The Effect of Information Disclosure on Industry Payments to Physicians
58 Pages Posted: 6 Nov 2017 Last revised: 25 Sep 2020
Date Written: September 8, 2017
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.
Keywords: Information Disclosure, Causal Inference, Machine Learning, Causal Forest, Heterogeneous Treatment Effect, Public Policy, Quasi-Experiment, Pharmaceutical Marketing, Physician Payments
JEL Classification: M31, M38, C31, C54, C55, I18,K20
Suggested Citation: Suggested Citation