JOURNAL ARTICLE
Law, Economics, and Privacy: Implications of Government Policies on Website and Third-Party Information Sharing.
Published In: Information Systems Research (INFORMS), 2023, v. 34, n. 4. P. 1375 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Gopal, Ram D.; Hidaji, Hooman; Kutlu, Sule Nur; Patterson, Raymond A.; Yaraghi, Niam 3 of 3
Abstract
This article analyzes the effects of two government-enforced data-protection policies—consent-based user information sharing (as exemplified by the European Union's General Data Protection Regulation and California's Consumer Privacy Act) and website subsidization of privacy-conscious sites—on websites, users, and third-party data sharing. Using an analytical model and empirical data from the CCPA rollout, the study finds that while consent-based policies can increase user surplus by lowering prices, they may unintentionally raise the number of third-parties involved in data sharing and reduce market competition by driving websites out of the market, thereby harming social welfare. Website subsidization policies offer a more targeted approach that can improve user surplus in specific markets but may also affect competition depending on the subsidized site's quality. The findings suggest that policy makers should carefully consider these trade-offs and potentially combine regulatory and nonregulatory mechanisms to better protect online privacy without undermining market dynamics.
Additional Information
- Source:Information Systems Research (INFORMS). 2023/12, Vol. 34, Issue 4, p1375
- Document Type:Article
- Subject Area:Economics
- Publication Date:2023
- ISSN:1047-7047
- DOI:10.1287/isre.2022.1178
- Accession Number:174317138
- Copyright Statement:Copyright of Information Systems Research (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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