A General Theory of Pass-Through in Channels with Category Management and Retail Competition
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Bibliographic record
Abstract
I provide a general formulation of the channel pass-through problem as a comparative static of the retail price equilibrium, and I analyze the impact of category management and retail competition on pass-through, focusing on brand and retailer differences, and the nature of the cost change being passed through—whether it is brand specific, retailer specific, both, or neither. With category management, a retailer's response to a brand-specific cost change is not limited to that brand; in general, a retailer will also change the prices of other brands. The cross-brand effect can be positive or negative, and, depending on its sign, it either enhances or attenuates pass-through. I explain the cross-brand effect as an interaction between two forces: a demand-substitution force that pushes for a negative cross-brand effect, and a strategic-complementarity force that pushes for a positive cross-brand effect. Retail competition adds another layer of strategic complementarity, causing other retailers to respond even for retailer-specific cost changes and increasing pass-through of categorywide cost changes. But its effect for brand-specific cost changes is ambiguous. I apply the theory to two commonly used demand functions—linear demand and nested logit—and show that they have significantly different pass-through properties. The paper concludes with a discussion of how the theory relates to the empirical literature, including the companion piece by Besanko et al. (Besanko, D., J-P. Dubé, S. Gupta. 2005. Own-brand and cross-brand retail pass-through. Marketing Sci. 24(1) 123–137.)
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it