Antitrust violations in their various forms result in considerable costs and disadvantages for competitors, customers and the public at large. Yet uncovering such violations is often difficult, as they frequently happen in secret. However, in the wake of digitalization, large amounts of data are often publicly available that may offer clues to (potential) antitrust violations. The DataComp project addresses open questions on the data-driven detection of competition law infringements both from the perspective of business analytics and from the perspective of competition law, while striving to set out the legal framework.
This project is joint work with Viktoria H.S.E. Robertson and her team at the Vienna University of Economics and Business. So far, we published a literature review outlining the state of research in application of computational antitrust approaches to real world data (Amthauer et al., 2023), our own study on detecting anomalies indicative of resale price maintainance, based on over 1 million offer prices webscraped over the course of 3 months (Amthauer et al., 2023), and finally an editorial outlining the future role of data in antitrust enforcement (Robertson & Fleiß, 2024).
Initial funding was provided by the Field of Excellence Smart Regulation at the University of Graz for the project DataComp.
References
2024
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Computational Antitrust and the Future of Competition Law Enforcement
GRUR International, 2024
Digitalisation has had a profound impact on the economy and, as such, also on EU competition law. While digitalisation has significantly challenged the cornerstones of competition analysis in substantive terms, it enabled the novel field of computational antitrust to provide competition authorities with a set of new data-driven tools that can support the enforcement of competition rules in an unprecedented way. While still in their infancy, these tools may well represent the future of competition law enforcement. In the following, we briefly revisit the far-reaching changes that digitalisation has led to in substantive competition law. We then turn to how digitalisation may now support the enforcement of competition rules and what prerequisites must be fulfilled for a successful application of computational antitrust tools.
2023
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Ready or not? A systematic review of case studies using data-driven approaches to detect real-world antitrust violations
Computer Law & Security Review, 2023
Cartels and other anti-competitive behaviour by companies have a tremendously negative impact on the economy and, ultimately, on consumers. To detect such anti-competitive behaviour, competition authorities need reliable tools. Recently, new data-driven approaches have started to emerge in the area of computational antitrust that can complement already established tools, such as leniency programs. Our systematic review of case studies shows how data-driven approaches can be used to detect real-world antitrust violations. Relying on statistical analysis or machine learning, ever more sophisticated methods have been developed and applied to real-world scenarios to identify whether an antitrust infringement has taken place. Our review suggests that the approaches already applied in case studies have become more complex and more sophisticated over time, and may also be transferrable to further types of cases. While computational tools may not yet be ready to take over antitrust enforcement, they are ready to be employed more fully.
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Detecting resale price maintenance for competition law purposes: Proof-of-concept study using web scraped data
Computer Law & Security Review, 2023
Computational antitrust tools can support competition authorities in the detection of antitrust infringements. However, these tools require the availability of suitable data sets in order to produce reliable results. The present proof-of-concept study focuses on the understudied area of resale price maintenance, that is, the fixing of retail prices between manufacturers and retailers. By applying web scraping to price data for washing machines in Austria from a publicly accessibly price comparison website, we compiled a comprehensive data set for a period of nearly three months. Visualised with the help of interactive dashboards, this data was then analysed using various benchmarks in order to determine whether individual washing machine manufacturers and their retailers may be engaging in resale price maintenance. We conclude that the availability of data is a strong driver for research into and the application of computational antitrust tools. If market data were publicly accessible and provided in a more structured format, researchers and competition enforcers could develop ever more refined computational antitrust applications and screens that would, ultimately, help safeguard competition in markets.