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Understanding the Implications of Big Data and Big Data Analytics for Competition Law

An Attempt for a Primer

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New Developments in Competition Law and Economics

Part of the book series: Economic Analysis of Law in European Legal Scholarship ((EALELS,volume 7))

Abstract

The chapter is conceptualized as a primer on the implications of Big Data and Big Data analytics for market dynamics and competition law. It provides an overview of the existing scholarship and the contested opinions on whether Big Data is a distinct phenomenon that demands adjustments in the currently applied competition law toolkit.

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Notes

  1. 1.

    On contemporary disruptive technologies and their intimate relationship with data, see Manyika et al. (2013). The classic reference to creative destruction remains Schumpeter (1950).

  2. 2.

    Marr (2015).

  3. 3.

    Cukier and Mayer-Schönberger (2013), p. 13; Cohen (2013), pp. 1920–1921; Richards and King (2014), p. 394.

  4. 4.

    Marr (2015).

  5. 5.

    For excellent analyses, see Brown et al. (2011), Cukier and Mayer-Schönberger (2013) and Bughin et al. (2016).

  6. 6.

    Schultz (2017). For updates, see Desjardins (2018).

  7. 7.

    Marr (2015).

  8. 8.

    One of the most recent reported period, the number of Internet users worldwide was 3.58 billion, up from 3.39 billion in the previous year. See https://www.statista.com/statistics/273018/number-of-Internet-users-worldwide/ (last accessed 18 May 2018).

  9. 9.

    One can also try to draw a line between Big and small data. Small data, although similarly to Big Data is not clearly defined, is thought of as solving discrete questions with limited and structured data. The data often is controlled by one institution. See e.g. Berman (2013), pp. 1–2. For an excellent analysis of both terms and review of the literature, see Hu (2015), pp. 794–799.

  10. 10.

    Marr (2015).

  11. 11.

    Gal and Rubinfeld (2017).

  12. 12.

    EDPS (2014), p. 9.

  13. 13.

    The Economist (2017).

  14. 14.

    Brown et al. (2011).

  15. 15.

    EDPS (2014), p. 9, citing Lokke (2014). Estimates of the added value of data vary according to context and methodology: revenues or net income per record/user for two global companies whose business models rely on personal data have been calculated at EUR 3–5 per year, while the digital value that EU consumers place on their data has been estimated at EUR 315 billion in 2011, forecast to rise to EUR 1 trillion by 2020. See EDPS, p. 9, referring to OECD (2013b) and Boston Consulting Group (2012). For a great explanation of correlation versus causality in the use of data and what the implications of this may be, see Cukier and Mayer-Schönberger (2013).

  16. 16.

    The project Google Flu Trends was launched in 2008 and is now discontinued (https://www.google.org/flutrends/about/). See Kou et al. (2015).

  17. 17.

    The GDPR provides the following definition of personal data: “‘personal data’ means any information relating to an identified or identifiable natural person (‘data subject’); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person”. See Directive 2016/680 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data by competent authorities for the purposes of the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties, and on the free movement of such data, and repealing Council Framework Decision 2008/977/JHA, OJ L [2016] 119/89 (hereinafter GDPR). The GDPR entered into force on 24 May 2016 and is effective as of 25 May 2018. For a brief introduction, see Burri and Schär (2016).

  18. 18.

    The protection of privacy and family life are fundamental human rights enshrined in a number of international and regional acts, such as the Council of Europe’s European Convention on Human Rights. Charter of Fundamental Rights of the European Union (CFREU) distinguishes between the right of respect for private and family life in Article 7 and the right to protection of personal data, which is explicitly enshrined in Article 8. This distinction is no coincidence but reflects the heightened concern of the EU and translates into a positive duty to implement an effective protection of personal data and to regulate the transmission of such data. See Charter of Fundamental Rights of the European Union, OJ C (2010) 83/2.

  19. 19.

    “As techniques like data fusion make big data analytics more powerful, the challenges to current expectations of privacy grow more serious. When data is initially linked to an individual or device, some privacy-protective technology seeks to remove this linkage, or ‘de-identify’ personally identifiable information—but equally effective techniques exist to pull the pieces back together through ‘re-identification’. Similarly, integrating diverse data can lead to what some analysts call the “mosaic effect,” whereby personally identifiable information can be derived or inferred from datasets that do not even include personal identifiers, bringing into focus a picture of who an individual is and what he or she likes. Many technologists are of the view that de-identification of data as a means of protecting individual privacy is, at best, a limited proposition”. See The White House (2014), p. 14.

