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Record W4310751652 · doi:10.5267/j.ac.2022.9.001

Ownership structure and audit fees: Evidence from Sub-Saharan Africa

2022· article· en· W4310751652 on OpenAlexvenueno aff
Gibson Munisi

Bibliographic record

VenueAccounting · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAuditAccountingBusinessNexus (standard)Corporate governanceJoint auditAudit committeeContext (archaeology)External auditorForeign ownershipFinanceInternal auditEconomicsForeign direct investment

Abstract

fetched live from OpenAlex

This study examines the factors affecting audit fees in firms listed primarily in Sub-Saharan Africa countries by focusing on the relationship between ownership structure and audit fees. The study uses an unbalanced panel dataset of 531 observations of non-financial firms collected from annual reports for the years 2005 to 2009. The findings show that audit fees vary with ownership structure. Particularly, the study shows managerial ownership and concentrated ownership are negatively related to audit fees, whereas foreign ownership is related positively to audit fees. This study provides valuable insights on effects of ownership structure on audit fees pricing. Specifically, the study emphasizes that decisions of pricing of audit fees should consider characteristics of the ownership structure of a firm. The study makes contributions to the literature that focuses on the nexus between corporate governance and audit fees. Particularly, the findings provide empirical evidence of impacts of ownership structure on audit fees in Sub-Saharan African context, which is characterized by less developed financial markets and a weak institutional environment relative to developed countries where most studies are conducted.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.027
GPT teacher head0.205
Teacher spread0.178 · 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 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

Citations6
Published2022
Admission routes1
Has abstractyes

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