Multisided Platforms, Big Data, and a Little Antitrust Policy

Abstract

Commentators on both the right and the left ends of the political spectrum have called for new and more forceful approaches to antitrust enforcement with respect to large multisided platforms: especially Amazon, Facebook, and Google. In part, these calls have been driven by the fact that these platforms have business models that make extensive use of data about their users. This article surveys what economics has to say about a wide range of antitrust issues—including the treatment of exclusionary conduct, merger, and privacy—that are raised by multisided platforms’ reliance on big data collected about their users.

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Notes

  1. 1.

    See, e.g., della Cava et al. (2017), Elliott (2018), and Smith (2018).

  2. 2.

    See, e.g., della Cava et al. (2017), Elliott (2018), and Khan (2017).

  3. 3.

    Competition authorities around the world—including France, Germany, and Singapore—have been assessing the implications of big data for antitrust enforcement. See, e.g., Autorité de la concurrence and Bundeskartellamt (2016) and Competition Commission of Singapore (2017).

  4. 4.

    See, e.g., Khan (2017).

  5. 5.

    The Bundeskartellamt initiated a proceeding against two Facebook entities to investigate whether they abused Facebook’s “possibly dominant” position in social networks to impose unfair terms of service regarding the use of user data. (Bundeskartellamt, “Bundeskartellamt initiates proceeding against Facebook on suspicion of having abused its market power by infringing data protection rules,” Press Release, March 2, 2016, available athttps://www.bundeskartellamt.de/SharedDocs/Meldung/EN/Pressemitteilungen/2016/02_03_2016_Facebook.html.).

  6. 6.

    I do not consider the antitrust implications of other types of big data. For example, I do not consider the use of factory data to improve production processes. I also limit attention to the utilization of data about users on one side of a platform to improve the platform’s offering to users on another side, as opposed to considering the role of analytics and algorithms generally. Hence, I do not address the concerns that are raised by some people that cross-platform information sharing and/or the use of algorithms might facilitate collusion among platforms.

  7. 7.

    In other cases, the information can be used to provide a product or service to users on the same side of the platform as those from which the data have been collected. For example, the service could be a recommendation engine that helps the consumers themselves better target their consumption decisions based on models of the behavior of similar consumers. Or, the data could be users’ social network postings that other users on the same side would like to see. Although the use of data in this way raises several issues that are similar to the ones that are addressed below, I believe that the use of data to provide same-side benefits is better viewed through the lens of same-side network effects, and the interested reader should consult the extensive literature on antitrust and network effects. See, for example, Economides and White (1994), Farrell and Katz (1998), Melamed (1999), Shapiro (1999), and Shelanski and Sidak (2001).

  8. 8.

    See, for example, Grunes and Stucke (2015) and Kahn (2017). The European Commission is widely perceived as having an aggressive posture towards platforms utilizing big data (see, for example, Harper Neidig “EU antitrust regulator concerned about big data’s effects on competition,” The Hill, January 2, 2018, available athttp://thehill.com/policy/technology/367043-eu-regulator-concerned-about-control-of-big-data.).

  9. 9.

    If user data play little role in a firm’s success or substitutes are readily available, then user big data are unlikely to raise antitrust issues. There is no question that big data are very valuable in many industries. There is considerable debate, however, with regard to how scarce big data are and whether a given dataset is unique. Lambrecht and Tucker (2017) conclude that big data often fail to meet one or more of the conditions that are necessary to serve as a source of sustainable competitive advantage: that they be inimitable, rare, valuable, and non-substitutable. For arguments that Google benefits from a data barrier to entry, see Newman (2014).

  10. 10.

    In addition to traditional economic concerns, there is a movement to utilize antitrust enforcement to limit the allegedly excessive political influence of large firms. Casual empiricism, however, suggests that the accumulation of extreme wealth by a small number of individuals (e.g., successful hedge fund managers) and the lack of campaign finance limits might be a larger problem—one that antitrust enforcement would be hard pressed to address.

  11. 11.

    Khan (2017, p. 792) expresses this concern with user transaction data. It should be noted that, although certain transaction data may be very broadly useful, it may also be the case that, the more widely given data can be used, the greater the range of alternative user transactions that can serve as substitute sources of data. Moreover, there may be diminishing marginal returns to the sizes of datasets: At some point additional data may lead to little improvement in the performance of the algorithms that are based on those data.

  12. 12.

    Grunes and Stucke (2015, p. 3) raise this concern.

  13. 13.

    For a survey of economic issues regarding the foundational elements of antitrust cases (e.g., market definition) involving multisided platforms, see Katz (2019).

  14. 14.

