Random Forests for Labor Market Analysis: Balancing Precision and Interpretability

Daniel Graeber, Lorenz Meister, Carsten Schröder and Sabine Zinn

No 1230, SOEPpapers on Multidisciplinary Panel Data Research from DIW Berlin, The German Socio-Economic Panel (SOEP)

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

Keywords: Machine learning; interpretability; labor market; random forests (search for similar items in EconPapers)
JEL-codes: C25 C45 C53 C83 I18 J08 J21 (search for similar items in EconPapers)
Pages: 29 p.
Date: 2025
New Economics Papers: this item is included in nep-lma
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