Managerial and Customer Costs of Price Adjustment: Direct Evidence from Industrial Markets
Bibliographic record
Abstract
We study the price adjustment practices and provide quantitative measurement of the managerial and customer costs of price adjustment using data from a large U.S. industrial manufacturer and its customers. We find that price adjustment costs are a much more complex construct than the existing industrial-organization or macroeconomics literature recognizes. In addition to physical costs (menu costs), we identify and measure three types of managerial costs (information gathering, decision-making, and communication costs) and two types of customer costs (communication and negotiation costs). We find that the managerial costs are more than 6 times, and customer costs are more than 20 times, the menu costs. In total, the price adjustment costs comprise 1.22% of the company's revenue and 20.03% of the company's net margin. We show that many components of the managerial and customer costs are convex, whereas the menu costs are not. We also document the link between price adjustment costs and price rigidity. Finally, we provide evidence of managers' fear of antagonizing customers.I have no answer to the question of how to measure these menu change costs, but these [menu cost] theories will never be taken seriously until an answer is provided. Edward Prescott (1987, p. 113)Given the large number of theoretical papers that evaluate the implications of [price] adjustment costs, obtaining direct evidence that such costs are present seems crucial. Margaret Slade (1998, p. 104) © 2004 President and Fellows of Harvard College and the Massachusetts Institute of Technology.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".