JOURNAL ARTICLE

Using Subsidies, Fines, and Restitution with Budget Balance to Combat Digital Piracy.

  • Published In: Management Science (INFORMS), 2025, v. 71, n. 9. P. 7863 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Fereidouni, Meysam; Nault, Barrie R. 3 of 3

Abstract

This article investigates the role of policymakers in combating digital piracy through a social welfare maximization framework that balances a budget among three instruments: fines on detected pirates, subsidies for legal purchases, and restitution payments to firms. The model segments users into subscribers, pirates, and non-users, and analyzes how firms set subscription fees and quality levels in response to policy instruments under budget constraints. Key findings reveal that imposing fines can be socially optimal when fine revenues are redistributed as subsidies or restitution, challenging prior literature that suggested fines reduce social welfare. Using two specific utility function forms—additive and multiplicative—the study shows that the impact of fines on a firm's investment in quality depends on user preferences and how fines are redistributed, with additive utility leading to increased quality investment with fines, while multiplicative utility yields ambiguous effects. Overall, the policymaker's optimal intervention improves social welfare and firm profits but may reduce consumer surplus, highlighting complex trade-offs in digital piracy enforcement policies.

Additional Information

  • Source:Management Science (INFORMS). 2025/09, Vol. 71, Issue 9, p7863
  • Document Type:Article
  • Subject Area:Politics and Government
  • Publication Date:2025
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2021.03463
  • Accession Number:188078579
  • Copyright Statement:Copyright of Management Science (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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