Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/331783 
Year of Publication: 
2025
Series/Report no.: 
SOEPpapers on Multidisciplinary Panel Data Research No. 1230
Publisher: 
Deutsches Institut für Wirtschaftsforschung (DIW), German Socio-Economic Panel (SOEP), Berlin
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.
Subjects: 
Machine learning
interpretability
labor market
random forests
JEL: 
C45
C53
C25
J08
I18
C83
J21
Document Type: 
Working Paper

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