The Lead-Lag Puzzle of Demand and Distribution: A Graphical Method Applied to Movies
Bibliographic record
Abstract
Understanding the lead-lag relationship between distribution and demand is an important and challenging issue for all marketers. It is particularly challenging in the movie industry, where the very short lifespan and decaying revenue and exhibition patterns of motion pictures means that the associated time series are short and nonstationary, rendering existing econometric methods unreliable. We propose an alternate method that uses state-space diagrams to determine lead-lag relationships. Straightforward to apply and interpret, it takes advantage of the eye’s ability to see patterns that algebra-based formulations cannot easily recognize. A number of validation tests are provided to illustrate the usefulness and limitations of the method. We study the weekly data for 231 major movies released in 2000–2001. While econometric methods do not provide consistent results, the graphical method of visually inferred causality clearly shows a pattern that demand leads distribution for most movies. In other words, the dominant industry pattern is one of movie exhibitors monitoring box office sales and then responding with screen allocation decisions. The managerial implications of these findings are discussed.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".