We investigate heterogenous employment effects of Flemish training programmes. Based on administrative individual data, we analyse programme effects at various aggregation levels using Modified Causal Forests (MCF), a causal machine learning estimator for multiple programmes. While all...

Matching-type estimators using the propensity score are the major workhorse in active labour market policy evaluation. This work investigates if machine learning algorithms for estimating the propensity score lead to more credible estimation of average treatment effects on the treated using a...

This paper examines the impact of an experiment in North Macedonia in which vulnerable unemployed individuals applying to a subsidized employment program were randomly selected to attend job interviews. Employers hiring a new employee from the target population receive a subsidy covering the...

Hiring subsidies are widely used to create (stable) employment for the long-term unemployed. This paper exploits the abolition of a hiring subsidy targeted at long-term unemployed jobseekers over 45 years of age in Belgium to evaluate its effectiveness in the short and medium run. Based on a...

Hiring subsidies are widely used to create (stable) employment for the long-term unemployed. This paper exploits the abolition of a hiring subsidy targeted at long-term unemployed jobseekers over 45 years of age in Belgium to evaluate its effectiveness in the short and medium run. Based on a...

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...