Title: Customer Discrimination and Quality Signals: A Field Experiment with Healthcare Shoppers
Abstract: This paper provides evidence that customer discrimination in the market for doctors can be largely accounted for by statistical discrimination. I evaluate customer preferences in the field with an online platform where cash-paying consumers can shop and book a provider for medical procedures based on an experimental paradigm called validated incentivized conjoint analysis (VIC). Customers evaluate doctor options they know to be hypothetical to be matched with a customized menu of real doctors, preserving incentives. Racial discrimination reduces patient willingness-to-pay for black and Asian providers by 12.7% and 8.7% of the average colonoscopy price respectively; customers are willing to travel 100–250 miles to see a white doctor instead of a black doctor, and somewhere between 50–100 to 100–250 miles to see a white doctor instead of an Asian doctor. Further, providing signals of provider quality reduces this willingness-to-pay racial gap by about 90%, which suggests that statistical discrimination is an important cause of the gap. Actual booking behavior allows cross-validation of incentive compatibility of stated preference elicitation via VIC.
Alex Chan, MPH
Alex Chan is a PhD candidate in Health Economics, and a Gerhard Casper Stanford Graduate Fellow. He has research interests in health economics, experimental economics, market design, and labor economics. His projects look at the causes and consequences of discrimination and diversity in medicine, U.S. Health Policy (especially organ transplantation), and market design in health policy and medicine. He holds an MPH from Harvard University. Before Stanford, he developed extensive experience in the healthcare industry starting as a McKinsey consultant, and most recently as Senior Vice President of Market Strategy with Optum/UnitedHealth before joining academia.
Personal Website: https://www.alexchan.net