"The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?", joint work with Conny Wunsch.
"Decisions and Performance Under Bounded Rationality: A Computational Benchmarking Approach", joint work with Uwe Sunde and Dainis Zegners.
"Religion and Terrorism: Evidence from Ramadan Fasting", joint work with Roland Hodler and Paul Raschky.
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 experiment entails a mix of positive and negative work incentives. Previous studies show that it is hard to tackle the effect heterogeneity of Jobs First by means of CATEs. I report evidence that CML methods can provide support for the theoretical labor supply predictions. Furthermore, I document reasons why some conventional CATE estimators fail and discuss the limitations of CML methods.
"Optimal Targeting in Fundraising: A Machine Learning Approach", joint work with Tobias Cagala, Ulrich Glogowsky, and Johannes Rincke.
This paper studies optimal targeting in fundraising. In a large-scale field experiment, we randomly provide potential donors with a small unconditional gift. We then use causal machine learning methods to derive the optimal targeting of the fundraising instrument based on socio-economic characteristics, donation history, and geo-spatial information. In the warm list, optimal targeting increases the charity’s profits significantly, even if the algorithm uses only the publicly available geo-spatial information. In the cold list, optimal targeting does not raise donations sufficiently to cover the costs of the gift. We conclude that without optimal targeting, charities' fundraising efforts may waste significant resources.