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Record W1482239271

Market Structure of Nepalese Banking Industry

2010· article· en· W1482239271 on OpenAlexaff
Dinesh Gajurel

Bibliographic record

VenueSSRN Electronic Journal · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsUniversity of FrederictonUniversity of New Brunswick
Fundersnot available
KeywordsMonopolistic competitionMarket concentrationMonopolyCapitalizationMarket structureMarket sizeEconomicsMarket capitalizationMonetary economicsCompetition (biology)Lerner indexBanking industryRevenueMarket sharePerfect competitionFactor marketMarket powerMarket economyFinancial systemIndustrial organizationMicroeconomicsInternational economicsStock marketFinance
DOInot available

Abstract

fetched live from OpenAlex

This paper examines the evolution of market concentration and tests the market competition of Nepalese banking industry for an unbalanced panel of 15-25 banks for the period of 2001-2009. The market concentration is measured by Hirschman-Herfindahl indices and concentration ratios whereas market competition is tested under Panzar-Rosse approach. The concentration measures indicate decreasing trend and low level of market concentration in Nepalese banking industry. The test of market competition/contestability by using Panzar-Rosse approach rejects both the hypotheses for monopoly and perfect competition indicating monopolistic market behaviors among banks. In addition, the market for interest-based income is found more competitive than that of the market for fee-based income. The results further indicate that size has positive and equity capitalization has negative impact on revenue generation. The results are robust across different specifications and across different estimation techniques.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.214
Teacher spread0.206 · 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
Published2010
Admission routes1
Has abstractyes

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