JOURNAL ARTICLE

Competitive Neutrality: OECD Recommendations and the Australian Experience.

  • Published In: Journal of Competition Law & Economics, 2023, v. 19, n. 2. P. 250 1 of 3

  • Database: Legal Source 2 of 3

  • Authored By: Smith, Rhonda L; Healey, Deborah; Bai, Xue 3 of 3

Abstract

This article analyzes the OECD’s May 2021 Recommendation on Competitive Neutrality, focusing on its practical implementation through the established Australian framework as a case study. Competitive neutrality, defined by the OECD as ensuring no entity operating in an economic market faces undue competitive advantages or disadvantages due to government ownership, regulation, or activity, aims to create a level playing field among enterprises. Australia’s policy, introduced in 1995 following the Hilmer Review, applies primarily to significant government-owned businesses and seeks to neutralize net competitive advantages arising from public ownership through measures such as corporatization and full cost recovery. While Australia’s approach is regarded as comprehensive, challenges remain, including inconsistent application across jurisdictions, limited transparency, absence of enforceable penalties for non-compliance, and complexities in balancing competitive neutrality with overriding public policy objectives, especially in privatization and infrastructure reforms. The article suggests that these Australian experiences offer valuable insights for other jurisdictions seeking to implement the OECD Recommendation while highlighting the need for clearer guidance, stronger enforcement mechanisms, and greater transparency.

Additional Information

  • Source:Journal of Competition Law & Economics. 2023/06, Vol. 19, Issue 2, p250
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2023
  • ISSN:17446414
  • DOI:10.1093/joclec/nhad003
  • Accession Number:164307387
  • Copyright Statement:Copyright of Journal of Competition Law & Economics 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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