Christopher Yoo on ‘Unpacking Data Portability’ (2020) Competition Policy International

Data portability has become a hot topic in competition law. Legislators and enforcement officials around the world have shown increasing interest in data portability as a competition law remedy. Although some commentators have suggested that data portability represents low hanging fruit compared with more complex remedies such as interoperability, the debate about how to implement any such mandate remains underdeveloped. This paper, available here, argues that data portability is not a panacea, and that enforcement officials will have to engage in the type of nuanced, fact-specific determinations that characterise classic antitrust analysis. Section 2 points out that not all data are created equal. To date, discussions have largely treated data as a monolithic phenomenon without drawing any distinctions among particular types of data and their different uses. Although advocacy rhetoric tends to talk about “big” data, the trade press repeatedly emphasises that size is not the only thing that matters. The most famous formulation claims that data consists of three…

Vikas Kathuria and Jure Globocnik ‘Exclusionary conduct in data-driven markets: limitations of data sharing remedies’ (2020) Journal of Antitrust Enforcement 8 511

By depriving its rivals of gaining scale in data, a dominant player can successfully exploit demand-side scale economies, i.e. network effects, to its benefit in a two-sided market. In effect, dominant undertakings may be able to exclude their rivals from accessing user data and thus deprive them of scale in markets that are characterised by network effects. In the face of exclusionary conduct by a dominant undertaking in data-driven markets, a critical question relates to the nature of the remedy that can offset the harm to consumer welfare and restore competition. Intuitively, mandating a delinquent dominant undertaking to share wrongly withheld data appears to be an optimal remedy. This article, , available here, analyses the viability of mandatory data sharing as a remedy to restore competition in the affected market – and concludes that mandatory data sharing is not the optimal solution to remedy loss to consumer welfare. Section 2 considers the objectives of remedies in EU competition law. To…

Gönenç Gürkaynak, Ali Kağan Uҫar and Zeynep Buharali ‘Data-Related Abuses in Competition Law’ in Standing Up for Convergence and Relevance in Antitrust – Frédéric Jenny Liber Amicorum – Volume I (eds. Ahmad and Charbit, 2019) Concurrences

Data has become an indispensable business tool, and, as a result, the collection and use of data by dominant undertakings can give rise to competition law concerns. This article, available here, examines data-related abuses in competition law, and seeks to provide an overview of specific types of abuses arising from the use of data. Section II looks at the definition of data. Data is often defined as “information that can be stored and used by a computer program.” Accordingly, “big data” refers to “large amounts of different types of data produced at high speed from multiple sources, requiring new and more powerful processors and algorithms to process and to analyse’. As “data” increases in volume, diversifies in nature and content, and keeps on flowing rapidly through the veins of the global economy, its collection and processing creates increasingly valuable commercial opportunities. Undertakings more and more see data as an indispensable tool for improving business decisions and strategies, and for improving…

Björn Lundqvist ‘Regulating competition in the digital economy’ in Competition Law for the Digital Economy (ed. Björn Lundqvist and Michal S. Gal) (2019, Elgar)

There is an intense academic discussion regarding whether consumers and business users are exposed to conduct that may amount to competition law abuses when using Internet services. The discussion is connected to the Internet phenomenon of ‘platforms’ or intermediaries. The multitude of direct customer–supplier transactions making up everyday business conduct are, to an increasing degree, replaced on the Internet by an intermediary, the platform, matching the customer with the supplier. Platforms are able to perform role because they provide efficient and easy matching. Further, internet platforms may, due to certain special and somewhat unique characteristics – like network effects, tipping and path dependency – become central ‘hubs’ between purchasers and suppliers. This chapter, available here, focuses on the application of competition law vis-à-vis the platforms collecting personal and non-personal data. It considers questions such as: may competition law be used to gain access to intermediaries’ data, and the infrastructure around that data? May competition law be used to limit the…

Klaus Wiedemann ‘A Matter of Choice: The German Federal Supreme Court’s Interim Decision in the Abuse-of-Dominance Proceedings Bundeskartellamt v. Facebook (Case KVR 69/19)’ (2020) IIC – International Review of Intellectual Property and Competition Law volume 51 1168

In June 2020, the German Federal Supreme Court (Bundesgerichtshof) upheld the 2019 interim decision of the Federal Cartel Office (Bundeskartellamt) ordering Facebook to stop collecting data about its users without their consent when they use apps and visit websites outside Facebook’s social network.Importantly, the Federal Supreme Court confirmed that Facebook’s data collection was an abuse of its dominance in the (German) market for personal social networks, overruling an earlier decision of the Düsseldorf Court of Appeal (Oberlandesgericht Düsseldorf). This piece, available here, explores the relevance of the case – and the courts’ different decisions – from a number of perspectives. Section II describes the Facebook case, up to the Supreme Federal Court’s judgment. In February 2019, the Bundeskartellamt found that Facebook was dominant on the market for social networks, and had abused this position by imposing terms of service allowing it: (i) to collect its users’ personal data (and data related to their terminal devices) from outside the actual social…

