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

Thomas Epper (), Kristoffer Ibsen (), Alexander Koch () and Julia Nafziger ()
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Thomas Epper: CNRS, IESEG School of Management, Univ. Lille, UMR 9221 – LEM – Lille Economie Management, F-59000 Lille, France
Kristoffer Ibsen: Aarhus University
Alexander Koch: Aarhus University
Julia Nafziger: Aarhus University

No 18439, IZA Discussion Papers from IZA Network @ LISER

Abstract: 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.

Keywords: dropout; non-cognitive skills; motivation; economic preferences; beliefs; education; machine learning (search for similar items in EconPapers)
JEL-codes: D91 I23 (search for similar items in EconPapers)
Date: 2026-03
New Economics Papers: this item is included in nep-cmp, nep-edu, nep-exp, nep-mac and nep-neu
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