Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/219115 
Year of Publication: 
2020
Series/Report no.: 
CESifo Working Paper No. 8297
Publisher: 
Center for Economic Studies and Ifo Institute (CESifo), Munich
Abstract: 
Based on administrative data of unemployed in Belgium, we estimate the labour market effects of three training programmes at various aggregation levels using Modified Causal Forests, a causal machine learning estimator. While all programmes have positive effects after the lock-in period, we find substantial heterogeneity across programmes and unemployed. Simulations show that “black-box” rules that reassign unemployed to programmes that maximise estimated individual gains can considerably improve effectiveness: up to 20% more (less) time spent in (un)employment within a 30 months window. A shallow policy tree delivers a simple rule that realizes about 70% of this gain.
Subjects: 
policy evaluation
active labour market policy
causal machine learning
modified causal forest
conditional average treatment effects
JEL: 
J68
Document Type: 
Working Paper
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