Gray Markets and Supply Chain Incentives
Bibliographic record
Abstract
“Gray markets” are unauthorized channels that distribute a branded product without the manufacturer's permission. Since gray markets are not officially sanctioned by the manufacturer, their existence is assumed to hurt the manufacturer. Yet manufacturers sometimes tolerate or even encourage gray market activities. We investigate the incentives of a manufacturer and its authorized retailer to engage in (or tolerate) gray markets. The firms need to consider the trade‐off between the positive effects of a gray market (price discrimination and cost savings) and the negative effects (cannibalization of sales and a loss in consumer valuation). Generally, gray markets can be categorized into two types: (i) a “local gray market,” where a retailer diverts products to unauthorized sellers operating in the same region as the retailer; and, (ii) “bootlegging,” where the retailer diverts products to unauthorized sellers in another market where the manufacturer sells through a direct channel. We characterize the equilibrium in each type of gray market and identify conditions under which the retailer will divert products to the gray market. Incentive problems are more complicated when the retailer bootlegs and, in this case, we show that conflicting incentives may lead to the emergence of a gray market where both the manufacturer's and retailer's profits decrease.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".