Greed for data and exclusionary conduct in data-driven markets

computer law & security review 35 (2019) 89–102

Available online at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/CLSR

Greed for data and exclusionary conduct in data-driven markets Vikas KathuriaR Max Planck Institute for Innovation and Competition, Marstallplatz 1, 80539 Munich, Germany

a r t i c l e

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Article history:

a b s t r a c t Several two-sided platforms base their business model on collecting user data, which not only is used for advertisements that generate revenue, but also improve the underlying algorithm that forms the core of any virtual platform. In such markets, big data generates

JEL:

network effects that sustain the market position of the dominant player. Further, scope in

K21

data adds a crucial competitive advantage to the advertisement-driven business model. The

L12

paper argues that by cutting the supply of user data to its competitors, a dominant player

L41

can successfully restrict its competitors from gaining critical mass (in terms of both scale

Keywords: Big data Machine learning Competition law Google

and scope) that is crucial to stay viable in a competitive market. The literature on the competition assessment of data-driven markets has predominantly been theoretical hitherto. This paper presents the competition assessment of two recent cases—European Commission’s decision against Google in the Android licensing case, and Bundeskartellamt’s (German Federal Cartel Office) action against Facebook— in their technological and economic context to ascertain foreclosure. While Google’s practices resulted in foreclosure, the technological

Android Facebook Network effects Foreclosure

and economic context in Bundeskartellamt’s case against Facebook does not present a convincing theory of foreclosure. The paper also draws common lessons from these cases that can guide the competition assessment in similar circumstances. The paper, therefore, contributes to the scant academic literature on the exclusionary conduct in data-driven markets from a practical standpoint.

1.

Introduction

Scale economies have long invited antitrust scrutiny—first, supply-side scale in the traditional economy such as steel, chemicals, automobiles, and oil; and, later demand-side scale in the network economy such as credit cards and Operating

Systems of computers.1 The pace of technological innovation has brought us into the age of data-driven economy, where aside from direct network effects (where they exist, e.g., social media2 ), indirect network effects between the past and new users (e.g., search engine) present even bigger challenges to ensure that these markets remain contestable. This has been the understanding from the very beginning that in virtual

E-mail address: [email protected] Senior Research Fellow at the Max Planck Institute for Innovation and Competition, Munich; extramural fellow at the Tilburg Law and Economics Center (TILEC), the Netherlands. I am grateful to Dennis Kann, Josef Drexl, Inge Graef, Marco Botta and Jure Globocnik for very helpful discussions and comments on the paper. Thanks are also due to the two anonymous reviewers. All mistakes are mine alone. 1 See, Carl Shapiro, “Exclusivity in Network Industries”, 7 George Mason law Review. 673 (1999). 2 Social media may also experience one-sided indirect network effects between advertisers and users. See the text accompanying footnote 59. R

https://doi.org/10.1016/j.clsr.2018.12.001 0267-3649

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computer law & security review 35 (2019) 89–102

networks harm from monopoly is greater on innovation than on consumer prices.3 Indeed, the hi-tech industry is characterized by rapid innovation where switching costs and network effects are not able to hold the products from displacing the incumbent.4 The innovation in data-driven markets depends upon the quantity and quality of user data that interacts with the underlying algorithm and improves it in turn. This paper deals with exclusionary conduct in the markets where user-generated data is a fundamental input first, for the purpose of data mining that facilitates targeted advertisements, which in turn increases the revenue of the platform; second, for the purpose of training the Machine Learning enabled algorithm that forms the core of their infrastructure. The paper argues that foreclosure in data-driven markets should be assessed against the technological nature of big data and the economic context of the relevant market. Conventionally, the European Commission ensures consumer welfare by assessing if the practices of a dominant player lead to anticompetitive foreclosure.5 The paper shows that in the data-driven economy by successfully restricting competitor’s access to data, both in terms of scale and scope, a dominant firm can foreclose the market and thus harm consumer welfare. Data as input is required by those virtual platforms that link buyers and sellers. Aside from Google and Facebook, Amazon, Airbnb and Uber among others follow this business model. Market players can engage in several types of exclusionary conducts in online markets. For instance, Commission’s Google Search (Shopping) Decision found Google’s practice of giving more favourable positioning and display to its own comparison shopping service compared to the competing comparison shopping services an exclusionary conduct.6 Also, the Commission recently started a preliminary investigation against Amazon. Amazon’s business model allows it to act in a dual capacity— a host to manufacturers/retailers in the upstream and a competitor to them in the downstream through Amazon’s own label. The Commission suspects that as a host Amazon has access to crucial data of its platform users related to prices, return

3

Carl Shapiro (n1). Christian Ahlborn, Vincenzo Denicolò, Damien Geradin, and A. Jorge Padilla, “DG Comp’s Discussion Paper on Article 82: Implications of the Proposed Framework and Antitrust Rules for Dynamically Competitive Industries” (accessed 29 November 2018) (citing S.E. Margolis and S.J. Liebowitz, “Causes and Consequences of Market Leadership in Application Software,” in Winners, Losers, & Microsoft: Competition and Antitrust in High Technology, Oakland: Independent Institute, 1999). 5 Communication from the Commission — Guidance on the Commission’s enforcement priorities in applying Article 102 of the EC Treaty to abusive exclusionary conduct by dominant undertakings (2009/C 45/02), paragraph 19. “The aim of the Commission’s enforcement activity in relation to exclusionary conduct is to ensure that dominant undertakings do not impair effective competition by foreclosing their competitors in an anti-competitive way, thus having an adverse impact on consumer welfare…”. 6 CASE AT.39740 Google Search (Shopping), Date: 27/06/2017. 4

rates and popularity of competitors’ products.7 Amazon can use this data to outcompete the platform users. This paper, however, deals only with those practices where big data is a fundamental input in the business model and the dominant undertaking forecloses the rivals from accessing data. While the stakeholders (competition authorities, market players, and academics) have been debating the role of competition law in the data-driven economy, two recent decisions—first one by the European Commission where it found Google’s licensing practices of its Android Operating System abusive, and the second one by Bundeskartellamt where it found Facebook’s collection of user data from third-party websites anticompetitive—spell out some early warnings for the market players.8 Although the facts and context of these two cases differ, at the core of both the cases is the motivation to illegally acquire data to score an advantage over the competitors. Some have argued that the data-driven markets do not pose any concern for competition law, as data is non-rivalrous.9 However, if an incumbent restricts rivals’ access to data, this may raise concern for competition law, warranting a case-by-case analysis.10 This thought is manifested in the Commission’s Google-Android decision and Bundeskartellamt’s case against Facebook.

7 (accessed 10 November 2018); The Bundeskartellamt as well recently started an investigation against Amazon along the similar lines. The “Bundeskartellamt is examining in particular the company’s [Amazon’s] terms of business and practices towards sellers on its German Amazon marketplace.” (accessed 2 December 2018). 8 The full text of the EU Decision in Google (Android) case has not been published yet. The assessment here is based on the press release available at (accessed 10 November 2018); the Bundeskartellamt came out with the initial assessment of the Facebook case in December 2017 along with a background information paper (accessed 10 November 2018). 9 Darren S. Tucker and Hill B. Wellford, “Big Mistakes Regarding Big Data”, The Antitrust Source, December 2014; speech given by CMA Chief Executive, Alex Chisholm, at the UEA Centre for Competition Policy Annual Conference in Norwich (accessed 10 November 2018) noting “But there are respectable counter arguments: that data is actually pretty freely available; that it is nonrivalrous (making a mockery of the notion of a land-grab); and that its value tends to degrade rapidly.”; ©OECD, Data-driven Innovation for Growth and Well-being: Interim Synthesis Report, October 2014, pages 24-25; Anja Lambrecht and Catherine E. Tucker, ‘Can Big Data Protect a Firm from Competition?’ Antitrust Chronicle 1 (January 2017), no. 12, 17. 10 Digital market consists of several sub-markets. The technological characteristics of each market are different, and in turn therefore have different economic features; see also, “Competition Law and Data” Autorité De La Concurrence and Bundeskartellamt, 10th May 2016, page 42.

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Even before the Google-Android decision, the Commission had the occasions to deal with complex issues such as the relevance of data for Machine Learning and the effect of pre-installation of software applications on market share. Predominantly, such issues arose in merger cases. Indeed, the General Court had upheld the Commission’s theory of foreclosure in the Microsoft case that dealt with the complex nuances of digital market.11 Thus, even before it had to grapple with the complexities of the Google-Android case, the Commission had the chance to do its homework well in other cases. This paper makes frequent reference to these cases. On the other hand, the Bundeskartellamt took a more novel approach to foreclosure in its decision against Facebook. The paper shows that in the peculiar technological and economic context of the case, the relevant market is unlikely to witness foreclosure. The paper is planned as follows. The technological and economic features of big data have been debatable. As the legal assessment of foreclosure is invariably based on the technological and economic context in such markets, Part I of the paper sets out some common features of big data after providing supporting arguments. Part II builds on the theoretical assessment discussed in the previous section and presents an analysis of Commission’s Google-Android decision and Bundeskartellamt’s Facebook decision. Part III draws common lessons from both the cases that can provide guidance to the stakeholders in a similar context.

