File Sharing and Film Revenues: Estimates of Sales Displacement at the Box Office
Bibliographic record
Abstract
Abstract This study examines the impact of peer-to-peer (P2P) file sharing on the Australian theatrical film industry. Using a large data set of torrent downloads observed on three popular P2P networks, we find evidence of a sales displacement effect on box office revenues. However, although statistically significant, the economic significance of this displacement appears relatively small. To establish causality, we make use of the state-day-level panel data structure permitting the use of film fixed effects to help mitigate the endogeneity between film revenues and downloads. To further assist identification, we propose a downloading cost function that considers other states’ downloading activities as a proxy for the number of peers in the download swarm; the US DVD release date as a supply shock to P2P networks; and the substantial structural progression within the Australian internet service provision industry that occurred over the sample period. We observe that the release gap between the US and Australian markets is a key contributor to piracy early in a film’s theatrical life. This finding provides a partial explanation for the industry’s move towards coordinated worldwide releases.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".