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Augmenting the Human Capital Earnings Equation with Measures of Where People Work

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  • Erling Barth
  • James Davis
  • Richard B. Freeman

Abstract

We augment standard log earnings equations for workers in US manufacturing with variables reflecting measured and unmeasured attributes of their employer. Using panel employee-establishment data, we find that establishment-level employment, education of coworkers, capital equipment per worker, and firm-level R&D intensity affects earnings substantially. Unobserved characteristics of employers captured by employer fixed effects also contribute to the variance of log earnings, although less than unobserved characteristics of individuals captured by individual fixed effects. The observed and unobserved measures of employers mediate the effects of individual characteristics on earnings and increase earnings inequality through sorting of workers among establishments.

Suggested Citation

  • Erling Barth & James Davis & Richard B. Freeman, 2018. "Augmenting the Human Capital Earnings Equation with Measures of Where People Work," Journal of Labor Economics, University of Chicago Press, vol. 36(S1), pages 71-97.
  • Handle: RePEc:ucp:jlabec:doi:10.1086/694187
    DOI: 10.1086/694187
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    1. Augmenting the Human Capital Earnings Equation with Measures of Where People Work
      by maximorossi in NEP-LTV blog on 2016-09-06 01:17:09

    Citations

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    Cited by:

    1. Molina-Domene, Maria, 2018. "Specialization matters in the firm size-wage gap," LSE Research Online Documents on Economics 88696, London School of Economics and Political Science, LSE Library.
    2. Jin Seo Cho & Peter C. B. Phillips & Juwon Seo, 2019. "Parametric Inference on the Mean of Functional Data Applied to Lifetime Income Curves," Working papers 2019rwp-153, Yonsei University, Yonsei Economics Research Institute.
    3. Giuseppe Berlingieri & Sara Calligaris & Chiara Criscuolo, 2018. "The Productivity-Wage Premium: Does Size Still Matter in a Service Economy?," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 328-333, May.
    4. Erling Barth & James C. Davis & Richard B. Freeman & Kristina McElheran, 2020. "Twisting the Demand Curve: Digitalization and the Older Workforce," Working Papers 20-37, Center for Economic Studies, U.S. Census Bureau.
    5. Knutsson, Polina, 2018. "Sorting on Unobserved Skills into New Firms," Working Papers 2018:38, Lund University, Department of Economics.
    6. Nathan Goldschlag & Ron Jarmin & Julia Lane & Nikolas Zolas, 2019. "Research Experience as Human Capital in New Business Outcomes," NBER Chapters, in: Measuring and Accounting for Innovation in the Twenty-First Century, National Bureau of Economic Research, Inc.
    7. John Haltiwanger & Henry Hyatt & Erika McEntarfer, 2018. "Who Moves Up the Job Ladder?," Journal of Labor Economics, University of Chicago Press, vol. 36(S1), pages 301-336.
    8. Maria Molina-Domene, 2018. "Specialization matters in the firm size-wage gap," CEP Discussion Papers dp1545, Centre for Economic Performance, LSE.

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    More about this item

    JEL classification:

    • J0 - Labor and Demographic Economics - - General
    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J3 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs
    • J40 - Labor and Demographic Economics - - Particular Labor Markets - - - General

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