2. Technological and economic features of the data-driven markets Data has always been important to firms. The present technology, however, makes the collection, storage and processing of data cheaper and easier. Of course, for this reason, the ‘three Vs’ of Big Data, Volume, Velocity and Variety have only recently become possible.12 The virtual networks continuously require expanding their user base. In terms of static efficiency, a two-sided platform characterized by network effects can be successful if it has a critical mass of users on both sides. Also, a large number of users provide more candidates for behavioural targeting of advertisements, which remains the prime source of revenue for most of the data-driven market players. Additionally, data from the users help these networks improve their Machine Learning based algorithms that are core to their operations. Machine Learning (ML hereafter) is a “field of study that gives computers the ability to learn without being explicitly programmed.”13 As early as 2004 the Commission noted : “ML is based on algorithms that can learn from, process and rank data to make useful predictions to its users.”14 The underlying algorithm in the case of ML evolves (alters)

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Microsoft v Commission, Case T-201/04. Big data: A Tool for Inclusion or Exclusion? FTC Report, January 2016, page 2. 13 Arthur Samuel,1959,IBM Community< https://www.ibm.com/ developerworks/community/blogs/jfp/entry/What_Is_Machine_ Learning?lang=en> (accessed 10 November 2018). 14 Case M.8124—Microsoft/LinkedIn, footnote 230. 12

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itself after interacting with big data.15 For example, the face recognition technology of Facebook keeps improving itself as users identify or correct the suggested tags. In the case of Machine Learning, the algorithm is given an objective, and it reaches that objective by interacting with data. Gathering of data and labelling it correctly is the fundamental part of training ML.16 Equally important for ML is the quality of data. An ML algorithm will be most optimal to achieve its given objective if it is provided with relevant data.17 As ML is driven by big data,18 the quantity of data positively influences the amount of learning.19 The Community case law, therefore, has recognised data as an input for ML.20 The following part discusses some common features of big data.

2.1.

Importance of scale in data

If big data is an input, the question arises whether the quantity of data positively influences an online platform. The importance of scale in data and its relationship with the effective performance of a search engine is reflected in the statement made by the US Department of Justice (DoJ) in the Microsoft/Yahoo search cooperation agreement: The transaction will enhance Microsoft’s competitive performance because it will have access to a larger set of queries, which should accelerate the auto-mated learning of Microsoft’s search and paid search algorithms and enhance Microsoft’s ability to serve more relevant search results and paid search listings, particularly with respect to rare or “tail” queries. The increased queries received by the combined operation will further provide Microsoft with a much larger pool of data than it currently has or is likely to obtain without this transaction. This larger data pool may enable more effective testing and thus more rapid innovation of potential new search-related products, changes in the presentation of search results and paid search listings, other changes in the user interface, and changes in the search or paid search algorithms. This enhanced performance, if realized, should exert correspondingly greater competitive pressure in the marketplace.21 15 These algorithms are, therefore, ‘learning algorithms’. Iain M. Cockburn and Rebecca Henderson, “The Impact of Artificial Intelligence on Innovation”, Paper prepared for the NBER Conference on Research Issues in Artificial Intelligence Toronto, September 2017. 16 (accessed 10 November 2018). 17 Ibid. 18 European Political Strategy Centre, “The Age of Artificial Intelligence Towards a European Strategy for Human-Centric Machines” Issue 29, 27 March 2018 (accessed 10 November 2018). 19 Greg Sivinski, Alex Okuliar & Lars Kjolbye , “Is big data a big deal? A competition law approach to big data”, (2017) 13(2-3) European Competition Journal, 199-227, page 209. “We can observe similar returns to scale and feedback effects with spell checkers, speech recognition software or other online shopping applications and services – the more frequently these systems are used, the more data they collect and the smarter they become.” 20 Microsoft/LinkedIn n (14) paragraphs 246 to 277. 21 Press Release, Dep’t 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!

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In the EU as well, the importance of scale in data vis-à-vis search engines has been recognized. In the 2010 MicrosoftYahoo! search deal, one of the reasons for finding the agreement procompetitive for internet search users was that the scale of data post-deal was likely to allow the merged entity to run more tests and experiments on the algorithm in order to improve its relevance.22 Researchers too have noted that scale in data leads to improvement in the efficiency of a search platform.23 It may be true that data suffers from diminishing returns.24 This does not undermine the importance of scale, however. Especially, when the threshold beyond which diminishing returns are observed is high, it may act as a substantial entry barrier.25 Scale is particularly important for the “tail queries”.26 More recently, in the Google (Shopping) Decision the Commission relying upon Google’s internal documents and the evidence given by other general search services noted that the ability of a search engine to quickly detect change in user behaviour pattern and accordingly improve its relevance is proportional to the number of queries a general search service receives.27 In this case, the Commission also acknowledged that a higher volume of traffic allows Machine Learning effects in comparison shopping service algorithms and thus increases their relevance.28 In the static sense as well, a larger set of users makes the platform more attractive for advertisers, thus bringing more revenue. Consequently, Cockburn and Hender-

Inc. (18 February 2010) 1 (accessed 10 November 2018). 22 Case No COMP/M.5727-Microsoft/Yahoo! Search Business, paragraphs 223, 225 and 226. 23 Maurice E. Stucke & Ariel Ezrachi, “When Competition Fails to Optimize Quality: A Look at Search Engines”, 18 YALE J.L. & TECH. 70 (2016), “With more users on the company’s platform of services, the company is better able to develop predictive user profiles, target users with sponsored and organic search results, and use behavioral ads to reach users via the platform’s different channels.” Page 86; the OECD also notes “The accumulation of data can lead to significant improvements of data-driven services which in turns can attract more users, leading to even more data that can be collected.” © OECD (n 9); on this, see in general, Inge Graef, “Market Definition and Market Power in Data: The Case of Online Platforms” (2015) World Competition: Law and Economics Review 38(4) page 484-487. 24 This was pointed by Google and accepted by the notifying parties (Microsoft and Yahoo!) in Microsoft/Yahoo! (n 22), paragraph 174. “However, Google argues that while scale is an important and necessary ingredient of having a successful search engine, its degree of importance has been largely overstated. In particular, Google underlines that the value of incremental data decreases as the amount of data increases, something which is acknowledged by the notifying party.”; see also, Stucke & Ezrachi (n 23), pages 93-96; Andres V. Lerner, “The Role of ’Big Data’ in Online Platform Competition” (2014)< https://papers.ssrn.com/sol3/papers. cfm?abstract_id=2482780> (accessed 10 November 2018) pages 3538; “Competition Law and Data” Autorité De La Concurrence and Bundeskartellamt (n 10) page 48. 25 Bruno Lasserre & Andreas Mundt, “ Competition Law And Big Data: The Enforcers’ View” (2017) Italian Antitrust Review 4(1), page 87-103. 26 Microsoft/Yahoo! DoJ (n 21). 27 Google (Shopping) (n 6) paragraph 287. 28 Ibid, paragraph 447.

son posit that even if the underlying algorithm is open, early entrants may score an advantage over their rivals where there are increasing returns to scale and scope in data acquisition.29

2.2.

Is big data fungible?

The next important issue with respect to data—that has a bearing on competition assessment of data-driven markets— is that of its substitutability. If the Internet is a huge cosmos of data, can access to one type of data from a particular relevant market facilitate entry in a different market? If this is a possibility, a market player present in one market can use the data from this market to train the algorithm in another market. However, the technical nature of data suggests that for the purpose of Machine Learning, data is not fungible. The Commission in the past has recognized that different types of data may be useful for different purposes. Third party data relevant for ML can be different depending to the use case and relevant industry. The data collected by LinkedIn are one source of the third party data which could be used for ML and may be relevant for certain use cases in certain industry sectors, but not for others.30 This finding is important for the purpose of competition in the search engine market. For instance, it could be argued that tying of Google Play with Google Search on mobile devices does not restrict Microsoft’s access to data from desktops/laptops (static devices) to train its mobile search algorithms. Arguably, the queries generating on static devices and mobile handsets are different. For instance, almost onethird of all mobile searches are related to location.31 Thus, the nature of data is different. In any case, Google gets more searches on mobile handsets compared to desktops, thus providing it far more data than its competitors.32

29 Cockburn and Henderson, (n 15) page 4; they authors also note “In each application sector, there is the possibility that firms that are able to establish an advantage at an early stage, and in doing so position themselves to be able to generate more data (about their technology, about customer behavior, about their organizational processes) will be able to erect a deep-learning-driven barrier to entry that will ensure market dominance over at least the medium term.” Page 26; See also, Greg Sivinski et al (n 19),the authors note “How a machine learns in each case will illuminate the relative value of the data, and focus the regulatory issues associated with the monetization of that data.”, page 203. 30 Microsoft/LinkedIn, (n 14), paragraph 261, In footnote 243, the Commission bases its reasoning regarding the non-substitutable nature of data on its interaction with the industry. It observes “For example, a customer active in the financial sectors, Provident Financial Management Services Ltd., explained that "We currently do not use social media as a way to communicate with our customers and do not recruit new customers by using Linkedin," response to questionnaire to Sales Intelligence Solutions Competitors Q6 of 14 October 2016, question 22.” 31 (accessed 10 November 2018). 32 (accessed 10 November 2018).

