- Assessing the value of data for prediction policies: The case of antibiotic prescribing (Open Access), with Shan Huang and Michael Ribers, Economics Letters, Vol. 213, 110360, 2022. DIW Discussion Paper Nr. 1939 (download pdf).
Column (in German): DIW Wochenbericht 13-14/2021.
- Career prospects and effort incentives: evidence from professional soccer, with Jeanine Miklós-Thal, Management Science, Vol. 62(6), pp. 1645-1667, 2016. Last working paper version. Online Appendix: Download. Reprinted in Learning in Labour Markets, edited by Michael Waldman, Edward Elgar Publishing, Cheltenham, 2017.
Column (in German): ZEW News 9/2009.
- Belief precision and effort incentives in promotion contests, with Jeanine Miklós-Thal, Economic Journal, Vol. 125(589), pp. 1952-1963, 2015. Last working paper version.
- Regulation of pharmaceutical prices: evidence from a reference price reform in Denmark, with Ulrich Kaiser, Susan Mendez, and Thomas Rønde, Journal of Health Economics, Vol. 36, pp. 174-187, 2014. Last working paper version.
Column (in German): DIW Wochenbericht 14/2014.
- Machine predictions and human decisions with variation in payoffs and skill: the case of antibiotic prescribing, with Michael A. Ribers, Berlin School of Economics Discussion Paper Nr. 27 (download pdf).
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.
- Machine learning and physician prescribing: a path to reduced antibiotic use, with Michael A. Ribers, former title “Battling antibiotic resistance: can machine learning improve prescribing?”, Berlin School of Economics Discussion Paper Nr. 19 (download pdf). Revise and resubmit at Quantitative Marketing and Economics.
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 machine learning provides new opportunities to reduce antibiotic use, with the help of physicians. 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. We find human-algorithm complementarity is essential to achieve efficiency gains with a potential reduction in antibiotic prescribing by 8.1 percent and in overprescribing by 20.3 percent.
- Provider effects in antibiotic prescribing: Evidence from physician exits, with Shan Huang, Berlin School of Economics Discussion Paper Nr. 18 (download pdf). Revise and resubmit at Journal of Human Resources.
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.
- Facebook shadow profiles, with Luis Aguiar, Christian Peukert, and Maximilian Schaefer, DIW Discussion Paper Nr. 1998 (download pdf).
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.
- Returns to web tracking data (Abstract), with Luis Aguiar, Tomaso Duso, Jonas Hannane, and Christian Peukert.
The tracking of online user behavior has been essential for the construction of consumer profiles to help platforms monetize their services by selling targeted advertisements. We analyze web browsing data to show how prediction quality of consumer profiles varies across platforms depending on the size and scope of user data available to them. We find decreasing returns to the number of users observed and the number of websites tracked. Combining web browsing data with demographic information, two heterogeneous sources of user information which are available to some online platforms, provides a sizable increase in prediction quality. For Google, we find more slowly decreasing returns compared to other trackers with an increase in both the number of users and websites tracked. Finally, we document that proposed data combination policies may level the playing field with respect to the returns to data.
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.
- A note on regressions with interval data on a regressor, with Daniel Cerquera and Francois Laisney. Online Appendix: Download
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
- Consumer welfare and unobserved heterogeneity in discrete choice models: the value of Alpine road tunnels, with Daniel Cerquera, Centre for European Economic Research Discussion Paper 2010-095
Work in progress
- Career concerns and managerial risk taking: evidence from the NFL, with Paul Bose and Florian Schütt
- Salience of antibiotic resistance and antibiotic prescribing in primary care (Preregistration), with Shan Huang, Michael Allan Ribers, Barbara Juliane Holzknecht, Jonas Bredtoft Boel, Jette Brommann Kornum, and Michael Pedersen
- The causal effect of antibiotic prescribing on population antibiotic resistance, with Shan Huang, Michael Allan Ribers, Barbara Juliane Holzknecht, Jonas Bredtoft Boel, Jette Brommann Kornum, and Michael Pedersen
My research repositories and social media profiles