An Economic Analysis of US Antitrust Enforcement in Data-Driven Markets
Introduction
In recent years, leadership at the Antitrust Division of the Department of Justice (DOJ) and the United States Federal Trade Commission (FTC) has emphasized the agencies’ increased focus on competition issues raised by the use of data. As stated in 2022 by the then Assistant Attorney General for Antitrust, Jonathan Kanter, “new market realities demand new approaches to competition enforcement”.[1] In addition, the 2023 Merger Guidelines,[2] which provide the framework used by the DOJ and FTC in evaluating antitrust markets and competition, more explicitly outline the agencies’ focus on potential harm to competition beyond increased prices. This includes assessments of potential “worsening terms along any dimension of competition”, including “quality, service, capacity investment, choice of product variety or features, or innovative effort”.[3] This change in focus is particularly important when assessing potential theories of harm involving digital platforms, where data plays a central role in competition.
Reflecting these new priorities, federal regulators have pursued several high-profile lawsuits against data-focused companies, such as Google, Meta (formerly Facebook), UnitedHealth Group, Amazon and Apple, alleging unilateral conduct that substantially lessened competition.[4] For example, in Federal Trade Commission v Amazon.com, the FTC and numerous state attorneys general alleged that Amazon is a monopolist and engaged in illegal practices to maintain its market power,[5] and that “access to valuable shopper data” allowed it to strengthen its alleged dominant position as a platform between sellers and consumers while “overcharging its customers” and “degrading the services it provides them”.[6]
In cases that allege monopolization, before assessing a firm’s alleged market power, it is necessary to understand the nature of competition and the antitrust market at issue. In this chapter, we first discuss how economic analysis and the definition of the relevant markets affects the assessment of competitive harm in data-focused markets.
We further discuss the specific data-focused theories of harm presented by the federal agencies in a recent case, USA v UnitedHealth and Change, involving two-sided platforms and how economic analysis was used to assess these theories of harm. In this particular case, the agencies have alleged that the firm used data to enhance its market power, thereby lessening competition and harming consumers through both higher prices and non-price terms, such as quality of service. In doing so, the federal agencies have adopted theories of harm focused on access to, and control of, specific types of data.
Finally, we consider the implications of data-driven technologies for antitrust enforcement. The federal agencies have discussed their focus on investigating potential harm to competition in the context of the digital revolution, including increased data collection and the use of automated decision-making.[7] We discuss algorithmic price-setting and its implications for assessing alleged coordinated conduct.
Analysis of antitrust markets in data-focused industries
In assessing claims of monopolization, a first step is usually to determine the relevant antitrust market for the product (or service) at issue.[8] There are two dimensions to assess when defining an antitrust market:
- the product and potential substitutes customers can turn to in response to a price increase (or quality decrease); and
- the geographic area in which competition for the relevant product (or service) takes place.[9]
In addition to assessing current market conditions, market definition analysis in the technology sector should also account for nascent competition and product development. The threat of entry through new technology or innovation can pose a meaningful competitive constraint that needs to be considered.
