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

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  • van den Berg, Gerard J.

    (University of Groningen)

  • Kunaschk, Max

    (Institute for Employment Research (IAB), Nuremberg)

  • Lang, Julia

    (Institute for Employment Research (IAB), Nuremberg)

  • Stephan, Gesine

    (Institute for Employment Research (IAB), Nuremberg)

  • Uhlendorff, Arne

    (CREST)

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.

Suggested Citation

  • van den Berg, Gerard J. & Kunaschk, Max & Lang, Julia & Stephan, Gesine & Uhlendorff, Arne, 2023. "Predicting Re-Employment: Machine Learning versus Assessments by Unemployed Workers and by Their Caseworkers," IZA Discussion Papers 16426, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp16426
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    Cited by:

    1. Homrighausen, Pia & Oberfichtner, Michael, 2024. "Do Caseworker Meetings Prevent Unemployment? Evidence from a Field Experiment," IZA Discussion Papers 16923, Institute of Labor Economics (IZA).
    2. 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).
    3. 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.

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    More about this item

    Keywords

    unemployment; expectations; prediction; random forest; unemployment insurance; information;
    All these keywords.

    JEL classification:

    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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