An Empirical Study of Uniform and Differential Pricing in the Movie Theatrical Market
Bibliographic record
Abstract
Movies vary widely in appeal, star power, cost, and other elements, and therefore, each might be expected to charge a different price. Multiplexes, however, typically charge the same price for all movies, except for such premium formats as 3D, a choice that has puzzled managers and researchers. Because of data limitations, minimal empirical work has directly addressed this issue. In Hong Kong, however, prices vary both within and across multiplexes. Using daily ticket prices and attendance by theater and movie, the authors empirically examine the potential gains from differentiated movie-specific pricing as well as the increasingly common two-tier (2D/3D) uniform pricing, as compared with a full uniform pricing strategy in which a theater charges the same price for all its movies. Their results show that differential pricing leads to higher profits than the two-tier uniform pricing practice, but that the improvement is limited. In contrast, the gains are substantial when compared with the full uniform pricing strategy, suggesting that only minimal differentiation (2D/3D) may obtain most of the gains available from fully differentiated prices.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".