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...

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...

In this paper we estimate, for the 1989-93 period in Belgium, the effect of vocational classroom training on the rate of transition from unemployment. We propose a "control function" estimator accounting for variable treatment effects. In the absence of interaction effects between explanatory...

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...

The public employment service (PES) makes use in many countries of vacancy referrals as to facilitate the matching between unemployed workers and vacancies. Based on a “timing-of-events” approach to control for selective participation, this study evaluates whether this policy instrument...