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Is it Who You Are, Where You Work, or With Whom You Work? Reassessing the Relationship Between Skill Segregation and Wage Inequality

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  • Paul A. Lengermann
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    Abstract

    In a recent paper, Kremer & Maskin (QJE, forthcoming) develop an assignment model in which increases in the dispersion and mean of the skill distribution can lead simultaneously to increases in wage inequality and skill segregation. They then present evidence that, concurrent with rising wage inequality, wage segregation increased for production workers in the United States between 1975 and 1986. My paper argues that relying on wages as a proxy for skill may be problematic. Using a newly developed longitudinal dataset linking virtually the entire universe of workers in the state of Illinois to their employers, I decompose wages into components due, not only to person and firm heterogeneity, but also to the characteristics of their co-workers. Such "co-worker effects" capture the impact of a weighted sum of the characteristics of all workers in a firm on each individual employee’s wage. While rising wage segregation can result from greater skill segregation, it may also be due to changes in the variance of co-worker effects in the economy, or to changes in the covariance between the person, firm, and co-worker components of wages. Due to the limited availability of demographic information on workers, I rely on the person specific component of wages to proxy for co-worker "skills." Because these person effects are unknown ex ante, I implement an iterative estimation approach where they are first obtained from a preliminary regression that excludes any role for co-workers. Because virtually all person and firm effects are identified, the approach yields consistent estimates of the co-worker parameters. My estimates imply that a one standard deviation increase in both a firm’s average person effect and experience level is associated, on average, with wage increases of 3% to 5%. Firms that increase the wage premia they pay workers appear to do so in conjunction with upgrading worker quality. Interestingly, the average effect masks considerable variation in the relative importance of co-workers across industries. After allowing the co-worker parameters to vary across 2 digit industries, I find that industry average co-worker effects explain 26% of observed inter-industry wage differentials. Finally, I decompose the overall distribution of wages into components due to persons, firms, and coworkers. While co-worker effects do indeed serve to exacerbate wage inequality, the tendency for high and low skilled workers to sort non-randomly into firms plays a considerably more prominent role.

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    File URL: ftp://ftp2.census.gov/ces/tp/tp-2002-10.pdf
    File Function: First version, 2002
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    Bibliographic Info

    Paper provided by Center for Economic Studies, U.S. Census Bureau in its series Longitudinal Employer-Household Dynamics Technical Papers with number 2002-10.

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    Length: 60 pages
    Date of creation: Jun 2002
    Date of revision:
    Handle: RePEc:cen:tpaper:2002-10

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    Related research

    Keywords: wage inequality; skill segregation; employer-employee data; panel data;

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    References

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    1. Gibbons, R. & Katz, L.F., 1989. "Layoffs And Lemons," Working papers 531, Massachusetts Institute of Technology (MIT), Department of Economics.
    2. Burgess, Simon & Lane, Julia & Stevens, David, 2000. "Job Flows, Worker Flows, and Churning," Journal of Labor Economics, University of Chicago Press, vol. 18(3), pages 473-502, July.
    3. Bruce C. Fallick, 1996. "A review of the recent empirical literature on displaced workers," Industrial and Labor Relations Review, ILR Review, Cornell University, ILR School, vol. 50(1), pages 5-16, October.
    4. Haltiwanger, John C. & Lane, Julia I. & Spletzer, James R., 2007. "Wages, productivity, and the dynamic interaction of businesses and workers," Labour Economics, Elsevier, vol. 14(3), pages 575-602, June.
    5. John M. Abowd & Francis Kramarz & David N. Margolis, 1999. "High Wage Workers and High Wage Firms," Econometrica, Econometric Society, vol. 67(2), pages 251-334, March.
    6. Brockner, Joel & Wiesenfeld, Batia M. & Martin, Christopher L., 1995. "Decision Frame, Procedural Justice, and Survivors' Reactions to Job Layoffs," Organizational Behavior and Human Decision Processes, Elsevier, vol. 63(1), pages 59-68, July.
    7. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
    8. John M. Abowd & Paul A. Lengermann & Kevin L. McKinney, 2002. "The Measurement of Human Capital in the U.S. Economy," Longitudinal Employer-Household Dynamics Technical Papers 2002-09, Center for Economic Studies, U.S. Census Bureau, revised Mar 2003.
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    Cited by:
    1. Del Bono, Emilia & Weber, Andrea & Winter-Ebmer, Rudolf, 2008. "Clash of Career and Family. Fertility Decisions after Job Displacement," Economics Series 222, Institute for Advanced Studies.
    2. Huttunen, Kristiina & Moen, Jarle & Salvanes, Kjell G., 2006. "How Destructive Is Creative Destruction? The Costs of Worker Displacement," IZA Discussion Papers 2316, Institute for the Study of Labor (IZA).
    3. Yolanda K. Kodrzycki, 2007. "Using unexpected recalls to examine the long-term earnings effects of job displacement," Working Papers 07-2, Federal Reserve Bank of Boston.
    4. Peter Thompson & Mihaela Pintea, 2007. "Sorting, Selection, and Industry Shakeouts," Working Papers 0702, Florida International University, Department of Economics.
    5. Kevin McKinney & Lars Vilhuber, 2006. "Using linked employer-employee data to investigate the speed of adjustments in downsizing firms," Longitudinal Employer-Household Dynamics Technical Papers 2006-03, Center for Economic Studies, U.S. Census Bureau.

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