Interaction between manufacturer's wholesale pricing and retailers' price‐matching guarantees
Bibliographic record
Abstract
In practice, many retailers employ price‐matching guarantees (PMGs), committing to meet the price of an identical product at a competitor's outlet. Despite the profound linkage between retailers and manufacturers, existing literature has predominantly explored retailers' PMGs without contemplating the influence of manufacturers' wholesale pricing strategies. Employing a supply chain model comprising one manufacturer and two retailers, we scrutinize the implications of wholesale pricing—uniform or discriminatory—on supply chain members and consumers when retailers have the option to extend PMGs. Our analysis uncovers that retailers refrain from offering PMGs when the manufacturer is granted the discretion to set discriminatory wholesale prices—even if such offers align with the manufacturer's preferences. Conversely, under uniform wholesale pricing, PMGs thrive at equilibrium—even if the manufacturer opposes the practice—as long as the degree of demand or cost asymmetry between retailers and average hassle costs remains relatively modest. Although firms' preferences regarding PMGs vary, a Pareto zone exists where all entities prefer that either the efficient retailer under demand asymmetry or the inefficient retailer under cost asymmetry extends the PMG. Despite the potential advantages of PMGs for the more efficient retailer, the enforcement of uniform wholesale pricing diminishes supply chain profit, consumer welfare, and overall social welfare. The detrimental impacts on welfare owing to the imposition of uniform wholesale pricing persist, even amid the presence of hassle costs associated with price matching. Our findings thus instigate a dialogue for policymakers concerning the validity of regulating wholesale pricing when PMGs are in effect.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".