Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/279124 
Erscheinungsjahr: 
2023
Schriftenreihe/Nr.: 
IZA Discussion Papers No. 16426
Verlag: 
Institute of Labor Economics (IZA), Bonn
Zusammenfassung: 
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.
Schlagwörter: 
unemployment
expectations
prediction
random forest
unemployment insurance
information
JEL: 
J64
J65
C55
C53
C41
C21
Dokumentart: 
Working Paper

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