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Random Forests for Labor Market Analysis: Balancing Precision and Interpretability

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  • Daniel Graeber
  • Lorenz Meister
  • Carsten Schröder
  • Sabine Zinn

Abstract

Machine learning is increasingly used in social science research, especially for prediction. However, the results are sometimes not as straight-forward to interpret compared to classic regression models. In this paper, we address this trade-off by comparing the predictive performance of random forests and logit regressions to analyze labor market vulnerabilities during the COVID-19 pandemic, and a global surrogate model to enhance our understanding of the complex dynamics. Our study shows that, especially in the presence of non-linearities and feature interactions, random forests outperform regressions both in predictive accuracy and interpretability, yielding policy-relevant insights on vulnerable groups affected by labor market disruptions

Suggested Citation

  • Daniel Graeber & Lorenz Meister & Carsten Schröder & Sabine Zinn, 2025. "Random Forests for Labor Market Analysis: Balancing Precision and Interpretability," SOEPpapers on Multidisciplinary Panel Data Research 1230, DIW Berlin, The German Socio-Economic Panel (SOEP).
    • Handle: RePEc:diw:diwsop:diw_sp1230
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    1. Andrew E. Clark & Conchita D’Ambrosio & Anthony Lepinteur, 2021. "The fall in income inequality during COVID-19 in four European countries," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(3), pages 489-507, September.
    2. Andrew E. Clark & Conchita D'Ambrosio & Anthony Lepinteur, 2020. "The Fall in Income Inequality during COVID-19 in Five European Countries," Working Papers 565, ECINEQ, Society for the Study of Economic Inequality.
    3. Adams-Prassl, Abi & Boneva, Teodora & Golin, Marta & Rauh, Christopher, 2020. "Inequality in the impact of the coronavirus shock: Evidence from real time surveys," Journal of Public Economics, Elsevier, vol. 189(C).
    4. Adams-Prassl, A. & Boneva, T. & Golin, M & Rauh, C., 2020. "Inequality in the Impact of the Coronavirus Shock: New Survey Evidence for the UK," Cambridge Working Papers in Economics 2023, Faculty of Economics, University of Cambridge.
    5. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    6. Jinjing Li & Yogi Vidyattama & Hai Anh La & Riyana Miranti & Denisa M Sologon, 2020. "The Impact of COVID-19 and Policy Responses on Australian Income Distribution and Poverty," Papers 2009.04037, arXiv.org.
    7. Stefanie Stantcheva, 2022. "Inequalities in the times of a pandemic," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 37(109), pages 5-41.
    8. Niccolò Gentile & Michela Bia & Andrew E. Clark & Conchita D'Ambrosio & Alexandre Tkatchenko, 2025. "What Makes a Satisfying Life? Prediction and Interpretation with Machine‐Learning Algorithms," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 71(2), May.
    9. Kühne, Simon & Kroh, Martin & Liebig, Stefan & Zinn, Sabine, 2020. "The Need for Household Panel Surveys in Times of Crisis: The Case of SOEP-CoV," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 14(2), pages 195-203.
    10. Andrew E. Clark & Conchita D’Ambrosio & Anthony Lepinteur, 2022. "Correction to: the Fall in Income Inequality during COVID-19 in Four European Countries," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(2), pages 503-507, June.
    11. Cathal O'Donoghue & Denisa M. Sologon & Iryna Kyzyma & John McHale, 2020. "Modelling the Distributional Impact of the COVID‐19 Crisis," Fiscal Studies, John Wiley & Sons, vol. 41(2), pages 321-336, June.
    12. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
    13. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    14. Mühlbauer, Sabrina & Weber, Enzo, 2022. "Machine Learning for Labour Market Matching," IAB-Discussion Paper 202203, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    15. Sendhil Mullainathan & Ziad Obermeyer, 2022. "Diagnosing Physician Error: A Machine Learning Approach to Low-Value Health Care [“The Determinants of Productivity in Medical Testing: Intensity and Allocation of Care,”]," The Quarterly Journal of Economics, Oxford University Press, vol. 137(2), pages 679-727.
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    Keywords

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    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • J08 - Labor and Demographic Economics - - General - - - Labor Economics Policies
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure

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