Reprinted in Learning in Labour Markets, edited by Michael Waldman, Edward Elgar Publishing, Cheltenham, 2017.

Working Papers

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

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.

Work in progress

  • Career concerns and managerial risk taking: evidence from the NFL (with Florian Schütt)
  • Regulation and equilibrium prices in pharmaceutical markets (with Jonas Lieber)

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