Agent Heterogeneity and Learning: An Application to Labor Markets
October 2002
Working Paper Number:
tp-2002-20
Abstract
I develop a matching model with heterogeneous workers, rms, and worker-firm
matches, and apply it to longitudinal linked data on employers and employees. Workers
vary in their marginal product when employed and their value of leisure when unemployed.
Firms vary in their marginal product and cost of maintaining a vacancy. The
marginal product of a worker-firm match also depends on a match-specific interaction
between worker and rm that I call match quality. Agents have complete information
about worker and rm heterogeneity, and symmetric but incomplete information about
match quality. They learn its value slowly by observing production outcomes. There
are two key results. First, under a Nash bargain, the equilibrium wage is linear in a
person-specific component, a firm-specific component, and the posterior mean of beliefs
about match quality. Second, in each period the separation decision depends only on
the posterior mean of beliefs and person and rm characteristics. These results have
several implications for an empirical model of earnings with person and rm e ects.
The rst implies that residuals within a worker-firm match are a martingale; the second
implies the distribution of earnings is truncated.
I test predictions from the matching model using data from the Longitudinal
Employer-Household Dynamics (LEHD) Program at the US Census Bureau. I present
both xed and mixed model specifications of the equilibrium wage function, taking
account of structural aspects implied by the learning process. In the most general
specification, earnings residuals have a completely unstructured covariance within a
worker-firm match. I estimate and test a variety of more parsimonious error structures,
including the martingale structure implied by the learning process. I nd considerable
support for the matching model in these data.
Document Tags and Keywords
Keywords
Keywords are automatically generated using KeyBERT, a powerful and innovative
keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant
keywords.
By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the
text, highlighting the most significant topics and trends. This approach not only enhances searchability but
provides connections that go beyond potentially domain-specific author-defined keywords.
:
economist,
econometric,
estimating,
employ,
employed,
labor,
longitudinal,
endogenous,
job,
heterogeneity,
employment estimates,
tenure,
hiring,
employing,
econometrician,
educated,
census bureau,
wage data,
employer household,
longitudinal employer,
research census,
labor markets,
employee data,
unemployed
Tags
Tags are automatically generated using a pretrained language model from spaCy, which excels at
several tasks, including entity tagging.
The model is able to label words and phrases by part-of-speech,
including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are
identified to contain references to specific institutions, datasets, and other organizations.
:
Standard Industrial Classification,
National Science Foundation,
Ordinary Least Squares,
American Immigration Council,
Current Population Survey,
Decennial Census,
Employer Identification Numbers,
Survey of Income and Program Participation,
Cornell University,
Council of Economic Advisers,
National Institute on Aging,
Alfred P Sloan Foundation,
Longitudinal Employer Household Dynamics,
Protected Identification Key,
CDF
Similar Working Papers
Similarity between working papers are determined by an unsupervised neural
network model
know as Doc2Vec.
Doc2Vec is a model that represents entire documents as fixed-length vectors, allowing for the
capture of semantic meaning in a way that relates to the context of words within the document. The model learns to
associate a unique vector with each document while simultaneously learning word vectors, enabling tasks such as
document classification, clustering, and similarity detection by preserving the order and structure of words. The
document vectors are compared using cosine similarity/distance to determine the most similar working papers.
Papers identified with 🔥 are in the top 20% of similarity.
The 10 most similar working papers to the working paper 'Agent Heterogeneity and Learning: An Application to Labor Markets' are listed below in order of similarity.
