Corporate governance, directors' and officers' insurance premiums and audit fees
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it