Big Data, Little Chance of Success: Why Precedent Does not Support Anti-Data Theories of Harm

As the digital economy has matured, “Big Data”— extremely large datasets that require sophisticated tools to analyze — has enabled extraordinary innovation, creating a number of benefits, including free products and greater efficiencies. Precisely because Big Data is such a powerful tool, though, scholars, governments, and litigants have called attention to what they view as its potential to harm both competition and consumers. In this article, we explore the advances enabled by Big Data, its competitive implications, and why applying an expansive interpretation of the antitrust laws regarding single firm conduct to Big Data would be out of step with legal precedent and sound economics.

By Kristen O’Shaughnessy, D. Daniel Sokol, Jaclyn Phillips & Nathan Swire^[1]

 

I. INTRODUCTION

As the digital economy has matured, “Big Data”— extremely large datasets that require sophisticated tools to analyze^[2] — has enabled extraordinary innovation, creating a number of benefits, including free products and greater efficiencies. Precisely because Big Data is such a powerful tool, though, scholars, governments, and litigants have called attention to what they view as its potential to harm both competition and consumers.^[3]

The debate over the effect of Big Data on competition is occurring amidst renewed interest in Section 2 of the Sherman Act, which governs single-firm monopolistic conduct. States, regulators, and private plaintiffs have brought numerous high-profile cases against large technology companies with access to Big Data in recent years, with varying degrees of success.^[4] Courts have generally remained skeptical of these attempts to use the competition laws to challenge large technology firms’ uses of and provision of access to their data. Government enforcers, though, are signaling that the near future may hold more litigation (both civil and criminal), and scrutiny of mergers involving Big Data and other forms of technology.^[5]

If we see more of these challenges, courts will have to consider the question of whether and how the use of Big Data can be anticompetitive. These cases may arise not just in traditional digital platforms, but also in the application of emerging technologies that enable new applications of Big Data, including healthcare, connected automobiles, smart cities, supply chain optimization, and energy efficiency.

Antitrust claims over the use of Big Data are most likely to proceed under the theory that the data constitutes an “essential facility” or that withholding a rival’s access to the data constitutes an anticompetitive refusal to deal. However, these arguments are dubious from a legal perspective given how the antitrust law has evolved over nearly four decades since the Supreme Court’s decision in Aspen Skiing Co v. Aspen Highlands Skiing Corp.^[6]

From an economic perspective as well, these theories may have high potential downsides because allowing claims based on the essential facilities or reinvigorated refusal to deal doctrines threaten to undermine the procompetitive benefits created by the new digital economy.^[7] In this article, we explore the advances enabled by Big Data, its competitive implications, and why applying an expansive interpretation of the antitrust laws regarding single firm conduct to Big Data would be out of step with legal precedent and sound economics.

 

II. POTENTIAL COMPETITIVE IMPLICATIONS OF BIG DATA

Data enables the modern internet, where many resources and services are available without charge. Many of the most popular online services and applications in the world are able to offer their services to consumers for free by harnessing and monetizing the data they collect from users.^[8] For instance, one way firms utilize data is to target and customize ads for consumers, increasing consumer surplus both by assisting them in finding the products they desire, and by allowing the firms to provide their products for free.^[9]

Big Data, though, allows these firms to go beyond focusing advertisements to deliver the right products to the right consumers; it also serves as an input for innovation. Search engines can continuously refine the design of their searches by querying which links users click, online markets can recommend products to users based on the purchase history of other users, and media companies can design new products based on data-driven metrics.^[10] Consumers place a high value on these free services. For instance, one study of European consumers showed that “WhatsApp, Facebook, and digital maps on phones are highly valued . . . with median compensations for losing 1 mo[nth] of access of €536, €97, and €59, respectively.”^[11]

A related use for Big Data, with applications far beyond the internet, is as an input for artificial intelligence-driven prediction modeling. Sectors as diverse as public health, pharmaceuticals, finance, education, logistics, and e-commerce have been able to develop new or more-effective products and services — such as higher-yield vaccines or more-accurate fraud detection — through the application of Big Data and artificial intelligence.^[12]

Critics contend that large firms’ access to Big Data may distort competition and harm consumers. One critique is that many digital platforms depend on network effects, whereby the utility of the platform increases based on the number of users and connections between users, which these critics say creates significant barriers to entry of new competitors.^[13] Another critique is that digital platforms can access real-time market data regarding both users and third-parties, which one Congressional committee characterized as “akin to near-perfect market intelligence.”^[14]

