Competition Law Concerns of Big Data
[Venkata Sai Aditya Santosh Badana] The author is a 4th year student of ICFAI Law School, Dehradun. Introduction: Several years ago, “The Economist Magazine” noted that data, particularly consumer data, had become “the new raw material of business: an economic input almost on a par with capital and labors.” The choice of buzzwords in the instant interplay between antitrust and the digital economy, however, is not merely “data”, but “big data”. Traits of “big data” that are frequently cited are large amounts of different types of data, produced at high speed from multiple sources, whose handling and analysis require new and more powerful processors and algorithms.[i] To simplify, “big data” is often defined by the three characteristics or “3 V”s – Variety, Velocity, Volume.[ii] This brings us to the question whether big data or data allied activities in general can be examined by the sector specific competition watchdog when inter alia Data protection Authorities are in place? To answer this intriguing question at its simplest, data is simply a product and a competition law analysis can be applied to it as is applied to any other product. Services founded on data can be analyzed in the same way under competition law as any other service.[iii] This article casts light on the debate of Antitrust concerns of Big data by examining some of the key issues and parameters that may need to be considered when assessing the nexus between market power, competition law and data. For this purpose it is necessary to first delineate what is by meant “big data”.The theories of anti-competitive harm usually associated with data collection and exploitation in digital markets are presented in section II, while theories to regulate Data driven combinations are dealt with in Section III. Anti-Competitive Arrangements and Big Data: Data collection may facilitate collusion when this data is employed to fix prices in tandem with the use of algorithmic technology.Factual examples of aforementioned scenario cases include United States v. Airline Tariff Publ’g Co.[iv] as well as the David Topkins case[v]. The ruling in that latter case stated that “in order to implement this agreement, David Topkins and his co-conspirators concurred to employ specific pricing algorithms for the agreed-upon posters with the object of corresponding changes to their assigned prices”. In furtherance of the conspiracy, David Topkins created a computer code that instructed Company A’s algorithm to set prices of the posters analogous to price fixing agreement, and the same algorithm was entrusted to ensure compliance. In pursuance with the agreements reached, The Northern District of California in San Francisco held that, “David Topkins and his co-conspirators sold, distributed, and accepted payment for the agreed upon posters at collusive, non-competitive prices on Amazon marketplace[vi].” Topkins also has agreed to pay a $20,000 criminal fine and cooperate with the department’s ongoing investigation. It is exigent here to outline that there that CCI as of date is yet to bring a successful prosecution or levelling of charges of employing big data for collusion. But there is glimmer of hope by drawing inferences from the recent development of CCI[vii] imposing a fine of Rs.258 crore on three leading airlines – Jet Airways, Indigo and Spice Jet after finding them guilty of tacit collusion and cartel like behavior in overcharging cargo freight in the garb of fuel surcharge. It essentially found that the three airlines had fixed a fuel surcharges at a uniform rate through algorithms on the very same day and they all increased the surcharges at the same time without any analogous rise in the fuel prices. Such conduct was found to have resulted in indirectly determining the rates of air cargo transport in contravention of section 3 of the Competition Act, 2002. Data-Driven Merger Control and Big Data: Mergers in technology sector beg the ever important question of whether an entity obtaining access to exclusive troves of big data can significantly foreclose or leverage competition? Access to data sets in a competitive markets can be of great value, for example when a data set facilitates more targeted advertising (behavioral targeting or micro targeting). While testifying before United States Congress, Mark Zuckerberg attested to the fact that, big data can potentially predict the outcome of elections.[viii] Few cases which have been subjected to merger scrutiny by the European Commission (the “Commission”) illustrate: Google/Double Click[ix]: takeover price 3.1 billion dollar; Facebook/WhatsApp: takeover price 19 billion dollar and Microsoft/LinkedIn[x]: takeover price 26 billion dollar). All the above amalgamations have been unconditionally cleared with a few contingent directions, placing reliance upon the traditional litmus test of “significant impediment to effective competition” disregarding data analytics and third party data sharing. The European Commission’s Horizontal Mergers Guidelines, 2004 and Non-Horizontal Mergers Guidelines 2008 respectively do not outline the element of big data in merger control. Competition Commission of India (CCI) did not raise concern about Facebook/WhatsApp, while concerns about competitiveness associated with net neutrality are still fresh from the Telecom Regulatory Authority’s decision against discriminatory access to data services.[xi] While the CCI is yet to examine Big Data and its effects on competition, it is pertinent to visit opinion/Exposition 83/2015 issued by the CCI, wherein dominant position of WhatsApp and Google was averred by the Informant under Section 19.[xii] The Commission noted that the informant had failed to satisfy the ingredients of section 4 of the Competition Act 2002, under which, imposition of unfair or discriminatory terms has to be shown in sale of goods or service or price in purchase or sale of goods or services.The Google/Double Click merger decision includes nevertheless a significant disclaimer at paragraph 360: “it is not excluded that (…) the merged entity would be able to combine DoubleClick’s and Google’s data collections, e.g., users’ IP addresses, cookies IDs, connection times to correctly match records from both databases. Such combination could result in individual users’ search histories being linked to the same users’ past surfing behaviour on the internet (…) the merged entity may know that the same user has searched for terms A, B and C and visited pages X, Y and
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