Predicting University Dropouts: Evidence on the Value of Student Expectations and Motivation

University dropout is costly, making it a policy priority to identify factors that predict dropout. Using a survey experiment with incoming first-year students linked to long-run administrative outcomes, we assess which information improves dropout prediction beyond standard university records. A small number of targeted, study-specific survey items - especially motivation and expectations about degree completion - substantially improve predictive performance. By contrast, widely used measures of general preferences and traits (such as grit and self-control) add little incremental value - a result that we qualitatively replicate in a large population. Our findings suggest inexpensive, scalable ways to improve dropout predictions.