Because many firms whose business model is based on Big Data offer their products to consumers for free, critics have noted that lack of competition can manifest not in increased prices but in reduced quality, such as more-intrusive ads or fewer privacy protections.^[15] Critics also contend that a firm exercising market power in a two-sided market can raise prices on only the enterprise side, which is invisible to consumers, but may ultimately result in higher prices.^[16]

However, the nature of data itself undermines many of these concerns about competition. For one, even though large firms benefit from network effects, data-driven markets as a whole are characterized by low barriers to entry.^[17] A new entrant to the market can quickly overtake an established player if it offers a novel product, targets an underserved demographic, or uses modest data more effectively.^[18] For example, the music video-based social media site TikTok became enormously popular beginning in 2018 by focusing narrowly on the youth demographic and by utilizing particularly powerful algorithms to guide users to content.^[19]

A second factor is that data is ubiquitous and easy to produce. In most cases, data is “non-rivalrous” in both its consumption and production.^[20] In other words, in terms of consumption, the data is not used up like a physical good when transferred from one party to another. In terms of production, a single act by a consumer — such as visiting a website — can provide data to many different parties simultaneously.^[21] A robust and competitive market of data brokers has therefore emerged, meaning that even a minor player or new entrant in the market can acquire enough data to be usable without having to collect that data itself.^[22]

Of course, this very ease of collecting data has spurred government and consumer concerns over privacy and security.^[23] But harms to consumer privacy or data security arising from the use of Big Data, are likely better answered through consumer protection and privacy rules rather than through an antitrust lens.^[24]  The collection and utilization of data is not the domain of a few dominant firms; rather, essentially all businesses in the online ecosystem are incentivized to collect consumer data because it can be used to improve services and marketing, or as an input to their products.^[25]

Indeed, the data ecosystem consists of data from numerous sources, often sold by data brokers: first party information, data scraping, sensors, devices, crowdsourcing, customer data, and “exhaust” data unrelated to a business transaction such as physical movement or actions.^[26] For example, the business model of the “people analytics” firm hiQ, discussed below, depends on collecting and analyzing large amounts of publicly available data.^[27] Antitrust is thus a poor fit for addressing privacy concerns; its use carries the risk of distorting competition by focusing on only the largest firms, while ignoring data collection by the many smaller players in the online ecosystem.^[28]

As to the critique that large platforms may use their access to data to directly compete with third parties in separate markets, there is empirical evidence that this type of market strategy can be procompetitive on the whole.^[29] This not a new business strategy either; brick-and-mortar stores such as Target, Walmart, and grocers have long provided private-label goods that compete for shelf space with third-party brands while benefiting from the stores’ ability to gather data about the market.^[30]

The concern for overall efficiency is particularly important in two-sided markets as well, as the Supreme Court held in the recent case Ohio v. American Express, where it recognized that the two sides of the market in credit card transactions (consumers and merchants) could not be analyzed in isolation, but must be analyzed together.^[31] And as will be explained below, the nature of single-firm conduct makes it especially tricky to determine when behavior that may appear at first glance to be anticompetitive, actually is.

 

III. POTENTIAL LEGAL FRAMEWORKS TO ANALYZE USES OF BIG DATA

Current trends in antitrust cases against large technology companies, and in the FTC’s increased scrutiny of mergers, are making lawsuits and enforcement involving Big Data more probable. To date, though, there is limited case law dealing directly with potential anticompetitive concerns involving Big Data, and courts have shown little interest in expanding antitrust doctrine regarding single-firm conduct.  

Single firm conduct, including a dominant firm’s refusal to deal with a rival, is governed by Section 2 of the Sherman Act.^[32]  Some commentators have suggested that such refusals to deal in the context of Big Data could give rise to a cause of action under the antitrust laws because large platforms with access to vast amounts of data could qualify as “essential facilities.”^[33]

Applying this theory to the digital economy, the Congressional Subcommittee of the Judiciary has suggested that “Congress consider revitalizing the ‘essential facilities’ doctrine, the legal requirement that dominant firms provide access to their infrastructural services or facilities on a nondiscriminatory basis. To clarify the law, Congress should consider overriding judicial decisions that have treated unfavorably essential facilities- and refusal to deal-based theories of harm.”^[34]

As the Subcommittee noted, however, modern case law is highly skeptical of both the refusal to deal and essential facilities doctrines. In essence, this is because the general rule under the antitrust laws is that a private actor is free “to exercise his own independent discretion as to parties with whom he will deal.”^[35]

The essential facility doctrine historically has been applied to natural monopolies such as bridges and power grids, price-regulated utilities, or state-owned enterprises, where the party controlling the facility refuses a rival access to a physical “essential input,” and it is impractical for the rival to obtain or duplicate the resource.^[36] The Supreme Court itself has never recognized the essential facilities doctrine, but it has been recognized (albeit rarely) by several circuit courts.^[37]