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2.3.

Barriers to entry and big data

The theoretical literature has seen established companies’ access to present big data as an insurmountable entry barrier for newcomers.33 The “free” services offered by established platforms attract users that generate a large volume of data that the competitors may not have access to.34 New entrants are at a disadvantage when they are unable to access (either collect on their own or buy) the same kind of data in terms of volume and/or variety, as incumbents.35 Importantly, the theoretical possibility of buying “third-party data” to enter a market has little practical value owing to the quantity and quality of the established company’s data set.36 Also, data protection legislations such as the General Data Protection Regulation (GDPR) in Europe limit the scope of trading users’ personal data.37 Further, real-time data collection is equally important to stay ahead of one’s competitors. This is the ‘velocity’ feature of big data.38 This explains, why just past data is not important enough to stay ahead in the business. Importantly, if a platform loses its popularity, very soon the competitor can pace ahead owing to the ‘volume’ feature of big data. Whether big data acts as an entry barrier depends upon the specific context. For example in the Nielsen-Arbitron merger in 2013, the Federal Trade Commission (FTC) found that merged individual-level demographic data for audience measurement purposes would act as an entry barrier. In this case, only Nielsen and Arbitron maintained large, representative panels capable of measuring television with the required individuallevel demographics. Thus, the FTC found that the proposed acquisition would harm competition for national syndicated cross-platform audience measurement services.39 Opposite to this, big data was not found to have any effect on entry barriers in Google/DoubleClick,40 Facebook/Whatsapp41 mergers, and Telefónica UK/ Vodafone UK/ Everything Everywhere42 joint venture in the specific context of these combinations.

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Autorité De La Concurrence and Bundeskartellamt (n 10). Ibid, page 12. 35 Ibid. 36 Ibid. 37 Article 5 (1) and 6 of the General Data Protection Regulation (GDPR). 38 (accessed 10 November 2018), the authors note “Real-time or nearly real-time information makes it possible for a company to be much more agile than its competitors. For instance, our colleague Alex “Sandy” Pentland and his group at the MIT Media Lab used location data from mobile phones to infer how many people were in Macy’s parking lots on Black Friday—the start of the Christmas shopping season in the United States. This made it possible to estimate the retailer’s sales on that critical day even before Macy’s itself had recorded those sales.” 39 In the Matter of Nielsen Holdings N.V. and Arbitron Inc. File No. 131 0058 (accessed 10 November 2018). 40 Google/Doubleclick, COMP/M. 4731, dated 11.03.2008. 41 Facebook/Whatsapp, COMP/M. 7217, dated 03.10.2014. 42 Case No COMP/M.6314 – Telefónica UK/ Vodafone UK/ Everything Everywhere/ JV. 34

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In the Google/DoubleClick merger, the Commission was of the view that access to relevant data for the purpose of advertisement targeting was available to the competitors from various sources. Microsoft and Yahoo! had access to a combination of data about searches with data about users’ web surfing behaviour. The option of purchasing relevant data from third parties such as comScore also existed. In addition, Internet service providers were also a source of data, which according to the Commission was richer and broader than the data collected even by the merged entity.43 Once again in the Facebook/Whatsapp merger, the Commission found that competitors could access a “large amount of Internet user data” for the advertising purpose even if Facebook started using WhatsApp user data.44 For the same reason, the Commission approved the joint venture of Telefónica UK/ Vodafone UK/ Everything Everywhere that had access to user data for data analytics services, which could be used for mobile advertisements.45 The above-discussed cases make it quite clear that big data may act as an entry barrier depending on the context— thus, necessitating a case-by-case analysis.46 The underlying economics is different for different digital economy platforms. For instance, a search engine and social media app such as WhatsApp have little in common so far as big data is concerned. If Whatsapp does not act as a gateway to data for Facebook, its business model depends far little on big data than the business model of a search engine. This is of course for the reason that Whatsapp does not act as a two-sided platform, and user data do not reinforce network effects. The context discussed in the remaining part of this paper is that of search engines and social media platforms. In both these markets, data collection leads to network effects that reinforce the market position of the incumbent.

2.3.1. Big data generated entry barriers in the search engine market High entry barriers including hardware, cost of indexing the web, human capital, cost of developing and updating the algorithm and IP patents characterize the search engine market.47 As a two-sided platform search engines offer a textbook example of network effects that act as an entry barrier. Present search for a certain set of keywords increases the quality of results for the future users. This phenomenon has been seen as an indirect network effect in search engines.48 This “positive feedback” between the old and new consumers has been well documented.49 The following text by the OECD

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Google/Doubleclick, (n 40) §365. Facebook/Whatsapp (n 41) §189. 45 Telefónica UK/ Vodafone UK/ Everything Everywhere/ JV (n 42), paragraphs 535-558. 46 The joint paper by the French and German competition authorities also suggests this approach in data-driven markets, Autorité De La Concurrence and Bundeskartellamt (n 10) page 13. 47 Microsoft/Yahoo! Search Business (n 22), paragraph 111. 48 Cédric Argenton and Jens Prüfer, “Search Engine Competition with Network Externalities” (2012) Journal of Competition Law & Economics, 8(1), 73–105. 49 “The accumulation of data can lead to significant improvements of data-driven services which in turns can attract more users, leading to even more data that can be collected. This ‘positive feedback makes the strong get stronger and the weak get 44

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emphatically summarises the ‘Chicken-and-Egg’ / “snowball effects”50 problem in the search engine market. As an illustration, if a search engine only has one thousand daily queries, its algorithms have less data to learn responsive search results (other than more straightforward inquiries) and fewer related searches that it can suggest to users. With poorer quality search results, it will be unlikely to attract many users from the larger search engines; with fewer users, the search engine will attract fewer advertisers, which means fewer occasions for users to click on paid search results and less advertising revenue to expand the platform to other services.51 It is also believed that the right type of advertisements contribute to the relevance of a search engine.52 Thus, if by virtue of more data, a search engine displays more relevant advertisements, consumers will value that search engine more. While the search engine market experiences high entry barriers, multi-homing cannot correct this market, as consumers do not tend to multi-home search engines.53 In this market, Google has the additional advantage of accessing user data from other services such as mail, video services, and phones.54 This data integration gives Google added advantage compared to its competitors.55

2.3.2.

Big data and entry barriers in social networks

Social media is a crucial source of big data.56 On an aggregate level, big data is needed for the purpose of social media mining, which is the process of representing, analysing, and extracting meaningful patterns from data in social media, resulting from social interactions.57 The results from this process are used for targeted marketing campaigns.58

weaker, leading to extreme outcomes’ (Shapiro and Varian, 1999). For example, the more people use services such as Google Search, or recommendation engines such as that provided by Amazon, or navigation systems such as that provided by TomTom, the better the services as they become more accurate in delivering requested sites and products, and providing traffic information, and the more users it will attract.” ©OECD (n 9), pages 29, citing Shapiro, C. and H. R. Varian, (1999) “Information Rules: A Strategic Guide to the Network Economy”, Harvard Business Press, BostonMA. 50 Autorité De La Concurrence and Bundeskartellamt (n 10), page 13. 51 © OECD, “Big Data: Bringing Competition Policy to The Digital Era” DAF/COMP(2016)14. 52 Microsoft/Yahoo! Search Business (n 22) paragraph 101. 53 Ibid, paragraph 221, “[t]he very limited share of user multihoming between Microsoft and Yahoo shows that users rarely run checks between these two platforms.”; Google Search (Shopping) (n 6) paragraphs 306–312. 54 Autorité De La Concurrence and Bundeskartellamt (n 10), page 40. 55 President’s Council of Advisors on Science and Technology, Report to the President, Big Data and Privacy: A Technological Perspective (May 2014) (hereafter PCAST Report). 56 Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu, “Social Media Mining: An Introduction” < http://dmml.asu.edu/smm/ SMM.pdf> (accessed 10 November 2018). 57 Ibid, page 21. 58 < https://law.yale.edu/mfia/case- disclosed/social- mediamining- effects- big- data- age- social- media> (accessed 10 November 2018).