-
Working PaperModeling Labor Markets with Heterogeneous Agents and Matches🔥
May 2002
Working Paper Number:
tp-2002-19
I present a matching model with heterogeneous workers, firms, and worker-fim matches. The model generalizes the seminal Jovanovic (1979) model to the case of heterogeneous agents. The equilibrium wage is linear in a person-specific component, a firm-specific component, and a match specific component that varies with tenure. Under certain conditions, the equilibrium wage takes a simpler structure where the match specific component does not vary with tenure. I discuss fixed- and mixedeffect methods for estimating wage models with this structure on longitudinal linked employer-employee data. The fixed effect specification relies on restrictive identification conditions, but is feasible for very large databases. The mixed model requires less restrictive identification conditions, but is feasible only on relatively small databases. Both the fixed and mixed models generate empirical person, firm, and match effects with characteristics that are consistent with predictions from the matching model; the mixed model moreso than the fixed model. Shortcomings of the fixed model appear to be artifacts of the identification conditions.View Full Paper PDF
-
Working PaperWage Dispersion, Compensation Policy and the Role of Firms🔥
November 2005
Working Paper Number:
tp-2005-04
Empirical work in economics stresses the importance of unobserved firm- and person-level characteristics in the determination of wages, finding that these unobserved components account for the overwhelming majority of variation in wages. However, little is known about the mechanisms sustaining these wage di'er- entials. This paper attempts to demystify the firm-side of the puzzle by developing a statistical model that enriches the role that firms play in wage determination, allowing firms to influence both average wages as well as the returns to observable worker characteristics. I exploit the hierarchical nature of a unique employer-employee linked dataset for the United States, estimating a multilevel statistical model of earnings that accounts for firm-specific deviations in average wages as well as the returns to components of human capital - race, gender, education, and experience - while also controlling for person-level heterogeneity in earnings. These idiosyncratic prices reflect one aspect of firm compensation policy; another, and more novel aspect, is the unstructured characterization of the covariance of these prices across firms. I estimate the model's variance parameters using Restricted (or Residual) Maximum Likelihood tech- niques. Results suggest that there is significant variation in the returns to worker characteristics across firms. First, estimates of the parameters of the covariance matrix of firm-specific returns are statistically significant. Firms that tend to pay higher average wages also tend to pay higher than average returns to worker characteristics; firms that tend to reward highly the human capital of men also highly reward the human capital of women. For instance, the correlation between the firm-specific returns to education for men and women is 0.57. Second, the firm-specific returns account for roughly 9% of the variation in wages - approximately 50% of the variation in wages explained by firm-specific intercepts alone. The inclusion of firm-specific returns ties variation in wages, otherwise attributable to firm-specific intercepts, to observable components of human capital.View Full Paper PDF
-
Working PaperA Formal Test of Assortative Matching in the Labor Market🔥
November 2009
Working Paper Number:
CES-09-40
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 his 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.View Full Paper PDF
-
Working PaperRanking Firms Using Revealed Preference🔥
January 2017
Working Paper Number:
CES-17-61
This paper estimates workers' preferences for firms by studying the structure of employer-toemployer transitions in U.S. administrative data. The paper uses a tool from numerical linear algebra to measure the central tendency of worker flows, which is closely related to the ranking of firms revealed by workers' choices. There is evidence for compensating differential when workers systematically move to lower-paying firms in a way that cannot be accounted for by layoffs or differences in recruiting intensity. The estimates suggest that compensating differentials account for over half of the firm component of the variance of earnings.View Full Paper PDF
-
Working PaperJob Referral Networks and the Determination of Earnings in Local Labor Markets🔥
December 2010
Working Paper Number:
CES-10-40
Referral networks may affect the efficiency and equity of labor market outcomes, but few studies have been able to identify earnings effects empirically. To make progress, I set up a model of on-the-job search in which referral networks channel information about high-paying jobs. I evaluate the model using employer-employee matched data for the U.S. linked to the Census block of residence for each worker. The referral effect is identified by variations in the quality of local referral networks within narrowly defined neighborhoods. I find, consistent with the model, a positive and significant role for local referral networks on the full distribution of earnings outcomes from job search.View Full Paper PDF
