Research

Publications

Working Papers

We analyze how machine learning predictions may improve antibiotic prescribing in the context of the global health policy challenge of increasing antibiotic resistance. Estimating a binary antibiotic treatment choice model, we find variation in the skill to diagnose bacterial urinary tract infections and in how general practitioners trade off the expected cost of resistance against antibiotic curative benefits. In counterfactual analyses we find that providing machine learning predictions of bacterial infections to physicians increases prescribing efficiency. However, to achieve the policy objective of reducing antibiotic prescribing, physicians must also be incentivized. Our results highlight the potential misalignment of social and heterogeneous individual objectives in utilizing machine learning for prediction policy problems.

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

Digital platforms increasingly observe individuals’ browsing behavior beyond the boundaries of their own services. This paper studies whether such off-platform tracking generates data externalities by enabling platforms to infer personal characteristics of individuals who do not disclose them. We examine this mechanism in the context of Facebook’s tracking technologies embedded on third-party websites. Using clickstream data on about 40,000 individuals, we document that Facebook can observe a substantial share of browsing activity both for its own users and for individuals outside its user base. We then train prediction models on Facebook users, for whom demographic characteristics are available to the platform, and apply these models to the trackable browsing behavior of non-users. The results show that demographic characteristics of non-users can be inferred above a zero-information benchmark, implying economically meaningful data externalities. We then study how privacy regulation affects these externalities, in particular, the introduction of the General Data Protection Regulation (GDPR). Although the GDPR sharply reduced Facebook’s off-platform tracking ability, its effect on prediction accuracy for non-users was much more limited. The findings suggest that privacy regulation focused on limiting data collection may leave important inference-based privacy risks unresolved when platforms can use data from some individuals to learn about others.

Column (in German): DIW Wochenbericht 27/2023. Media coverage in taz, Table.Media, finanzen.net, and ntv (in German).
AI-generated podcast (by Google’s NotebookLM)

Tracking online user behavior is essential for targeted advertising and is at the heart of the business model of major online platforms. We analyze tracker-specific web browsing data to show how the prediction quality of consumer profiles varies with data size and scope. We find decreasing returns to the number of observed users and tracked websites. However, prediction quality increases considerably when web browsing data can be combined with demographic data. We show that Google, Facebook, and Amazon, which can combine such data at scale via their digital ecosystems, may thus attenuate the impact of regulatory interventions such as the GDPR. In this light, even with decreasing returns to data small firms can be prevented from catching up with these large incumbents. We document that proposed data-sharing provisions may level the playing field concerning the prediction quality of consumer profiles.

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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