The Pricing Responses of Non-Bag Fee Airlines to the Use of Bag Fees in the US Air Travel Market
Bibliographic record
Abstract
Using a panel data for Southwest and JetBlue in the 1st quarter of 2009, 2011, and 2013, we develop an empirical study and find evidence suggesting that the amount of bag fee charged by bag fee airlines, including those direct rivals at the route level and indirect rivals competing from adjacent airports, has a positive and significant effect on the airfare of non-bag fee airlines and such a positive association is smaller on vacation-oriented routes, but greater on routes with higher per capita income at endpoint cities. Moreover, the results are found that on the routes where Southwest is the only non-bag fee airline, the amount of bag fee charged by other airlines has a positive and significant effect on the traffic volume of Southwest. This traffic increasing effect, however, may be offset by the higher airfare of Southwest in response to other airlines’ imposition of bag fees.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".