JOURNAL ARTICLE
Fairness Regulation of Prices in Competitive Markets.
Published In: Manufacturing & Service Operations Management (M&SOM) (INFORMS), 2024, v. 26, n. 5. P. 1897 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Yang, Zongsen; Fu, Xingyu; Gao, Pin; Chen, Ying-Ju 3 of 3
Abstract
The article focuses on the economic impacts of price fairness regulation aimed at addressing the loyalty penalty—a pricing strategy where firms charge higher prices to loyal customers while offering lower prices to attract new ones. Using a theoretical duopoly model with two symmetric markets and consumer loyalty, the study finds that the effects of such regulation depend critically on the intensity of market competition. When competition is intense, mild price fairness regulation can reduce cutthroat competition, leading to Pareto improvements that benefit both firms and consumers. Conversely, when competition is weak, fairness regulation may strengthen firms' monopoly power, inducing collusive high prices that harm consumers and social welfare. Extensions of the model reveal that regulating relative price discounts can cause price inflation, and that fairness regulation may widen profit gaps between large incumbents and smaller entrants. The authors propose a two-pronged policy combining price fairness constraints with price caps to mitigate negative effects like collusion, highlighting the importance of tailoring regulatory intensity and form to market conditions.
Additional Information
- Source:Manufacturing & Service Operations Management (M&SOM) (INFORMS). 2024/09, Vol. 26, Issue 5, p1897
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2024
- ISSN:1523-4614
- DOI:10.1287/msom.2022.0552
- Accession Number:179561474
- Copyright Statement:Copyright of Manufacturing & Service Operations Management (M&SOM) (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences 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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