The USA v Google case illustrates the importance of identifying the relevant market in assessing alleged competitive harm. In October 2020, the DOJ and several state attorneys general sued Google for violating section 2 of the Sherman Act by “unlawfully maintaining monopolies in the markets for general search services, search advertising, and general search text advertising in the United States through anticompetitive and exclusionary practices”.[10] The complaint alleged that Google entered into exclusionary agreements with wireless device manufacturers, carriers and browser developers, which resulted in Google becoming the default search engine for their products.[11]
The government alleged the agreements “deny rivals scale to compete effectively [with Google]” as “the volume, variety, and velocity of data accelerates the automated learning of search and search advertising algorithms”.[12] In other words, greater scale improves the quality of Google search and search advertising services over rivals, which in turn allegedly reinforces Google’s market dominance. The government further claimed that Google’s conduct “harmed consumers by reducing the quality of general search services” (across dimensions such as privacy and data protection), lessened consumers’ choices and impeded innovation.[13]
A key area of debate in this case was the definition of the relevant markets. Two-sided technology platforms have unique features that should be considered when evaluating the relevant antitrust markets. Platforms such as Google provide products or services to multiple groups, or “sides”, that “benefit from each other’s participation”.[14] Because there are distinct sets of customers on each side of the platform, the assessment of alleged competitive harm should consider the supply and demand conditions that affect each side of the platform and the nature of competition on each side:[15]
- on the user side of the platform, the government proposed an antitrust market of general search services that includes general search engines (eg, Google, Bing, Yahoo! and DuckDuckGo);[16] and
- on the advertiser side of the platform, the DOJ proposed two antitrust markets:
- general search text advertisements, which are primarily text that appears on the search engine results page (SERP); and
- the broader search advertisements, which encompass any advertisements shown on a SERP in response to a search query, including text advertisements, shopping advertisements and travel advertisements.[17]
These relevant markets informed the government’s analysis of Google’s alleged market power; for example, the government contended that Google controlled almost “90 percent of all general search engine queries in the United States, and almost 95 percent of queries on mobile devices”.[18] In addition, the government argued that “Google has 16 times more fresh search data than [Microsoft’s] Bing, its nearest competitor”.[19]
Google’s economic experts argued that the US government’s market definition analyses was overly narrow, omitting important competitors for both users and advertisers, namely specialized vertical providers as well as social media sites.[20] On the user side of the market, Google argued that the government “distort[ed] the commercial reality that users routinely substitute other search providers for general search engines—such as Amazon when they shop, or Expedia when they travel”.[21] That is, the relevant competitive conditions to assess the alleged conduct would be different if, instead of general search services, the relevant markets analysed included specialised search providers, which Google argued were “many of [their] strongest competitors”.[22]
On the advertiser side of the market, Google argued that economic analysis and substitution patterns show that advertisers allocate their spending across Google, Meta, Amazon and others.[23] As a result, Google’s expert concluded that these other options must be included in the relevant markets for advertisers.
Judge Mehta found that Google maintained an illegal monopoly in two antitrust markets:
- general search services on the user side of the platform; and
- general search text advertising on the advertiser side of the platform.[24]
The court’s consideration of the relevant markets for each side of the platform highlights the unique characteristics of two-sided technology platforms that should be considered when determining relevant antitrust markets. On the user side of the platform, the court relied on evidence that “users can access Google’s rivals by switching the default search access point or by downloading a rival search app or browser. But the market reality is that users rarely do so”.[25]
In assessing the two proposed markets on the advertiser side of the platform, the court reached different conclusions. It found that Google maintained an illegal monopoly in the proposed market for general search text advertising (eg, text advertisements that are found on the SERP).[26] However, with respect to the broader market for search advertisement, the court concluded there was sufficient evidence showing that the strength and growth of entry by competitors supported the conclusion that Google did not have monopoly power in this market.[27] Judge Mehta found that the government did not prove that Google had “sufficient power in that market” to support a violation of the monopolization laws.[28]
The recent decision in Federal Trade Commission v Meta Platforms similarly shows how the boundaries of the relevant market impact the assessment of alleged competitive harm. In this case, the FTC alleged that Meta held a monopoly in the Personal Social Networking (PSN) market and “preserved its monopoly” by purchasing “upstart rivals Instagram and WhatsApp”.[29] The FTC and Meta disagreed over the “bounds of the relevant product market” in which Facebook and Instagram compete with other apps.[30] The FTC argued that “Meta competes in a separate market for PSN apps, which people use to keep up with their friends and family”, while Meta argued that it competes within a broader social media market and that “the empirical evidence shows that consumers treat other apps—especially TikTok and YouTube—as substitutes” for Meta’s apps.[31] The court agreed with Meta, citing the “widespread substitution between these apps” and ultimately found that “Meta holds no monopoly in the relevant market” for social media.[32] Both of these judgments underscore the fact-specific nature of market definition analysis, which involves, as the 2023 Merger Guidelines state, “leverage[ing] market realities to identify an area of effective competition”.[33]
Data-focused theories of harm involving two-sided platforms
The methodology for assessing competition between multi-sided platforms is a key change to the 2023 Merger Guidelines. The Guidelines lay out the agencies’ approach to assessing potential harm to competition from access to or control of data by these platforms.[34] Specifically, the Guidelines point to four types of mergers involving platforms that would potentially lessen competition:
- mergers between a “dominant” platform operator and “smaller competing platforms”;
- acquisitions that deprive rivals of network effects, which “may weaken rival operators or increase barriers to entry and expansion”;
- acquisitions of companies that “help sellers manage listings on multiple platforms, or software that helps users switch among platforms”; and
- mergers involving a firm that provides inputs (eg, data) that “may enable the platform to weaken rival platforms by denying them that data”.[35]
As with market definition analysis, an economic analysis of alleged anticompetitive conduct is a fact-specific inquiry. In assessing data-focused theories of harm, it is critical to understand how the firms at issue compete, including how they use (or potentially use) the data at issue. Below, we discuss how data-focused theories of harm involving two-sided platforms were assessed in the government’s case against UnitedHealth Group (United).
