data publishers to protect the privacy of their respondents. We review the concept of statistical disclosure limitation for an audience of economists who may be unfamiliar with these methods. We characterize what it means for statistical disclosure limitation to be ignorable. When it is not ignorable, we consider the effects of statistical disclosure limitation for a variety of research designs common in applied economic research. Because statistical agencies do not always report the methods they use to protect confidentiality, we also characterize settings in which statistical disclosure limitation methods are discoverable; that is, they can be learned from the released data. We conclude with advice for researchers, journal editors, and statistical agencies.

Modeling Endogenous Mobility in Wage Determiniation

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.

Sorting Between and Within Industries: A Testable Model of Assortative Matching

We test for sorting of workers between and within industrial sectors in a directed search model with coordination frictions. We fit the model to sector-specific vacancy and output data along with publicly-available statistics that characterize the distribution of worker and employer wage heterogeneity across sectors. Our empirical method is general and can be applied to a broad class of assignment models. The results indicate that industries are the loci of sorting-more productive workers are employed in more productive industries. The evidence confirms assortative matching can be present even when worker and employer components of wage heterogeneity are weakly correlated.

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 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-of-sample 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. Supplementary materials for this article are available online.