Christopher Townley, King's College London – The Dickson Poon School of Law; A Dickson Poon Transnational Law Institute, Eric Morrison, King's College London, and Karen Yeung, The Dickson Poon School of Law provide a theory on Big Data and Personalised Price Discrimination in EU Competition Law.
ABSTRACT: The networked digital revolution is ushering in a new data-driven age, powered by the engine of Big Data. We generate a massive volume of digital data in our everyday lives via our on-line interactions, which can now be tracked on a continuous and highly granular basis. Being able to track this data has radically disrupted the retail sector through, amongst other things, digital personalisation. However, this is no longer limited to shopping recommendations and advertising delivered to our smartphones, laptops and other mobile devices, but may extend to the prices at which goods and services are offered to customers in on-line environments, making it possible for two individuals to be offered exactly the same product, at precisely the same time, but at different prices, based on an algorithmic assessment of each shopper’s predicted willingness to pay. This is done by mining consumers’ digital footprints, using machine learning algorithms to enable digital retailers to predict the price that individual consumers (‘final end users’) are willing to pay for particular items, and thus offer them different prices. This phenomenon, which we dub ‘algorithmic consumer price discrimination’ (ACPD) forms the focus of this paper.
The practice of price discrimination, which we define as “… charging different customers or different classes of customers different prices for goods or services whose costs are the same or, conversely, charging a single price to customers for whom supply costs differ…” is hardly a new phenomenon. Familiar forms include loyalty discounts, volume or multi-buy discounts, and the offering of status based discounts for students, old-age pensioners and the unemployed. However, the technological capacities of Big Data substantially enhance the ability of digital retailers to engage in much more precise, targeted and dynamic forms of price discrimination that were not previously possible.
There are many areas of law that might mount a response to rising public anxieties associated with these practices. Our paper examines ACPD from the perspective of competition law through which we seek to evaluate ACPD by reference to two contrasting normative values: economic efficiency, on the one hand, and fairness or equity on the other. Competition law provides a unique lens for interrogating the social implications of ACPD due to its distinctive character as a form of ‘economic law’ that is intended to protect and strengthen the process of rivalry in the marketplace. Although ‘traditional’ forms of price discrimination have long been the subject of economic analysis to evaluate whether they are economically efficient, algorithmic price discrimination has hitherto attracted relatively little critical analysis. As we demonstrate in Section 2, the incentives for firms to engage in ACPD often exist. We find that consumers are in the aggregate often better off, economically, when sellers can price discriminate in this way, thereby enhancing consumer surplus. However, this is not always the case. Furthermore, whether EU competition law is solely and exclusively concerned with economic efficiency, or whether it provides scope for non-efficiency based considerations in the application of its provisions, is a matter of debate. Accordingly, in Section 3 we evaluate ACPD by reference to its fairness or justice (which we also call equity) understood in three distinct (and sometimes overlapping) ways: (a) the perceived fairness of pricing practices; (b) unfair dealing between online retailers and consumers (corrective justice); and (c) fairness as a requirement of distributive (or collective) justice. For each of these understandings of fairness, we identify points of convergence and conflict with economic evaluations of the effects of ACPD on aggregate consumer welfare. No Article 102 cases have directly considered the legality of ACPD. Section 4 therefore interrogates existing Article 102 case law to ascertain whether ACPD would likely breach this provision. Because the current legal position is unclear, Section 5 draws together the efficiency and fairness evaluations by considering whether ACPD should be regarded as unlawful under EU competition law. We argue that where ACPD increases both consumer surplus and fairness, it should not breach Article 102. Conversely, where ACPD undermines both consumer welfare and fairness, then such practices should be unlawful under Article 102. However, because economic and fairness evaluations of ACPD may conflict in specific cases, Section 5 also considers whether, in the light of the underlying justifications for EU competition law and the EU’s foundational principles, ACPD should be considered a violation of Article 102 where it undermines justice or equity, even though it may enhance consumer surplus, and vice versa. We deal with the clashes between these goals in two ways: first, we offer a partial reconciliation between these goals, by supplementing conventional economic analysis with insights from behavioural economics, thus enabling some fairness considerations that affect consumer welfare to be taken into account. Secondly, we suggest that fairness should have a secondary role when Article 102 is applied to ACPD, in the form of a ‘defence’ to an allegation of abuse of market power. On our suggested account, ACPD which reduces consumer surplus may nonetheless avoid falling foul of Article 102 if it can be justified on grounds of fairness. Section 6 concludes, suggesting that EU competition law may have a valuable but limited role to play in redressing some of the adverse impacts of ACPD, primarily by focusing on the consumer welfare effects of ACPD, and in which considerations of fairness and justice play a relevant, but nonetheless subsidiary, role. Competition law cannot, and should not, seek to solve all the social problems associated with market behaviour, including data-driven forms of personalised pricing.
November 22, 2017 | Permalink
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