Bibliographic record
Abstract
This paper measures the productivity and unit costs of a set of U.S. and non-U.S. airlines to evaluate the differences in productivity and cost between the world's airlines. Results from a recent cost function analysis of U.S. and non-U.S. carriers are used to decompose productivity and unit cost differentials in an attempt to determine what factors are most influential in explaining the differences, and therefore what forms of deregulation are likely to improve productive efficiency. Results show a productivity advantage for the U.S. over non-U.S. carriers of 12% in 1983. The U.S. productivity advantage is 1% over a sample of Canadian firms, 19% over a sample of European firms and 48% over a sample of other non-U.S. airlines. However, a sample of East Asian firms has a 15% productivity advantage over the U.S. Non-U.S. firms have unit costs approximately equal to that of the U.S. The U.S. has a 7% unit cost advantage over European firms and a 26% higher unit cost than East Asian firms. Non-U.S. firms pay lower labor prices, which gives them a unit price advantage. The U.S. firms make up for this disadvantage through higher levels of productivity that are the result of higher traffic density. Unless carriers are allowed to increase their traffic density through pricing and route freedom, deregulation of bilaterals is unlikely to close the productivity gap between U.S. and non-U.S. firms. Consolidation of existing airlines would also increase density, but is unlikely in parts of the world where airlines are government-owned.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.097 | 0.034 |
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".