An analysis of selected labor market outcomes of college dropouts in Germany: A machine learning estimation approach. Research report - Julia Heigle; Friedhelm Pfeiffer (ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung, 2019)
[Introduction ...] The results indicate that college dropouts aged between 25 and 65 do, in expectation, not experience significant losses in terms of hourly wages. Further-more, in terms of expectations, college dropouts end up in occupations with higher occupational prestige scores relative to individuals with a college en-trance qualification but no college experience. There seem to be no significant differences in employment status between the two groups. A further descriptive analysis shows that college dropouts are more likely to end up in smaller firms. The rest of the paper is structured as follows. Section 2 reviews the relevant literature on college dropout. Section 3 describes the data set used for the anal-ysis. Section 4 discusses the integration of machine learning techniques into the causal inference framework. Section 5 introduces the method used for treat-ment effect estimation. In Section 6 treatment effect estimation results are pre-sented for hourly wages and occupational prestige scores, and a multinomial logit model is estimated to investigate the relationship between employment and treatment group status. Section 7 concludes by critically discussing the empirical estimation strategy. – – Die Studie untersucht ausgewählte Arbeitsmarktergebnisse von Studierenden, die die Universität ohne Abschluss verlassen haben. Datengrundlage sind Stichproben aus dem SOEP. Es werden ökonometrische Schätzverfahren mit Hilfe des maschinelles Lernen verwendet.
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