- 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.
- ! UPDATED version (May 2022) Machine predictions and human decisions with variation in payoffs and skill: the case of antibiotic prescribing, with Michael A. Ribers, DIW Discussion Paper Nr. 1911 (download pdf). Twitter thread.
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.
- Physician effects in antibiotic prescribing: evidence from physician exits, with Shan Huang, DIW Discussion Paper Nr. 1958 (download pdf). Twitter thread.
Human antibiotic consumption is considered the main driver of antibiotic resistance. Reducing human antibiotic consumption without compromising health care quality poses one of the most important global health policy challenges. A crucial condition for designing effective policies is to identify who drives antibiotic treatment decisions, physicians or patient demand. We measure the causal effect of physician practice style on antibiotic intake and health outcomes exploiting variation in patient-physician relations due to physician exits in general practice in Denmark. We estimate that physician practice style accounts for 53 to 56 percent of between-clinic differences in all antibiotic consumption, and for 74 to 81 percent in the consumption of second-line antibiotic drugs. We find little evidence that low prescribing styles adversely affect health outcomes measured as preventable hospitalizations due to infections. Our findings suggest that policies to curb antibiotic resistance are most effective when aimed at improving physician decision-making, in particular when they target high prescribers. High prescribing practice styles are positively associated with physician age and negatively with staff size and the availability of diagnostic tools, suggesting that improvements in the quality of diagnostic information is an important path to improved decisions.
- ! UPDATED version (February 2022) Machine learning and physician prescribing: a path to reduced antibiotic use, with Michael A. Ribers,
Old working paper version: Battling antibiotic resistance: can machine learning improve prescribing?, DIW Discussion Paper Nr. 1803 (download pdf). Twitter thread.
Inefficient human decisions are driven by biases and limited information. The health care sector is one leading example where machine learning is hoped to deliver efficiency gains. Antibiotic resistance constitutes a major challenge to health care systems largely due to human antibiotic overuse. In this paper, we investigate how machine learning provides new opportunities to design decision rules that reduce antibiotic use. We focus on urinary tract infections in primary care which constitute one of the leading causes for antibiotic use. Laboratory testing can diagnose bacterial infections but with considerable delay such that physicians often prescribe prior to attaining diagnostic certainty. Using Danish administrative and laboratory data, we find that machine learning methods are capable of predicting the presence of bacteria out-of-sample and complement physician prescribing. We optimize policy rules which delegate a share of prescription decisions to physicians and base the remaining decisions on machine learning predictions. The policy shows a potential to reduce antibiotic prescribing by 8.4 percent and overprescribing by 20.9 percent without assigning fewer prescriptions to patients with bacterial infections. We find that human-algorithm cooperation is essential to achieve efficiency gains.
- 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 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.
- 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
- Selection bias in routine surveillance of antibiotic resistance, with Michael Ribers
- The value of data: evidence from web tracking, with Luis Aguiar, Tomaso Duso, Jonas Hannane, and Christian Peukert
- Career concerns and managerial risk taking: evidence from the NFL, with Paul Bose and Florian Schütt