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Record W4200365964 · doi:10.1080/00779954.2021.2020325

Bank size, competition, and efficiency: a post-GFC assessment of Australia and New Zealand

2021· article· en· W4200365964 on OpenAlexaff
Salah U‐Din, David Tripe, M. Humayun Kabir

Bibliographic record

VenueNew Zealand Economic Papers · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsMount Royal University
FundersGrantová Agentura České Republiky
KeywordsMarket concentrationFinancial crisisCompetition (biology)Profit (economics)Lerner indexIndex (typography)EconomicsHerfindahl indexFrontierMonetary economicsBanking industryMarket structureBusinessFinancial systemMarket powerIndustrial organizationMarket economyMacroeconomicsMonopoly

Abstract

fetched live from OpenAlex

This paper investigates the impact of the global financial crisis (GFC) on banking market structure and efficiency in both countries, and the relationship of bank size and market competition with cost and profit efficiencies. Efficiencies of 11 Australian and New Zealand large commercial banks are estimated with a one-stage stochastic frontier approach (SFA) for the period 2003–2017. The Herfindahl- Hirschman Index (HHI) and Lerner index are used as proxies for market competition along with eight banking environment variables. Cost and profit efficiencies significantly declined during 2008 and 2009, but the impact of the GFC persisted longer in New Zealand than in Australia. The level of risk and competition has reduced, and bank size increased in the post-GFC period. Bank size is found to be positively associated with bank efficiency. Market competition negatively influenced cost and profit efficiencies during the study period, especially after the GFC.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score0.607

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.020
GPT teacher head0.248
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2021
Admission routes1
Has abstractyes

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