Column (in German): DIW Wochenbericht 13-14/2021.

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

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

Working Papers

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

Inefficient human decisions are driven by biases and limited information. Health care is one leading example where machine learning is hoped to deliver efficiency gains. Antibiotic resistance constitutes a major challenge to health care systems due to human antibiotic overuse. We investigate how a policy leveraging the strengths of a machine learning algorithm and physicians can provide new opportunities to reduce antibiotic use. We focus on urinary tract infections in primary care, a leading cause for antibiotic use, where physicians often prescribe prior to attaining diagnostic certainty. Symptom assessment and rapid testing provide diagnostic information with limited accuracy, while laboratory testing can diagnose bacterial infections with considerable delay. Linking Danish administrative and laboratory data, we optimize policy rules which base initial prescription decisions on machine learning predictions and delegate decisions to physicians where these benefit most from private information at the point-of-care. The policy shows a potential to reduce antibiotic prescribing by 8.1 percent and overprescribing by 20.3 percent without assigning fewer prescriptions to patients with bacterial infections. We find human-algorithm complementarity is essential to achieve efficiency gains.

Human decision-making varies due to differences in information and incentives. This constitutes a substantial challenge for evaluating how machine learning predictions can improve decision outcomes. We tackle this challenge in the context of the major health policy problem of improving efficiency in antibiotic prescribing, the leading cause of antibiotic resistance. We incorporate machine learning predictions on large-scale administrative data into a treatment choice model featuring heterogeneity in patients’ disease risk, physician payoffs, and diagnostic skill. We find substantial variation in the skill to diagnose bacterial urinary tract infections and in how physicians trade off the antibiotic resistance externality against curative benefits. Counterfactual policy evaluation shows that providing predictions to physicians increases efficiency but does not reduce antibiotic use. To reduce prescribing, by close to 10 percent, physicians must be incentivized. Our results highlight the importance of the potential misalignment of decision-makers’ and social planners’ objectives in considering prediction policies, while accentuating the complementarity of machine learning and human expertise.

In the fight against antibiotic resistance, reducing antibiotic consumption while preserving healthcare quality presents a critical health policy challenge. We investigate the role of practice styles in patients’ antibiotic intake using exogenous variation in patient-physician assignment. Practice style heterogeneity explains 49% of the differences in overall antibiotic use and 83% of the differences in second-line antibiotic use between primary care providers. We find no evidence that high prescribing is linked to better treatment quality or fewer adverse health outcomes. Policies improving physician decision-making, particularly among high-prescribers, may be effective in reducing antibiotic consumption while sustaining healthcare quality.

Column (in German): DIW Wochenbericht 29-30/2022. Media coverage in Tagesspiegel, Der StandardKronenzeitungHeise, PC-Welt, BR, and Computerwoche podcast (in German).

We quantify Facebook’s ability to build shadow profiles by tracking individuals across the web, irrespective of whether they are users of the social network. For a representative sample of US Internet users, we find that Facebook is able to track about 40% of the browsing time of both users and non-users of Facebook, including on privacy-sensitive domains and across user demographics. We show that the collected browsing data can produce accurate predictions of personal information that is valuable for advertisers, such as age or gender. Because Facebook users reveal their demographic information to the platform, and because the browsing behavior of users and non-users of Facebook overlaps, users impose a data externality on non-users by allowing Facebook to infer their personal information.

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.

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

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