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Heterogeneity and Learning in Labor Markets

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  • Simon D. Woodcock

    (Simon Fraser University)

Abstract

This paper examines the role of agent heterogeneity and learning on wage dispersion and employment dynamics. In the first half of the paper, I present an equilibrium matching model where heterogeneous workers and firms learn about match quality and bargain over wages. The model generalizes Jovanovic (1979) to the case of heterogeneous workers and firms. Equilibrium wage dispersion arises due to productivity differences across workers, technological differences across firms, and heterogeneity in beliefs about match quality. Under a simple CRS technology, the equilibrium wage is additively separable in worker- and firm-specific components, and in the posterior mean of beliefs about match quality. This parallels the 'person and firm effects' empirical specification of Abowd et. al. (1999, AKM) and others. It consequently provides a theoretical context for the AKM model, and a formal economic interpretation of their empirical person and firm effects. The model also yields an assortative matching result that predicts a negative correlation between estimated person and firm effects, which is consistent with most empirical evidence. Finally, the model makes novel predictions about the relationship between the person and firm effects and separation behavior, job duration, and firm size. In the second half of the paper, I test the model's empirical predictions. I estimate fixed and mixed effects specifications of the equilibrium wage function on the LEHD database. The mixed effect specifications generalize the earlier work of AKM and others. The learning component of the matching model implies a specific structure for the error covariance. I exploit this structure to test whether earnings residuals are consistent with Bayesian learning, and to estimate structural parameters of the matching model. I find considerable support for the matching model in these data.

Suggested Citation

  • Simon D. Woodcock, 2005. "Heterogeneity and Learning in Labor Markets," Labor and Demography 0511012, EconWPA.
  • Handle: RePEc:wpa:wuwpla:0511012
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    Cited by:

    1. Woodcock, Simon D., 2015. "Match effects," Research in Economics, Elsevier, vol. 69(1), pages 100-121.
    2. Cristian Bartolucci & Francesco Devicienti, 2012. "Better Workers Move to Better Firms: A Simple Test to Identify Sorting," Carlo Alberto Notebooks 259, Collegio Carlo Alberto.
    3. Araújo, Bruno César & Paz, Lourenço S., 2014. "The effects of exporting on wages: An evaluation using the 1999 Brazilian exchange rate devaluation," Journal of Development Economics, Elsevier, vol. 111(C), pages 1-16.
    4. Woodcock, Simon D., 2008. "Wage differentials in the presence of unobserved worker, firm, and match heterogeneity," Labour Economics, Elsevier, vol. 15(4), pages 771-793, August.
    5. Sónia Torres & Pedro Portugal & John T. Addison & Paulo Guimarães, 2013. "The sources of wage variation: a three-way high-dimensional fixed effects regression model," Working Papers w201309, Banco de Portugal, Economics and Research Department.
    6. Torben Sørensen & Rune Vejlin, 2013. "The importance of worker, firm and match effects in the formation of wages," Empirical Economics, Springer, vol. 45(1), pages 435-464, August.
    7. Albert Berry, 2017. "Employment and Efficiency Effects of Social Security (SS) and Social Protection (SP) Systems in the Context of an Informal Sector and Market Imperfections: A Conceptual Review," DOCUMENTOS DE TRABAJO 015567, UNIVERSIDAD DEL ROSARIO.
    8. Carl Sanders, 2012. "Skill Uncertainty, Skill Accumulation, and Occupational Choice," 2012 Meeting Papers 633, Society for Economic Dynamics.

    More about this item

    Keywords

    matching; learning; heterogeneity; longitudinal linked data; mixed model;

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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