Rachel Scheele ‘Facebook: From Data Privacy to a Concept of Abuse by Restriction of Choice’ (2021) Journal of European Competition Law & Practice 12(1) 34

On 23 June 2020, the German Federal Supreme Court found that Facebook violated German competition law by abusing its dominance in the market for social networks. The ruling, upholding the decision by the competition authority, is a major victory for advocates of addressing data-related competition concerns under Article 102 TFEU and its national equivalents. However, instead of focusing on the intersection between competition and data protection law in its reasoning, as the competition authority had, the Federal Supreme Court relied on the concept of restriction of consumer choice. This article, available here, casts light on the Facebook case and its practical relevance. Section 2 reviews the Facebook infringement decision. In 2019, the German Bundeskartellamt found that Facebook had abused its dominant position on the German market for personal social networks by imposing unfair terms and conditions on its users. The Bundeskartellamt’s case linked antitrust violations with data protection law, and relied on alleged infringements of the EU’s General Data Protection…

Emilio Calvano, Giacomo Calzolari, Vincenzo Denicolò, Joseph E. Harrington Jr. and Sergio Pastorello ‘Protecting consumers from collusive prices due to AI’ (2020) Science 370 Issue 6520

This paper is available here. The efficacy of a market system is rooted in competition. Nothing more fundamentally undermines this process than collusion, when firms agree not to compete with one another and consumers are harmed by higher prices. The increasing delegation of price setting to algorithms has the potential to open a back door through which firms could collude lawfully. Such algorithmic collusion can occur when artificial intelligence (AI) algorithms learn to adopt collusive pricing rules without human intervention, oversight, or even knowledge. A first section looks at human collusion. Collusion among humans typically involves three stages. First, firm staff with price-setting authority communicate with the intent of agreeing on a collusive rule of conduct. Second, successful communication results in the mutual adoption of a collusive rule of conduct. A crucial component of this rule is retaliatory pricing: each firm raises its price and maintains that higher price under the threat of a “punishment,” such as a temporary price…

Zach Y. Brown and Alexander MacKay ‘Competition in Pricing Algorithms’ Harvard Business School Working Paper, No. 20-067

Increasingly, retailers have access to better pricing technology, especially in online markets. Using hourly data from five major online retailers, the authors show that retailers set prices at regular intervals that differ across firms. Faster firms appear to use automated pricing rules that are functions of rivals’ prices. These observations are inconsistent with the standard assumptions about pricing technology used in the empirical literature. Motivated by this, the present paper – available here – considers a model of competition in which firms can differ in pricing frequency, and can choose pricing algorithms rather than prices. Relative to the standard simultaneous price-setting model, pricing technology with these features can increase prices. A simple counterfactual simulation implies that pricing algorithms can lead to meaningful increases in mark-ups, especially for firms with the fastest pricing technology. Section 2 highlights key features of pricing algorithms used by online retailers. The authors identify three characteristics of the use of high-frequency price data for over-the-counter allergy…

Cento Veljanovski on ‘Pricing Algorithms as Collusive Devices’ (2020)

This paper, available here, undertakes a critical review of the prospect that self-learning pricing algorithms will lead to widespread collusion independently of the intervention and participation of humans. It reviews the arguments and evidence that self-learning pricing algorithms pose a new and significant threat to competition and antitrust enforcement. It argues that there is no concrete evidence, no example yet, and no antitrust case that self-learning pricing algorithms have colluded, let alone increased the prospect of collusion across the economy. Part I explains why algorithmic collusion may be a problem. Academic lawyers, who argued that algorithmic pricing poses a real threat to competition which cannot be dealt with by existing antitrust provisions, initiated a debate over the threat posed by algorithmic pricing. The prospect that pricing algorithms can facilitate collusion by firms is not the principal worry of this academic literature. Rather, the concern is with a class of machine-based algorithms that can collude without human involvement. Through self-learning and…

Emmanuel Combe and Constance Monnier ‘Why Managers Engage in Price Fixing? An Analytical Framework’ (2020) World Competition 43(1) 35

With the exception of the United States, individual cartelists are rarely subject to criminal proceedings. However, it cannot be ruled out that a managers may obtain private gains from their cartel participation, and therefore that they might have a personal incentive to set them up. This article, available here, analyses the incentives for a manager to engage in a cartel by mobilising the theoretical framework of the ‘economics of crime’. It also examines the various solutions – both at company and public authority level – to limit individual incentives to engage in this type of practice. Section II looks at the costs and benefits for a manager of participating in a cartel. In most detected cartels, individuals who participated in the practice held relatively high positions within their company: they were often commercial directors, and sometimes even general managers or CEOs. This means that, typically, a cartelists’ remuneration includes a large variable part linked to the achievement of short-term objectives….