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

Author

Listed:
  • 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:

    1. is not listed on IDEAS
    2. Gerard J. van den Berg & Sarah Bernhard & Gesine Stephan & Arne Uhlendorff, 2024. "Investigating the Impact of Integration Agreements on Labor Market Outcomes for Welfare Recipients: A Randomized Controlled Trial," Working Papers 2024-12, Center for Research in Economics and Statistics.
    3. Altmann, Steffen & Mahlstedt, Robert & Rattenborg, Malte Jacob & Sebald, Alexander, 2023. "Which Occupations Do Unemployed Workers Target? Insights from Online Job Search Profiles," IZA Discussion Papers 16696, Institute of Labor Economics (IZA).
    4. Homrighausen, Pia & Oberfichtner, Michael, 2024. "Do Caseworker Meetings Prevent Unemployment? Evidence from a Field Experiment," IZA Discussion Papers 16923, Institute of Labor Economics (IZA).

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    Keywords

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    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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