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...

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 use (donut) regression discontinuity design and difference-in-differences estimators to estimate the impact of a one-shot hiring subsidy targeted at low-educated unemployed youths during the Great Recession recovery in Belgium. The subsidy increases job-finding in the private sector by 10...

A first objective of this paper is to emphasise the role of a correct diagnosis of unemployment persistence for the design of effective active labour market policies. A second is to stress the importance of adequate incentives for programme administrators of active labour market policies, and...

In July 2004, the Belgian government intensified monitoring within the Unemployment Insurance scheme. Workers claiming unemployment benefits for more than 13 months are notified that past job-search behavior will be monitored 8 months later. In one region the target group is counselled shortly...

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...

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...

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...