Using Subsidies, Fines, and Restitution with Budget Balance to Combat Digital Piracy
Bibliographic record
Abstract
Despite utilizing technical prevention methods and enacting copyright protection legislation, digital piracy has remained a persistent problem. We examine policy remedies to digital piracy whereby the policymaker has to balance its budget between fines on detected pirates, subsidies for legal purchases, and restitution to the firm. In our model, users choose whether to subscribe, copy, or not use the good, a firm decides on subscription fee and quality, and a policymaker determines subsidies, fines, and restitution. We find that the firm’s subscription fee is always increasing in subsidies, fines, and restitution under a budget balance constraint. The impact of these policy instruments on the firm’s investment in quality depends on how user marginal utility is influenced by the quality of the good and how the policymaker redistributes fines back to society, if at all. Using two specific functional forms with additive and multiplicative utility, we explain how these factors come into play. With additive utility, fines increase the firm’s investment in quality. However, with multiplicative utility the firm’s investment in quality decreases with fines if fines are used alone or alongside subsidies. In contrast to prior research, findings of our general framework illustrate that imposing fines on detected pirates “can” be socially optimal when accounting for the policymaker’s budget balance. Our specific functional forms also serve as two instances where imposing fines is always welfare maximizing. Finally, we find that although the policymaker’s optimal intervention improves social welfare and mitigates digital piracy, it leads to a reduction in consumer surplus. This paper was accepted by Hemant Bhargava, information systems. Funding: This work was supported by the Social Sciences and Humanities Research Council of Canada [Grant 435-2016-0431]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2021.03463 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".