Efficiency Benchmarking of North American Airports: Comparative Results of Productivity Index, Data Envelopment Analysis and Stochastic Frontier Analysis
Bibliographic record
Abstract
Using three common methodologies for measuring airport efficiency, namely the productivity index method, Data Envelopment Analysis (DEA) method, and stochastic frontier analysis (SFA) method, this study examines the efficiency performances of 62 Canadian and U.S. airports. Unlike most previous studies, this study includes aeronautical and non-aeronautical outputs of airports as they are inexplicably tied to each other in airport production. The empirical results reveal that the efficiency scores and rankings measured by these alternative methods are quite similar to each other in the top 15 and bottom 15 ranked airports, whereas considerable differences exist among the airports in the middle range. We also found that the percentage of non-aeronautical revenue, passenger volume, average aircraft size, percentages of international and connecting traffic significantly affect our airport efficiency estimates in all of the three alternative approaches used.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".