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

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IZA Discussion Papers ; No. 16426

Classification and Topics

• Classification: Wirtschaft ; Unemployment: Models, Duration, Incidence, and Job Search ; Unemployment Insurance; Severance Pay; Plant Closings ; Large Data Sets: Modeling and Analysis ; Forecasting Models; Simulation Methods ; Duration Analysis; Optimal Timing Strategies ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
• Subject: unemployment ; expectations ; prediction ; random forest ; unemployment insurance ; information

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): van den Berg, Gerard J. ; Kunaschk, Max ; Lang, Julia ; Stephan, Gesine ; Uhlendorff, Arne
• Event: Veröffentlichung
• (who): Institute of Labor Economics (IZA)
• (where): Bonn
• (when): 2023

Further information

• Handle: http://hdl.handle.net/10419/279124
• Last update: 10.03.2025, 11:44 AM CET