An Empirical Investigation of Audit Fees, Nonaudit Fees, and Audit Committees*
Bibliographic record
Abstract
Abstract This study examines the association between audit committee characteristics and the ratio of nonaudit service (NAS) fees to audit fees, using data gathered under the Securities and Exchange Commission's (SEC's) fee disclosure rules. Issues related to NAS fees have been of concern to practitioners, regulators, and academics for a number of years. Prior research suggests that audit committees possessing certain characteristics are important participants in the process of managing the client‐auditor relationship. We hypothesize that audit committees that are independent and active financial monitors have incentives to limit NAS fees (relative to audit fees) paid to incumbent auditors, in an effort to enhance auditor independence in either appearance or fact. Our analysis using a sample of 538 firms indicates that audit committees comprised solely of independent directors meeting at least four times annually are significantly and negatively associated with the NAS fee ratio. This evidence is consistent with audit committee members perceiving a high level of NAS fees in a negative light and taking actions to decrease the NAS fee ratio.
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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 | 0.007 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".