JOURNAL ARTICLE

Rethinking the legal test for excessive pricing: insights from the landmark UK CMA v Pfizer/Flynn Case and its legal implications.

  • Published In: Journal of Antitrust Enforcement, 2025, v. 13, n. 1. P. 115 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Marinova, Miroslava 3 of 3

Abstract

This article focuses on the legal treatment of excessive pricing in the pharmaceutical sector, analyzing the UK Competition and Market Authority's (CMA) approach in the Pfizer/Flynn case and its subsequent appeal. It examines how UK courts have interpreted the two-limb United Brands test—which assesses whether prices are excessive relative to costs and unfair either "in themselves" or compared to competing products—and the impact of these rulings on the CMA's enforcement strategy. The article highlights that while the CMA primarily uses a cost-plus methodology incorporating profitability benchmarks such as return on capital employed (ROCE) and return on sales (ROS) to assess excessiveness and unfairness, UK courts have imposed procedural burdens requiring the CMA to consider both limbs of unfairness cumulatively, potentially complicating future investigations. Comparative analysis with other European National Competition Authorities and the European Commission's Aspen decision reveals consistent application of the United Brands test but differing procedural approaches, with the CMA's expanded evidentiary requirements possibly leading to increased investigatory challenges.

Additional Information

  • Source:Journal of Antitrust Enforcement. 2025/03, Vol. 13, Issue 1, p115
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2025
  • ISSN:2050-0688
  • DOI:10.1093/jaenfo/jnae033
  • Accession Number:184296657
  • Copyright Statement:Copyright of Journal of Antitrust Enforcement is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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