Corporate governance, directors' and officers' insurance premiums and audit fees
Bibliographic record
Abstract
Purpose – This study aims to examine the association between corporate governance and audit fees using directors' and officers' (D&O) insurance premiums as a proxy for overall governance quality. The use of an overall governance measure that captures both structural and non-structural governance features may shed light on the association between governance and audit fees, which is known to be inconclusive in the literature. Design/methodology/approach – The authors employ D&O insurance premiums as a proxy for governance quality that reflects both the structural features and non-structural features of governance. D&O insurance premiums are hand-collected from a proxy circular of Canadian firms. Multivariate regression analyses are used for testing. Findings – The authors find a positive association between D&O premiums and audit fees, suggesting that auditors charge higher fees to firms with heightened corporate governance risk. Even after controlling for structural governance variables in the regression model, the authors find a significantly positive association between D&O premiums and audit fees. Research limitations/implications – The findings suggest that mandatory disclosures of D&O insurance policies can be useful for market participants. This study uses a relatively small sample of Canadian firms. A larger sample could strengthen the implications of the findings. Originality/value – The findings suggest that structural features of governance may be insufficient to provide a full understanding of the impact of corporate governance on audit pricing and add to the understanding of the determinants of audit fees.
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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 | 0.002 | 0.016 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".