Scandinavian Working Papers in Economics

Working Paper Series,
IFAU - Institute for Evaluation of Labour Market and Education Policy

No 2023:22: Predicting re-employment: machine learning versus assessments by unemployed workers and by their caseworkers

Gerard J. van den Berg (), Max Kunaschk, Julia Lang, Gesine Stephan and Arne Uhlendorff
Additional contact information
Gerard J. van den Berg: IFAU and University of Gronigen
Max Kunaschk: IAB Nuremberg
Julia Lang: IAB Nuremberg
Gesine Stephan: IAB Nuremberg
Arne Uhlendorff: CNRS and CREST i Paris

Abstract: We analyze unique data on three sources of information on the probability of re-employment within 6 months (RE6), for the same individuals sampled from the inflow into unemployment. First, they were asked for their perceived probability of RE6. Second, their caseworkers revealed whether they expected RE6. Third, random-forest machine learning methods are trained on admin istrative data on the full inflow, to predict individual RE6. We compare the predictive performance of these measures and consider how combinations im prove this performance. We show that self-reported (and to a lesser extent caseworker) assessments sometimes contain information not captured by the machine learning algorithm.

Keywords: Unemployment; expectations; prediction; random forest; unemloyment insurance; information

JEL-codes: C21; C41; C53; C55; J64; J65

Language: English

57 pages, November 10, 2023

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