PEAK-LOAD PRICING IN A VERTICAL SETTING: THE CASE OF AIRPORTS AND AIRLINES
Bibliographic record
Abstract
Economists have long approached the airport congestion problem by calling for the use of the price mechanism, under which landing fees are based on a flight’s contribution to congestion. In this paper we extend the existing work on airport congestion pricing, which does not consider inter-temporal pricing across different travel periods, to peak-load pricing (PLP) and analyze both price level and price structure (peak vs. non-peak). A major innovation of our analysis lies in the basic model structure used, in which an airport makes its capacity and price decisions prior to the airlines’ decisions. This vertical structure gives rise to sequential PLP : the PLP schemes implemented by the downstream airlines induce a different periodic demand for the upstream airport, with the shape of that demand depending on the number of downstream carriers and the type of competition they exert. The airport then would have an incentive to use PLP as well, which in turn affects the downstream firms’ PLP. We carry out the analysis for a public airport, for a private airport and for a private airport that has a strategic agreement with the airlines. The comparison between private and public airports is important because it has been argued that private airports would use efficient peak-load and congestion pricing. Our results show that private airports will not only have higher peak and off-peak prices (levels), but also have higher price differentials, inducing a quite different allocation of flights and passengers to peak and-off peak periods. Further, it may be possible that a public airport find it optimal to have a peak price that is lower than the off-peak price. Finally, we note that, while there is an extensive body of literature on PLP, the case of sequential peak-load pricing has yet been analyzed. Since this sequential structure is highly relevant to many other industries (such as telecommunications), our results not only contribute to the understanding of airport policy and management, but should be useful for other sectors as well.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".