I investigate causal machine learning (CML) methods to estimate effect heterogeneity by means of conditional average treatment effects (CATEs). In particular, I study whether the estimated effect heterogeneity can provide evidence for the theoretical labour supply predictions of Connecticut's...

Recent studies have proposed causal machine learning (CML) methods to estimate conditional average treatment effects (CATEs). In this study, I investigate whether CML methods add value compared to conventional CATE estimators by re-evaluating Connecticut's Jobs First welfare experiment. This...

Over time the international development community has advocated various development paradigms, but countries following these paradigms have often performed poorly. I provide an explanation for this poor performance. In my model the political leader of a developing country chooses a policy and...

The objective of providing vocational training for the unemployed is to increase their chances of re-employment and human capital accumulation. In comparison to mandatory course assignment by case workers, the awarding of vouchers increases recipients’ freedom to choose between different...