Vertical Control, Dynamics and the Strategic Role of Inventories
Bibliographic record
Abstract
When consumers select retail outlets on the basis of both price and the “fill rate” (the probability of the desired product being available) inventory has an ex ante, demand-enhancing, effect. Greater inventory becomes a competitive strategy rather than just a means of satisfying random demand. We consider the coordination of inventory and pricing incentives in a distribution system when inventory has this ex ante effect on the demand facing each retailer. The key characteristic in predicting the nature of incentive distortions and their contractual resolutions is the degree of perishability of the product. In a static “newsvendor” model or with sufficiently high perishability of the product, downstream retailers are biased towards excessive price competition and inadequate inventories. Vertical price floors can coordinate incentives in both pricing and inventories. In a dynamic setting, where the product is less perishable, the distortion is reversed and vertical price ceilings coordinate incentives. ∗Sauder School of Business, University of British Columbia. harish.krishnan@sauder.ubc.ca; ralph.winter@sauder.ubc.ca. We gratefully acknowledge support from the Social Sciences and Humanities Research Council and the Natural Sciences and Engineering Research Council.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".