Insider imitation with product differentiation

Abstract Online marketplace providers such as Amazon may also offer own-brand products, thus competing with third-party traders on their platform. Moreover they may exploit nonpublic data on third-party sales to identify opportunities for profit and design their own products, in this way engaging in a form of partial imitation that is exclusive to them. Such an asymmetry in the ability to imitate has led to advocating for stricter regulation of online platforms in order to limit their ability to exploit nonpublic data. We propose a dynamic simulation model to study the effects of insider imitation by a platform provider under product differentiation. Using different parametrizations, we are able to show how such a market may evolve in time with or without the presence of insider imitation under different initial conditions. The model also allows to account for subsequent innovation via imitation, which is an aspect not considered by previous studies. Our results add to the existing literature and casts further doubts on current regulatory approaches aiming at limiting data exploitation. Our results suggest that the presence of insider imitation is more likely to lead to higher consumer welfare and lower market concentration and should not be banned on the grounds of standard Antitrust concerns. JEL Classifications: D40, D82, D83, L40, O3