An Economic Analysis of US Antitrust Enforcement in Data-Driven Markets

In this article, authors Sophie Meadows, Matthew Milner & Monica (Sidong) Zhong (Edgeworth Economics) explore how data has become a central focus of modern U.S. antitrust enforcement, particularly as regulators examine the competitive impact of digital platforms and artificial intelligence. They explain that recent DOJ and FTC actions increasingly consider not only price effects but also factors such as innovation, quality, consumer choice, and access to valuable data when assessing market power.

The authors examine several landmark antitrust cases, including those involving Google and Meta, to demonstrate how defining the relevant market is critical to evaluating competition in digital ecosystems. They show that courts have closely scrutinized substitution patterns, two-sided platform dynamics, and the role of user and advertiser data in determining whether companies possess monopoly power.

The article also discusses data-focused theories of harm in merger enforcement through the DOJ’s challenge to UnitedHealth’s acquisition of Change Healthcare. While regulators argued that access to rivals’ claims data would strengthen UnitedHealth’s competitive position, the court concluded that much of the information was already available to the company and that the merger was unlikely to materially lessen competition, underscoring the importance of rigorous economic analysis grounded in business realities.

Finally, the authors consider how AI is reshaping antitrust analysis, particularly through algorithmic pricing and the growing importance of data access. They note that regulators are increasingly concerned that AI-powered pricing systems could facilitate tacit collusion or reinforce market dominance, while emphasizing that traditional economic tools remain valuable for assessing whether AI-driven conduct has genuinely harmed competition…

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