Timing of Sale, Pricing, and Cost Information: Evidence from the Airline Industry*
Bibliographic record
Abstract
Abstract This study examines the association between when an airline sells its passenger seats and the pricing method (marginal cost or full cost) it employs. Prior literature suggests that when firms are able to change prices during the selling period, the optimality of full cost pricing or marginal cost pricing depends on when demand information is revealed during the period between capacity commitment decisions and time of sale. Full cost‐based pricing is appropriate in determining capacity commitment and prices simultaneously, while marginal cost provides more relevant information for pricing when capacity has been committed. Using the price and cost data from a sample of four U.S. domestic airlines, we find that full cost explains price variations of first‐day sales robustly. The adjusted R2 of the marginal cost pricing model is larger in the sample of sales two days prior to departure than in the sample of first‐day sales. In the analysis of the sample of sales two days prior to departure, we find that, based on the adjusted R2 of the full cost pricing and marginal cost pricing models, the explanatory power of marginal cost pricing is relatively weaker than full cost pricing. Our results document the use of different cost information along the dynamic change of price and provide implications in understanding the role of cost information in setting prices.
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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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".