  20. 20.

    The White House (2014), pp. 14–15; see also Ohm (2010).

  21. 21.

    Rubinstein (2013), p. 77.

  22. 22.

    Rubinstein (2013), p. 77, referring to Polonetsky and Tene (2013).

  23. 23.

    Rubinstein (2013), p. 78.

  24. 24.

    Rubinstein (2013), p. 78.

  25. 25.

    Group profiles that apply to individuals as members of a reference group, even though a given individual may not actually exhibit the property in question. Rubinstein (2013), p. 78.

  26. 26.

    Polonetsky and Tene (2013).

  27. 27.

    For a comprehensive analysis, see Gasser (2015, 2016) and Pan (2016).

  28. 28.

    Bughin et al. (2016), p. 6.

  29. 29.

    Bughin et al. (2016), p. 26, referring also to Chui and Manyika (2015).

  30. 30.

    Bughin et al. (2016), p. 26.

  31. 31.

    OECD (2013a), p. 5.

  32. 32.

    Much of the literature on two-sided markets goes back to the work by Rochet and Tirole. See e.g. Rochet and Tirole (2002, 2003). For a literature overview, see Evans and Schmalensee (2015), pp. 408–410; also Gebicka and Heinemann (2014).

  33. 33.

    Many media outlets followed Zuckerberg’s testimonies. See e.g. Buncombe (2018).

  34. 34.

    EDPS (2014), p. 11.

  35. 35.

    Discussions of network effects have traditionally focused on “direct” effects, whereby increases in usage directly increase the value of the network. There are multiple examples, ranging from telephones to coffee machines. Some of the classic references include: Katz and Shapiro (1994) and Liebowitz and Margolis (1995). Direct network effects may present a variety of antitrust problems, in that companies with larger networks may entrench their dominance or leverage it onto other markets. See e.g. Cass (2013), pp. 175–176.

  36. 36.

    Johnson and Moazed (2016), p. 95.

  37. 37.

    Bamberger and Lobel (2018), p. 1068.

  38. 38.

    Bamberger and Lobel (2018), p. 1068.

  39. 39.

    Bamberger and Lobel (2018), p. 1069.

  40. 40.

    EDPS, p. 11.

  41. 41.

    EDPS, p. 11.

  42. 42.

    Gasser (2015), p. 392.

  43. 43.

    Gasser (2015), p. 392.

  44. 44.

    For a great variety of excellent examples in the fields of Big Data and the Internet of Things, see Gasser (2015), pp. 392–402.

  45. 45.

    Just a few weeks after the scandal, Facebook shares were traded a bit higher than before it. It is at the same time fair to note that the Cambridge Analytica itself filed for bankruptcy. See e.g. the New York Times report on the topic: Confessore and Rosenberg (2018).

  46. 46.

    Gasser (2015), pp. 405–406.

  47. 47.

    This discrepancy between attitude and behaviour is often referred to as the “privacy paradox”. See Gasser (2015), p. 367, referring also to Berendt et al. (2001), Barnes (2006) and Acquisti et al. (2015).

  48. 48.

    We follow the taxonomy and the great analyses offered by Sokol and Comerford. See Comerford and Sokol (2016), pp. 1133–1140.

  49. 49.

    Comerford and Sokol (2016), p. 1133, referring to Walker (2015), pp. 141–142; Lerner (2014). See also Case COMP/M.7217—Facebook/WhatsApp, Commission Decision (10 March 2014), para. 47 (noting that “[t]he vast majority of social networking services are provided free of monetary charges”).

  50. 50.

    Comerford and Sokol (2016), p. 1134, citing Lerner (2014), p. 13.

  51. 51.

    Comerford and Sokol (2016), p. 1134, referring to Lerner (2014), p. 50; Edlin and Harris (2013), p. 177. See also Case COMP/M.7217—Facebook/WhatsApp, Commission Decision (10 March 2014), para. 73 (noting that datasets should not have an impact in a market for online advertising because there are so many different sources of user data available).

  52. 52.

    Comerford and Sokol (2016), p. 1133, referring to Lambrecht and Tucker (2015).

  53. 53.

    Comerford and Sokol (2016), p. 1136, referring to Lambrecht and Tucker (2015), pp. 11–15; Tucker and Wellford (2014), pp. 6–9.

