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Record W3022437401 · doi:10.1017/s0022109016000648

Does Competition Matter for Corporate Governance? The Role of Country Characteristics

2016· article· en· W3022437401 on OpenAlexaff
Jean‐Claude Cosset, Hyacinthe Somé, Pascale Valéry

Bibliographic record

VenueJournal of Financial and Quantitative Analysis · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsCorporate governanceDeveloping countryCompetition (biology)BusinessProductivityInvestment (military)Market economyCapital (architecture)Competitive advantageInternational economicsIndustrial organizationEconomicsEconomic growthFinancePoliticsMarketing

Abstract

fetched live from OpenAlex

We investigate the role of country characteristics on the competition-governance relation. We find that competition is associated with higher ratings in corporate governance, but only in developing countries. Further, corporate governance is associated with greater firm value, but only in less competitive industries from developed countries. For developing countries, the evidence suggests that corporate governance is valuable mostly in competitive industries. Additional tests show that corporate governance increases labor productivity and cost efficiency, mostly in less competitive industries in both developed and developing countries. Furthermore, in developing countries, corporate governance increases investment in capital, but primarily in competitive industries.

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)0.010
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 score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.013
GPT teacher head0.219
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

Citations56
Published2016
Admission routes1
Has abstractyes

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