Determinants of External Audit Fees: Evidence from the Banking Sector in Nigeria
Bibliographic record
Abstract
Studies abound on market structures for audit services in developed economies of the USA, UK, Canada and Australia with abysmal very few on the African continent. Across these studies is the continuous trend of exclusion of the financial sector. This study seeks to provide empirical examination of client attributes which significantly explain variations in the amount of external audit fees charged by bank auditors in Nigeria. A standard audit fee model, modified accordingly, is used to investigate the specific effect of bank size, risks and complexities on audit fees for top ten (10) publicly quoted commercial banks, which constitute over 70% of the total assets of the industry. Multiple OLS regression was adopted as the estimation technique on the panel data gathered through content analysis of annual reports and accounts of these banks over a 4-year post consolidation periods covering 2009-2012. The findings from this study reveal that bank size is also an important factor that is priced by bank auditors having shown a positive and significant influence accounting for 63% variations. Risk proxied by capital adequacy and non performing loans ratios was insignificant but positive and negative respectively; while only the number of branches used to operationalise complexities associated with bank audit displayed a negative and significant influence. The massive deployment of Information Technology (IT) in the industry, especially for the rendering of returns by branches of these banks to their head offices could account for this result. Keywords: Audit fees, External audit, Banking sector, Nigeria.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".