In this case, the DOJ claimed that United’s acquisition of healthcare billing and payment processing service provider Change Healthcare (Change) would allow United “to use its rivals’ [electronic claims and payment data] to gain an unfair advantage and harm competition in health insurance markets”.[36] In February 2022, the DOJ filed a lawsuit to block the merger under section 7 of the Clayton Act, alleging two vertical theories of harm:
- the combined firm would enable United to use its rival’s claims data tracked by Change’s digital platforms “to extract intelligence about its health insurance rivals”; and
- the combined firm would “disadvantage its health insurance rivals by raising their costs and reducing or withholding quality improvements and innovations from rivals that rely on Change’s technologies”.[37]
Focusing on this first theory of harm, the key disagreements between the government and the merging parties centered on the incremental value of competitive intelligence that Change’s claims data provided United. To support this theory, the government relied on an economic analysis of healthcare insurance claims data. This analysis showed that, through the acquisition, United would gain access to data on competitor claims that make up approximately 40% of all commercial claims, potentially giving United access to detailed information about a large number of rival insurers’ customers.[38] In response, the defendants argued that Change’s electronic claims data was not unique, and that United could access key elements of the claims data at issue from publicly available sources.[39]
Ultimately, the court ruled in favor of the merging parties and allowed the merger to proceed.[40] Specifically, the court found that:
- the claims data at issue may have competitive value, but there was sufficient overlap between the types of data to which United already had access prior to the merger and the data to which it would have access through the acquisition of Change;[41] and
- the evidence presented by the merging parties demonstrated that United had strong incentives to maintain a “multi-payer business strategy” rather than withhold innovative technologies from rival insurers.[42]
Based on the defendants’ analysis, the court concluded that the competitive intelligence gained by United from access to additional data post-merger would be similar to the intelligence it would have absent the proposed acquisition. The court concluded that the testimony from United executives on the strategic direction of the company was “far more probative of post-merger behavior than [the plaintiff’s expert’s] independent weighing of costs and benefits”.[43]
As this decision shows, to directly test the merits of an allegation and be probative to the court, an economic analysis must be able to reliably isolate the effects of purported anticompetitive conduct. In data-focused markets, it is important for this type of economic analysis to account for how the firms actually use (or would use) the data at issue. In this case, the court found that United had access to similar data such that access to additional data from Change would not result in United gaining materially different potential business intelligence post-merger. An economic analysis should account for these business realities so as to reliably model the effect (if any) of the alleged conduct.
Implications of rapidly evolving technologies for antitrust enforcement
Artificial intelligence (AI) is a diverse domain that incorporates various technologies, such as machine learning and natural language processing. These can be used in various capacities, such as chatbots (which process and generate humanlike text in real time) and healthcare tools (which assist with diagnosing and monitoring patients).[44] The increased use of AI and other data-driven technologies raises potential competitive concerns.