Courts have had little opportunity to consider the application of the essential facilities doctrine to Big Data. One of the few cases to touch on the issue is hiQ Labs, Inc. v. LinkedIn Corp.,^[38] where the plaintiff hiQ, a data analytics firm, alleged that defendant LinkedIn had denied it access to an essential facility because it refused to allow hiQ to “scrape” public information about LinkedIn’s users. HiQ asserted this public data was essential to its business model, which relied on LinkedIn users’ profiles to create predictive insights of worker behavior.^[39] Yet the court never assessed the viability of hiQ’s essential facility claims because it determined hiQ had failed to properly define the market.^[40]

The court refused to accept hiQ’s contention that the “people analytics” market depended, as a practical matter, on LinkedIn’s dataset, explaining: “that does not mean that useful publicly available information cannot be gleaned [f]rom other sources such as Google and Facebook or other industry directories and sources.”^[41] As explained above, the court’s reasoning here tracks with one of the main theoretical reasons why it is so difficult for data to be truly essential: it is often available from another source.

The recent case of Sanborn Library LLC v. Eris Info., while not involving Big Data directly, dealt with a similar issue of access to a copyrighted database.^[42] In this copyright infringement case, counterclaim plaintiff ERIS, a provider of environmental data, alleged that Sanborn Library, the owner of the world’s largest collection of fire insurance maps, engaged in monopolistic conduct by refusing to license its map database.^[43] While the court agreed with ERIS that Sanborn Library’s database could theoretically constitute an essential facility, the court stated ERIS had failed to allege facts sufficient to show it was not feasible for a rival to recreate the database, therefore recommending dismissal of the claims.^[44]

The Sanborn Library case illustrates one of the major flaws in applying the essential facilities doctrine to any kind of dataset, including Big Data—the likelihood of a dataset being truly “essential.” As Hovenkamp notes: “A particular facility or input is not ‘essential’ simply because one particular firm would prefer to rent it from the monopoly rather than provide it for itself. Rather, it must be shown that rivals in general are unable to duplicate the facility.”^[45] And as explained above, data as a resource is far easier to duplicate or obtain from third parties than is physical infrastructure.^[46] It is therefore uniquely unsuited to being treated as “essential.”

A second, and perhaps more serious problem with applying the essential facilities doctrine to Big Data is that it calls for forced sharing, which carries a high risk of negative economic consequences.^[47] These efficiency problems are baked into any refusal to deal theory.

The legal standard controlling a firm’s unilateral refusal to deal stems from Aspen Skiing.^[48] There, the Supreme Court ruled that a monopolist can face liability for refusing to deal when it withdraws from a voluntary and profitable prior course of dealing with a rival, sacrificing short-term profits to harm that rival or harm competition.^[49]

The Supreme Court subsequently narrowed the reach of the refusal to deal doctrine in Verizon Communs., Inc. v. Law Offices of Curtis V. Trinko where it stated that the ruling from Aspen Skiing was “at or near the outer boundary of [Section] 2 liability.”^[50] The Court reiterated that there is no general duty to aid competitors, cautioning that a court compelling a monopolist to deal with its rivals “may lessen the incentive for the monopolist, the rival, or both to invest in those economically beneficial facilities.”^[51] After Trinko, courts do still occasionally find a firm can be liable for refusal to deal—for example, in the recent case Viamedia, Inc. v. Comcast Corp.^[52] the Seventh Circuit applied Aspen Skiing to find potential refusal-to-deal liability in Comcast’s decision to block a rival from accessing a certain advertising platform over which it held a monopoly — but such cases remain rare.

Many scholars (and courts) have concluded that a firm’s unilateral refusal to deal can only be truly anticompetitive when that firm gives up a profitable cooperative arrangement for a less profitable strategy of exclusion.^[53] Thus, most courts have refused invitations to engage in broad interrogations of unilateral conduct.^[54]   

Furthermore, even if one assumes that a large technology company is a monopolist, forcing it to share that monopoly does not ultimately benefit the consumer, because such an intervention would only lead to multiple firms sharing the same monopoly profits—reducing their incentive to compete.^[55] Such forced sharing also stifles innovation by discouraging both the monopolist and the rival from making economically beneficial investments in the shared product, and by discouraging the rival from developing alternatives.^[56]

A similar dynamic exists for intellectual property. Just as the protections provided by intellectual property laws incentivize innovation, so too may forced sharing counteract those incentives.^[57] Thus, the DOJ/FTC’s 2017 guidance on the intersection of antitrust and intellectual property laws recognized that the antitrust laws generally impose no duty to deal, “in part because doing so may undermine incentives for investment and innovation.”^[58]

 

IV. REGULATORY TRENDS IN TECHNOLOGY AND BIG DATA

While plaintiffs have so far been generally unsuccessful in bringing antitrust claims related to technology platforms and data, the power of the FTC and DOJ is not as constrained by current essential facilities and refusal to deal jurisprudence. This has manifested recently in their attitude towards mergers. While regulators have not yet blocked any mergers specifically because of the parties’ data assets, they have recently signaled the intent to pursue a more aggressive stance.