-
Working PaperSorting Between and Within Industries: A Testable Model of Assortative Matching
January 2017
Working Paper Number:
CES-17-43
We test Shimer's (2005) theory of the sorting of workers between and within industrial sectors based on directed search with coordination frictions, deliberately maintaining its static general equilibrium framework. We fit the model to sector-specific wage, vacancy and output data, including 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 confirm that strong assortative matching can be present even when worker and employer components of wage heterogeneity are weakly correlated.View Full Paper PDF
-
Working PaperDistribution Preserving Statistical Disclosure Limitation
September 2006
Working Paper Number:
tp-2006-04
One approach to limiting disclosure risk in public-use microdata is to release multiply-imputed, partially synthetic data sets. These are data on actual respondents, but with confidential data replaced by multiply-imputed synthetic values. A mis-specified imputation model can invalidate inferences because the distribution of synthetic data is completely determined by the model used to generate them. We present two practical methods of generating synthetic values when the imputer has only limited information about the true data generating process. One is applicable when the true likelihood is known up to a monotone transformation. The second requires only limited knowledge of the true likelihood, but nevertheless preserves the conditional distribution of the confidential data, up to sampling error, on arbitrary subdomains. Our method maximizes data utility and minimizes incremental disclosure risk up to posterior uncertainty in the imputation model and sampling error in the estimated transformation. We validate the approach with a simulation and application to a large linked employer-employee database.View Full Paper PDF
-
Working PaperLabor Reallocation, Employment, and Earnings: Vector Autoregression Evidence
January 2017
Working Paper Number:
CES-17-11R
Analysis of the labor market has given increasing attention to the reallocation of jobs across employers and workers across jobs. However, whether and how job reallocation and labor market 'churn' affects the health of the labor market remains an open question. In this paper, we present time series evidence for the U.S. 1993-2013 and consider the relationship between labor reallocation, employment, and earnings using a vector autoregression (VAR) framework. We find that an increase in labor market churn by 1 percentage point predicts that, in the next quarter, employment will increase by 100 to 560 thousand jobs, lowering the unemployment rate by 0.05 to 0.25 percentage points. Job destruction does not predict future changes in employment but a 1 percentage point increase in job destruction leads to an increase in future unemployment 0.14 to 0.42 percentage points. We find mixed results on the relationship between labor reallocation rates and earnings: we nd that, especially for earnings derived from administrative records data, a 1 percentage point increase to either job destruction or churn leads to increased earnings of less than 2 percent. Results vary substantially depending on the earnings measure we use, and so the evidence inconsistent on whether productivity-enhancing aspects of churn and job destruction provide earnings gains for workers in aggregate. Our findings on churn leading to increased employment and a lower unemployment rate are consistent with models of replacement hiring and vacancy chains.View Full Paper PDF
-
Working PaperThe Long-Term Effects of Job Mobility on the Adult Earnings of Young Men: Evidence from Integrated Employer-Employee Data
June 2005
Working Paper Number:
CES-05-05
The paper follows a population of 18-year-old men to examine the impact that early job mobility has on their earnings prospects as young adults. Longitudinal employer-employee data from the state of Maryland allow me to take into consideration the endogenous determination of mobility in response to unobserved worker as well as firm characteristics, which may lead to spurious results. The descriptive portion of the paper shows that mobility patterns of young workers differ considerably with the characteristics of the firm; however, growth patterns are not significantly different on average. Workers employed in high-turnover firms (such as those in retail and services) experience more job turnover but similar rates of wage growth compared to workers employed in low turnover firms (such as those in manufacturing); however, their wage levels remain below and the wage gap actually increases over time. Regression results controlling for unobservable show that employers in the low-turnover sector discount earnings of workers who displayed early market mobility. By contrast, I find no evidence that mobility has negative effects for workers that remain employed in the high turnover sector.View Full Paper PDF
-
Working PaperModeling Endogenous Mobility in Wage Determiniation
June 2015
Working Paper Number:
CES-15-18
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.View Full Paper PDF