  54. 54.

    Comerford and Sokol (2016), p. 1136.

  55. 55.

    Tucker (2013), p. 1030.

  56. 56.

    Comerford and Sokol (2016), p. 1137, referring to the locus classicus on the economics of information: Shapiro and Varian (1999), p. 24.

  57. 57.

    Comerford and Sokol (2016), p. 1138, referring to Renda (2015), p. 30. The European Commission found the same way in the Facebook/WhatsApp merger: it said that usage of one particular messaging app did not exclude the use of competing messaging apps by the same user and multi-homing was common and facilitated by the ease of downloading a consumer communications application and its very low cost, as almost all apps were available free of charge. See Case COMP/M.7217—Facebook/WhatsApp, Commission Decision (10 March 2014), paras 133–134.

  58. 58.

    Comerford and Sokol (2016), p. 1138, referring to Chiou and Tucker (2014).

  59. 59.

    Comerford and Sokol (2016), p. 1138.

  60. 60.

    Lambrecht and Tucker (2015), pp. 12–16.

  61. 61.

    See e.g. Grunes and Stucke (2015a, 2016) and Ezrachi and Stucke (2016a). For a very nuanced analysis, see Shelanski (2013).

  62. 62.

    See also in this sense OECD (2014), pp. 58–60.

  63. 63.

    Comerford and Sokol (2016), p. 1142 (emphasis in the original), referring to Ezrachi and Stucke (2016b), pp. 91–102.

  64. 64.

    Grunes and Stucke (2015a).

  65. 65.

    Comerford and Sokol (2016), p. 1143.

  66. 66.

    Comerford and Sokol (2016); also Gebicka and Heinemann (2014). Gebicka and Heinemann develop interesting thoughts on the so-called “small but significant non-transitory decrease in the quality (SSNDQ)” test as takes quality into account in contrast to the standard “small but significant non-transitory increase in price’ (SSNIP) test. See also in this sense, Shelanski (2013) and Grunes and Stucke (2016).

  67. 67.

    Grunes and Stucke (2015a), p. 5.

  68. 68.

    Grunes and Stucke (2015a); also Graef (2015), pp. 476–477.

  69. 69.

    Grunes and Stucke (2015a), p. 5.

  70. 70.

    Comerford and Sokol (2016), p. 1142, citing Pamela Jones Harbour, Commissioner, Federal Trade Commission, Dissenting Statement regarding in re Google/DoubleClick, FTC File No. 071-0170, 20 December 2007, p. 10.

  71. 71.

    FTC, Statement concerning Google/DoubleClick, FTC File No. 071-0170, 20 December 2007, p. 2.

  72. 72.

    Case COMP/M.4731, Google/DoubleClick, Commission Decision, 2008 OJ C 927, 5, at paras 2–3.

  73. 73.

    Id. at para. 368.

  74. 74.

    Letter from Jessica L. Rich, Director, Bureau of Consumer Protection, FTC, to Erin Egan, Chief Privacy Officer, Facebook, Inc. and Anne Hoge, General Counsel, WhatsApp Inc., 10 April 2014.

  75. 75.

    Case COMP/M.7217—Facebook/WhatsApp, Commission Decision (10 March 2014), para. 164.

  76. 76.

    Autorité de la concurrence and Bundeskartellamt (2016), pp. 23–24. In this study, the agencies jointly analysed which consequences and challenges arise out of the collection of data in the digital economy and other industries.

  77. 77.

    EDPS (2014).

  78. 78.

    Graef (2015).

  79. 79.

    Gal and Rubinfeld (2017).

  80. 80.

    Graef (2015), p. 483.

  81. 81.

    Graef (2015), p. 483, referring to Grunes and Stucke (2015b).

  82. 82.

    Graef (2015), p. 483.

  83. 83.

    Shelanski (2013); Graef (2015), p. 497.

  84. 84.

    Graef (2015), pp. 479–480.

  85. 85.

    Case COMP/M.7217—Facebook/WhatsApp, Commission Decision (10 March 2014), paras 188–189.

  86. 86.

    Case COMP/M.7217—Facebook/WhatsApp, Commission Decision (10 March 2014), para. 189.

  87. 87.