It is not difficult to see that direct network effects lead to an increase in the size of a social network platform. Further, critical mass achieved due to direct network effects on the user side makes the platform more attractive to advertisers, triggering one-sided positive indirect network effects.59 These direct and indirect network effects lead to high entry barriers in the social media market. In the case of social media, more and different type of data related to a user enables better targeting of advertisements. If Facebook harvests data on third-party websites, it can access data that goes beyond geographical location, language, and likes. Moreover, data from disparate sources –known as “data fusion” —gives better insight into consumer behaviour.60 Data fusion and data integration lead to better inferences.61 This, in turn, can make a platform more lucrative for advertisers. Thus, scope in data coupled with network effects can give an incumbent unassailable market position. The Commission saw the possibility of multi-homing in Facebook/WhatsApp merger vis-à-vis these two apps, suggesting that these two apps were complimentary.62 But multi-homing cannot have disciplining effects when two apps are in direct competition. Further, direct network effects can tip the market in favour of a particular social networking app. More recently, the Bundeskartellamt observed that multi-homing does not lead to deconcentration in the social network market.63 The discussion above makes it clear that big data is a fundamental input in the business model of data-driven virtual platforms. The technological nature of big data makes data from different markets non-substitutable for the purpose of Machine Learning. Virtual platforms also benefit from scale and scope in big data. Also, network effects in these markets lead to high entry barriers.

2.4.

Analysis of case law

The advent of big data is a recent development. The promises that machine Learning hold for the future, makes big data a fundamental input the market players will compete for. Naturally, therefore, this competition will fall under the scrutiny of competition agencies. This part of the paper analyses two such recent cases where the dominant firms were alleged to have engaged in exclusionary behaviour. The analysis presented in this part draws upon the theoretical findings of the previous section to ascertain foreclosure effect in the relevant market.

2.5.

The EU Google-android decision

In July of 2018, the European Commission found three licensing practices by Google in violation of the EU competition law. These practices are 1. Tying Google Search app and browser app (Chrome) to Google’s app store (the Play Store), which 59

Bundeskartellamt, Background information paper (n 8). PCAST Report (55) page 10. 61 Ibid, page 26. 62 Facebook/Whatsapp (n 41) paragraph 105. 63 Bundeskartellamt, Background information paper (n section 5. 60

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the Commission found to be a ‘must-have’ app. 2. Making payments to some large manufacturers and mobile network operators to exclusively pre-install Google Search app on their devices 3. Preventing manufactures, who wished to pre-install Google apps from selling mobile devices running on alternative versions of Androids that were not approved by Google (these alternative versions are called ‘Android forks’).64 At first, it seems that these practices are benign in nature as it is easy to download the competing search apps or browsers from the Internet. However, this part of the paper will confirm the theory of harm adopted by the Commission that these practices were aimed at invoking the ‘status-quo bias’ of the users and prevented them from downloading rival search engines. At the core of all the three violations by Google is the objective to gain exclusivity in the search engine market. This way Google ensures that search traffic on Android devices does not go to rival search engines. This means Google remains the most attractive platform for advertisers and receives the lion’s share of advertisement revenues. So far as the static efficiency is concerned, it is clear that consumers do not suffer in the short run, inasmuch as search engines are free for the consumers. However, the advertisers may end up paying more money if there is no competition. In the static sense, a two-sided platform, such as a search engine, can be successful if it is able to attract a large number of participants on both sides.65 The competition in the search engine market, however, is dynamic in nature. In the year 2010 itself, the Commission had noted that quality of the search results is critical in the search market as “[c]ompetition for the users mainly takes place on the basis of the quality of the search results (i.e. their relevance to the users but also speed of returning results) and the user interface.”66 In the same case, the Commission had noted that the search engine market is characterized by incremental innovation, where market players strive to continuously improve the search algorithm and their ability to match users and advertisers.67 An innovative new platform can easily compete away advertisers from the incumbent. By curtailing the traffic on rivals’ search engine, Google can restrict them from gaining a critical mass of big data that is the fundamental source for improving their algorithms. For this reason, the press release in the Google (Android) Decision states that “Google’s strategy has also prevented rival search engines from collecting more data from smart mobile devices, including search and mobile location data, which helped Google to cement its dominance as a search engine.”68 Exclusivity imposed on its consumers by an incumbent in network industry has been found to be anticompetitive, as “new and improved technologies will be unable to gain the critical mass necessary to truly threaten the current market leader”.69 Google aimed at achieving exclusivity by invoking

64

Google (Android) Press Release (n 8). Microsoft/Yahoo! Search Business (n 22) Paragraph 48. 66 Ibid, paragraph 101. 67 Ibid, paragraph 109. 68 Google (Android) Press Release (n 8). 69 Shapiro (n 1), Shapiro mentions the following cases to show how exclusive contracts entered into by an incumbent in network industries were anticompetitive. Atari Corp. v. Nintendo, No. 89-0824 65

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the ‘status quo bias’ through the first two practices, i.e., tying and pre-installation. Through its practice of stunting the development of ‘Android forks’, Google ensured there was no room for competitors to gain data traffic, which could make them more competitive.

2.5.1.

Status quo bias

Faced with alternative options, individuals tend to stick to the status quo position even if it is not the optimal choice.70 This lesson from behavioural economics is in contrast with the neoclassical economics assumption that market participants always make rational and logical decisions that maximise their utility.71 The psychological foundations of behavioural economics enable more realistic predictions about human behaviour in the marketplace.72 In the Google (Android) case, the epicentre of Commission’s theory of harm is based on ‘consumer inertia’ or ‘status quo bias’ that predisposes users to stick to the default option on their device. Because of this behaviour of consumers, a new entrant struggles to achieve critical mass in a market that is characterized by network effects. In order to exploit the ‘status quo bias’ of the users, not only did Google tie its search engine with Play Store, it also incentivised OEMs and mobile network operators to exclusively preinstall its search app on their devices. This gave Google search the default position on mobile handsets. To prove that the ‘status quo bias’ is real and not merely perceived, the Commission states that more than 75% searches are conducted on Bing on Windows OS where it comes pre-installed, whereas more than 95% searches are made via Google on Android platforms where it comes preinstalled.73 This is a solid argument. The Decision can be termed convincing largely because of this argument. There is another evidence as well to show that the status quo bias in the search engine market is not merely perceived. Yandex saw its market share rising from 48 to 51 per cent (N.D. Cal. verdict May 1, 1992); United States v. FTD Corp., 60 Fed. Reg. 40,859 (E.D. Mich. 1995) (proposed enforcement order); United States v. Florists’ Telegraph Delivery Ass’n, 1956 Trade Cas. (CCH) 168,367 (E.D. Mich. 1956); United States v. Electronic Payment Servs., Inc., 1994-2 Trade Cas. (CCH) 70,796 (D.Del. 1994); United States v. Visa U.S.A., Inc. (S.D.N.Y. Oct. 7, 1998) (No. 98-civ.7076); aside from network economies, where the exclusive dealing agreements have increased its competitors’ cost and hence led to foreclosure have been found to be anticompetitive. The FTC found that McWane’s program [exclusive dealing] “deprived its rivals . . . of distribution sufficient to achieve efficient scale, thereby raising costs and slowing or preventing effective entry.” McWane, Inc. v. F.T.C., No. 1411363, 2015 WL 1652200, at ∗ 19 (11th Cir. Apr. 15, 2015) (citing FTC findings), this case is also mentioned in Maurice E. Stucke & Allen P. Grunes, “Debunking the Myths about Big Data”, CPI Antitrust Chronicle, May 2015 (2). 70 William Samuelson and Richard Zeckhauser “Status Quo Bias in Decision Making,” (1988) Journal of Risk and Uncertainty, 1 (March), 7–59. 71 See in general, Herbert A. Simon, “Theories of Decision-Making in Economics and Behavioral Science” The American Economic Review Vol. 49, No. 3 (Jun., 1959), pp. 253-283. 72 Colin F. Camerer and George Loewenstein, “Behavioral Economics: Past, Present, Future” in Colin F. Camerer, George Loewenstein and Mathew Rabin, Advances in Behavioral Economics (Princeton University Press, 2004) page 3–51. 73 Google (Android) Press Release (n 8).