    The Supreme Court summarized the situation as follows:

    Firms may acquire monopoly power by establishing an infrastructure that renders them uniquely suited to serve their customers. Compelling such firms to share the source of their advantage is in some tension with the underlying purpose of antitrust law, since it may lessen the incentive for the monopolist, the rival, or both to invest in those economically beneficial facilities. Enforced sharing also requires antitrust courts to act as central planners, identifying the proper price, quantity, and other terms of dealing, a role for which they are ill-suited. Moreover, compelling negotiation between competitors may facilitate the supreme evil of antitrust: collusion.

    (Verizon Communications Inc. v. Law Offices of Curtis V. Trinko, LLP, 540 U.S. 398, 402 (2004).)

  15. 15.

    It should also be noted that there generally is no special duty to deal or share knowledge that is accumulated through experience—even though such knowledge clearly is an important source of competitive advantage in many industries. One might argue that big data are more readily transferred than is tacit knowledge acquired through experience, but the other issues identified in the text would remain.

  16. 16.

    Jessica Davies, “German publishers are pooling data to compete with Google and Facebook,” Digiday, June 8, 2016. See, also, emetriq, “Profitieren Sie von unserer Datenintelligenz,” available athttps://www.emetriq.com/loesungen/data/.

  17. 17.

    For a discussion of the legal status of a duty to deal and the distinction between multifirm and single-firm conduct, see Areeda (1990).

  18. 18.

    For a survey of the economics literature, see Farrell and Klemperer (2007).

  19. 19.

    The effects are very similar to those associated with network effects; see Farrell and Katz (2005).

  20. 20.

    See, e.g., Melamed (2006), Popofsky (2006), Salop (2006), and Werden (2006).

  21. 21.

    See, e.g., Brief of the Appellees United States and the State Plaintiffs at 48, United States v. Microsoft Corp., 253 F.3d 34 (D.C. Cir. 2001) (Nos. 00-5212, 00-5213); Brief for Appellant United States at 2, 30, United States v. AMR Corp., 335 F.3d 1109 (10th Cir. 2003) (No. 01-3202) (public redacted version); Brief for Appellant United States at 28, United States v. Dentsply Int’l, Inc., 399 F.3d 181 (3d Cir. 2005) (No. 03-4097) (public redacted version).

  22. 22.

    See, e.g., Werden (2006).

  23. 23.

    Brooke Group, Ltd. v. Brown & Williamson Tobacco Corporation, 509 U.S. 209, 222 (1993). Many other national competition authorities consider price–cost tests as well. For a survey of practices, see Unilateral Conduct Working Group (2008), § II.1.

  24. 24.

    Areeda and Turner (1975).

  25. 25.

    Brooke Group, Ltd. v. Brown & Williamson Tobacco Corporation, 509 U.S. 209, 224 (1993).

  26. 26.

    Another issue is that, under the no-economic-sense test, pricing above cost can be exclusionary if the firm would have found it profitable to have set its prices even higher if not for the value of weakening its rivals. Even though above-cost pricing can be deemed predatory in some circumstances, this approach has been rejected by some of the antitrust literature as undesirable because such a rule would be hard to implement and could be subject to high rates of error. This issue is not unique to markets in which user data are important. For a discussion of the debate, see Elhauge (2003).

  27. 27.

    The benefits that arise because sales generate data that improve the platform’s performance are a form of experience effect. For formal analyses of predatory pricing in the presence of experience (or learning-by-doing) effects, see Cabral and Riordan (1994, 1997).

  28. 28.

    Some commentators view the recoupment prong as a test of whether predation is rational and ask: Will the firm’s profits be higher in the long run because of its lower short-run prices? A fundamental problem with this view is that, in this form, the recoupment prong is a test that any economically rational investment—predatory or otherwise—would have to meet.

  29. 29.

    Khan (2017, pp. 792, 796) suggests that merger policy should seek to prevent firms from acquiring data that they can “cross-leverage” across markets. This suggestion seeks to limit entry and the resulting competition for the sake of preventing the growth of large firms. Khan’s approach is similar to an “efficiencies offense” for mergers, and it suffers from similar weaknesses.

  30. 30.

    U.S. Department of Justice, “Statement of the Department of Justice Antitrust Division on Its Decision to Close Its Investigation of the Internet Search and Paid Search Advertising Agreement Between Microsoft Corporation and Yahoo! Inc.,” February 18, 2010, available athttps://www.justice.gov/opa/pr/statement-department-justice-antitrust-division-its-decision-close-its-investigation-internet.

  31. 31.