Up to the time of this writing, the DOJ and FTC’s treatment of data assets in mergers has been relatively less aggressive than the rhetoric. For example, when Turbotax owner Intuit acquired personal financial company Credit Karma in 2020, the DOJ required divestiture of Credit Karma’s tax services, but placed no restraints on Intuit’s acquisition of data from Credit Karma’s hundred million users.^[59] Similarly, though the DOJ did probe the deal, Google acquired the fitness tracking company Fitbit in 2021 — and Fitbit’s user health data — without any conditions.^[60] In contrast, the European Commission approved the deal only upon the conditions that Google segregate user data collected by Fitbit and that Google continue to maintain its smartphone Android’s compatibility with competitors’ smartwatches.^[61] Where regulators in the United States have placed conditions on mergers, those have generally been to require the merging party to license or sell its data to third parties who would not otherwise have access.^[62]

Over the past year, though, regulators have been setting the groundwork to put all mergers, including those involving data and technology, under a much finer microscope. In August of 2021, the FTC issued a “warning letter” regarding its required review of mergers pursuant to the Hart-Scott-Rodino (“HSR”) Act.^[63] Under the HSR Act, the FTC and DOJ have 30 days from the merging parties’ submission of their filing in order to pursue an initial investigation. The letter informed industry that the FTC was changing its policy regarding these 30-day reviews by reserving the right to investigate or challenge mergers even after closing the initial investigation.^[64] Then in September 2021, the FTC and DOJ reversed their 2020 guidelines on vertical mergers, saying their theories in those guidelines regarding “purported procompetitive benefits of mergers” were “flawed.”^[65] Most recently, on January 18, 2022, the FTC and DOJ announced their intent to strengthen current merger guidelines.^[66] Regulators have signaled that technology and data will be a particular priority.

In September 2021, the FTC also released a study examining past acquisitions by the largest technology firms that did not reach the threshold for review under the HSR Act.^[67] FTC Chair Lina Khan characterized these acquisitions as part of a strategy by these large tech firms to systematically “buy their way out of competing.”^[68] On the DOJ side, in prepared remarks on April 22, 2022 before the Stigler Center, Assistant Attorney General Jonathan Kanter signaled the Department’s intent to pursue greater enforcement in digital markets, “vigorously enforce” Section 2 of the Sherman Act, and “litigate, not settle” to block anticompetitive conduct or anticompetitive mergers.^[69]

Legislators are also focusing on the competitive implications of technology. The May 25, 2022 update to the American Innovation and Choice Online Act (“AICOA”), which would bar certain types of self-preferencing and refusal to deal by large online platforms, included a new carve-out for banking and telecommunications — placing the impact squarely on technology firms.^[70] Another bill, the Augmenting Compatibility and Competition by Enabling Service Switching Act of 2021 (“ACCESS Act”) would attempt to reduce barriers to entry in data-driven industries by requiring covered platforms to maintain data in a manner that is both portable (allowing transfer to other platforms) and interoperable (allowing transfer to other businesses).^[71] These are just two of the changes to the antitrust laws that politicians taking aim at data and tech-driven industries have proposed.

The FTC has also taken preliminary steps to expand its authority to police competition through the use of rulemaking, though competition rulemaking remains murky legally.^[72] Section 5(a) of the FTC Act grants the FTC the power to bring actions against any company for unfair or deceptive practices.^[73] Between 2015 and 2021, the FTC maintained a policy of bringing Section 5(a) competition-related enforcement actions only in accordance with the principles underlying the antitrust laws: the promotion of consumer welfare, and the assessment of a firm’s conduct under the rule of reason standard, which includes weighing efficiencies and business justifications.^[74] On July 1, 2021, the FTC rescinded this policy.^[75] On the same day, the FTC updated its rulemaking procedures under Section 18 of the FTC Act to streamline and speed up rulemaking.^[76] As the FTC announced in its 2022 Statement of Regulatory Priorities, these changes will enable it to develop specific rules defining what constitutes unfair or deceptive acts or practices under Section 5(a).^[77]

