Gürkaynak, Güner, Diniz and Filson (2016) proposed that a leading competition authority issue antitrust guidelines for most-favored-nation (MFN) clauses containing presumptions and safe harbors, but deliberately left the numerical boundaries — market-share thresholds and Herfindahl–Hirschman Index (HHI) concentration cut-offs — uncalibrated, analyzed retail MFNs only under the agency distribution model, and confined the guideline to traditional and retail MFNs rather than the wider family of rival-referencing clauses (across-seller price guarantees, English clauses). The completion problem they left open has three linked components: (i) calibrating and empirically validating the safe-harbor and presumption thresholds in real-world markets; (ii) establishing the competitive effects of retail MFNs under non-agency distribution structures (resale, hybrid); and (iii) extending the framework to other rival-referencing clauses. The later literature shows partial movement — the EU's 2022 Vertical Block Exemption Regulation with an uncalibrated 30% narrow-parity threshold, the Digital Markets Act's gatekeeper parity prohibition, the Court of Justice's 2024 Booking.com ruling entrenching market-by-market analysis, and event studies of rate-parity bans — but still no calibration of thresholds, no analysis of non-agency settings, and no scope extension, leaving the open core intact.

Background

A most-favored-nation (MFN) clause — also called a best-price or most-favored-customer clause — is a contractual promise by a seller to give a particular buyer terms at least as favorable as those given to any other buyer. Antitrust economics distinguishes two variants. In a traditional MFN, a supplier promises a wholesale customer that no other customer will receive better prices. In a retail (or platform) MFN, a seller distributing through an intermediary platform — for example a hotel listed on an online travel agency — promises the platform not to offer the same product more cheaply through rival platforms or its own direct sales channel; retail MFNs are closely related to price parity clauses, which require uniform end-user prices across sales channels.

These clauses typically arise under an agency distribution model, in which the supplier keeps control of the retail price and pays the platform a commission on each sale. Under a resale (wholesale) model, by contrast, the platform buys the product at a wholesale price and independently sets its own retail price — at which point a supplier-side retail MFN becomes difficult to observe and enforce, since the supplier no longer controls retail prices. Hybrid platforms combine both roles.

The economics of MFNs is double-edged. They can protect buyers' relationship-specific investments and support efficient long-term contracting, but they can also act as a "tax" on discounting (any selective price cut must be extended to the MFN-protected buyer), soften price competition, facilitate collusion, and raise rivals' costs. Formal theory bears out the harm in agency settings: Boik and Corts (2016) show that platform MFNs raise platform fees and prices, at times above collusive levels; Johnson (2017) shows that combining the agency model with MFN clauses raises retail prices and lowers consumer surplus; Edelman and Wright (2015) show that "price coherence" requirements can induce excessive intermediation. Enforcement, however, has remained case-by-case (the US and EU e-book proceedings, the German Federal Cartel Office's hotel-portal decisions, the UK private motor insurance investigation).

Gürkaynak, Güner, Diniz and Filson (2016) argue that the case-by-case approach wastes enforcement resources and leaves businesses without legal certainty, and propose that a leading competition authority issue MFN guidelines containing presumptions and safe harbors. A safe harbor is a rule under which conduct is presumed lawful when defined conditions hold — in their proposal, low market shares of the contracting parties or low market concentration, measured for instance by the Herfindahl–Hirschman Index (HHI), the sum of the squared market shares of all firms in the market (HHI=∑isi2HHI=\sum_i s_i^2, with higher values indicating a more concentrated market); a presumption of illegality symmetrically shifts the burden of proof to the contracting parties when shares or concentration exceed a cut-off. Their model guideline deliberately leaves these numerical cut-offs unspecified, saying only that presumption cut-offs should be "very high to be on the conservative side," analyzes retail MFNs only under the agency-model assumption, and restricts itself to traditional and retail MFNs rather than the wider family of rival-referencing clauses — provisions that reference rivals' prices, such as across-seller price guarantees (promises to match any competitor's price, also called meeting-the-competition clauses) or English clauses (clauses tying the buyer's terms to the terms of the seller's other deals).

Since 2015, regulation has moved partly toward rules: the EU's 2022 Vertical Block Exemption Regulation (Regulation (EU) 2022/720) removes the block exemption for wide retail parity clauses while allowing narrow ones under a generic 30% market-share threshold, and the Digital Markets Act (Regulation (EU) 2022/1925) prohibits wide parity conditions outright for designated "gatekeeper" platforms. Yet the Court of Justice's Booking.com ruling of 2024 (Case C-264/23) confirmed that applying even the market-share threshold requires a concrete, market-by-market substitutability analysis, so the treatment of MFNs remains case-by-case in practice. No competition authority has issued MFN guidelines with empirically calibrated numerical boundaries, the competitive effects of retail MFNs outside agency settings are unmodeled, and the rival-referencing family beyond MFNs still lacks a coherent guideline treatment.

