Large-scale data show promise to provide efficiency gains through individualized risk predictions in many business and policy settings. Yet, assessments of the degree of data-enabled efficiency improvements remain scarce. We quantify the value of the availability of a variety of data...

While several studies suggest that stress-related mental health problems among school children are related to specific elements of schooling, empirical evidence on this causal relationship is scarce. We examine a German schooling reform that increased weekly instruction time and study its...

"Sin taxes" are high on the political agenda in the global fight against obesity. According to theory, they are welfare improving if consumers with low self-control are at least as price responsive as consumers with high self-control, even in the absence of externalities. In this paper, we...

We develop a structural model to address the question whether, and to what extent, expert panelists engage in herd behavior when voting on important policy questions. Our data comes from FDA advisory committees voting on questions concerning the approval of new drug applications. We utilize a...

Human decision-making differs due to variation in both incentives and available information. This generates substantial challenges for the evaluation of whether and how machine learning predictions can improve decision outcomes. We propose a framework that incorporates machine learning on...

We estimate the impact of parental health on adult children’s labor market out- comes. We focus on health shocks which increase care dependency abruptly. Our estimation strategy exploits the variation in the timing of shocks across treated families. Empirical results based on Austrian...

We investigate how the economic consequences of the pandemic, and of the government-mandated measures to contain its spread, affected the self-employed relative to employed individuals in Germany and, secondly, to what extent the female self-employed were more strongly hit than their male...

Antibiotic resistance constitutes a major health threat. Predicting bacterial causes of infections is key to reducing antibiotic misuse, a leading driver of antibiotic resistance. We train a machine learning algorithm on administrative and microbiological laboratory data from Denmark to predict...

We develop a structural model to address the question whether, and to what extent, expert panelists engage in herd behavior when voting on important policy questions. Our data comes from FDA advisory committees voting on questions concerning the approval of new drug applications. We utilize a...

Many important decisions within public and private organizations are based on recommendations from expert committees and advisory boards. A notable example is the U.S. Food and Drug Administration's advisory committees, which make recommendations on new drug applications. Previously the voting...