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Air Transport Liberalization and its Effects on Airline Competition and Traffic Growth – An Overview

2014· book-chapter· en· W2504865965 on OpenAlexafffund
Xiaowen Fu, Tae Hoon Oum

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLiberalizationExternalityCompetition (biology)International economicsAir transportIndustrial organizationAir traffic controlEconomicsAviationAir travelFree tradeBusinessInternational tradeMarket economyMicroeconomicsTransport engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract This chapter reviews the effects of air transport liberalization, and investigates the roles played by airport-airline vertical arrangements in liberalizing markets. Our investigation concludes that liberalization has led to substantial economic and traffic growth. Such positive outcomes are mainly due to increased competition and efficiency gains in the airline industry, and positive externalities to the overall economy. Liberalization allows airlines to optimize their networks, and thus may introduce substantial demand and financial uncertainty to airports. Vertical arrangements between airlines and airports may offer a wide range of benefits to the parties involved, yet such arrangements could also lead to airline entry barriers which reduce the effects of liberalization. Three approaches have been developed to model the effects of liberalization in complex market conditions, which include the analytical, econometric and computational network methods. These approaches should be selectively utilized in policy studies on liberalization.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.796
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.039
GPT teacher head0.223
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations36
Published2014
Admission routes2
Has abstractyes

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