Asymmetric Wholesale Pricing: Theory and Evidence
Bibliographic record
Abstract
Asymmetric pricing or asymmetric price adjustment is the phenomenon where prices rise more readily than they fall. We offer and provide empirical support for a new theory of asymmetric pricing in wholesale prices. Wholesale prices may adjust asymmetrically in the small but symmetrically in the large, when retailers face cost of price adjustment. Such retailers will not adjust prices for small changes in their costs. Manufacturers then see a region of inelastic demand where small wholesale price changes do not translate into commensurate retail price changes. The implication is asymmetric—a small wholesale price increase is more profitable because manufacturers will not lose customers from higher retail prices; yet, a small decrease is less profitable, because it will not lower retail prices; hence, there is no extra revenue from greater sales. For larger changes, this asymmetry in the behavior of wholesale price vanishes as the price adjustment cost is compensated by the increase in retailers’ revenue resulting from correspondingly large retail price changes. We present a formal economic model of a channel with forward-looking retailers and cost of price adjustment, test the derived propositions on the behavior of manufacturer prices using a large supermarket scanner data set, and find that the results are consistent with the predictions of our theory. We then discuss the implications for asymmetric pricing, channels, and cost of price adjustment literatures, as well as public policy.
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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 | 0.010 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".