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Identifying Labor Market Sorting with Firm Dynamics

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  • Andreas Gulyas

    (University of Mannheim)

Abstract

Studying wage inequality requires understanding how workers and firms match. I propose a novel strategy to identify the complementarities in production between unobserved worker and firm attributes, based on the idea that positive (negative) sorting implies that firms upgrade (downgrade) their workforce quality when they grow in size. I use German matched employer-employee data to estimate a search and matching model with worker-firm complementarities, job-to-job transitions, and firm dynamics. The relationship between changes in workforce quality and firm growth rates in the data informs the strength of complementarities in the model. Thus, this strategy bypasses the lack of identification inherent to environments with constant firm types. I find evidence of negative sorting and a significant dampening effect of worker-firm complementarities on wage inequality. Worker and firm heterogeneity, differential bargaining positions, and sorting contribute 71%, 20%, 32% and -23% to wage dispersion, respectively. Reallocating workers across firms to the first-best allocation without mismatch yields an output gain of less than one percent.

Suggested Citation

  • Andreas Gulyas, 2018. "Identifying Labor Market Sorting with Firm Dynamics," 2018 Meeting Papers 856, Society for Economic Dynamics.
  • Handle: RePEc:red:sed018:856
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    Cited by:

    1. Heiland, Inga & Kohler, Wilhelm, 2022. "Heterogeneous workers, trade, and migration," European Economic Review, Elsevier, vol. 144(C).
    2. Bachmann, Rüdiger & Bayer, Christian & Merkl, Christian & Seth, Stefan & Stüber, Heiko & Wellschmied, Felix, 2017. "Worker Churn and Employment Growth at the Establishment Level," IZA Discussion Papers 11063, Institute of Labor Economics (IZA).
    3. Bachmann, Rüdiger & Bayer, Christian & Merkl, Christian & Seth, Stefan & Stüber, Heiko & Wellschmied, Felix, 2021. "Worker churn in the cross section and over time: New evidence from Germany," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 781-797.
    4. Andreas Gulyas & Krzysztof Pytka, 2019. "Understanding the Sources of Earnings Losses After Job Displacement: A Machine-Learning Approach," CRC TR 224 Discussion Paper Series crctr224_2019_131, University of Bonn and University of Mannheim, Germany.
    5. Bachmann, Rüdiger & Bayer, Christian & Merkl, Christian & Seth, Stefan & Stüber, Heiko & Wellschmied, Felix, 2017. "Job and worker flows: New stylized facts for Germany," FAU Discussion Papers in Economics 02/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.

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