Price Discrimination on the Spokes Model with Data Sales

This Master’s Thesis examines the strategies employed by a Data Broker (DB) who sells information to firms operating in a non-localized spatial competition environment represented by the Spokes Model. Each firm perceives itself as competing in various submarkets, categorized based on the availability of consumers’ first and second preferred brands. In certain submarkets, the firm acts as a monopolist, serving captive consumers with no alternative brand option. In other submarkets, the firm operates as a duopolist, competing against an alternative brand. The focus is on a market characterized by low-utility valued products, where uninformed firms are motivated to carve out a portion of captive consumers facing an elastic monopolistic demand. The DB offers a data segment of equal length to all firms through a Take-It-or-Leave-It (TIOLI) arrangement, maximizing firms’ willingness to pay while leveraging the threat of an Outside Option. The data enable first-degree price discrimination, specifically designed for competitive and monopolistic markets, while allowing informed firms to serve at a unique basic price all unidentified consumers. Data have mainly three effects: firstly, they reduce the scope of non-discriminatory markets, thereby facilitating monopolistic market coverage; secondly, they intensify competition among firms for consumers in competitive sub-markets; thirdly, they grant total surplus extraction from captive consumers. In contrast to well-known oligopolistic spatial price discrimination models (i.e. Hotelling), even when the data broker offers overlapping segments that include non-exclusive information about all consumers in competitive markets, each informed firm does not lower its basic price to zero but adjusts it to serve unidentified captive consumers. When the number of firms is sufficiently low and there is a large turf of captive consumers, firms strive to leverage the rent extraction from monopolistic sub-markets and avoid basic prices war for unidentified consumers in competitive sub-markets. Consequently, they target a specific kink of the demand function and grant full market coverage with marginal captive consumers’ surplus at zero. In this scenario, the primary focus of the DB is to enable every informed company to maximize surplus extraction from monopolistic sub-markets. This involves selling equal-sized data partition, almost in its entirety, to all firms involved. However, when there is a low market concentration informed firms succeed in maximizing the rent extraction from monopolistic sub-markets only when they are not forced to defend their non-discriminatory consumers in each duopolistic segment. Indeed, when data segments do not overlap there is a fringe of unidentified consumers in competitive sub-markets that triggers a price war. We find that the DB finds it convenient to mitigate competition among firms by providing a low quantity of non-exclusive information. Furthermore, even in cases where duopolistic segments dominate, the presence of a small group of captive consumers, where informed firms can fully exploit surplus, significantly hampers the performance of the uninformed firm. As a result, even in this scenario, the DB chooses to sell equal-sized partitions encompassing nearly all available data to maximize the threat posed by being uninformed and ensures full market coverage.