Based on innovative data sources and modern statistical methods, this project carried out a comprehensive study on the causes of college dropout and its dynamics over time. A traditional, theory-driven econometric analysis was accompanied by machine-learning algorithms to elicit as-good-as possible predictions of student dropout based on information available through the administrative process. Such models, tailored to the respective institutions, may be used as "early warning system for college dropouts" to provide cost-effective implementations of early intervention (e.g. mentoring programs or career centers) to identified high-risk groups.
Two IZA Research Reports summarize the research findings of this project: