An Empirical Analysis of the Competitiveness in the U.S. Airline Industry
Bibliographic record
Abstract
The purpose of this paper is to investigate the competitiveness of the airline industry in the United States from 1982 to 2012. We examine the stochastic behavior of corporate profitability ratios using a balanced panel of publicly-traded U.S. Airline firms. In particular, we use a panel unit root approach to examine the persistence of profitability. Using a second generation panel unit root test developed by Pesaran (2007) that controls for cross-sectional dependence, we find some evidence that is consistent with airlines¡¯ return on assets (ROA) exhibiting mean-reversion during a part of our examination period, but not for the full period. In particular, our findings show that when ROA is used as a profitability measure, profits are mean-reverting in the earlier years of our examination period but not mean-reverting following the demand shocks of the terrorist attacks of September 11, 2011 and the 2008 recession. Therefore, our findings produce some evidence in support of the long-standing ¡°competitive environment¡± hypothesis originally set forward by Mueller (1977).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".