Insider Imitation

Abstract

We study how regulating data usage impacts innovation in digital markets. Platforms commonly use proprietary data about third-party sellers to inform their own competing offerings, dampening incentives for innovation. We model this interaction and characterize how data usage restrictions reshape these incentives. We apply our results to show that a ban on data usage has an ambiguous impact on innovation dictated by the thickness of the right tail of demand for new products. We then generalize our analysis to more flexible regulations controlling when and what data is made available, showing how each of these additional levers should be used to improve the effectiveness of regulation. Our results contribute to an ongoing policy discussion regarding competition in digital markets. Joint with Erik Madsen (NYU).