The FTC already uses its Section 5 authority regarding all manner of alleged unfair or deceptive conduct, some of it involving misuse of data.^[78] But the precedent from states’ laws barring unfair and deceptive practices suggests that any new FTC rules may broaden its reach with respect to competition. For example, returning to hiQ v. LinkedIn, the district court denied hiQ’s competition claims only after it already granted hiQ a preliminary injunction in 2017 on the theory that LinkedIn’s decision to block it could constitute breach of contract, tortious interference, or unfair competition.^[79] The Ninth Circuit affirmed the preliminary injunction in April of 2022.^[80] With respect to the unfair competition claims, the district court concluded that LinkedIn’s conduct in blocking hiQ could be potentially unfair because it violated the “spirit” of the antitrust laws.^[81] Notably, the district court did not conduct a rigorous market analysis in analyzing the unfair competition claims (as it would, later, for the federal antitrust claims), but instead looked primarily to LinkedIn’s intent.^[82] The unfair competition laws remain at issue in the ongoing litigation.^[83]

HiQ v. LinkedIn is therefore notable in illustrating how a court may deny federal antitrust claims and allow state unfair competition claims for the exact same conduct based on their differing standards. At the federal level, an expansion in the FTC’s definition of anticompetitive conduct could lead to the same result.

What is certain is that the landscape of antitrust enforcement is shifting in a manner that will affect the future of Big Data. Currently, the expansive theories espoused by plaintiffs and enforcers are out of touch with the narrow scope of the relevant case law. However, given the movement in this area, including regulatory and agency proposals, theories involving Big Data are likely to continue to evolve in the short term.

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[1] Any views expressed in this publication are strictly those of the authors and should not be attributed in any way to White & Case LLP.

[2] Anja Lambrecht & Catherine E. Tucker, Can Big Data Protect a Firm From Competition? at 1, Competition Policy International (2017), https://www.competitionpolicyinternational.com/wp-content/uploads/2017/01/CPI-Lambrecht-Tucker.pdf.  

[3] E.g. Filippo Lancieri & Patricia Morita Sakowski, Competition in Digital Markets: A Review of Expert Reports at 4-8, Chicago Stigler Center (2020), https://www.chicagobooth.edu/-/media/research/stigler/pdfs/workingpapers/303competitionindigitalmarketslawreview.pdf.

[4] See, e.g. New York v. Facebook, Inc., 549 F. Supp. 3d 6 (D.D.C. 2021); Epic Games v. Apple Inc., 2021 U.S. Dist. LEXIS 172303 (N.D. Cal. Sep. 10, 2021).

[5] Assistant Attorney General Jonathan Kanter, Antitrust Enforcement: The Road to Recovery, Address before the Chicago Stigler Center (Apr. 21, 2022), https://www.justice.gov/opa/speech/assistant-attorney-general-jonathan-kanter-delivers-keynote-university-chicago-stigler#_ftnref11.

[6] 472 US 585 (1985).

[7] Marco Iansiti & Karim R. Lakhani, Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World (2020).

[8] See Sokol & Comerford, Antitrust and Regulating Big Data at 1133-34.

[9] D. Daniel Sokol & Roisin Comerford, Antitrust and Regulating Big Data, 23 George Mason Law Review 1129, 1133-34 (2016); Sigurd Naess-Schmidt, et al., Empowering the European Business Ecosystem – An Impact Study of Businesses Using Facebook Apps and Technologies at 28, Copenhagen Economics, (2020).

[10] Andres V. Lerner, The Role of ‘Big Data’ in Online Platform Competition at 10-11 (August 26, 2014), SSRN: https://ssrn.com/abstract=2482780 or http://dx.doi.org/10.2139/ssrn.2482780.

[11] Erik Brynjolfsson, et al., Using Massive Online Choice Experiments to Measure Changes in Well-Being 116 Proc. of the Nat’l Acad. of Sciences 7250, 7252 (2019), https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6462102/pdf/pnas.201815663.pdf.

[12] See American Bar Association, Antitrust Law Section, Artificial Intelligence & Machine Learning: Emerging Legal and Self-Regulatory Considerations Part II: Competition Implication of Big Data and Artificial Intelligence/Machine Learning 9-10 (David D. Golden & Wyatt Fore, eds., 2021), https://www.americanbar.org/content/dam/aba/administrative/antitrust_law/comments/feb-21/aba-big-data-task-force-white-paper-part-two-final-215.pdf.

