We use panel data from the US Health and Retirement Study 1992-2002 to estimate theeffect of self-assessed health limitations on active labor market participation of men aroundretirement age. Self-assessments of health and functioning typically introduce anendogeneity bias when studying the...

We consider the identification of and inference on a partially linear model, when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked. This type of data combination problem arises very frequently in empirical microeconomics. Using...

We investigate the impact of the presence of university dropouts on the academic success of first-time students. Our identification strategy relies on quasi-random variation in the proportion of returning dropouts. The estimated average zero effect of dropouts on first- time students' success...

In cases of non-compliance with a prescribed treatment, estimates of causal effects typically rely on instrumental variables. However, when participation is also misreported, this approach can be severely biased. We provide an instrumental variable method that researchers can use to identify the...

Binary treatments are often ex-post aggregates of multiple treatments or can be disaggregated into multiple treatment versions. Thus, effects can be heterogeneous due to either effect or treatment heterogeneity. We propose a decomposition method that uncovers masked heterogeneity, avoids...

In response to strong revenue and income losses that a large share of the self-employed faced during the COVID-19 pandemic, the German federal government introduced a €50bn emergency aid program. Based on real-time online-survey data comprising more than 20,000 observations, we analyze the...

We use data from the National Longitudinal Study of Adolescent to Adult Health to investigate whether the quality of tertiary education -measured by college selectivity- causally affects obesity prevalence in the medium run (by age 24-34) and in the longer run (about 10 years later). We use...

Predicting entrepreneurial development based on individual and business-related characteristics is a key objective of entrepreneurship research. In this context, we investigate whether the motives of becoming an entrepreneur influence the subsequent entrepreneurial development. In our analysis,...

We propose a complete framework for model-free difference-in-differences analysis with covariates, where model-free means data-driven, in particular nonparametric estimation and testing, variable and scale choice. We start with searching for the preferred data setup by simultaneously choosing...

We introduce the package ddml for Double/Debiased Machine Learning (DDML) in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects of endogenous variables in settings with unknown functional forms and/or...