Incentives for Transshipment in a Supply Chain with Decentralized Retailers
Bibliographic record
Abstract
We examine transshipment incentives in a decentralized supply chain where a monopolist distributes a product through independent retailers. A key insight is that the transshipment price determines whether the firms benefit from, or are hurt by, transshipment. In particular, we show that the manufacturer prefers to set the transshipment price as high as possible, whereas retailers prefer a lower transshipment price. Given the important role of the transshipment price in determining the benefits that each firm gets from transshipment, it is useful to consider transshipment in the case where retailers are under joint ownership (a “chain store”) and the transshipment price does not play a role. This comparison yields two surprising results. First, if decentralized retailers control the transshipment price, they will choose a relatively low transshipment price as a way to mitigate the manufacturer's ability to extract profits by increasing wholesale prices; therefore, the manufacturer may prefer dealing with the chain store, which does not have a transshipment price, rather than with decentralized retailers. Similarly, the decentralized retailers can use a low transshipment price to achieve higher total profits than a chain store.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".