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Matching for three: big data evidence on search activity of workers, firms, and employment service

Author

Listed:
  • Hartl, Tobias

    (Institute for Employment Research (IAB), Nuremberg, Germany)

  • Hutter, Christian

    (Institute for Employment Research (IAB), Nuremberg, Germany)

  • Weber, Enzo

    (Institute for Employment Research (IAB), Nuremberg, Germany ; Univ. Regensburg)

Abstract

"We generate measures for search intensity of employers and job seekers and – as a novel feature – for placement intensity of employment agencies. For this purpose, we tap big data on online activity from the job exchange of the German Federal Employment Agency and its internal placement-software. We use these data to estimate an enhanced matching function where the efficiency parameter varies with the search and placement intensities. The results show that the intensity measures significantly contribute to the variation in job findings." (Author's abstract, IAB-Doku) ((en))

Suggested Citation

  • Hartl, Tobias & Hutter, Christian & Weber, Enzo, 2021. "Matching for three: big data evidence on search activity of workers, firms, and employment service," IAB-Discussion Paper 202101, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    • Handle: RePEc:iab:iabdpa:202101
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    File URL: https://doku.iab.de/discussionpapers/2021/dp0121.pdf
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    References listed on IDEAS

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    1. Federico Ravenna & Carl E. Walsh, 2012. "Screening and Labor Market Flows in a Model with Heterogeneous Workers," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(s2), pages 31-71, December.
    2. Regis Barnichon & Andrew Figura, 2015. "Labor Market Heterogeneity and the Aggregate Matching Function," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(4), pages 222-249, October.
    3. Scott R. Baker & Andrey Fradkin, 2017. "The Impact of Unemployment Insurance on Job Search: Evidence from Google Search Data," The Review of Economics and Statistics, MIT Press, vol. 99(5), pages 756-768, December.
    4. Ay?egül ?ahin & Joseph Song & Giorgio Topa & Giovanni L. Violante, 2014. "Mismatch Unemployment," American Economic Review, American Economic Association, vol. 104(11), pages 3529-3564, November.
    5. Andreas Hornstein & Marianna Kudlyak, 2015. "Estimating Matching Efficiency with Variable Search Effort," Working Paper Series 2016-24, Federal Reserve Bank of San Francisco.
    6. R. Jason Faberman & Marianna Kudlyak, 2019. "The Intensity of Job Search and Search Duration," American Economic Journal: Macroeconomics, American Economic Association, vol. 11(3), pages 327-357, July.
    7. Sabine Klinger & Enzo Weber, 2016. "Decomposing Beveridge Curve Dynamics By Correlated Unobserved Components," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(6), pages 877-894, December.
    8. Alan B. Krueger & Andreas Mueller, 2011. "Job Search and Job Finding in a Period of Mass Unemployment: Evidence from High-Frequency Longitudinal Data," Working Papers 1295, Princeton University, Department of Economics, Center for Economic Policy Studies..
    9. Christopher A. Pissarides, 2000. "Equilibrium Unemployment Theory, 2nd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262161877, December.
    10. Peter Kuhn & Mikal Skuterud, 2004. "Internet Job Search and Unemployment Durations," American Economic Review, American Economic Association, vol. 94(1), pages 218-232, March.
    11. Gomme, Paul & Lkhagvasuren, Damba, 2015. "Worker search effort as an amplification mechanism," Journal of Monetary Economics, Elsevier, vol. 75(C), pages 106-122.
    12. Steven J. Davis & R. Jason Faberman & John C. Haltiwanger, 2013. "The Establishment-Level Behavior of Vacancies and Hiring," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 128(2), pages 581-622.
    13. repec:pri:cepsud:215krueger is not listed on IDEAS
    14. Christian Hutter & Enzo Weber, 2017. "Mismatch and the Forecasting Performance of Matching Functions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(1), pages 101-123, February.
    15. Alan B. Krueger & Andreas Mueller, 2011. "Job Search, Emotional Well-Being and Job Finding in a Period of Mass Unemployment: Evidence from High-Frequency Longitudinal Data," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 42(1 (Spring), pages 1-81.
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    17. Krueger, Alan B. & Mueller, Andreas I., 2011. "Job Search and Job Finding in a Period of Mass Unemployment: Evidence from High-Frequency Longitudinal Data," IZA Discussion Papers 5450, Institute of Labor Economics (IZA).
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Bauer Anja & Keveloh Kristin & Mamertino Mariano & Weber Enzo, 2023. "Competing for Jobs: How COVID-19 Changes Search Behaviour in the Labour Market," German Economic Review, De Gruyter, vol. 24(4), pages 323-347, December.
    2. Wolter, Marc Ingo & Mönnig, Anke & Maier, Tobias & Schneemann, Christian & Steeg, Stefanie & Weber, Enzo & Zika, Gerd, 2021. "Langfristige Folgen der Covid-19-Pandemie für Wirtschaft, Branchen und Berufe," IAB-Forschungsbericht 202102, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

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    More about this item

    Keywords

    Bundesrepublik Deutschland ; Datengewinnung ; Jobbörse ; matching ; Personalbeschaffung ; Arbeitsagenturen ; Big Data ; Vermittlungserfolg ; Arbeitsuche ; Arbeitsvermittlung;
    All these keywords.

    JEL classification:

    • C78 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Bargaining Theory; Matching Theory
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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