For example, in the USA v Google case discussed above, the court found that between the liability and remedies phases of the case, generative AI products moved “front and center as a nascent competitive threat” to general search engines.[45] The court’s remedies decision, in part, ordered Google to make “data available to competitors”, including generative AI companies.[46] As part of this decision, the court highlighted the importance of access to relevant data, finding that making “data available to competitors would narrow the scale gap created by Google’s exclusive distribution agreements and, in turn, the quality gap that followed”.[47]
Below, we consider the concerns surrounding potential collusion facilitated by AI models, specifically with respect to pricing decisions. As these technologies have advanced, firms have adopted AI-powered pricing algorithms to recommend prices that would optimize profits based on input data such as production cost, overhead charges, consumer demand, inventories and, most critically in this context, competitor pricing.[48]
Many economists and lawyers have discussed how the use of AI pricing algorithms may facilitate collusion among competitors, even in the absence of an explicit agreement.[49] One publication noted that advancements in data processing that enable real-time observation of the market and the use of AI to engage in autonomous decision-making may “amplify tacit collusion to a new level of stability and scope”.[50] For example, firms may “unilaterally create […] an algorithm” while knowing that “the industry-wide use of pricing algorithms will facilitate tacit collusion” or unintentionally align prices with those of competitors by using similar algorithms to monitor prices.[51]
The updated Merger Guidelines outline several factors that the DOJ and FTC consider when assessing the extent to which competition may be harmed by pricing algorithms, stating that:
- pricing algorithms that “track or predict competitor prices or actions” can increase risk of collusion as companies’ pricing and strategies are easily observable across the market; and
- faster pricing algorithms can result in more predictable strategic responses from rivals.[52]
Ultimately, it is important for firms to actively understand the AI models they use. Empirical questions, such as whether there are barriers to access relevant data in certain marketplaces and whether the use of AI models by competitors potentially facilitates collusive behavior, can only be answered by studying the specific AI tools and relevant data.
Economic analysis can provide an understanding of the algorithms deployed by companies, including the data inputs that feed into the pricing algorithms and how the algorithm leverages these inputs to determine a price recommendation. Existing economic tools that have been widely used in traditional competition matters are still applicable in assessing the competitive effects of pricing decisions influenced by AI; for example, economic modelling can be used to compare the actual prices set by companies using pricing algorithms to those in a “but-for” world where no anticompetitive conduct occurred. Similarly, “natural experiments” can be used to assess whether prices set by algorithms are higher, lower or unchanged relative to comparable transactions where other pricing methods were used.
This piece was originally published in the GCR Data & Antitrust Guide.
Acknowledgements
The authors wish to thank Dr. Ashley Zhou, formerly managing principal at Edgeworth Economics, for her contributions to previous editions of this chapter. The authors are also grateful for the research assistance conducted by Anav Singh and the collegial support from Stephanie Cheng and Michael Kheyfets.
CITATIONS
[1] US Department of Justice (DOJ), Office of Public Affairs (OPA), “Assistant Attorney General Jonathan Kanter Delivers Keynote at the University of Chicago Stigler Center” (21 April 2022) (Kanter speech), https://www.justice.gov/opa/speech/assistant-attorney-general-jonathan-kanter-delivers-keynote-university-chicago-stigler.
[2] United States FTC and DOJ, “Merger Guidelines” (18 December 2023) (2023 Merger Guidelines), https://www.ftc.gov/system/files/ftc_gov/
pdf/2023_merger_guidelines_final_12.18.2023.pdf.
[3] 2023 Merger Guidelines, supra note 2, at 42.
[4] Amended Complaint, United States of America, et al v Google, LLC, No. 1:20-cv-03010 (15 January 2021) (USA v Google Complaint); First Amended Complaint, FTC v Facebook, Inc, No. 1:20-cv-03590 (19 August 2021); Complaint, United States of America, et al v UnitedHealth Group Incorporated and Change Healthcare Inc, No. 1:22-cv-00481 (24 February 2022) (USA v UnitedHealth and Change Complaint); Second Amended Complaint, FTC, et al v Amazon.com, Inc, No. 2:23-cv-01495 (31 October, 2024) (FTC v Amazon Complaint); First Amended Complaint, United States of America, et al v Apple Inc, No. 2:24-cv-04055 (11 June 2024) (USA v Apple Complaint).
[5] FTC, Press release, “FTC Sues Amazon for Illegally Maintaining Monopoly Power” (26 September 2023), https://www.ftc.gov/news-events/news/press-releases/2023/09/ftc-sues-amazon-illegally-maintaining-monopoly-power.
[6] FTC v Amazon Complaint, supra note 4, at ¶¶5, 182, 209.
[7] Kanter speech, supra note 1.