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in Russia, when as part of the agreement between the rival search engines, Google agreed that phone makers could preinstall Yandex on Android devices and let consumers decide which app would be their default search engine.74

2.5.1.1. Tying The first abuse that the Commission mentions relates to tying of Google Search and Chrome browser (which provides an additional gateway to Google search) apps with the Play Store app. The tying and the tied products are distinct markets. In both the markets Google is dominant. Tying was practised vis-à-vis OEMs and mobile network operators. The motivation behind this practice is to maintain the dominant position in the search market by invoking the ‘status quo bias’ —the users will stay with the Google search engine if it comes bundled with Play Store. Thus, while Microsoft was a case of ‘defensive leverage’75 in tying product, Google-Android reflects a strategy to defend the tied market. Consumer coordination is a recognised way to mitigate technology lock-in.76 Katz and Shapiro note that in a market where network effects exist “[a] single large user, or a coordinated group of users, can take control and move the market to the new product if it is superior for their needs”.77 The same holds true for status quo bias. Consumers can coordinate through electronic word-of-mouth (e-WoM) to offset the effect of status quo bias vis-à-vis search engines that are experience goods.78 Indeed, researchers show that Microblogging word of mouth (MWOM) influences other consumers’ early adoption decisions and thus the new product’s success.79 In the information age, it is much easier to try out new services, especially when one does not have to travel to another brickand-mortar store. In principle, an electronic-word-of-mouth can easily spread the word about a new more efficient search platform. Unfortunately, e-WoM can also not check the market power of the incumbent in this case, as in the absence of a fundamental input, i.e., big data, rival search engines struggle to achieve efficiency that can make them desirable to users. This is again a ‘chicken-and-egg’ problem. If the services provided by the newcomers are not as good as that of the incumbent, 74 (accessed 10 November 2018). 75 Giorgio Monti, EC Competition Law (Cambridge University Press, 2007) page 190. 76 Daniel F. Spulber, “Unlocking Technology: Antitrust and Innovation”, 4 J. COMP. L. & ECON. 915 (2008), page 918. 77 M.L. Katz and C Shapiro (1999) Antitrust in Software Markets. In: Eisenach J.A., Lenard T.M. (eds) Competition, Innovation and the Microsoft Monopoly: Antitrust in the Digital Marketplace. Springer, Dordrecht, page 34. 78 e-WoM is known to influence consumer behaviour by checking information asymmetry and building trust in online markets. See, Chrysanthos Dellarocas, Federico Dini and Giancarlo Spagnolo, Designing Reputation (Feedback) Mechanisms, Handbook of Procurement, Nicola Dimitri, Gustavo Piga, Giancarlo Spagnolo, eds., Cambridge University Press, 2006. 79 Exploring the “Twitter Effect:” An Investigation of the Impact of Microblogging Word of Mouth on Consumers’ Early Adoption of New Products. (accessed 10 November 2018).

there is little incentive to switch to the new service providers. In fact, Larouche notes that if the quality of a tied product is suboptimal, users may then start searching for better alternatives going by the experience of more tech-savvy users.80 Google search has acquired the status of a ‘must-have’ product. This explains why Amazon Fire and Nokia X that were offered without Google apps were not successful.81 In the US Microsoft case, the District Court noted that Internet Explorer had become as good as Netscape Navigator.82 Perhaps, this ensured the success of tying strategy to replace Netscape as the market leader. Thus, tied to an efficient search engine, users have little incentive to look for alternatives. In the 1990s Microsoft tied the Operating System (OS) with Internet Explorer (in the US and later in the EU) and Window Media Player (in the EU) and imposed contractual and technical restrictions on OEMs from removing the browser from the OS.83 This tying was aimed at creating/maintaining ‘application entry barrier’ —indirect network effects between the applications and the operating system. It was a case of protective tying, where the dominant position holder wanted to protect the tying market by foreclosing the tied market. The effect of tying to maintain the ‘application barrier’ was so efficient in the US Microsoft case that Internet Explorer was able to replace Netscape—that most people associated the Internet and cutting-edge browsing technology with—as the most preferred browser.84 Google’s practice of tying its Play Store with Google Search is not to achieve network effects, as Google search engine is compatible with other Operating Systems as well.85 Unlike the Microsoft case,86 it is easy to delete the default search engine and download another one.87 Further, unlike a media player, a second search engine does not consume extra capacity on users’ phone, as it is merely a cloud-based application. This way the facts of the Google-Android are different from the Microsoft case. In the Google-Android case, the tying was aimed at achieving the ‘status quo’ bias, to retain the search traffic. Here the motivation was not to protect the Android OS, as it is licensed free of cost. The eventual commercial aim of tying is to protect Google’s advertisement market by maintaining the dominance of Google Search. Once again, the crucial point of distinction is that unlike WMP, Google is already the dominant

80 Pierre Larouche, “The European Microsoft Case at the Crossroads of Competition Policy and Innovation: Comment on Ahlborn And Evans” (2009) 75(3) Antitrust Law Journal, Page 933-963. 81 See, Benjamin Edelman & Damien Geradin (2016) “Android and competition law: exploring and assessing Google’s practices in mobile” (2016) European Competition Journal, 12:2-3,159-194, page 10. 82 United States of America Plaintiff, v. Microsoft Corporation, 84 F. Supp. 2d 9 (D.D.C. 1999 (findings of fact). page 68. 83 Ibid, page 78. 84 Ibid, page 187. 85 Network effects do not exist when two programs are compatible. See, Katz and Shapiro (n 77). 86 Microsoft v Commission (n 11) “The coercion thus applied to OEMs is not just contractual in nature, but also technical. In effect, it is common ground that it was not technically possible to uninstall Windows Media Player”, paragraph 963. 87 Ibid, paragraph 1050.

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Table 1 – Comparison between the EU Microsoft case and Google (Android) Decision. Parameters

Microsoft Case

Google-Android

Nature of entry barriers Technical restrictions Restrictions on OEMs

Network effects Not possible to remove WMP 1. Contractually obligated to install WMP 2. Technically not viable to provide more than one media player Preserve the market share of the Windows Operating System that was the source of revenue

Status quo bias of the consumers Possible to remove Google search 1. Contractually obligated to install Google search and Chrome browser 2. Exclusive pre-installation of Google Search Preserve the market share in the advertisement market that is the source of revenue through maintaining its market share in search engine market Sustain the dominant position of Google Search

Motivation for tying

Means

Increase the market share of WMP

search engine and thus the natural preferred choice of consumers. When Microsoft started resorting to tying in the EU, RealPlayer ‘had a significant commercial advantage as market leader’ .88 Contrary to this, Google merely has to maintain its market position in the search engine market. Consequently, the effect of ‘status quo bias’ need not be exactly the same as the effect of contractual and technical tying in the Microsoft case. The following table lays down the differences between the EU Microsoft and the Google case Table 1. The Microsoft case heralded an era where it was clear that the Commission would not shy away from testing new theories (of course new, as the underlying technology warranted an uncharted path to assess consumer harm) so long as the factual reality supported the theory.89 It can be said that the threat of foreclosure in the Microsoft Decision was more theoretical than real as even seven years after the Commission first stated its case in 2001, the media player market was still competitive with the presence of iTunes, Adobe Flash Player and Real Player.90 However, it is critical to note that the purpose of tying was not to completely foreclose the tied market. To the extent, Microsoft was able to maintain its ‘application barriers to entry” to eventually safeguard the tying (OS) market, the purpose of Microsoft was served. Thus, the presence of fringe players did not mean the absence of consumer harm in the Microsoft case.91 Further, innovation may open up new windows to the relevant market. What is more, competitive assessment has to be based on the factual circumstances existing at that particular time when the case was decided.92 Similarly, the presence of fringe search engines cannot negate the finding of foreclosure in the search engine market.

2.5.1.2. Pre-installation The second way to invoke the ‘status quo bias’ was through paying some large device manufacturers and mobile network operators to exclusively pre-install Google Search across their entire portfolio of Android devices. It is noteworthy that in its Microsoft Decision itself, the Commission had referred to ‘end-users inertia’ that prevented

users from ignoring the pre-installation of WMP.93 However, this did not factor in the reasoning provided by the Commission (and later endorsed by the General Court) to support its conclusion regarding how tying foreclosed the relevant market. The Commission once again reiterated this point in the Microsoft/LinkedIn merger and noted that software installation “in principle” can make switching more difficult owing to the users’ inertia induced by the ‘status quo bias’.94 So far, the Commission has avoided a broad-brush approach regarding the effect of pre-installation on market share. Even before the Google Android case, the Commission had taken a pragmatic approach towards the effect of pre-installation of an application on its user base. In the Microsoft/LinkedIn merger decision, the Commission held that LinkedIn and Skype belonged to different product markets, which differed in terms of user behaviour. Consequently, it held that the pre-installation of both the applications on Windows PCs would have a different effect on their market position.95 In this merger, the notifying parties, referring to the Microsoft/Skype merger, had submitted that Skype’s pre-installation on Windows and integration with Office did not increase Skype’s market share.96 The facts in the Microsoft/Skype merger were different, as new (perhaps better) entrants such as Whatsapp had already hit the market and grew rapidly.97 Moreover, Skype’s pre-installation was not on an exclusive basis, and OEMs could still have the choice to install other apps. Above all, even before the merger, Skype was pre-installed on more than half of Windows PCs sold.98 Unlike this merger, the Commission found pre-installation would lead to foreclosure in the Microsoft/LinkedIn merger, as Windows PCs were the most important channel for PSNs (Professional Social Networks) to acquire new customers.99 In the Google-Android case pre-installation of Google Search was on an exclusive basis. This in turn induced users’ ‘status quo bias’.

