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
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Gerard J. van den Berg: University of Groningen, University Medical Center Groningen ; IFAU Uppsala ; ZEW ; IZA ; CEPR
Max Kunaschk: Institute for Employment Research (IAB), Nuremberg, Germany
Julia Lang: Institute for Employment Research (IAB), Nuremberg, Germany

No 202403, IAB-Discussion Paper from Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany]

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 administrative data on the full inflow, to predict individual RE6. We compare the predictive performance of these measures and consider how combinations improve this performance. We show that self-reported (and to a lesser extent caseworker) assessments sometimes contain information not captured by the machine learning algorithm." (Author's abstract, IAB-Doku) ((en))

Keywords: Bundesrepublik Deutschland; IAB-Open-Access-Publikation; berufliche Reintegration; Fremdbild; Integrierte Erwerbsbiografien; Langzeitarbeitslosigkeit; Profiling; Prognosegenauigkeit; Risikoabschätzung; Selbsteinschätzung; Arbeitsberater; Machine learning; Arbeitslose; Arbeitslosenversicherung; Arbeitslosigkeitsdauer; Arbeitsmarktchancen; 2012-2013 (search for similar items in EconPapers)
JEL-codes: C21 C41 C53 C55 J64 J65 (search for similar items in EconPapers)
Pages: 56 pages
Date: 2024-02-08
New Economics Papers: this item is included in nep-big, nep-cmp and nep-lab
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https://doi.org/10.48720/IAB.DP.2403

Related works:
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)
Working Paper: Predicting Re-Employment: Machine Learning versus Assessments by Unemployed Workers and by Their Caseworkers (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:iab:iabdpa:202403

DOI: 10.48720/IAB.DP.2403

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