    Etan Vlessing, “AT&T Head Says Time Warner Deal Is About Advertising,” The Hollywood Reporter, May 15, 2018, available athttps://www.hollywoodreporter.com/news/att-head-says-time-warner-deal-is-advertising-1111942. I was retained by AT&T as an expert witness in this matter but did not testify with respect to efficiencies.

  32. 32.

    The text discusses data transactions between platforms. In the case of transactions between a platform and its users, there are circumstances in which the data can be used to provide analytics while preserving the platform’s ownership and control of the data. For example, Facebook does not transfer its data to advertisers. Instead, it uses analyses of its data to offer advertisers precisely targeted audiences.

  33. 33.

    See, e.g., Nocera (2018).

  34. 34.

    For a review of the literature, see Varian (1989).

  35. 35.

    For a review of the literature, see Stole (2007).

  36. 36.

    For other examples of pairs of opposing arguments regarding the effects of price discrimination on competition, see Katz (1987, fn. 2).

  37. 37.

    See, e.g., Nitasha Tiku, “Digital Privacy is Making Antitrust Exciting Again,” Wired, June 4, 2017 (“Andreas Mundt, president of Germany’s antitrust agency, Bundeskartellamt, said he was ‘deeply convinced privacy is a competition issue.’”); Al Franken, “How Privacy Has Become an Antitrust Issue, Huffpost, May 30, 2012.

  38. 38.

    A second conception of privacy is autonomy: the right to be left alone by others, whether the state or private parties. See Hirshleifer (1980).

  39. 39.

    It is sometimes argued that the combination of competition and users’ ownership of their data will lead to efficient privacy outcomes. However, Hermalin and Katz (2006) show that, to be effective, a privacy policy may need to ban certain uses of personally identifiable data outright rather than simply assign users control rights to their data. Intuitively, having the ability to withhold information from trading partners does not protect an individual if trading partners assume the worst about anyone who does not voluntarily relinquish the information.

  40. 40.

    As this model is purely expositional, I will assume that all of the functions discussed here are sufficiently well behaved.

  41. 41.

    There is a second distortion, which arises from the interaction of the monopolist’s quality and output choices. As is well known, a monopolist restricts its output level conditional on quality. Spence shows that, as a result of these two distortions, a profit-maximizing monopolist’s choice of quality can be higher or lower than the total-surplus-maximizing quality, both conditional on quantity and relative to the first-best quality.

  42. 42.

    Hirshleifer (1971) provides an early analysis that demonstrates that parties may invest inefficient amounts in collecting (or concealing) information in order to affect the distribution of rents.

  43. 43.

    Such a case could arise, for example, when third-degree price discrimination allows a monopolist profitably to serve a new market segment in which the firm appropriates a smaller share of the total surplus than in its current segments.

  44. 44.

    In theory, reduced privacy could also benefit viewers by exposing them to better-targeted ads. Absent regulatory restrictions, a platform’s equilibrium choice of privacy will never be at a point where the net effect of reducing privacy would be to benefit the marginal viewer—if it were, the platform could reduce privacy, attract additional viewers, and earn more money from advertisers, which would contradict the optimality of the original choice.

  45. 45.

    I assume that the net effect of an increase in privacy and corresponding increase in the number of viewers is not to raise the value of any given advertisement. In other words, I assume that the reduction in data per user offsets any gains that are associated with having a larger data set.

  46. 46.

    As observed above (note 5 supra), the German antitrust authority opened an investigation into whether Facebook used market power to impose unfair privacy policies.

  47. 47.

    Note that similar arguments can be used to show that any type of platform quality that affects viewer demand may be inefficiently low or high. The arguments are simpler when changes in the platform’s quality do not affect the advertisers’ willingness to pay.

  48. 48.

    Samuel Gibbs, “Google has been tracking Android users even with location services turned off,” The Guardian, November 22, 2017, available athttps://www.theguardian.com/technology/2017/nov/22/google-track-android-users-location-services-turned-off-sim.

  49. 49.

    Sacher and Yun (2017) offer a different critique of Hubbard’s arguments and factual claims than I do here.

  50. 50.

    United States v. Grinnell Corp., 384 U.S. 563, 571 (1966).

  51. 51.

    In this respect, perhaps one should breathe a sigh of relief that U.S. federal antitrust law—with its focus on harm to competition—applies to few circumstances that involve price discrimination against final consumers.

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Katz, M.L. Multisided Platforms, Big Data, and a Little Antitrust Policy. Rev Ind Organ 54, 695–716 (2019). https://doi.org/10.1007/s11151-019-09683-9

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Keywords

  • Multisided platforms
  • Big data
  • Antitrust policy
  • Privacy