[8] 2023 Merger Guidelines, supra note 2, at 39–40 (“The Agencies engage in a market definition inquiry in order to identify whether there is any line of commerce or section of the country in which the merger may substantially lessen competition or tend to create a monopoly…. Market definition can also allow the Agencies to identify market participants and measure market shares and market concentration.”).
[9] ibid. See also American Bar Association, “Market Power Handbook: Competition Law and Economic Foundations”, at 62 (8 March 2012).
[10] USA v Google Complaint, supra note 4, at 3.
[11] id., at ¶4.
[12] id., at ¶8.
[13] id., at ¶167. In this case, although Google does not charge a cash price to users, the search is not free as “the consumer provides personal information and attention in exchange for search results. Google then monetizes the consumer’s information and attention by selling ads”, id., at ¶25.
[14] 2023 Merger Guidelines, supra note 2, at 23.
[15] For example, in Ohio v American Express, an antitrust case involving the credit card market, the Supreme Court of the United States stated: “Unlike traditional markets, two-sided platforms exhibit ‘indirect network effects’, which exist where the value of the platform to one group depends on how many members of another group participate. Two-sided platforms must take these effects into account before making a change in price on either side, or they risk creating a feedback loop of declining demand”. Opinion, Ohio et al v American Express Co et al, No. 16-1454 (25 June 2018), at 1, https://www.supreme
court.gov/opinions/17pdf/16-1454_5h26.pdf.
[16] USA v Google Complaint, supra note 4, at section IV.A.1–2.
[17] Plaintiffs’ Pre-Trial Brief, United States of America, et al v Google, LLC, No. 1:20-cv-03010 (28 August 2023) (Plaintiffs’ Pre-Trial Brief, USA v Google), at 5–7. The government’s economic expert opined that: text ads are distinct from other types of search ads (eg, shopping ads) in meaningful ways; and search ads are distinct from other forms of advertising such as social or display ads because the latter are not returned in response to a real-time user query.
[18] USA v Google Complaint, supra note 4, at ¶5.
[19] Plaintiffs’ Pre-Trial Brief, USA v Google, supra note 18, at 1.
[20] Defendant Google LLC’s Pre-Trial Brief, United States of America, et al v Google, LLC, No. 1:20-cv-03010 (8 September 2023), at 2, USA v Google.
[21] ibid.
[22] ibid.
[23] id., at 5–6.
[24] Memorandum Opinion, United States of America, et al v Google, LLC, No. 1:20-cv-03010 (5 August 2024), at 276. Specifically, Judge Mehta concluded that Google violated section 2 of the Sherman Act in each of the markets through exclusive agreements that foreclosed a substantial share of the market, deprived rivals of scale and reduced rivals’ incentives to invest and innovate in that market. id., at 216, 226, 236 and 258.
[25] id., at 221.
[26] id., at 185–191.
[27] id., at 166–185.
[28] id., at 180.
[29] Memorandum Opinion, FTC v Meta Platforms, Inc, No. 20-3590 (18 November 2025), at 1.
[30] id., at 36.
[31] id., at 36–37.
[32] id., at 2, 76.
[33] 2023 Merger Guidelines, supra note 2, at 41.
[34] 2023 Merger Guidelines, supra note 2, at 25.
[35] ibid. Lawsuits brought by the government have alleged theories of harm that are consistent with these considerations; for example, in United States of America v Apple, Inc, the DOJ and state attorneys general alleged that Apple “sustained the most dominant smartphone platform and ecosystem in the United States”. The government alleged that “Apple’s iPhone platform is protected by several additional barriers to entry and expansion, including strong network and scale effects and high switching costs and frictions”. USA v Apple Complaint, supra note 4, at ¶¶4 and 185.
[36] DOJ, OPA, Press release, “Justice Department Sues to Block UnitedHealth Group’s Acquisition of Change Healthcare” (24 February 2022), https://www.justice.gov/archives/
opa/pr/justice-department-sues-block-unitedhealth-group-s-acquisition-change-healthcare.
