Bibliographic record
Abstract
We examine the financial consequences of a reseller excluding a certain supplier. We take advantage of a recent conflict between American Airlines and two leading U.S. online travel agencies (Expedia and Orbitz), which led to the suspension of American Airlines fares during the first quarter of 2011. We analyze price data for the first quarter of 2010 and 2011, employing a simple difference-in-differences identification strategy to evaluate changes in American Airlines ’ domestic fares due to this conflict. After controlling for across market heterogeneity, carrier-specific time-invariant effects, and time-specific carrier-invariant effects, American Airlines ’ domestic fares during the conflict were 2.7-4.2 percent lower than similar fares charged by American’s main competitors (United, Continental, Delta, and US Airways). The fare effect is most pronounced in the sub-sample of one-stop itineraries, where competition is stronger, and customers are more likely to have to rely on travel agents – rather than carriers ’ own web-sites – for flight bookings. While American’s fares may have dropped, we find minimal impact on passenger counts and load factors. In sum, we find between a $35 to $40 million short-term reduction in American’s revenue due to this online travel dispute. The long-term impact on American’s profit, however, is unclear because the carrier may have gained some bargaining power and negotiated more favorable terms in their most recent confidential contracts with Expedia and Orbitz.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.678 | 0.407 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".