Abstract: This paper investigates whether financial incentives for curbing antibiotic prescriptions are effective and how the design of incentives plays a role in influencing physician behavior. Using prescription-level data from French general practitioners over six years, I provide evidence of the incentives' effectiveness by exploiting variation in the set of diseases that the physicians treat as well as in the reward scheme. To understand how they respond, I propose a model that incorporates financial incentives into physician's decision-making and test the predictions of the model. The results highlight that the reduction in antibiotic prescriptions varies across different diseases, in line with the physicians' altruism and, hence, the patient's needs. Moreover, forward-looking physicians are influenced by the marginal cost of antibiotic prescriptions and the design of the incentives. While the program is effective, the magnitude is moderate, with a 2 percentage point drop in the antibiotic prescription rate. Comparing the effect to the cost of the program, conditioning the rewards on prescription rates rather than the improvement over time plays a role. As a result, while aggregate bonus payments per physician remain modest (on average 0.2% of physicians' annual income), the cost per avoided prescription is substantial (on average 56% of the fixed visit fee).
Abstract: The differentiated products demand model initiated by Berry (1994) and Berry, Levinsohn, and Pakes (1995) is the workhorse model for demand estimation with market-level data. This model uses random coefficients to account for unobserved preference heterogeneity. The shape of the distribution of random coefficients matters greatly for many counterfactual quantities, such as the pass-through. In this paper, we develop new econometric tools to test this distribution and improve its estimation under a flexible parametrization. In particular, we construct new instruments that are designed to detect deviations from the underlying distribution of random coefficients. Then, we develop a formal moment-based specification test on the distribution of random coefficients. Next, we show that our instruments can strengthen the identifying power of the moment conditions used for estimation. Finally, we validate our approach with Monte Carlo simulations and an empirical application using data on car purchases in Germany. We show that these methods extend to the mixed logit demand model (with individual-level data).
Abstract: Antimicrobial resistance (AMR) increases hospital stays, medical costs and mortality. Antibiotic consumption and resulting selective pressure on bacteria can create AMR. We study the role of AMR on changes in prescriptions of antibiotics in France for treating bladder inflammation (cystitis) using a representative sample of general practitioners between 2002 and 2019. Effects of resistance on demand and substitution behavior are identified via a random coefficient logit model, controlling for the endogeneity of resistance using antibiotics sales in veterinary medicine. As resistance increases, physicians substitute to other drugs, and we test whether physicians consider predictable resistance evolution in their decisions. We perform counterfactual analysis assessing the impact of decreasing veterinary use of antibiotics and limiting fluoroquinolone use to treat cystitis. Both policies reduce resistance against fluoroquinolones but have opposite effects on substitution behavior and consumer surplus. Finally, we propose a method for the optimal pricing of rapid bacterial detection and antibiotic susceptibility testing.