The Effect of Information Disclosure on Industry Payments to Physicians
55 Pages Posted: 6 Nov 2017 Last revised: 27 Apr 2020
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 causal forest algorithm 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 changed insignificantly on average due to disclosure. However, there is considerable heterogeneity in the treatment effects with 12% of the drug-physician pairs showing a significant change in their monthly payment. Moreover, the decline in payment is smaller among more expensive drugs and among physicians who prescribed more heavily. Drugs that were heavily marketed, more prescribed and higher priced tend to place more stakes towards consulting and speaker fees after the disclosure. Thus, the effect of information disclosure is limited on more expensive drugs and bigger 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
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