The effect of governance structures on airport efficiency performance – the North American case
Bibliographic record
Abstract
Over the last two decades, there have been widespread moves to corporatize, privatize, and deregulate airports around the world. These changes have created a great diversity of airport ownership and governance structures. Against this backdrop, this paper applies a stochastic cost frontier model to examine how the two dominant governance forms of publically owned airports in North America, namely operation and governance by a government branch and by an airport authority, affect airport efficiency performance. The data for this study is taken over the 2002-2008 period from 54 airports in Canada and the US and provided for this thesis in confidence by the ATRS Global Airport Performance Benchmarking Project. This study sets out to prove that these two types of governance structures can have significant effects on the efficiency performance of airports in North America, with the results showing that (1) the airports operated by an airport authority achieve higher cost efficiency than those operated by a government branch; and (2) the airports operated by a government branch tend to have lower labour share than those operated by an airport authority. Moreover, by separating Canadian and US airport authorities, our study also attempts to determine whether Canadian and US airport authorities differ in their impact on airport (cost) efficiency performance and hence should be considered as different types of airport governance. However, our regression models have not discerned there is any statistically significant difference as to the efficiency performance between airports operated by US and Canadian airport authorities. It seems therefore that US and Canadian airport authorities are similar in nature and should not be considered as different types of airport governance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".