88

93

89

94

Ibid, paragraph 1046. In a way, it was not completely uncharted, as the US District Court and later the Circuit Court as well had already found the allegation of monopolisation correct. 90 Larouche (n 80) pages 954–955. 91 Ibid. 92 Microsoft v Commission (n 11), paragraph 260.

95 96 97 98 99

Case COMP/C-3/37.792 Microsoft, paragraph 870. Microsoft/LinkedIn (n 14) paragraph 309. Ibid, paragraph 313. Ibid, paragraph 299. Ibid, paragraph 299. Ibid, paragraph 313. Ibid, paragraph 318.

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Foreclosure

In the Guidance Paper on Article 102, ‘anti-competitive foreclosure’ has been defined as a situation where “effective access of actual or potential competitors to supplies or markets is hampered or eliminated as a result of the conduct of the dominant undertaking”.100 The discussion above has shown that big data is an essential input in the data-driven markets. Further, paragraph 20 of the Guidance Paper on Article 102 already mentions taking account of conditions of entry and expansion on the relevant market while determining foreclosure. These conditions are economies of scale and/or scope and network effects.101 As has been shown in Part I of this paper, big data leads to indirect network effects in the search engine market. The three-prong strategy of Google for restricting competitor search engines from accessing user data culminates with obstructing the development of ‘Android forks’. As the Press Release states, ‘Android forks’ could have acted as an alternative platform for the rival search engines to collect more data including search and location data, which would have strengthened those platforms.102 Other search engines cannot grow stronger even on iOS, as Google has exclusivity agreement even with iOS.103 Google’s practices impede to a certain extent consumers’ access to rivals’ product. This, in turn, prevents rivals from gaining critical mass to survive in a market that is characterised by network effects. In general, product development depends on user feedback. Also, an increase in demand may lead to learning effects.104 In the case of search engine market the use of the product itself feeds into the development of search engines. The General Court held in the Microsoft case that the degree of interoperability in order to ensure the “maintenance of effective competition on market”105 needs to be seen from the perspective of competitors, i.e., if the competitors can operate viably on the relevant market.106 Likewise in the Google case, by restricting rivals from gaining data traffic, Google rendered them unviable in terms of both static and dynamic efficiency. Even if the exclusionary effect is assessed based on the ‘as efficient competitor’ test suggested by the Guidance Paper,107 it is easy to find a foreclosure, as the competitor’s access to data is restricted.

100

Art 102 Guidance Paper (n 5) paragraph 19. Ibid, paragraph 20. 102 Google (Android) Press Release (n 8). 103 Joel Rosenblatt and Adam Satariano, ‘Google Paid Apple $1 Billion to Keep Search Baron iPhone’ Bloomberg (Any new report?) (accessed 10 November 2018). 104 “The Commission will take a dynamic view of that constraint, given that in the absence of an abusive practice such a competitor may benefit from demand-related advantages, such as network and learning effects, which will tend to enhance its efficiency.” Paragraph 24, Art 102 Guidance Paper (n 5). 105 Joined Cases C-359/96 P and C-396/96 P Compagnie Maritime Belge Transports and Others v Commission [2000] ECR I-1365, paragraph 34. 106 Microsoft v Commission (n 11), paragraph 228 & 229. 107 Art 102 Guidance Paper (n 5) paragraph 23. 101

In the Microsoft case, the General Court underscored the freedom of choice that OEMs should be able to exercise when it comes to catering to the consumer demand for software and applications.108 This is the correct approach, as in the absence of specialised knowledge about the technical nature of a product, users can rely on the more nuanced experience of OEMs. Let us assume a scenario where OEMs are not forced to preinstall a particular application. On one hand, it will give some space to other apps to compete; on the other hand, it can also increase the search cost for consumers. In this case, OEMs can install the application for which they see a consumer demand. The General Court acknowledged that OEMs played the role of intermediary in the PC market, who follow the consumer demand and provide media player, which would not necessarily be Windows Media Player.109 The OEMs have a more important role in the Google case, as while the consumer may suffer from ‘status quo bias’, being specialists in their trade, OEMs may not suffer from the same. Further, a big OEM can make a choice in favour of a rival search engine leaving a dent in Google’s strategy.

2.5.3.

Can there be an entry from a neighbouring market?

In order to train their algorithms, it remains a possibility for competitors to access data through PCs and/or through Windows mobile phones even if the Android platform is foreclosed. Desktops and mobile phones are different devices so far as user behaviour is concerned. For instance, while explaining why majority of the users who sign-up to LinkedIn do so on Windows PCs as opposed to other PCs or mobile, the Commission noted in the Microsoft/LinkedIn merger that “users find it more convenient to carry out the tasks associated with signing up to a PSN platform (i.e. typing and pasting career related information) on a PC than on a mobile device.” 110 Arguably, therefore, search queries and thus concomitant data generating on PCs is different from the data that comes from mobile devices.111 As Part I of the paper shows this data cannot be relevant for the purpose of training a search engine algorithm best suited for mobile devices. Moreover, more searches occur on mobile as compared to desktops.112 Alternatively, competitors may get sufficient data from a different OS platform and challenge the dominance of Google. In a way, Bing’s dominance on the Windows platform suggests that it is an efficient search engine, and does not suffer from the ‘chicken-and-egg’ problem of the search engine market. Thus, it can be argued that if Bing is successful on a non-Android platform, its success can seep into the Android 108

Microsoft v Commission (n 11) paragraph 904. Ibid, paragraph 923. 110 Microsoft/LinkedIn (n 14) , paragraph 316. 111 Autorité De La Concurrence and Bundeskartellamt (n 10), page 44-45; In the Google (Shopping) decision, the Commission left the final determination of substitutability between general search services offered on static devices and on mobile devices an open question, Google (shopping) (n 6) Paragraphs 186-190. 112 In 2015 itself Google declared that “more Google searches take place on mobile devices than on computers in 10 countries including the US and Japan.” < https://searchengineland. com/its- official- google- says- more- searches- now- onmobile- than- on- desktop- 220369> (accessed 10 November 2018) 109

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platform too. This does not seem plausible as not many users have Windows-based mobile phones.113 The three-pronged licensing strategy of Google deprived its rivals of scale in data that could make them a viable alternative for the users. The fact that search queries need to be updated in logs shows that scale advantage of the incumbent is not permanent, pushing the search engines on the perennial quest for new data.114 By one account, Google encounters 15 per cent new searches every day. This also suggests that a search engine continuously needs to accumulate new data.115 This technological reality of the search engine market provides an opportunity to the competitors to wrest the market share from the incumbent. The role of competition agencies in this scenario is to ensure that this market remains contestable.

2.6.

