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Foundations for Antitrust and Policy on Digital Platforms

Periodic Reporting for period 1 - FAPoD (Foundations for Antitrust and Policy on Digital Platforms)

Okres sprawozdawczy: 2022-09-01 do 2025-02-28

The internet marketplace landscape has become increasingly dominated by a few major platforms, which serve as both marketplace operators and participants. These platforms exert significant influence over the digital economy, affecting consumers, sellers, and advertisers through practices that some critics argue exploit monopolistic power. Concerns include “self-preferencing” (prioritizing their own products), aggressive data collection, and creating “monopoly positions” that reduce competition. They may also use acquisition strategies or product replication to curb competition, limiting consumer choice and imposing higher fees on users due to the lack of alternatives.

This project aims to dissect these dynamics to understand the precise ways in which dominant platforms may distort market competition. Specifically, it addresses how the structure and practices of these platforms affect pricing, consumer choice, third-party sellers, and potential competitors. The project is organized into four parts, each examining a different facet of platform conduct: (1) the dual marketplace and seller role, (2) consumer steering through algorithms, (3) recommendation algorithms and search behavior, and (4) acquisition strategies of incumbents targeting smaller competitors.
In the first two years, the project I have finished three important papers and two more are nearing completion. All five study key issues in platform-driven marketplaces, algorithms, and consumer behavior. The first paper, “Retailer-led Marketplaces”, examines hybrid marketplaces where platforms act as both retailers and operators, developing a model to analyze interactions with third-party sellers. Findings indicate that marketplace fee regulation could improve welfare, and the paper lays the groundwork for a follow-up study to explore additional factors. The second paper, “Monetizing Steering”, investigates how platforms choose between algorithmic steering and ad auctions to direct consumers. The study reveals that the optimal steering method varies depending on market conditions, offering policy insights on vertical integration and platform competition. The third, "The “Kill Zone”: When a Platform Copies to Eliminate a Potential Threat" built on our earlier work but incorporated some of the novel ideas from FAPoD and tackles key issue of platform conduct with regards to potential rivals. We show how large platforms may eliminate competitors by copying their products at an early stage and thus forcing them to pursue no-threatening postures. Our results have implications for new approaches of competition policy with regards to dominant platforms. Two further studies are progressing toward working paper status. The first, “How to Impress an Algorithm,” explores how sellers attempt to gain algorithmic favor, such as by setting higher off-platform prices or manipulating reviews, showing that platforms may influence these behaviors to align with their own goals. The second project, “Search Model of (Behavioral) Retargeting,” analyzes consumer search behavior and the dual impacts of retargeting ads, finding that this practice may lower prices by fostering competition, though concentration in ad platforms can affect outcomes. These studies provide critical insights into how platform practices shape competition and consumer welfare, paving the way for evidence-based regulatory guidance.
This project has achieved several significant advancements beyond the current understanding of digital marketplaces. First, it has made a valuable contribution to the policy discourse surrounding hybrid marketplaces by developing a comprehensive yet manageable model that, for the first time, incorporates multiple key policy concerns within a unified framework. By addressing these interconnected issues—such as self-preferencing and high platform fees—within a single model, the project has shown that these concerns cannot be effectively tackled in isolation. For example, regulating self-preferencing has implications for platform fee structures, illustrating that any policy targeting one area will likely influence the other. This integrated model offers novel insights for both competition policy and platform management strategies.

Secondly, the project is the first of its kind to explore the deeper forces influencing platform decisions on consumer steering methods. This previously underexamined area has revealed a critical tension between double marginalization and fee flexibility, both of which significantly impact the policy discussion around steering practices. Surprisingly, the findings indicate that a platform’s optimal steering choice may align with a “second-best” consumer preference. This discovery underscores the need for careful policy deliberation, as the forces identified here will be crucial in shaping balanced, effective regulations on platform behavior.
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