Column (in German): ZEW News 9/2009.

Column (in German): DIW Wochenbericht 14/2014.

Working Papers

  • Machine predictions and human decisions with variation in payoffs and skill (with Michael A. Ribers), DIW Discussion Paper Nr. 1911 (download pdf).

Human decision-making differs due to variation in both incentives and available information. This constitutes a substantial challenge for the evaluation of whether and how machine learning predictions can improve decision outcomes. We propose a framework that incorporates machine learning on large-scale data into a choice model featuring heterogeneity in decision maker payoff functions and predictive skill. We apply this framework to the major health policy problem of improving the efficiency in antibiotic prescribing in primary care, one of the leading causes of antibiotic resistance. Our analysis reveals large variation in physicians’ skill to diagnose bacterial infections and in how physicians trade off the externality inherent in antibiotic use against its curative benefit. Counterfactual policy simulations show that the combination of machine learning predictions with physician diagnostic skill results in a 25.4 percent reduction in prescribing and achieves the largest welfare gains compared to alternative policies for both estimated physician as well as conservative social planner preference weights on the antibiotic resistance externality.

  • Battling antibiotic resistance: can machine learning improve prescribing? (with Michael A. Ribers), DIW Discussion Paper Nr. 1803 (download pdf).

Columns: DIW Weekly Report 19/2019 (in English), DIW Wochenbericht 19/2019 and Oekonomenstimme (in German).

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 diagnostic test outcomes for urinary tract infections. Based on predictions, we develop policies to improve prescribing in primary care, highlighting the relevance of physician expertise and policy implementation when patient distributions vary over time. The proposed policies delay antibiotic prescriptions for some patients until test results are known and give them instantly to others. We find that machine learning can reduce antibiotic use by 7.42 percent without reducing the number of treated bacterial infections. As Denmark is one of the most conservative countries in terms of antibiotic use, this result is likely to be a lower bound of what can be achieved elsewhere.

We analyze the effects of a hypothetical payment card fee regulation on bank profits, consumer welfare, and merchant welfare. We model consumers’ and merchants’ bank choices for debit card services, cardholders’ demand for card usage (conditional on bank choice), and how banks account for these in setting card fees to their customers. To estimate the model, we use bank-level data and survey data from the Norwegian debit card scheme, BankAxept. We conduct counterfactual exercises to analyze the effects of interchange fee regulations in the debit card scheme.

  • Antibiotic prescribing under uncertainty about resistance (with Michael Ribers)

The increasing level of antibiotic resistance constitutes a major worldwide health threat. Inappropriate antibiotic prescribing is considered one of the main drivers of increasing resistance. Hence, rational prescribing is an important policy objective. We develop a dynamic structural model of antibiotic prescribing for forward-looking general practitioners in the presence of uncertainty about antibiotics’ effectiveness. Our model endogenises information acquisition and features cross-patient learning from observed clinical microbiological testing. Reducing uncertainty is costly so that general practitioners have incentives to under-diagnose antibiotic resistance and prescribe inappropriately. We propose a framework for counterfactual simulations to evaluate policy measures such as mandatory resistance testing, a tax on (broad-spectrum) antibiotics, and the introduction of rapid testing technology.

Former title: Considerations on partially identified regression models, Centre for European Economic Research Discussion Paper 2012-024, BETA (Bureau d’Economie Théorique et Appliquée) Working Paper No. 2012-07, Online Appendix: Download

Work in progress

  • Physician effects in antibiotic prescribing: evidence from changes in clinic composition (with Shan Huang)
  • Selection bias in routine surveillance of antibiotic resistance (with Michael Ribers)
  • Career concerns and managerial risk taking: evidence from the NFL (with Florian Schütt)
  • Product consideration and equilibrium prices: evidence from Danish pharma (with Jonas Lieber)

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