[37] USA v UnitedHealth and Change Complaint, supra note 4, at 2, 9. The government also asserted a horizontal theory of harm that “Change and United are the largest and second largest vendors of first-pass claims editing solutions” and the acquisition would eliminate competition in this between them (id., at ¶¶108–13). The merging party entered a divestiture agreement to sell Change’s first-pass claims editing business to a third party. Amended Pretrial Brief of Defendants (ECF 90-1 (Def. Br.)), United States of America, et al v UnitedHealth Group Incorporated and Change Healthcare Inc, No. 1:22-cv-00481 (Amended Pretrial Brief of Defendants, USA v UnitedHealth and Change), at 1.
[38] Dr Gautam Gowrisankaran Slide Presentation, United States of America, et al v UnitedHealth Group Incorporated and Change Healthcare Inc, No. 1:22-cv-00481 (Gowrisankaran Slide Presentation).
[39] Amended Pretrial Brief of Defendants, USA v UnitedHealth and Change, supra note 32, at 49–50.
[40] Memorandum Opinion, United States of America, et al v UnitedHealth Group Incorporated and Change Healthcare Inc, No. 1:22-cv-00481 (21 September 2022), at 58.
[41] id., at 35–36. Moreover, the court stated that “based on all the evidence presented at trial, . . . United’s incentives to protect external customers’ data outweigh its incentives to ‘misuse’ that data”. id., at 39.
[42] id., at 39–40, 54.
[43] id., at 57.
[44] IBM, “What is artificial intelligence (AI)?”, https://www.ibm.com/topics/artificial-intelligence.
[45] Memorandum Opinion, United States of America, et al v Google, LLC, No. 1:20-cv-03010 (2 September 2025), at 2.
[46] id., at 103 and 130.
[47] id., at 130. The large amount of data required to develop AI models may potentially act as a barrier to entry for new model developers. That is, owing to the demand for significant computational power and extensive data sets, large companies may have a competitive advantage over smaller firms. See Daniel L Rubinfeld and Michal S Gal, “Access Barriers to Big Data”, Arizona Law Review, Volume 59:339 (2017) at section II.B.1. Additionally, as the FTC has discussed, the large amounts of data required by AI models raises concerns about the developers of these models “undermining people’s privacy”. FTC, Technology Blog, “AI Companies: Uphold Your Privacy and Confidentiality Commitments” (9 January 2024), https://www.ftc.gov/policy/advocacy-research/tech-at-ftc/2024/01/ai-companies-uphold-your-privacy-confidentiality-commitments.
[48] UK Competition and Markets Authority, Working Paper, “Pricing algorithms: Economic working paper on the use of algorithms to facilitate collusion and personalized pricing” (8 October 2018), https://assets.publishing.service.gov.uk/government/uploads/
system/uploads/attachment_data/file/746353/Algorithms_econ_report.pdf.
[49] See D Bamberger, “Antitrust Practitioners Should Address AI’s Collusive Potential”, Law360 UK (22 January 2024), https://www.law360.co.uk/articles/1788145/
antitrust-practitioners-should-address-ai-s-collusive-potential; E Calvano, et al, “Algorithmic Pricing What Implications for Competition Policy?”, Review of Industrial Organization, Vol 55:1 (2019) (Calvano, et al); T McSweeny and B O’Dea, “The Implications of Algorithmic Pricing for Coordinated Effects Analysis and Price Discrimination Markets in Antitrust Enforcement”, Antitrust, Vol 32:1 (Autumn 2017). The FTC further points to this market dominance as potentially making it easier for major players to collude and violate antitrust laws by using AI models to “facilitate collusive behavior that unfairly inflates prices, precisely target price discrimination, or otherwise manipulate outputs”. See FTC, Comment at 4, “Artificial Intelligence and Copyright” (30 October 2023), https://www.ftc.gov/system/files/ftc_gov/pdf/p241200_ftc_comment_to_copyright_office.pdf.
[50] Ariel Ezrachi and Maurice E Stucke, Virtual Competition: The Promise and Perils of the Algorithm-Driven Economy (Harvard University Press, 29 October 2019), at 71.
[51] id., at 48 and 56. See also J E Harrington, “Developing Competition Law for Collusion by Autonomous Artificial Agents”, Journal of Competition Law & Economics, Vol 331:3 (2018).
[52] 2023 Merger Guidelines, supra note 2, at 9.
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