Bundeskartellamt’s case against Facebook

The Bundeskartellamt initiated its probe against Facebook in March 2016 on the suspicion that Facebook violated data protection rules.116 The Bundeskartellamt accepted that while the violation of every law by a dominant undertaking did not automatically violate competition law, in the specific context of the case, Facebook’s terms and conditions could amount to the imposition of unfair conditions on users. Admittedly, the imposition of unfair conditions on users by a dominant undertaking is an exploitative abuse.117 However, it was not clear if the Bundeskartellamt wanted to pursue that theory of harm. Interestingly, at this stage, the Bundeskartellamt did not disclose the exact nature of ‘unfair conditions’ . In December 2017, the Bundeskartellamt issued its preliminary assessment in the case where it found Facebook to have abused its dominant position by ‘limitlessly amass[ing]’ user data by using ‘third-party websites’ (through embedded Facebook APIs) and merging it with users’ Facebook profile.118 For 88 per cent of all mobiles run on Android (accessed 10 November 2018). 114 “Other types of data (such as the particular products a consumer has been searching for) will be more transient in value, being relevant over a shorter period of time.” CMA, The Commercial Use of Consumer Data, June 2015, paragraph 3.6; “Some data loses value over time, so it is hard to see how persistent, unmatchable competitive advantage could be maintained. However some data has persistent value – for example in relation to customer transaction history on auction sites – and it is easier to see how the control of this data could become a barrier to entry.” Speech given by CMA Chief Executive, Alex Chisholm (n 9). 115 Lerner (n 24). 116 (accessed 10 November 2018). 117 See in general, Robert O’Donoghue and Jorge Padilla, The Law and Economics of Article 102 TFEU, (Hart Publishing, 2013) Second Edition, pages 846-860. 118 Bundeskartellamt, Press Release, 19 December 2017 (accessed 10 November 2018); Application Programming Interface (API) is an intermediary that 113

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instance, certain news websites have Facebook API in the form of Facebook ‘like’ button. As Bundeskartellamt found Facebook dominant in the social network market, the users had no option but to give consent to Facebook’s data collection policy. The Bundeskartellamt also issued a Background information paper on the proceeding.119 The present assessment is based on this paper. The Bundeskartellamt terms the practice of Facebook making its services conditional upon users granting extensive permission to use their personal data as ‘exploitative business terms’ .120 The theory of harm in this case seems atypical, however. Unlike conventional exploitative abuse cases, users of Facebook do not pay anything extra when Facebook gathers their data from the partner websites. In order to be a competition law abuse, it ought to result in some harm to consumers. There are two ways in which harm can be perceived in such a scenario. First, data from users may have a notional value for the purpose of competition law. Indeed, this data acts as an infrastructural resource or as a capital good on the other side (advertisers’ side) of the platform.121 It is important to note that there can only be a notional value of data in ex ante analysis, as the real value of data is impossible to determine ex ante “because the information derived from it is context dependent.” 122 The supply of data from users to platforms has also been seen as ‘by-product’ of the use of platforms.123 The Bundeskartellamt avoids this approach and relies upon the German Civil Law to determine if the conditions imposed on users, in an imbalanced negotiation, were unfair. This is in departure from the EU law on Article 102.124 Regulation 1/2003, however, permits such a departure.125 The Bundeskartellamt begins its assessment by observing “[w]here access to the personal data of users is essential for the market position of a company, the question of how that

allows two applications to communicate with each other< https://www.mulesoft.com/resources/api/what- is- an- api> (accessed 15 November 2018). 119 Bundeskartellamt, Background information paper (n 8). 120 Ibid Section 6. 121 ©OECD (n 9) pages 22 and 25. 122 “As OECD (2012b) highlighted assessing the value of data ex ante (before its use) is almost impossible, because the information derived from it is context dependent. Because information is context dependent, data value and quality typically depends on the intended use: Data that is of good quality for certain applications can thus be of poor quality for other applications.” OECD (n 9) page 25, citing OECD (2012b), “Exploring the Economics of Personal Data: A Survey of Methodologies for Measuring Monetary Value”, OECD Digital Economy Papers, No. 220, OECD Publishing. 123 “[T]he provision of data seems to be a side effect or a byproduct of the use of these platforms rather than a supply of a product by users in exchange for being able to employ search or social networking functionalities.” Inge Graef,(n 23) page 490. 124 For the case law and legal test for exploitative contractual terms see in general, Robert O’Donoghue and Jorge Padilla (n 117) 846-860. 125 Council Regulation (EC) No 1/2003 of 16 December 2002 on the implementation of the rules on competition laid down in Articles 81 and 82 of the Treaty. Recital 9 “This Regulation…does not preclude Member States from implementing on their territory national legislation, which protects other legitimate interests provided that such legislation is compatible with general principles and other provisions of Community law.”

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company handles the personal data of its users is no longer only relevant for data protection authorities. It becomes a relevant question for the competition authorities, too.”126 Thus, in the beginning itself the Bundeskartellamt differentiates between the role of competition authorities and the role of data protection authorities. The Bundeskartellamt goes on to elaborate on this difference by observing “[t]he legislator has acknowledged this relevance and in §18(3a) of the German Competition Act made access to personal data a criterion for market power, especially in the case of online platforms and networks. Monitoring the data processing activities of dominant companies is therefore an essential task of the competition authority which cannot be fulfilled by a data protection authority.”127 In a subsequent and arguably the most critical part it notes that “ [i]n its assessment of whether the company’s terms and conditions on data processing are unfair, the competition authority does, however, take account of the legal principles of data protection laws. For this purpose, the Bundeskartellamt works closely with data protection authorities.”128 Once again in the subsequent part it reiterates “[a]ccording to the caselaw of the German Federal Court of Justice, civil law principles can also be applied to determine whether business terms are exploitative.”129 The fuller manifestation of this approach features towards the end in Section 9 when the Bundeskartellamt quotes two decisions by the Federal Court of Justice (“VBL Gegenwert II” and “Pechstein”) to show how exploitative conducts in competition cases are informed by the German Civil Code and constitutional rights. Thus, in order to determine if data has been taken ‘unfairly’ by a dominant undertaking, the Bundeskartellamt will see if legal principles of data protection laws have been infringed. Seemingly, users do not give their ‘effective’ consent to Facebook to harvest their data from the ‘third-party websites’ which the Facebook API.130 The embedded Facebook API through the ‘like’ button or a ‘Facebook login’ or analytical services such as ’Facebook Analytics’ will transmit user data to Facebook even if a user has not used these services.131 This practice of Facebook of gathering data from unsuspecting users through ‘third-party websites’ and merging it with the data that users generate on its own website could be, therefore, in violation of data protection law. To this end, this practice may be termed ‘unfair’ under competition law as well. However, here the problems start with the assessment. If this were a simple exploitative abuse case, a determination of ‘unfair’ under data protection law would have been sufficient. However, it seems, the Bundeskartellamt eventually chose to base its theory of harm on exclusionary conduct. In Section 7 while discussing harm for users and for consumers, it notes “[w]ith the merging of data (taken from partner websites through APIs) the identity-based network effects” and, consequently, the “locking-in” of users increase,

to the detriment of other providers of social network”.132 The Press Release as well quotes the President of Bundeskartellamt Andreas Mundt as stating "Data protection, consumer protection and the protection of competition interlink where data, as in Facebook’s case, are a crucial factor for the economic dominance of a company.”133 Thus, the Bundeskartellamt also seems to be concerned about the market position of the firm and the resulting harm to competitors. This takes the theory of harm in the direction of exclusionary conduct. By choosing a theory of harm that is based on the exclusionary effects of Facebook’s action, the Bundeskartellamt has opted for a more difficult task, as showing the possibility of foreclosure will be challenging. In its defence, Facebook can argue that its business model is based on ‘data fusion’ that provides the most relevant user inference to advertisers. In the absence of more optimal inference, advertisers may choose other platforms. It is also a possibility that other social media platforms as well gather data from their partner websites through their APIs and thus benefit from scope. Note that scope in data is an important factor of competition as shown in Part I. Unlike the Google (Android) case discussed above, it is not clear if Facebook insists on exclusive embedment of its APIs on its partner platforms. This may also be a possibility that to attract advertisers in the non-search targeted advertisement market, Facebook competes with search engines such as Google that has the advantage of ‘data fusion’ through its various apps.134 To restrict Facebook from benefiting from ‘data fusion’ in this market will give a free-hand competitive advantage to Google.

3. Early lessons on the competition scrutiny in data-driven markets The present approach of the Commission and Bundeskartellamt shows that—in the specific context of these cases— these antitrust bodies were not impressed with the argument that data is non-rivalrous. In the respective cases, both the authorities are of the view that collection of user data by a dominant undertaking can result in anticompetitive foreclosure of the relevant market. Thus, the non-rivalrous nature of data that had redeemed several mergers in the past is not the sacrosanct principle in the data-driven markets.135 On a broader level, the approach adopted by these agencies makes it clear that there is no need to devise a brand new antitrust approach to the data-driven markets. Analytically coherent and logically convincing theory of harm in abuse of dominance cases is the requirement. A sound theory of harm must take account of both ability and incentives of a dominant undertaking to stifle competition.136 With the 132

Ibid, section 7. Bundeskartellamt Press Release 19 December 2017 (n 118). 134 In the Google (Shopping) Decision, the Commission had the occasion to look at the substitutability between general search services and social networking sites. The Commission found that there is a limited substitutability between general search services and social networking sites. Google (shopping) (n 6) paragraph 178. 135 See the accompanying text to footnotes 43, 44 and 45. 136 See in general, Zenger, Hans and Walker, Mike, Theories of Harm in European Competition Law: A Progress Report 133

126 127 128 129 130 131

Bundeskartellamt, Background Information (n 8) Page 1–2. Ibid, Section 3 page 2. Ibid. Ibid, section 6. Bundeskartellamt Press Release 19 December 2017 (n 118). Bundeskartellamt, Background Information (n 8) Page 2.

