ACADEMIC ARTICLE SUMMARY
Big Data, Little Chance of Success: Why Precedent Does Not Support Anti-Data Theories of Harm
Article Source: Antitrust Chronicle: Competition Policy International, Vol. 1, July, Summer 2022
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ARTICLE SUMMARY
Summary:
Some worry that tech firms could use big data to harm consumers and competition. Forcing firms to share data would reduce incentives to innovate and compete. Concerns about privacy are better addressed by privacy law.
POLICY RELEVANCE
Policy Relevance:
The expansion of antitrust law to regulate tech firms’ uses of big data would harm consumers.
KEY TAKEAWAYS
Key Takeaways:
- Technologies and services that use big data have many benefits for consumers.
- New applications of big data include healthcare, smart cities, and energy efficiency.
- Internet content is free to consumers because providers monetize data by selling ads.
- Big data helps train artificial intelligence-based systems to detect fraud or improve vaccines.
- Critics of big data worry that large firms use big data to harm consumers and competition; their concerns include:
- Network effects, which tend to make larger networks more useful to consumers, could create barriers to entry for new competitors.
- Rather than raising prices, firms might reduce quality, perhaps by cutting corners on privacy.
- In two-sided markets, where a firm serves both consumers and advertisers, the firm could increase advertising prices.
- The nature of data undermines critics’ concerns about competition.
- Barriers to entry in data-driven markets are low.
- Use of data by one firm does not use it up, so the same data can be used by many firms.
- New entrants can buy data from data brokers.
- Using antitrust to address privacy issues will distort competition by focusing on large firms while ignoring small firms; consumer protection and privacy law would better safeguard privacy.
- Some critics propose using Section 2 of the Sherman Act, which governs monopolistic conduct by a single firm, to limit allegedly anticompetitive uses of big data; plaintiffs proceeding on this theory have usually failed.
- In theory, a large platform’s data could qualify as an "essential facility," which must be shared with rivals; however, this theory conflicts with key legal precedents.
- In hiQ Labs, Inc. v. LinkedIn Corp., the court found that the data was not “essential,” because it could be obtained from other sources.
- Data is easier to duplicate or obtain from another source than from physical facilities.
- Forced sharing of resources harms consumers because it leads to multiple firms sharing monopoly profits; it also reduces firms’ incentives to innovate, compete, and invest in additional facilities.
- The Federal Trade Commission (FTC) and the Department of Justice have suggested they will scrutinize technology firms and data practices more closely in merger reviews.
- The FTC could expand its regulation of unfair and deceptive practices to include some uses of big data as “unfair competition.”
- The FTC has moved away from traditional antitrust principles, which call for enforcers to bring cases only to promote consumer welfare, and to consider the justifications for the firm's conduct under the “rule of reason.”
- Legislators have proposed new laws to require firms to offer access to data, or to require portability of data to allow its transfer to other platforms and business.
- Expanding the reach of antitrust laws to target big data would conflict with fundamental antitrust precedents and violate principles of sound economics.
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About Daniel Sokol
D. Daniel Sokol is the Carolyn Craig Franklin Chair in Law and Business at the USC Gould School of Law and an Affiliate Professor of Business at the Marshall School of Business, where he teaches in the marketing department. He serves as faculty director of the Center for Transnational Law and Business and the co-director of the USC Marshall Initiative on Digital Competition.
