The Effect of Information Disclosure on Industry Payments to Physicians
48 Pages Posted: 6 Nov 2017 Last revised: 7 Apr 2018
Date Written: September 8, 2017
U.S. pharmaceutical companies paid $2.6 billion to physicians in the form of gifts to promote their medicine in 2015. 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. We investigate the causal impact of this increased transparency on subsequent payments between firms and physicians by exploiting the phased rollout of the disclosure laws across states. In essence, we 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. The novel algorithm (Wager and Athey 2017) is computationally efficient and robust to model mis-specifications, while preserving consistency and asymptotic normality. Using a 29-month national panel covering $100 million in payments between 16 anti-diabetics brands and 50,000 physicians, we find that the monthly payments declined by 2% on average due to disclosure. However, there is considerable heterogeneity in the treatment effects with 14% of the drug-physician pairs showing a significant increase in their monthly payment. Moreover, the decline in payment is smaller among drugs with larger marketing expenditure, and among physicians who were paid more heavily pre-disclosure and prescribed more heavily. Thus, while information disclosure did lead to reduction in payments on average (as intended by policy makers), the effect is limited on big drugs and popular physicians. We further explore potential mechanisms that are consistent with 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