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Data Tracking and Competition
Abstract: We explore the welfare implications of data tracking technologies that enable firms to collect consumer data and potentially use it for price discrimination. The model we develop centers around two features: first, competition between firms and, second, consumers' level of sophistication. Our baseline environment features a firm that can collect information about the consumers it transacts with in a duopoly market, which it can then use in a second monopoly market. We characterize and compare the equilibrium outcomes in three settings of interest: (i) an economy with myopic consumers, who, when making purchase decisions, do not internalize the fact that firms have the ability to track their behavior and use this information in future transactions, (ii) an economy with forward-looking consumers, who take into account the implications of data tracking when determining their actions, and (iii) an economy where no data tracking technologies are used either due to technological or regulatory constraints. We find that the absence of data tracking may lead to a decrease in consumer surplus, even when consumers are myopic. Importantly, this result relies critically on competition: consumer surplus is higher when data tracking technologies are used in the marketplace only when multiple firms offer substitutable products to consumers. Our results contribute to the debate of whether to regulate firms’ use of data tracking technologies by illustrating that their effect on consumers depends not only on their level of sophistication, i.e., the extent to which they internalize how their data may be used, but also on the degree of competition in the market. Finally, in contrast to earlier work, we show that firms may have no incentive to self-regulate their use of consumer data even when consumers fully internalize and anticipate how their data may be used.
Bio: Kostas Bimpikis is an associate professor of Operations, Information and Technology at the Stanford Graduate School of Business. He received his PhD in Operations Research at MIT working with Daron Acemoglu and Asu Ozdaglar. Before going to Stanford, he spent a year as a postdoctoral fellow at Microsoft Research New England. His research agenda broadly explores the following two themes:
- Strategic interactions in networks and their implications on firms' operational decision making;
- The design and operations of online marketplaces with a particular emphasis on optimizing information disclosure policies.
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