Bibliographic record
Abstract
It has been argued in the literature that privatized airports would charge more efficient congestion prices and would be more responsive to market incentives for capacity expansions. Furthermore, the privatized airports would not need to be regulated since price elasticities are low, so allocative inefficiencies would be small, and collaboration between airlines and airports, or airlines countervailing power, would solve the problem of airports’ market power. However, as important as this issue may appear, not much has been done to analytically examine what the outcomes of privatization or divestment of regulation may be. This paper uses a model of vertical relations between airports and airlines to examine, both analytically and numerically, how ownership affects airports prices and capacities. Results show a rather unattractive picture for privatization. We find that: (i) private airports would be too small in terms of both, traffic and capacity and, despite the fact that they may be less congested, they induce important deadweight losses; (ii) the arguments that airlines countervailing power or increased cooperation between airlines and airports may make regulation unnecessary seem to be overstated; and (iii) things may deteriorate further if privatization is done on an airport by airport basis rather than in a system. We also show that two features of air travel demand that have not been incorporated previously in the literature –demand differentiation and schedule delay cost– play important roles on airports’ preferences regarding the number of airlines using the airport.
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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".