Problem Statement

Complete the antitrust framework for MFN clauses that Gürkaynak et al. (2016) only outlined, so that it becomes an administrable, economically grounded guideline. The problem has three linked components serving this single objective:

  1. Calibration. Determine, and empirically validate, the numerical boundaries of the proposed safe harbors and presumptions: the market-share thresholds below which MFN clauses should be presumed legal, the concentration (HHI) cut-offs — or combined market-share/concentration rules — performing the same function, and the corresponding high cut-offs for presumptions of illegality, including the treatment of widespread parallel use of MFN clauses covering a substantial share of a market. Validation means testing, in real-world markets, whether the presumptions classify MFN uses correctly — including in markets that look benign on the presumptions' own indicators (low individual market shares or low concentration) yet in which widespread parallel use of MFN clauses covers a substantial share of the market.

  2. Model robustness. Analyze the competitive effects of retail MFNs under distribution structures other than the agency model — resale and hybrid platform arrangements, in which the supplier's control over retail prices differs — and establish whether the presumptions carry over to those settings.

  3. Scope extension. Extend the guideline framework to the rival-referencing clauses the source paper excluded — across-seller price guarantees (meeting-the-competition clauses) and English clauses.

The problem is complete when a defensible, evidence-backed determination of the numerical boundaries and scope rules a competition authority could adopt as guidelines exists — whatever that determination concludes about which boundaries and scope treatments can be defended — together with an assessment of the presumptions' empirical performance in real-world markets. The completion boundary is expert-reviewable rather than mechanical: the source specifies no dataset, welfare criterion or harm metric, so operationalizing "competitive harm" (for example in terms of retail prices, platform fees, consumer surplus, or entry) and its significance standard, and judgment of what counts as adequately calibrated and validated, are part of the answer, not a defect in it.

References

  1. Gürkaynak, Gönenç; Güner, Ayşe; Diniz, Sinan; Filson, Janelle (2016). Most-favored-nation clauses in commercial contracts: legal and economic analysis and proposal for a guideline. European Journal of Law and Economics 42(1):129–155. DOI: 10.1007/s10657-015-9515-y. https://doi.org/10.1007/s10657-015-9515-y
  2. Boik, Andre; Corts, Kenneth S. (2016). The Effects of Platform Most-Favored-Nation Clauses on Competition and Entry. The Journal of Law and Economics 59(1):105–134. DOI: 10.1086/686971. https://doi.org/10.1086/686971
  3. Johnson, Justin P. (2017). The Agency Model and MFN Clauses. The Review of Economic Studies 84(3):1151–1185. DOI: 10.1093/restud/rdx007. https://doi.org/10.1093/restud/rdx007
  4. Edelman, Benjamin; Wright, Julian (2015). Price Coherence and Excessive Intermediation. The Quarterly Journal of Economics 130(3):1283–1328. DOI: 10.1093/qje/qjv018. https://doi.org/10.1093/qje/qjv018
  5. Regulation (EU) 2022/720 of the Commission of 10 May 2022 on the application of Article 101(3) TFEU to categories of vertical agreements and concerted practices (Vertical Block Exemption Regulation). CELEX: 32022R0720. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32022R0720
  6. Regulation (EU) 2022/1925 of the European Parliament and of the Council of 14 September 2022 on contestable and fair markets in the digital sector (Digital Markets Act). CELEX: 32022R1925. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32022R1925
  7. Court of Justice of the European Union (2024). Judgment of the Court (Second Chamber) of 19 September 2024, Case C-264/23, Booking.com BV and Booking.com (Deutschland) GmbH v 25hours Hotel Company Berlin GmbH and Others. ECLI:EU:C:2024:764, CELEX: 62023CJ0264. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:62023CJ0264
  8. Sharma, Abhinav; Nicolau, Juan L. (2019). Hotels to OTAs: "Hands off my rates!" The economic consequences of the rate parity legislative actions in Europe and the US. Tourism Management 75:427–434. DOI: 10.1016/j.tourman.2019.06.009. https://doi.org/10.1016/j.tourman.2019.06.009
  9. Nicolau, Juan L.; Sharma, Abhinav (2019). To Ban or not to ban rate parity, that is the question… or not? International Journal of Hospitality Management 77:523–527. DOI: 10.1016/j.ijhm.2018.08.016. https://doi.org/10.1016/j.ijhm.2018.08.016
  10. González Romo, Miguel (2024). 'Most Favored Nation' Clauses in the Online Food Delivery Platform Market: Beyond Booking.com Cases. Legal Issues of Economic Integration 51(3):263–282. DOI: 10.54648/leie2024010. https://doi.org/10.54648/leie2024010
  11. Campayo-Sanchez, Fernando; Sharma, Abhinav; Mas-Ruiz, Francisco José; Nicolau, Juan Luis (2025). Setting the Course: CEO Beliefs as the North Star in the Hotel-OTA Relationship. International Journal of Hospitality Management 125:103990. DOI: 10.1016/j.ijhm.2024.103990. https://doi.org/10.1016/j.ijhm.2024.103990