[13] Stucke, Data-opolies, at 320-21. But this approach overlooks the established economics and strategy work on issues such as network saturation, diminishing returns for network effects, and inter-temporal network effects. See e.g. Zhou Zhou et. al., How Users Drive Platform Value, ICIS 2020 Proceedings (2020), https://scholars.cityu.edu.hk/en/publications/how-users-drive-platform-value(c1c92a04-4913-4099-8da5-28d34f68784f).html; Carmelo Cennamo, Building the Value of Next-Generation Platforms: The Paradox of Diminishing Returns, 44 J. Mgmt. 3038 (2018); N. Venkatraman & Chi-Hyon Lee, Preferential Linkage and Network Evolution: A Conceptual Model and Empirical Test in the U.S. Video Game Sector, 47 Acad. Mgmt. 876 (2004).

[14] Committee of the Judiciary, Subcommittee on Antitrust, Commercial and Administrative Law, Investigation of Competition in Digital Markets at 378 (2020), https://judiciary.house.gov/uploadedfiles/competition_in_digital_markets.pdf.

[15] Stucke, Data-opolies, at 285-290.

[16] Stucke, Data-opolies, at 298-299.

[17] D. Daniel Sokol & Jingyuan Ma, Understanding Online Markets and Antitrust Analysis, 15 Northwestern J. of Tech. and I.P. 44, 48-49 (2017).

[18] John M. Yun, The Role of Big Data in Antitrust, 28 Global Antitrust Inst. Rep. Digital Economy 220, 229-232 (2020), https://gaidigitalreport.com/2020/08/25/big-data-and-barriers-to-entry/; Sokol & Ma, Understanding Online Markets at 49.

[19] Joe Tidy & Sophia Smith Galer, TokTok: The story of a social media giant, BBC (Aug. 5, 2020), https://www.bbc.com/news/technology-53640724.

[20] Sokol & Comerford, Antitrust and Regulating Big Data at 1137-38.

[21] Catherine Tucker, Digital Data, Platforms and the Usual [Antitrust] Suspects: Network Effects, Switching Costs, Essential Facility at 13-15 (2019), SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3326385.  

[22] Tucker, Usual [Antitrust] Suspects at 14-15 (2019).

[23] Federal Trade Commission Chair Lina M. Khan, remarks before IAPP Global Privacy Summit 2022 (Apr. 11, 2022), https://www.ftc.gov/system/files/ftc_gov/pdf/Remarks%20of%20Chair%20Lina%20M.%20Khan%20at%20IAPP%20Global%20Privacy%20Summit%202022%20-%20Final%20Version.pdf.

[24] Sokol & Comerford at 1144-45.

[25] See Statement of the Federal Trade Commission Concerning Google/DoubleClick at 1, Federal Trade Commission File No. 071-0170 (2007) (“Of course, the consumer privacy issues presented by ‘behavioral advertising’ are not unique to Google and DoubleClick. To the contrary, these issues extend to the entire online advertising marketplace.”); Data is the new gold – how and why it is collected and sold, Usercentrics (Oct. 21, 2021), https://usercentrics.com/knowledge-hub/data-is-the-new-gold-how-and-why-it-is-collected-and-sold/  (describing how online businesses collect and utilize user data).

[26] See generally Laia Pujol Priego et al., Data Sharing Practice in Big Data Ecosystems, ESADE Business School Research Paper No. 273 (2019), SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3355696; Llewellyn D. W. Thomas & Aija Leiponen, Big Data Commercialization, 44 IEEE Engineering Mgmt. Rev. 74 (2016).

[27] hiQ Labs, Inc. v. LinkedIn Corp., 2022 U.S. App. LEXIS 10349, at *9-10 (9th Cir. Apr. 18, 2022).

[28] Olivia T. Creser, In Antitrust We Trust?: Big Tech Is Not the Problem – It’s Weak Data Privacy Protections 73 Federal Communications Law Journal 289, 310-11 (2021).

[29] Feng Zhu & Qihong Liu, Competing With Complementors: An Empirical Look at Amazon.com at 28 (2018), https://www.hbs.edu/ris/Publication%20Files/amazon_2018-06-05_4a83c515-af0c-4366-9fba-8fb059d0b4f6.pdf.

[30] See PLMA’s 2021 Private Label Yearbook: A Statistical Guide to Today’s Store Brands at 4, Private Label Manufacturers Association (2021), https://plma.com/sites/default/files/files/2021–05/plma2021yearbook2.pdf  (showing that 20 percent of the value of goods bought at surveyed stores in 2021 was from private-label brands).

[31] 138 S. Ct. 2278, 2285-86 (2018).

[32] Phillip E. Areeda & Herbert J. Hovenkamp, Antitrust Law: An Analysis of Antitrust Principles and Their Application ¶ 773a.

[33] See, e.g. Lao, Search, Essential Facilities, at 317-18; Committee of the Judiciary at 397-98.

[34] Committee of the Judiciary at 397-98.

