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 Uhlendorf
Additional contact information
Gerard J. van den Berg: University of Groningen, University Medical Center Groningen, IFAU Uppsala, ZEW, IZA, CEPR
Max Kunaschk: IAB Nuremberg
Julia Lang: IAB Nuremberg
Arne Uhlendorf: CNRS and CREST, IAB Nuremberg, DIW, IZA
Authors registered in the RePEc Author Service: Arne Uhlendorff

No 2023-09, Working Papers from Center for Research in Economics and Statistics

Abstract: Predictions of whether newly unemployed individuals will become long-term unemployed are important for the planning and policy mix of unemployment insurance agencies. 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 administrative data on the full inflow, to predict individual RE6. We compare the predictive performance of these measures and consider whether combinations improve this performance. We show that self-reported and caseworker assessments sometimes contain information not captured by the machine learning algorithm.

Keywords: unemployment; expectations; prediction; random forest; unemployment insurance; information (search for similar items in EconPapers)
JEL-codes: C21 C41 C53 C55 J64 J65 (search for similar items in EconPapers)
Pages: 57 pages
Date: 2023-08-28
New Economics Papers: this item is included in nep-big, nep-cmp and nep-eur
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Working Paper: Predicting Re-Employment: Machine Learning Versus Assessments by Unemployed Workers and by Their Caseworkers (2024)
Working Paper: Predicting re-employment: machine learning versus assessments by unemployed workers and by their caseworkers (2023)
Working Paper: Predicting Re-Employment: Machine Learning versus Assessments by Unemployed Workers and by Their Caseworkers (2023)
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