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Microeconometric search-matching models and matched employer-employee data

Author

Listed:
  • Fabien Postel-Vinay

    (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - X - École polytechnique - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - CNRS - Centre National de la Recherche Scientifique, LEA - Laboratoire d'Economie Appliquée - INRA - Institut National de la Recherche Agronomique, Department of Economics - UCL - University College of London [London], PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CEPR - Center for Economic Policy Research - CEPR, University of Bristol [Bristol], ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique, IZA - Institute for the Study of Labor - Institute for the Study of Labor, DELTA - Département et Laboratoire d'Economie Théorique et Appliquée - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique, INRA)

  • Jean-Marc Robin

    (Economics department - MIT - Massachusetts Institute of Technology, UP1 - Université Paris 1 Panthéon-Sorbonne)

Abstract

The recent advent of matched employer-employee data as part of the labor market scholar's toolbox has allowed a great deal of progress in our understanding of individual labor earnings. A growing number of empirical analyzes of available matched employer-employee data sets now combine with the already voluminous literature on empirical wage equations based on individual or household survey data to draw an ever richer picture of wage dispersion, individual wage dynamics, and the productivity-wage relationship. In this chapter we tour the empirical wage equations literature along these three lines and make a case that viewing it through the lens of structural job search models can help clarify and unify some of its recurring findings. Among other things, we emphasize and quantify the role of matching frictions in explaining the share of "residual" wage dispersion that is left unexplained by the reduced-form approach. Secondly, we quantitatively assess the importance of labor market competition between employers relative to non-competitive wage formation mechanisms (namely, wage bargaining) as a theoretical underpinning of the wage-productivity relationship. Thirdly, we show how search frictions combined with a theoretically founded wage formation rule based on renegotiation by mutual consent can account for the widely documented dynamic persistence of individual wages. We conclude with a list of questions that are open to further research.

Suggested Citation

  • Fabien Postel-Vinay & Jean-Marc Robin, 2006. "Microeconometric search-matching models and matched employer-employee data," Post-Print hal-03587666, HAL.
  • Handle: RePEc:hal:journl:hal-03587666
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    Citations

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    Cited by:

    1. Fabien Postel-Vinay & Hélène Turon, 2010. "On-The-Job Search, Productivity Shocks, And The Individual Earnings Process," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 51(3), pages 599-629, August.
    2. Dale T. Mortensen, 2011. "Markets with Search Friction and the DMP Model," American Economic Review, American Economic Association, vol. 101(4), pages 1073-1091, June.
    3. Dostie, Benoit & Li, Jiang & Card, David & Parent, Daniel, 2023. "Employer policies and the immigrant–native earnings gap," Journal of Econometrics, Elsevier, vol. 233(2), pages 544-567.
    4. Gene Grossman, 2013. "Heterogeneous workers and international trade," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 149(2), pages 211-245, June.
    5. Jean Flemming, 2018. "Costly Commuting and the Job Ladder," 2018 Meeting Papers 100, Society for Economic Dynamics.
    6. James Harrigan & Ariell Reshef & Farid Toubal, 2016. "The March of the Techies: Technology, Trade, and Job Polarization in France, 1994-2007," Working Papers 2016-15, CEPII research center.
    7. Alessia Matano & Paolo Naticchioni, 2009. "Wage distribution and the spatial sorting of workers and firms," Working Papers - Dipartimento di Economia 8-DEISFOL, Dipartimento di Economia, Sapienza University of Rome, revised 2009.
    8. Philipp Ehrl, 2019. "On The Use Of Firm Fixed Effects As A Productivity Measure For Analyzing Labor Market Matching," Bulletin of Economic Research, Wiley Blackwell, vol. 71(2), pages 195-208, April.
    9. Giuseppe Moscarini & Fabien Postel-Vinay, 2009. "The Timing of Labor Market Expansions: New Facts and a New Hypothesis," NBER Chapters, in: NBER Macroeconomics Annual 2008, Volume 23, pages 1-51, National Bureau of Economic Research, Inc.
    10. Philipp Ehrl, 2014. "High-wage workers and high-productivity firms - a regional view on matching in Germany," Working Papers 149, Bavarian Graduate Program in Economics (BGPE).
    11. Borrs, Linda & Knauth, Florian, 2016. "The impact of trade and technology on wage components," DICE Discussion Papers 241, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    12. Dale T. Mortensen & Bent Jesper Christensen & Jesper Bagger, 2010. "Wage and Productivity Dispersion: Labor Quality or Rent Sharing?," 2010 Meeting Papers 758, Society for Economic Dynamics.
    13. Dean R Hyslop & David C Mare, 2009. "Job Mobility and Wage Dynamics," Global COE Hi-Stat Discussion Paper Series gd09-107, Institute of Economic Research, Hitotsubashi University.
    14. Borrs, Linda & Knauth, Florian, 2021. "Trade, technology, and the channels of wage inequality," European Economic Review, Elsevier, vol. 131(C).
    15. Alessia Matano & Paolo Naticchioni, 2011. "Is There Rent Sharing in Italy? Evidence from Employer-Employee Data," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 8(2), pages 265-279, December.
    16. Luigi Guiso & Luigi Pistaferri & Fabiano Schivardi, 2006. "Disentangling employment and wage rigidity," 2006 Meeting Papers 536, Society for Economic Dynamics.
    17. Sebastian Galiani & Juan Pantano, 2021. "Structural Models: Inception and Frontier," NBER Working Papers 28698, National Bureau of Economic Research, Inc.
    18. Kim P. Huynh & Yuri Ostrovsky & Robert J. Petrunia & Marcel C. Voia, 2017. "Industry shutdown rates and permanent layoffs: evidence from firm-worker matched data," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 6(1), pages 1-31, December.

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