Disclosing marketing payments to physicians resulted in a robust decline in both branded and generic prescriptions in three best-seller classes, likely because of physician self-monitoring.

We test whether heavy or binge drinkers are overly optimistic about probabilities of adverse consequences from these activities or are relatively accurate about these probabilities. Using data from a survey in eight cities, we evaluate the relationship between subjective beliefs and drinking. We assess accuracy of beliefs about several outcomes of heavy/binge drinking: reduced longevity, liver disease onset, link between alcohol consumption and Driving While Intoxicated (DWI), probability of an accident after drinking, accuracy of beliefs about encountering intoxicated drivers on the road, and legal consequences of DWI—ranging from being stopped to receiving fines and jail terms. Overall, there is no empirical support for the optimism bias hypothesis. We do find that persons consuming a lot of alcohol tend to be more overconfident about their driving abilities and ability to handle alcohol. However, such overconfidence does not translate into over-optimism about consequences of high levels of alcohol consumption.

In 2019, U.S. pharmaceutical companies paid $3.6 billion to physicians in the form of gifts to promote their drugs. The practice of offering financial incentives has raised concerns about potential conflict of interest. To curb such inappropriate financial relationships between healthcare providers and firms, several states have instituted disclosure laws wherein firms were required to publicly declare the payments that they made to physicians. In 2013, this law was rolled out to all 50 states as part of the Affordable Care Act. The authors investigate the causal impact of this increased transparency on subsequent payments between firms and physicians. While firms and physicians were informed of the disclosure regulation at data collection, complete transparency did not occur until the data were published online. The authors estimate the causal impact of the online data disclosure by exploiting the phased rollout of the disclosure laws across states. They use a quasi-experimental difference-in-difference research design to find control “clones” for every physician-product pair in the states with and without prior disclosure laws, facilitated by recent advances in machine learning methods. Using a 29-month national panel covering $100 million in payments between 16 anti-diabetics brands and 50,000 physicians, the authors find that the monthly payments changed insignificantly on average due to disclosure. However, the average null effect masks some unintended consequences of disclosure, where payments may have gone up for more expensive drugs and among physicians who prescribed more heavily. Interestingly, more popular physicians and heavier prescribers continue to receive high consulting and speaker fees after the disclosure. The authors further explore potential mechanisms that can parsimoniously describe the data pattern.

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