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...

By merging administrative data on public finances of all municipalities in Germany with individual data from the German Socio-Economic Panel, we explore whether local public expenditures on sports facilities influences individual labor market outcomes. Our identification strategy follows a...