Priority of Unemployed Immigrants? A Causal Machine Learning Evaluation of Training in Belgium
CESifo, Munich, 2020
CESifo Working Paper No. 8297
![](https://cesifo.org/DocImg/cesifo1_wp8297.jpg?c=1689237095)
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.
Social Protection
Labour Markets