Air Transport Liberalization and Its Impacts on Airline Competition and Air Passenger Traffic
Bibliographic record
Abstract
Abstract This study examines the impacts of air transport liberalization policies on economic growth, traffic volume, and traffic flow patterns, and investigates the mechanisms leading to those changes. Our investigation concludes that (1) liberalization has led to substantial economic and traffic growth. Such positive effects are mainly due to increased competition and efficiency gains in the airline industry, as well as positive externalities to the overall economy; (2) liberalization allows airlines to optimize their networks within and across continental markets. As a result, traffic flow patterns will change accordingly. Strategic alliance is a second-best solution and will have a reduced role when foreign ownership restrictions are relaxed; (3) there is a two-way relationship between the expansion of low-cost carriers (LCCs) and liberalization. The rapid growth of LCCs leads to increased competition and stimulated traffic, calling for the removal of restrictions on capacity, frequency, pricing and entry. In addition, development of LCCs in domestic markets can promote liberalization policy for international aviation by increasing the competitiveness of the national aviation industry. On the other hand, the existing regulations hindered the growth of LCCs. Further liberalization is needed for the full realization of associated benefits.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".