Arbeitspapier

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

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.

Language
Englisch

Bibliographic citation
Series: IZA Discussion Papers ; No. 16426

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

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

Handle
Last update
10.03.2025, 11:44 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • van den Berg, Gerard J.
  • Kunaschk, Max
  • Lang, Julia
  • Stephan, Gesine
  • Uhlendorff, Arne
  • Institute of Labor Economics (IZA)

Time of origin

  • 2023

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