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The Effects of Mergers on Airline Performance and Social Welfare

2016· article· en· W3125715543 on OpenAlexaff
Jia Yan, Xiaowen Fu, Tae Hoon Oum, Kun Wang

Bibliographic record

VenueAdvances in airline economics · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCompetition (biology)WelfareMerger controlIndustrial organizationBusinessSocial WelfareQuality (philosophy)Market shareService (business)MicroeconomicsEconomicsMarketingMarket economyFinance

Abstract

fetched live from OpenAlex

Abstract This chapter reviews the key results obtained in previous studies of airline mergers. It is found that the effect of mergers on airfares is dependent on the network configurations of merging airlines. Fare increases are frequently observed on overlapped routes. However, if the networks of two merging airlines are complementary, the expanded network after the merger leads to cost savings, increase in travel options, and improvement in service quality. Therefore, in a deregulated market, with few entry barriers, relaxing merger regulations is likely to improve welfare. However, most welfare evaluations do not incorporate quality changes or dynamic competition effects. Empirical investigations are primarily ex post analysis of mergers that have already passed antitrust reviews. The relationship between market concentration and welfare might be nonlinear and market specific. Therefore, airline mergers and alliances should be reviewed case by case. Methodological improvements are needed in future studies to control for the effects of complicating factors inherent in ex post evaluations.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.008
GPT teacher head0.210
Teacher spread0.202 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations0
Published2016
Admission routes1
Has abstractyes

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