Cost pass-through in the U.S. aviation industry
Bibliographic record
Abstract
This paper analyzes disparities in the pass-through of fuel costs across legacy, low-cost, and ultra-low-cost carriers in the U.S. civil aviation industry from 2000 to 2019. We examine how the distinct business models of these carriers influence their pricing mechanisms. Using panel data and a fixed-effects regression approach, we assess which carrier type transfers more of the increased fuel costs on to consumers, drawing on detailed airfare and operational cost data. Our findings reveal that low-cost carriers are more inclined than legacy carriers to raise ticket prices in response to rising fuel costs, while ultra-low-cost carriers exhibit the lowest degree of cost pass-through. • Study of the disparities in the pass-through of fuel costs across U.S. airline companies from 2000 to 2019. • Low-cost carriers are more inclined than legacy carriers to raise ticket prices in response to rising fuel costs. • Ultra-low-cost carriers exhibit the lowest degree of cost pass-through. • When facing a 10 % increase in fuel prices, legacy carriers’ (low-cost carriers’) airfare increases by 0.172 % (0.351 %).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".