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Predicting Re-Employment: Machine Learning Versus Assessments by Unemployed Workers and by Their Caseworkers

Author

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  • Berg, Gerard J. van den

    (University of Groningen, University Medical Center Groningen ; IFAU Uppsala ; ZEW ; IZA ; CEPR)

  • Kunaschk, Max

    (Institute for Employment Research (IAB), Nuremberg, Germany)

  • Lang, Julia

    (Institute for Employment Research (IAB), Nuremberg, Germany)

  • Stephan, Gesine

    (Institute for Employment Research (IAB), Nuremberg, Germany)

  • Uhlendorff, Arne

    (Institute for Employment Research (IAB), 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))

Suggested Citation

  • Berg, Gerard J. van den & Kunaschk, Max & Lang, Julia & Stephan, Gesine & Uhlendorff, Arne, 2024. "Predicting Re-Employment: Machine Learning Versus Assessments by Unemployed Workers and by Their Caseworkers," IAB-Discussion Paper 202403, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    • Handle: RePEc:iab:iabdpa:202403
    DOI: 10.48720/IAB.DP.2403
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    Cited by:

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    More about this item

    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;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search
    • J65 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment Insurance; Severance Pay; Plant Closings
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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