Modeling Cost Competitiveness: An Application to the Major North American Airlines
Bibliographic record
Abstract
Significant changes occurred in the North American aviation market during the 1990s and early 2000s: the growth of low cost carriers, the open skies agreement between Canada and the United States, the formation of global alliance networks such as Star Alliance, OneWorld, Sky Team, and Wings, mergers between major airlines such as American (AA) and TWA, Air Canada (AC) and Canadian Airlines International (CAI), etc. These events have affected productivities, unit costs, average yields, and consequently financial situations of airlines. Therefore, it is useful to measure the consequences of these changes on airline performance.This chapter (Chapter 38) measures and compares performance of 10 major full service carriers in Canada and the United States in terms of their unit cost competitiveness (see also Chapter 20). To accomplish this objective, in the first stage, the total factor productivity (TFP) of the 10 sample airlines is measured, and the sources of TFP differentials are investigated in order to compute the residual TFP index which is a measure of (pure) productive efficiency. In the second stage, a neoclassical variable cost function is estimated, and the variable cost function is used to decompose unit cost differentials of the sample airlines into various sources including differences in input prices, network characteristics, output composition, and productive efficiency.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".