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end objective of ensuring consumer welfare, the competition agencies can follow any theory to assess foreclosure in the relevant market so long it is supported by cogent evidence. While Google’s practices certainly led to foreclosure in the search engine market, the same cannot be said about Bundeskartellamt’s case against Facebook. In order for a theory to come out from the realm of behavioural economics and be accepted in the applied field of competition law, it must be supported by evidence including survey reports. In the EU Microsoft case, in view of the special circumstances of the case where it was possible to download rival media players through the Internet, the Commission made a departure from the ‘classical tying cases’ and did not just merely assume the foreclosure effect.137 The Commission went on to show foreclosure based on data on the development of the market. The General Court later endorsed this approach. Thus, while formulating a complex theory of foreclosure, the Commission took the more onerous path to counter any allegations of its theory being merely speculative. In the recent Google (Shopping) Decision as well, Commission relied heavily on survey reports based on questionnaires from both users and competitors. In the Yandex v Google case in Russia, the Russian antitrust authority also cited a report by the Moscow State University of Radio Engineering, Electronics and Automation (MIREA/MGUPI) to substantiate its theory of harm.138 Further, the Commission also relies upon information from the internal documents of the undertaking in question. Just like these cases, if the theory of harm is supported by strong evidence, antitrust bodies can counter the allegation that their action is merely presumptive. While this is the most optimal approach in the data-driven markets, it also increases the workload of antitrust authorities. The above-discussed cases also suggest that a dominant player may try other novel strategies as well to foreclose the relevant market, especially when the rivals require a critical mass to be viable. One way could be by gaming consumer expectations in network industries. In network industries, consumer expectations, as to which product will ultimately become the standard, play a critical role in the success or failure of market players.139 This is because in network industries, a consumer also cares about the future success of the product.140 There can be two ways in which an incum-

(February 22, 2012). Ten Years of Effects-Based Approach in EU Competition Law, Jacques Bourgeois and Denis Waelbroeck, eds., pp. 185-209, Bruylant, 2012. Available at SSRN:< https://ssrn.com/ abstract=2009296>(accessed 10 November 2018). 137 Commission Decision Microsoft (n 93) Microsoft, paragraph 841; The General Court subsequently approved this approach, Microsoft v Commission (n 11) paragraph 977. 138 See Yandex v Google, Federal Antimonopoly Service of the Russian Federation, Resolution on Case No. 1-14-21/00-1115, Unofficial English Translation (accessed 10 November 2018). 139 Carl Shapiro and Hal R. Varian, Information Rules: A Strategic Guide to the Network Economy (Harvard Business Press, 1998), page 14. 140 See, Yikuan Lee and Gina Colarelli O’Connor, “New Product Launch Strategy for Network Effects Products” (2003) 31:241 Journal of the Academy of Marketing Science.

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bent may manipulate consumer expectations and stunt the new entry. The incumbent may engage in aggressive preannouncements regarding the superiority of its future developments of its product, thus restricting the consumers from switching to a new product.141 Eliashberg and Robertson define pre-announcement as “deliberate communication before a form actually undertakes a particular marketing action such as a price change, a new advertising campaign, or a product line change”.142 Pre-announcing and even vaporware (announced products that do not yet exist) act as psychological positioning that halts the entry if rivals believe that someone else will lock in the market.143 Indeed, in the 1995 US Microsoft case while rejecting the consent decree, Judge Stanley Sporkin found ‘vaporware’ to be in violation of antitrust law.144 Unfortunately, this could not be decided as the case was remanded to another district judge.145 In the US, ‘vaporware’ has been the subject matter of some antitrust cases.146 The legality of ‘vaporware’ has not been tested within the EU competition law framework so far. Indeed, to the extent pre-announcements or ‘vaporware’ are detrimental for consumers they can amount to Unfair Commercial Practice in the EU.147 In the data-driven markets a dominant player by gaming the consumer expectations may restrict them from using an entrant’s platform, thus depriving the rivals of user data. Alternatively, an incumbent may engage in a disparaging campaign against a new entrant, which can prevent the new entrant from gaining critical mass. It is not difficult to imagine such a scenario. For instance, an incumbent may allege that the new platform is lax about privacy rules or compromises the privacy illegally. These theoretical possibilities should, however, pose no challenges for the competition law enforcement, as although the strategy to refrain rivals from gaining scale may be novel,

141

Shapiro and Varian (n 139) page 14 and 15. Shapiro and Varian give the example of Microsoft and IBM that used preannouncement to stifle competition. 142 Jehoshua Eliashberg and Thomas S. Robertson, “New Product Preannouncing Behavior: A Market Signaling Study” (1988) Journal of Marketing Research 25(3), page 282. 143 W. Brian Arthur, “Increasing Returns and the New World of Business.” Harvard Business Review (1996) 74(4), pages 100-109; see also, Barry L. Bayus, Sanjay Jain and Ambar G. Rao, “Truth or Consequences: An Analysis of Vaporware and New Product Announcements” (2001) Journal of Marketing Research 38(1), pages 3-13. 144 United States v. Microsoft Corp. 159 F.R.D. 318 (D.D.C. 1995), rev’dper curiam, 56 F.3d 1448 (D.C. Cir.1995). pages 334-337; see also, Robert Prentice, “Vaporware: Imaginary High-Tech Products and Real Antitrust Liability in a Post-Chicago World”, 57 OHIO ST. L.J. 1163, 1234 (1996). 145 Robert Prentice (n 144). 146 See in general, Maurice E. Stucke, “How Do (and Should) Competition Authorities Treat a Dominant Firm’s Deception”, 63 SMU L. Rev. 1069 (2010), pages 1097-1101. 147 Directive 2005/29/Ec Of The European Parliament And Of The Council of 11 May 2005 concerning unfair business-to-consumer commercial practices in the internal market and amending Council Directive 84/450/EEC, Directives 97/7/EC, 98/27/EC and 2002/65/EC of the European Parliament and of the Council and Regulation (EC) No 2006/2004 of the European Parliament and of the Council (‘Unfair Commercial Practices Directive’)

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the foreclosure effect can be assessed in its technological and economic context as argued in this paper.

4.

Conclusion

Big data is a fundamental input and very often the decisive determinant of competition for virtual networks that are based on Machine Learning algorithm. Rapid innovation—the hallmark feature of competition in the hi-technology markets– in virtual networks is based on user data. If a virtual platform stops innovating, users will not find it lucrative enough to port their data to this platform even if such a right is provided by law.148 With the advancement in Machine Learning, the value of user data is bound to increase. It is, therefore, logical that market players will compete for user data. This perennial quest for data may, however, be the motivation for some market players to engage in anticompetitive behaviour to acquire valuable user data. The objective of this paper, therefore, was to scrutinise the practices of dominant undertakings in the data-driven markets that could result in anti-competitive foreclosure. This Paper has argued that any assessment of foreclosure in the markets where big data is the fundamental input should be undertaken within the technological and economic context of that market. Thus, the paper first underlined the technological and economic characteristics of two important datadriven markets, i.e. search engine and social network. It was important to delineate the underlying characteristic of big data also for the reason that the assessment of foreclosure depends on these characteristics which are still being debated. Thereafter, the paper analysed the Google (Android) decision by the Commission and the action against Facebook by

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E.g., right to data portability enshrined in Article 20 of the GDPR.

Bundeskartellamt. The Paper concurs with the findings of the European Commission that Google’s practices foreclosed the search engine market. The paper showed that the licensing practices of Google were aimed at restraining rivals from gaining scale in data. This way Google achieved exclusivity in the search engine market by rendering its rivals unviable. Not only is this exclusivity bad in the static sense, as rival search engines do not receive traffic, it also deprives rivals of user data that could turn them into an efficient alternative to Google’s search engine in the future. The common objective of tying Google Search with Play Store and exclusive pre-installation of its search app by OEMs and mobile network operators was to reinforce the ‘status quo bias’ of users that prevented them from looking for alternatives. Once again, refusing to license Play Store and Google Search apps to OEMs who also sold ‘Android Fork’ was undertaken to stunt the growth of any alternative channel where rivals could get a foothold in the market. The second case that this paper analysed was Bundeskartellamt’s action against Facebook. It would have been fairly uncontroversial and simple if Bundeskartellamt had seen Facebook’s practice of harvesting data from third-party websites using API as exploitative abuse. Evidently, however, Bundeskartellamt is concerned about foreclosure effect in this market that seems unlikely against the technological and economic backdrop. Based on the analysis of these cases, the paper also delineates some common lessons that can guide the assessment in similar cases.

Supplementary material Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.clsr.2018.12.001.