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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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    References listed on IDEAS

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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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