Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/245537 
Year of Publication: 
2021
Series/Report no.: 
IZA Discussion Papers No. 14486
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
We investigate the effectiveness of three different job-search and training programmes for German long-term unemployed persons. On the basis of an extensive administrative data set, we evaluated the effects of those programmes on various levels of aggregation using Causal Machine Learning. We found participants to benefit from the investigated programmes with placement services to be most effective. Effects are realised quickly and are long-lasting for any programme. While the effects are rather homogenous for men, we found differential effects for women in various characteristics. Women benefit in particular when local labour market conditions improve. Regarding the allocation mechanism of the unemployed to the different programmes, we found the observed allocation to be as effective as a random allocation. Therefore, we propose data-driven rules for the allocation of the unemployed to the respective labour market programmes that would improve the status-quo.
Subjects: 
policy evaluation
Modified Causal Forest (MCF)
active labour market programmes
conditional average treatment effect (CATE)
JEL: 
J08
J68
Document Type: 
Working Paper

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