    In the case, Twitter informed PeopleBrowsr, a company that analyses Twitter data in order to provide a data analytics service to its clients, that it would be losing its full access to the stream of tweets as of December 2012 and instead had to approach one of Twitter’s certified data resellers to gain access to the data. PeopleBrowsr argued that it needed access to the full stream of tweets to be able to deliver its services to customers and stated in a court document that Twitter data is a unique and essential input. In addition, it claimed that the way in which Twitter enables users to respond to each other by retweeting content or mentioning each other in their own tweets, forms a web of interactions that “provides unique insight about which members of communities are influential”. While data from social networking sites as Facebook may serve as a valuable complement, Twitter data could in PeopleBrowsr’s view not be replaced by data from these sources. Unfortunately, the case was settled, so we do not have a final judgment. See Graef (2015), pp. 498–499, referring to PeopleBrowsr, Inc. et al. v. Twitter, Inc. (PeopleBrowsr), No. C-12-6120 EMC, 2013 WL 843032 (N.D. Cal. 6 March 2013), p. 1.

  88. 88.

    Graef (2015), p. 504.

  89. 89.

    Comerford and Sokol (2016), p. 1153, citing European Commission, “Mergers: Commission Approves Acquisition of WhatsApp by Facebook”, Press Release IP/14/1088, 3 October 2014.

  90. 90.

    Their combined share in the EEA market for consumer communications apps on iOS and Android smartphones in the period between November 2013 and May 2014 was around [30–40]% (WhatsApp: [20–30]%; Facebook Messenger: [10–20]%), followed by Android’s messaging platform ([5–10]%), Skype ([5–10]%), Twitter ([5–10]%), Google Hangouts ([5–10]%), iMessage ([5–10]%), Viber ([5–10]%), Snapchat ([0–5]%) and other market players with a share of [0–5]% or less. The Parties submitted that they have no reason to believe that their usage of consumer communications apps globally is higher than it is in the EEA. Case COMP/M.7217—Facebook/WhatsApp, Commission Decision (10 March 2014), para. 96.

  91. 91.

    Case COMP/M.7217—Facebook/WhatsApp, Commission Decision (10 March 2014), para. 99. This builds upon previous case-law. See in particular Case T-79/12, Cisco Systems Inc. and Messagenet SpA v. Commission, judgment of 11 December 2013.

  92. 92.

    Case COMP/M.7217—Facebook/WhatsApp, Commission Decision (10 March 2014), para. 179. It should be noted that in May 2017 Facebook was fined €110 million for misleading the Commission during the review of its takeover of WhatsApp. During the merger process in 2014, Facebook claimed it was technically impossible to combine user information from Facebook and WhatsApp automatically. However, WhatsApp announced thereafter that it would begin sharing user information with its parent company, admitting that personal details, such as phone numbers and device information, would now be used to target advertisements and improve products on Facebook. The decision on the fine has no impact on the Commission’s conclusion as to the legality of the merger. It is also independent from proceedings undertaken by data protection authorities in certain EU Member States.

  93. 93.

    Autorité de la concurrence and Bundeskartellamt (2016), pp. 12–13, referring to US Department of Justice, Antitrust Division, Competitive Impact Statement, 13-cv-00133 WHO, 08 May 2014.

  94. 94.

    Autorité de la concurrence and Bundeskartellamt (2016), pp. 12–13.

  95. 95.

    Comerford and Sokol (2016), p. 1130.

  96. 96.

    Shelanski (2013), pp. 1670–1671, referring also to Katz and Shelanski (2015) and Geroski (2003).

  97. 97.

    Grunes and Stucke (2015b), p. 10; also in this sense, Shelanski (2013), EDPS (2014) and Autorité de la concurrence and Bundeskartellamt (2016).

  98. 98.

    Shelanski (2013), p. 1705.

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Acknowledgement

I am indebted to the organizers of the 7th Law and Economics Conference “New Developments in Competition Behavioural Law and Economics”, and in particular to Prof. Klaus Mathis from the University of Lucerne. The comments and suggestions by the conference participants have helped me to improve the chapter; all errors remain my own.

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    Mira Burri

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Correspondence to Mira Burri .

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  1. Faculty of Law, University of Lucerne, Lucerne, Switzerland

    Klaus Mathis

  2. The School of Law, University of Notre Dame, Notre Dame, IN, USA

    Avishalom Tor

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Burri, M. (2019). Understanding the Implications of Big Data and Big Data Analytics for Competition Law. In: Mathis, K., Tor, A. (eds) New Developments in Competition Law and Economics. Economic Analysis of Law in European Legal Scholarship, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-030-11611-8_12

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