This paper investigates the link between variation in the supply of workers who participate in specific types of active labour market policies (ALMPs) and firm performance using a new exceptionally informative German employer-employee data base. For identification we exploit that German local...

We survey the literature on the link of labour market related outcomes to individ-ual physical activity and sports participation. The first part of the survey is devoted to the individual participation decision and is based on papers from various disciplines. The sec-ond part summarises parts of...

This paper evaluates the effect of a voucher award system for assignment into vocational training on the employment outcomes of unemployed voucher recipients in Germany, along with the causal mechanisms through which it operates. It assesses the direct effect of voucher assignment net of actual...

This paper investigates the link between variation in the supply of workers who participate in spe­cific types of active labour market policies (ALMPs) and firm performance using a new exception­ally informative German employer-employee data base. For identification we ex­ploit that German...

Uncovering the heterogeneity of causal effects of policies and business decisions at various levels of granularity provides substantial value to decision makers. This paper develops new estimation and inference procedures for multiple treatment models in a selection-on-observables frame-work by...

We systematically investigate the effect heterogeneity of job search programmes for unemployed workers. To investigate possibly heterogeneous employment effects, we combine non-experimental causal empirical models with Lasso-type estimators. The empirical analyses are based on rich...

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...

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...