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Record W1983380500 · doi:10.5539/ijef.v2n4p151

Efficient Structure Versus Market Power:Theories and Empirical Evidence

2010· article· en· W1983380500 on OpenAlexvenueno aff
Sami Mensi, Abderrazak Zouari

Bibliographic record

VenueInternational Journal of Economics and Finance · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicEfficiency Analysis Using DEA
Canadian institutionsnot available
Fundersnot available
KeywordsAllocative efficiencyEconomicsMarket powerMarket structureData envelopment analysisEconometricsDiversification (marketing strategy)MicroeconomicsProfit (economics)Empirical researchEfficient-market hypothesisSample (material)Financial economicsStock marketBusinessMarketingMonopolyMathematicsStatistics

Abstract

fetched live from OpenAlex

In this paper, we investigate the market structure-performance relationship within the Tunisian banking system during the period 1990-2005. We attempt to distinguish between two theories, namely the Efficient Structure Theory and Market Power Theory. Using the Data Envelopment Analysis Method, we estimate efficiency measures under X-efficiency, Technical Efficiency, Scale Efficiency and Allocative Efficiency. By incorporating into our analyses these forms of efficiency, this study allowed us to test the validity of new hypotheses. The empirical investigation is conducted on profit and price regressions for a sample of 10 commercial banks. The results reject the SCP and Quite life hypotheses under the market power theory but retain the RMP hypotheses. Also, all the hypotheses under Efficient Structure theory are rejected. This result suggests that Tunisian banks do not exert a monopole power entailing the exploitation of customers, yet they are able to extend their market share and generate profits thanks to a diversification of products.

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)0.007
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.005
Science and technology studies0.0010.006
Scholarly communication0.0040.007
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.053
GPT teacher head0.362
Teacher spread0.309 · 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 designTheoretical or conceptual
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

Citations43
Published2010
Admission routes1
Has abstractyes

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