AIRLINE COST STRUCTURE AND POLICY IMPLICATIONS: A MULTI-PRODUCT APPROACH FOR CANADIAN AIRLINES. IN: AIR TRANSPORT
Bibliographic record
Abstract
This paper seeks to extend the knowledge on airline cost structure where there is a significant variation in the size of carriers, and to clarify the issue of scale economies for small carriers. The results of the single output cost structure is extended to a case of multiple outputs, where carriers produce substantial charter and/or freight services, to see whether or not the empirical results on returns to scale and traffic density would hold for multiple output producers. The study also tests whether the fixed firm effect model suggested in a previous study is useful when an extended time-series data set is used for each firm. Data from the Canadian airline industry is used to develop a multi-product total cost model that emphasizes the roles of the firm's network and output attributes in determining costs. Parameter estimates of the total costs model are presented, and key findings on cost structures are compared with those obtained from a variable cost function. The key empirical results on cost structure obtained from the variable cost function are found to be similar to those of the total cost function. Since both the total and variable cost models have weaknesses, the true empirical results are likely to be somewhere between those obtained from the total and variable cost functions. The costs structure estimated in this paper can help determine the likely outcomes of Canadian air transport deregulation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".