Holiday Price Rigidity and Cost of Price Adjustment
Bibliographic record
Abstract
The Thanksgiving–Christmas holiday period is a major sales period for US retailers. Due to higher store traffic, tasks, such as restocking shelves, handling customers' questions and inquiries, running cash registers, cleaning and bagging, become more urgent during holidays. As a result, the holiday‐period opportunity cost of price adjustment may increase dramatically for retail stores, which should lead to greater price rigidity during holidays. We test this prediction using weekly retail scanner price data from a major Midwestern supermarket chain. We find that, indeed, prices are more rigid during holiday periods than non‐holiday periods. For example, the econometric model we estimate suggests that the probability of a price change is lower during holiday periods, even after accounting for cost changes. Moreover, we find that the probability of a price change increases with the size of the cost change, during both the holiday as well as non‐holiday periods. We argue that these findings are best explained by higher price adjustment costs (menu cost) the retailers face during the holiday periods. Our data provides anatural experimentfor studying variation in price rigidity because most aspects of market environment such as market structure, industry concentration, the nature of long‐term relationships, contractual arrangements, etc. do not vary between holiday and non‐holiday periods. We, therefore, are able to rule out these commonly used alternative explanations for the price rigidity, and conclude that the menu cost theory offers the best explanation for the holiday period price rigidity.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".