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Worker productivity and wages: Evidence from linked employer-employee data

Author

Listed:
  • Ana Sofia Lopes

    (STM/Polytechnic Institute of Leiria, Portugal and GEMF)

  • Paulino Teixeira

    (Faculty of Economics, University of Coimbra, Portugal and GEMF)

Abstract

This study compares the determinants of productivity and wages at both firm and worker level. In the firm-level analysis, we follow Hellerstein, Neumark and Troske (1999) and provide improved estimates based on an extended set of covariates including the intensity of firm-provided training. In the worker-level analysis we take a new turn and generate a proxy for unobserved worker productivity. Our results point to the presence of sizeable spillover effects from schooling and training as their impact is bigger on firm-level productivity equations than on the corresponding worker-level equations. In turn, our fully disaggregated model at worker level shows that, by using all possible combinations of worker attributes, we obtain that the wage differences across different worker groups are mostly productivity based and that the gap can be as high as 33%.

Suggested Citation

  • Ana Sofia Lopes & Paulino Teixeira, 2012. "Worker productivity and wages: Evidence from linked employer-employee data," GEMF Working Papers 2012-17, GEMF, Faculty of Economics, University of Coimbra.
    • Handle: RePEc:gmf:wpaper:2012-17
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    References listed on IDEAS

    as
    1. Addison, John T. & Teixeira, Paulino & Zwick, Thomas, 2006. "Works Councils and the Anatomy of Wages," IZA Discussion Papers 2474, Institute of Labor Economics (IZA).
    2. Hellerstein, Judith K & Neumark, David, 1999. "Sex, Wages, and Productivity: An Empirical Analysis of Israeli Firm-Level Data," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(1), pages 95-123, February.
    3. Moretti, Enrico, 2004. "Estimating the social return to higher education: evidence from longitudinal and repeated cross-sectional data," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 175-212.
    4. Boyan Jovanovic & Rafael Rob, 1989. "The Growth and Diffusion of Knowledge," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 56(4), pages 569-582.
    5. Lorraine Dearden & Howard Reed & John Van Reenen, 2006. "The Impact of Training on Productivity and Wages: Evidence from British Panel Data," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 68(4), pages 397-421, August.
    6. Hellerstein, Judith K & Neumark, David & Troske, Kenneth R, 1999. "Wages, Productivity, and Worker Characteristics: Evidence from Plant-Level Production Functions and Wage Equations," Journal of Labor Economics, University of Chicago Press, vol. 17(3), pages 409-446, July.
    7. Gérard Ballot & Fathi Fakhfakh & Erol Taymaz, 2006. "Who Benefits from Training and R&D, the Firm or the Workers?," British Journal of Industrial Relations, London School of Economics, vol. 44(3), pages 473-495, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Worker productivity; wages; human capital; LEED; inter-firm spillovers.;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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