The German government provides discretionary investment grants to structurally weak regions to reduce regional disparities. We use a regression discontinuity design that exploits an exogenous discrete jump in the probability of receiving investment grants, to identify the causal effects of the...

This paper studies the association between the unemployment experience of fathers and their sons. Based on German survey data that cover the last decades we find significant positive correlations. Using instrumental variables estimation and the Gottschalk (1996) method we investigate to what...

This paper shows that import exposure affects voting behavior because it affects local labor markets. We develop a new framework for mediation analysis where one instrumental variable is sufficient to identify three causal effects. Using German data from 1987-2009, we find that import exposure...

This paper examines the effects of Islamic banking on the causal linkages between credit and GDP by comparing two sets of seven emerging countries, the first without Islamic banks, and the second with a dual banking system including both Islamic and conventional banks. Unlike previous studies,...

This paper provides an introduction into the estimation of Marginal Treatment Effects (MTE). Compared to the existing surveys on the subject, our paper is less technical and speaks to the applied economist with a solid basic understanding of econometric techniques who would like to use MTE...

This paper proposes a difference-in-differences approach for disentangling a total treatment effect on some outcome into a direct impact as well as an indirect effect operating through a binary intermediate variable - or mediator - within strata defined upon how the mediator reacts to the...

The 'saving for a rainy day' hypothesis implies that households' saving decisions reflect that they can (rationally) predict future income declines. The empirical relevance of this hypothesis plays a key role in discussions of fiscal policy multipliers and it holds under the null that the...

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...