IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp7036.html
   My bibliography  Save this paper

Worker Productivity and Wages: Evidence from Linked Employer-Employee Data

Author

Listed:
  • Lopes, Ana Sofia

    (Polytechnic Institute of Leiria)

  • Teixeira, Paulino

    (University of Coimbra)

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

  • Lopes, Ana Sofia & Teixeira, Paulino, 2012. "Worker Productivity and Wages: Evidence from Linked Employer-Employee Data," IZA Discussion Papers 7036, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp7036
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp7036.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    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. 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.
    5. 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.
    6. 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.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Enrico Moretti, 2004. "Workers' Education, Spillovers, and Productivity: Evidence from Plant-Level Production Functions," American Economic Review, American Economic Association, vol. 94(3), pages 656-690, June.
    3. Van Biesebroeck, Johannes, 2011. "Wages Equal Productivity. Fact or Fiction? Evidence from Sub Saharan Africa," World Development, Elsevier, vol. 39(8), pages 1333-1346, August.
    4. Jozef Konings & Stijn Vanormelingen, 2015. "The Impact of Training on Productivity and Wages: Firm-Level Evidence," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 485-497, May.
    5. Bernhard Mahlberg & Inga Freund & Alexia Prskawetz, 2013. "Ageing, productivity and wages in Austria: sector level evidence," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 40(4), pages 561-584, November.
    6. Mahlberg, Bernhard & Freund, Inga & Crespo Cuaresma, Jesús & Prskawetz, Alexia, 2013. "Ageing, productivity and wages in Austria," Labour Economics, Elsevier, vol. 22(C), pages 5-15.
    7. Eleonora Bartoloni & Andrea Marino & Maurizio Baussola & Davide Romaniello, 2023. "Urban Non-urban Agglomeration Divide: Is There a Gap in Productivity and Wages?," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 9(2), pages 789-827, July.
    8. Derek C. Jones & Panu Kalmi & Antti Kauhanen, 2012. "The effects of general and firm-specific training on wages and performance: evidence from banking," Oxford Economic Papers, Oxford University Press, vol. 64(1), pages 151-175, January.
    9. Judith K. Hellerstein & David Neumark, 2003. "Ethnicity, Language, and Workplace Segregation: Evidence from a New Matched Employer-Employee Data Set," Annals of Economics and Statistics, GENES, issue 71-72, pages 1-15.
    10. Duranton, Gilles & Puga, Diego, 2014. "The Growth of Cities," Handbook of Economic Growth, in: Philippe Aghion & Steven Durlauf (ed.), Handbook of Economic Growth, edition 1, volume 2, chapter 5, pages 781-853, Elsevier.
    11. Peters, Jan Cornelius, 2016. "Quantifying the effect of labor market size on learning externalities," Economics Working Papers 2016-11, Christian-Albrechts-University of Kiel, Department of Economics.
    12. Annemarie Künn-Nelen & Andries de Grip & Didier Fouarge, 2013. "Is Part-Time Employment Beneficial for Firm Productivity?," ILR Review, Cornell University, ILR School, vol. 66(5), pages 1172-1191, October.
    13. Gathmann, Christina & Helm, Ines & Schönberg, Uta, 2014. "Spillover Effects in Local Labor Markets: Evidence from Mass Layoffs," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100378, Verein für Socialpolitik / German Economic Association.
    14. Stephan Brunow & Georg Hirte, 2009. "The age pattern of human capital and regional productivity: A spatial econometric study on german regions," Papers in Regional Science, Wiley Blackwell, vol. 88(4), pages 799-823, November.
    15. Navon, Guy, 2009. "Human Capital Spillovers in the Workplace: Labor Diversity and Productivity," MPRA Paper 17741, University Library of Munich, Germany.
    16. Pierre-Philippe Combes & Gilles Duranton & Laurent Gobillon & Sébastien Roux, 2010. "Estimating Agglomeration Economies with History, Geology, and Worker Effects," NBER Chapters, in: Agglomeration Economics, pages 15-66, National Bureau of Economic Research, Inc.
    17. Valentine Fays & Benoît Mahy & François Rycx & Mélanie Volral, 2021. "Wage discrimination based on the country of birth: do tenure and product market competition matter?," Applied Economics, Taylor & Francis Journals, vol. 53(13), pages 1551-1571, March.
    18. Rickne, Johanna, 2010. "Gender, Wages and Social Security in China’s Industrial Sector," Working Paper Series 2010:8, Uppsala University, Department of Economics.
    19. David Neumark, 2016. "Experimental Research on Labor Market Discrimination," NBER Working Papers 22022, National Bureau of Economic Research, Inc.
    20. André Mollick & Marie Mora, 2012. "The impact of higher education on Texas population and employment growth," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 48(1), pages 135-149, February.

    More about this item

    Keywords

    worker productivity; wages; human capital; LEED;
    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iza:izadps:dp7036. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Holger Hinte (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.