Regulators on both sides of the Atlantic are conducting investigations into the practice of self-preferencing by digital platforms, particularly Amazon and Google. Self-preferencing occurs when a platform prioritizes its own first-party offerings over those of third parties in terms of visibility in the platform market in order to attain a competitive advantage. There is some evidence for this practice: Chen and Tsai (2023) find that products are more frequently recommended to consumers on Amazon Marketplace if Amazon sells them.
However, the idea that a platform would foreclose third-party rivals is somewhat puzzling. As Etro (2024) notes, it ‘conflicts with the decision of hosting third party sellers to collect commission revenues’ in the first place, ‘especially for a platform [such as Amazon] that […] collects most of its revenues from third parties’ (p.1517).
Florian Dendorfer demonstrates in a recent article that a platform commits to refraining from self-preferencing, provided this commitment is credible to consumers. Generally, the platform profits from selling a first-party product as this reduces double marginalization. By the same token, first-party selling enhances market efficiency because it leads to lower prices for consumers. This is true regardless of whether the platform engages in self-preferencing. If the platform allows the first-party product to compete directly with third-party offerings, rather than shielding it from competition, third-party seller profit margins are squeezed due to increased competitive pressure, and double marginalization is further reduced. Not only do consumers benefit from this but the platform earns more revenue from commission fees. Thus, the platform will not engage in self-preferencing.
This result critically hinges on the platform’s ability to commit to refrain from self-preferencing. At the point of sale, the platform has a strong incentive to unfairly promote the first-party product to maximize its retail profit margin. It can attract additional demand to the market only if it convincingly demonstrates beforehand—when consumers sign up—that it will treat third-party products and its own product equally. This finding suggests that in practice a platform is more likely to engage in self-preferencing if it lacks a reputation for fairness and transparency of search results or if the functioning of its search algorithm is opaque or is not disclosed to the public.
The article’s findings are largely robust to various extensions. Among other things, they hold regardless of whether the platform introduces an entirely new variety or replicates an existing product. Qualitatively, it makes no difference whether the platform’s self-preferencing ability is unrestricted or constrained.
Ultimately, the article contributes to our understanding of self-preferential behavior of digital platforms and challenges the common assumption that these platforms always have an incentive to engage in this practice. It underscores a platform’s motivation to lower consumer prices when selling a first-party product and highlights the critical role of transparency in preventing self-preferencing.
The full paper is published in IJIO Volume 97, December 2024.
References
Chen, Nan and Hsin-Tien Tsai, ‘Steering via algorithmic recommendations’, The RAND Journal of Economics, 2023.
Etro, Federico, ‘e-Commerce platforms and self-preferencing’, Journal of Economic Surveys, 2024, 38 (4), 1516–1543.