[35] Verizon Communs., Inc. v. Law Offices of Curtis V. Trinko, LLP, 540 U.S. 398, 408 (2004).

[36] Herbert J. Hovenkamp, Unilateral Refusals to Deal, Vertical Integration, and the Essential Facility Doctrine, 1779 Faculty Scholarship at Penn Law 1, 5 (2008); Phillip E. Areeda, Essential Facilities: An Epithet in Need of Limiting Principles, 58 Antitrust L. J. 841, 852 (1989); MCI Commc’ns. Corp. v. American Tel. & Tel. Co., 708 F. 2d 1081, 1132-33 (7th Cir. 1983).

[37] Trinko, 540 U.S. at 411 (“We have never recognized [the essential facilities] doctrine, and we find no need either to recognize it or to repudiate it here.”) (citations omitted); see, e.g. MetroNet Servs. Corp. v. Qwest Corp., 383 F.3d 1124, 1128-29 (9th Cir. 2004); MCI Commc’ns Corp., 708 F.2d at 1132-33.

[38] 485 F. Supp. 3d 1137, 1143, 1151-52 (N.D. Cal. 2020)

[39] 485 F. Supp. 3d at 1143.

[40] 485 F. Supp. 3d at 1152.

[41] 485 F. Supp. 3d at 1148-49.

[42] 2021 U.S. Dist. LEXIS 165496 (S.D.N.Y. Aug. 30, 2021), affirmed Order, Sanborn Library LLC v. Eris Info., 19-cv-2049 (S.D.N.Y. Sep. 15, 2021), ECF No. 136.

[43] 2021 U.S. Dist. LEXIS 165496, at *5-7.

[44] 2021 U.S. Dist. LEXIS 165496, *38-41.

[45] Hovenkamp, Essential Facility Doctrine at 24.

[46] Tucker, Usual [Antitrust] Suspects at 13-15.

[47] See, e.g. Thom Lambert & Alden F. Abbott, Recognizing the Limits of Antitrust: The Roberts Court Versus the Enforcement Agencies, 11 J. Competition L. & Econ. (2015); Frank H. Easterbrook, The Chicago School and Exclusionary Conduct, 31 Harv. J.L. & Pub. Pol’y 439, 442 (2008).

[48] 472 U.S. 585.

[49] Aspen Skiing, 472 US at 608.

[50] 540 U.S. 398, 409 (2004).

[51] Trinko, 540 U.S. at 407-08.

[52] 951 F.3d 429 (7th Cir. 2020).

[53] See, e.g. Janusz A. Ordover & Robert D. Willig, Access and Bundling in High-Technology Markets, in Competition, Innovation and the Microsoft Monopoly: Antitrust in the Digital Marketplace 103, 109 (J.A. Eisenach and T.M. Lenard eds., 1999); Areeda & Hovenkamp, Antitrust Law ¶ 772d3.

[54] See Trinko, 540 U.S. at 409; Herbert Hovenkamp, The Monopolization Offense, 61 Ohio St. L.J. 1035, 1044–1045 (2000); Geoffrey Manne & Joshua Wright, If Search Neutrality Is the Answer, What’s the Question?, 2012 Colum. Bus. L. Rev. 152, 192-193 (2012).

[55] Herbert J. Hovenkamp, Essential Facility Doctrine, at 35; Gus Hurwitz, Digital Duty to Deal, Data Portability, and Interoperability, 28 Global Antitrust Inst. Rep. Digital Economy 1024, 1056 (2020).

[56] Trinko, 540 U.S. 407-408; Areeda & Hovenkamp, Antitrust Law ¶ 771b.

[57] Richard J. Gilbert & Carl Shapiro, An Economic Analysis of Unilateral Refusals to License Intellectual Property, 93 Proc. Nat’l Acad. Sci. 12749, 12754 (1996).

[58] Antitrust Guidelines for the Licensing of Intellectual Property at 3, Department of Justice & Federal Trade Commission (2017), https://www.justice.gov/atr/IPguidelines/download.

[59] See Justice Department Requires Divestiture of Credit Karma Tax for Intuit to Proceed with Acquisition of Credit Karma, Department of Justice (25 November 2020); Press Release, Intuit, Intuit and Credit Karma Receive Clearance from Department of Justice for Acquisition of Credit Karma (3 December 2020), https://investors.intuit.com/news/news-details/2020/Intuit-Completes-Acquisition-of-Credit-Karma/default.aspx.

[60] See Clare Cho, Cong. Rsch. Serv., R46739, Mergers and Acquisitions in Digital Markets at 13-15 (2021).

