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Understanding the Sources of Earnings Losses After Job Displacement: A Machine-Learning Approach

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  • Andreas Gulyas
  • Krzysztof Pytka
Abstract
We implement a generalized random forest (Athey et. al. 2019) to a difference-in-difference setting to identify substantial heterogeneity in earnings losses across displaced workers. Using administrative data from Austria over three decades we document that a quarter of workers face cumulative 11-year losses higher than 2 times their pre-displacement annual income, while almost 10% of individuals experience gains. Our methodology allows us to consider many competing theories of earnings losses. We find that the displacement firm's wage premia and the availability of well paying jobs in the local labor market are the two most important factors. This implies that earnings losses can be understood by mean reversion in firm wage premia and losses in match quality, rather than by a destruction of firm-specific human capital. We further show that 94% of the cyclicality of earnings losses is explained by compositional changes of displaced workers over the business cycle.

Suggested Citation

  • Andreas Gulyas & Krzysztof Pytka, 2020. "Understanding the Sources of Earnings Losses After Job Displacement: A Machine-Learning Approach," CRC TR 224 Discussion Paper Series crctr224_2020_131v2, University of Bonn and University of Mannheim, Germany.
    • Handle: RePEc:bon:boncrc:crctr224_2020_131v2
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    Cited by:

    1. Paul Brandily & Camille Hémet & Clément Malgouyres, 2022. "Understanding the Reallocation of Displaced Workers to Firms," Working Papers halshs-03082302, HAL.
    2. Andreas Gulyas & Sebastian Seitz & Sourav Sinha, 2023. "Does Pay Transparency Affect the Gender Wage Gap? Evidence from Austria," American Economic Journal: Economic Policy, American Economic Association, vol. 15(2), pages 236-255, May.
    3. Hannah Illing & Johannes Schmieder & Simon Trenkle, 2024. "The Gender Gap in Earnings Losses After Job Displacement," Journal of the European Economic Association, European Economic Association, vol. 22(5), pages 2108-2147.
    4. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    5. Ines Helm & Alice Kuegler & Uta Schoenberg, 2023. "Displacement Effects in Manufacturing and Structural Change," RF Berlin - CReAM Discussion Paper Series 2313, Rockwool Foundation Berlin (RF Berlin) - Centre for Research and Analysis of Migration (CReAM).
    6. Costanza Naguib, 2023. "Is the Impact of Opening the Borders Heterogeneous?," Diskussionsschriften dp2312, Universitaet Bern, Departement Volkswirtschaft.
    7. Antoine Bertheau & Edoardo Maria Acabbi & Cristina Barceló & Andreas Gulyas & Stefano Lombardi & Raffaele Saggio, 2023. "The Unequal Consequences of Job Loss across Countries," American Economic Review: Insights, American Economic Association, vol. 5(3), pages 393-408, September.
    8. Gulyas, Andreas & Meier, Matthias & Ryzhenkov, Mykola, 2024. "Labor market effects of monetary policy across workers and firms," European Economic Review, Elsevier, vol. 166(C).
    9. Bertheau, Antoine & Acabbi, Edoardo & Barcelo, Cristina & Gulyas, Andreas & Lombardi, Stefano & Saggio, Raffaele, 2022. "The Unequal Cost of Job Loss across Countries," IZA Discussion Papers 15033, Institute of Labor Economics (IZA).
    10. Patrick Arni & Pether H. Egger & Katharina Erhardt & Matthias Gubler & Philip Sauré, 2024. "Heterogeneous Impacts of Trade Shocks on Workers," Working Papers 2409, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    11. Arntz, Melanie & Ivanov, Boris & Pohlan, Laura, 2022. "Regional structural change and the effects of job loss," ZEW Discussion Papers 22-019, ZEW - Leibniz Centre for European Economic Research.
    12. Evan D. Peet & Dana Schultz & Susan Lovejoy & Fuchiang (Rich) Tsui, 2024. "The infant health effects of doulas: Leveraging big data and machine learning to inform cost‐effective targeting," Health Economics, John Wiley & Sons, Ltd., vol. 33(6), pages 1387-1411, June.
    13. Valente, Marica, 2023. "Policy evaluation of waste pricing programs using heterogeneous causal effect estimation," Journal of Environmental Economics and Management, Elsevier, vol. 117(C).
    14. Effrosyni Adamopoulou & Marta De Philippis & Enrico Sette & Eliana Viviano, 2020. "The Long Run Earnings Effects of a Credit Market Disruption," CRC TR 224 Discussion Paper Series crctr224_2020_169v3, University of Bonn and University of Mannheim, Germany.
    15. Jon Ellingsen & Caroline Espegren, 2022. "Lost in transition? Earnings losses of displaced petroleum workers," Working Papers No 06/2022, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    16. Henry Redondo, 2023. "From bricklayers to waiters: Reallocation in a deep recession," French Stata Users' Group Meetings 2023 27, Stata Users Group.
    17. Holmberg, Johan, 2021. "Earnings and Employment Dynamics: Capturing Cyclicality using Mixed Frequency Data," Umeå Economic Studies 991, Umeå University, Department of Economics.

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    More about this item

    Keywords

    Job displacement; Earnings losses; Causal machine learning;
    All these keywords.

    JEL classification:

    • J3 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs
    • 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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