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

Working Papers

  • Battling antibiotic resistance: using machine prediction to improve prescribing (with Michael Ribers) – paper coming very soon!

Increasing antibiotic resistance constitutes a major worldwide health threat. Predicting bacterial causes of infections is key to tackle the problem of antibiotic misuse, a leading cause of antibiotic resistance. We combine administrative and laboratory data from Denmark to evaluate how machine prediction of bacterial causes for urinary tract infections can improve primary care prescribing. We propose an assessment of machine prediction-based policies against human expert decision making by defining feasible policies based on information available prior to their implementation. Contrasting existing work tackling prediction policy problems, in our setting patient test outcomes are observed independent of physician prescription choices. This allows us to address the relevance of unobservables for physician decisions and to directly evaluate prescription policies based on machine prediction. We find that policies using a combination of machine prediction-based rules and physician expertise can lower antibiotic use by 7.24 percent without reducing the number of treated bacterial infections. As Denmark is one of the most conservative countries in terms of antibiotic prescribing, this result may 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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