[61] European Commission, Mergers: Commission Clears Acquisition of Fitbit by Google, Subject to Conditions, European Commission Press Corner, December 17, 2020, https://ec.europa.eu/commission/presscorner/detail/en/ip_20_2484.

[62] See Corelogic/DataQuick merger: Press Release, FTC Puts Conditions on CoreLogic, Inc’s Proposed Acquisition of DataQuick Information Systems, Federal Trade Commission (24 March 2014) https://www.ftc.gov/news-events/press-releases/2014/03/ftc-puts-conditions-corelogic-incs-proposed-acquisition-dataquick; see also Decision and Order, In the Matter of The Dun & Bradstreet Corporation, Federal Trade Commission (Sept. 10, 2010) https://www.ftc.gov/sites/default/files/documents/cases/2010/09/100910dunbradstreetdo.pdf (requiring Dun & Bradstreet to sell a copy of the existing K-12 teacher database to a competitor).

[63] Holly Vedoca, Federal Trade Commission Bureau of Competition, Adjusting merger review to deal with the surge in merger filings (Aug. 3, 2021).

[64] Holly Vedoca, Federal Trade Commission Bureau of Competition, Adjusting merger review to deal with the surge in merger filings (Aug. 3, 2021).

[65] Press Release, Federal Trade Commission, Federal Trade Commission Withdraws Vertical Merger Guidelines and Commentary (Sep. 15, 2021).

[66] Press Release, Federal Trade Commission, Federal Trade Commission and Justice Department Seek to Strengthen Enforcement Against Illegal Mergers (Jan. 18, 2022).

[67] Press Release, Federal Trade Commission, FTC Staff Presents Report on Nearly a Decade of Unreported Acquisitions by the Biggest Technology Companies (Sep. 15, 2021).

[68] Press Release, Federal Trade Commission, FTC Staff Presents Report on Nearly a Decade of Unreported Acquisitions by the Biggest Technology Companies (Sep. 15, 2021).

[69] Kanter, Remarks on Antitrust Enforcement.

[70] American Innovation and Choice Online Act (AICOA), S. 2992, 117th Cong. (2022); see also Ben Brody & Issie Lapowsky, Big Tech antitrust is moving. Here are the newest rules., protocol (May 27, 2022), https://www.protocol.com/newsletters/policy/big-tech-antitrust-changes?rebelltitem=10#rebelltitem10.  

[71] HR 3849, 117th Cong. (2021).

[72] Richard J. Pierce Jr, Can the Federal Trade Commission Use Rulemaking to Change Antitrust Law?, GW Law Faculty Publications & Other Works 1561 (2021), https://scholarship.law.gwu.edu/faculty_publications/1561.  

[73] 15 U.S.C. § 45(a)(1)–(2).

[74] Statement of Enforcement Principles Regarding “Unfair Methods of Competition” Under Section 5 of the FTC Act, Federal Trade Commission (Aug. 2015).

[75] Statement of Chair Lina M. Khan Joined by Commissioner Rohit Chopra and Commissioner Rebecca Kelly Slaughter on the Withdrawal of the Statement of Enforcement Principles Regarding “Unfair Methods of Competition” Under Section 5 of the FTC Act, Federal Trade Commission (July 1, 2021).

[76] Statement of Commissioner Rebecca Kelly Slaughter, Joined by Chair Lina Khan and Commissioner Rohit Chopra, Regarding the Adoption of Revised Section 18 Rulemaking Procedures, Federal Trade Commission (July 1, 2021).

[77] Statement of Regulatory Priorities, Federal Trade Commission (Dec. 10, 2021).

[78] See, e.g. Complaint, In re Flo Health, Inc, Commission File No 1923133 (Jan. 13, 2021) https://www.ftc.gov/system/files/documents/cases/flo_health_complaint.pdf.

[79] 273 F. Supp. 3d 1099, 1117-18.

[80] 2022 U.S. App. LEXIS 10349. The long temporal gap came about because one of LinkedIn’s justification for blocking hiQ’s access was that hiQ’s data scraping violated the federal Computer Fraud and Abuse Act (CFAA), and the Ninth Circuit was awaiting the Supreme Court’s ruling in Van Buren v. United States, 141 S. Ct. 1648 (2021), regarding the scope of that Act. Based on Van Buren, the Ninth Circuit determined that hiQ’s scraping of publicly-available data on LinkedIn was probably not illegally “without authorization” as defined by the CFAA, meaning hiQ itself had not clearly violated the law so as to nullify its request for a preliminary injunction. 2022 U.S. App. LEXIS 10349, at *7.

[81] 273 F. Supp. 3d at 1117.

[82] 273 F. Supp. 3d at 1117-18.

[83] 2022 U.S. App. LEXIS 10349 at *27-28.