BIG DATA & ANTITRUST

Propensity of Data Accumulation to Raise ‘Barriers to Entry’

A dynamic digital environment requires antitrust authorities to meticulously and creatively assess a broad range of determinants in deciding on the competitive effect of some merger. Data accumulation in that regard can be taken as a red flag, but not as solid proof of detrimental anti-competitive effect.

By Miljana Todorovic

April 11, 2021

Entry barriers can be defined as factors that could make a new entry on the market less likely or less rapid by affecting the expected sunk costs of entry and/or the expected profits for new entrants once they are in the market, or by establishing physical, geographic or legal obstacles to entry. Those difficulties may prevent the competitor from expanding his production or discourage potential competitors in providing effective competition to the dominant undertaking. That is why barriers to entry are important to be examined when assessing if an undertaking abuses its dominant position, which is prohibited by Article 102 TFEU. 

In the case of digital markets, the collection and use of large volumes of data have attracted substantial attention, also in regard to barriers to entry. There are several strategies for companies to get better access to data. For example, acquiring another company owning large datasets or merging with it, mergers of two companies that already hold strong market positions in separate upstream or downstream markets, or acquisitions by dominant platforms of small start-ups with a quickly growing user base.

Is Big Data a Big Problem for competition?

Apart from the undoubtedly procompetitive effects of these mergers, its frequency over the last decade gave rise to concerns about whether the mergers with specific data resources can significantly impede competition. They could result in a concentration in control over valuable and non-replicable data resources, and consequently in better data access for the merging parties than for their competitors. The question that needs to be examined is whether the collection and exploitation of data may raise barriers to entry and be a source of market power. 

We talk about entry barriers resulting from data collection when new entrants are unable either to collect the data or to buy access to the same kind of data, in terms of volume and/or variety, as established companies. The French-German study on competition law and data identifies five types of conduct that can lead to exclusion of competition: refusal to provide access to data, discriminatory access, exclusive contracts, tied sales and cross-usage of datasets, and data as a vehicle for discriminatory pricing.

However, even though there is a big hype around data, there are challenging opinions that the anticompetitive danger of data accumulation cannot be taken as a general statement. Examples have shown that data-driven markets are characterized by low entry barriers and new entrants can fastly establish themselves despite existing network effects (some examples are Slack, Tinder, and Snapchat). Data accumulation, by itself, does not create a barrier to entry and does not automatically endow a firm with either the incentive or the ability to foreclose rivals, expand its monopoly, or harm competition in other ways.

Important pieces in the assessment puzzle

The importance of data varies from market to market and its propensity to raise or lower barriers to entry depends on the nature and use of the data and the availability of alternative data sources. Big Data is well-known to be defined by three V’s, Volume, Velocity, and Variety, however, there is much more to know about the characteristics of particular data to make a right assessment of its importance for a specific market and its influence on competition.

One should make a distinction between first-party and third-party data, and further between those collected on its own assets, and those on the company’s customers. Next to it, other features of data have to be explored, such as their uniqueness, replaceability, portability, and interoperability. It is also pointed out that a combination of different data troves could raise competition concerns if the combination of data makes it impossible for competitors to replicate the information extracted from it. This shows the necessity to evaluate data on a case-by-case basis.

It is, however, important to notice that a thorough assessment of data is not sufficient to conclude on possible entry barriers. Another major part of this equation is market concentration because only when the level of market concentration is relatively high, data collection can provoke the rise of entry barriers and reduced competition. 

With everything noted above, the story on the assessment of barriers to entry is still not complete. It is emphasized that antitrust authorities will need to look more closely not only at the substitutability of different types of data for different purposes but also at the role of related but distinct factors such as the availability of algorithms or other software to process such data.

Final notes

Data accumulation does not necessarily pose a significant antitrust risk. Competitive concerns of raising barriers to entry are limited to a specific set of circumstances. They include certain features of particular data, the level of market concentration, and other factors related to data processing. A dynamic digital environment requires antitrust authorities to meticulously and creatively assess a broad range of determinants in deciding on the competitive effect of some merger. Data accumulation in that regard can be taken as a red flag, but not as solid proof of detrimental anti-competitive effect.

Miljana Todorovic is a doctor in law from the University of Belgrade. She has publications in the field of Constitutional Law, Criminal law and Property Law, and participated in numerous scientific conferences. At the Université libre de Bruxelles she completed an LL.M. in International Business Law. Miljana is a scientific collaborator at the University of Ghent and a mother of two. In the Institute for Internet & the Just Society she is co-leader of the Big Data & Antitrust Research Cycle.