Subsidised employment is an important tool of active labour market policies to improve the chances of the unemployed to find permanent employment. Using informative individual administrative data we investigate the effects of two different schemes of subsidised temporary employment implemented...

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...

In the second part of the 1990's Switzerland conducted an ambitious active labour market policy (ALMP) encompassing a wide variety of programmes. We evaluate the effects of these programmes on the individual employment probability of potential participants. Our econometric analysis uses...

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 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...

In East Germany, active labour market policies (ALMPs) are used on a large scale to contain fast rising unemployment after unification. This paper evaluates the effects for participants in public employment programmes (PEPs), that are an important part of ALMPs. It focuses on individual...

This study analyses the effects of public-sector-sponsored continuous vocational training and retraining in East Germany after unification with West Germany in 1990. It presents econometric estimates of the average gains from training participation in terms of employment probabilities, earnings,...