Modeling the Main Determinants of Movie Sales: An Econometric Study of Chinese Marketplace
Bibliographic record
Abstract
This paper investigates the financial performance of movies in China, a fast-growing commercial exhibition marketplace. Movie sales and Chinese market returns, movie characteristics and cultural contexts are operationalized in ordinary least squares (OLS) regression and quantile regression models to explain the highly varied acceptance across different products released since 2009. The samples comprise most of the widely released motion pictures in China. We posit that production budgets, sequels, audience ratings, cultural contexts and movie genres can significantly account for the variation in box office (BO) revenue and sales-revenue-to-cost (SRTC) within the Chinese market. Movies produced in countries with similar cultural contexts capture more audiences measured by box office proceeds, but it is noteworthy that SRTC falls at a decreasing rate as cultural differences increase. An increase in production budget generates more sales in China, but reduces the SRTC ratios with other factors controlled. Although aggregate cinema attendance may fluctuate with releasing date, this is not always true for an individual movie's financial success. Quantile regressions provide us with a richer characterization of the relationship, enabling us to analyze the entire distribution of box office proceeds and SRTC ratios, as well as their determinants at key quantiles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".