Insider imitation with product differentiation

Online marketplace providers such as Amazon may also offer own-brand products, in this way 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, thus 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. I propose a dynamic simulation model to study the effects of insider imitation by a platform provider under product differentiation. Using different parametrizations, I am 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 I refer to as copy-and-be-copied dynamics), an aspect not considered by previous studies. These results add to the existing literature and cast further doubts on current regulatory approaches aiming at limiting data exploitation. Results suggest that the presence of insider imitation is unlikely to lead to smaller consumer welfare and larger market concentration and should not be banned on the grounds of standard antitrust concerns.