Bibliographic record
Abstract
The issue of whether auditor fees affect auditor independence has been extensively debated by regulators, investors, investment professionals, auditors, and researchers. The revised Securities and Exchange Commission ( SEC ) requirements that resulted from the implementation of the Sarbanes‐Oxley Act (2002) limit nonaudit services ( NAS ) and mandate NAS fee disclosure. The SEC 's requirements are based on the argument that auditor independence could be impaired—and hence audit quality may be reduced—when auditors become economically dependent on their clients or audit their own work. Economic bonding leads to reduced independence, which can lead to reduced audit quality. We study a sample of firms sanctioned by the SEC for fraudulent financial reporting in Accounting and Auditing Enforcement Releases ( SEC ‐sanctioned fraud firms) and examine whether there is a relationship between auditor fee variables and the likelihood of being sanctioned by the SEC for fraud. We use SEC sanction as a measure of audit quality that has not previously been used in the auditor fee literature and is more precise than some of the other proxies used for flawed financial/auditor reporting. We find, in univariate tests, that fraud firms paid significantly higher (total, audit, and NAS ) fees. However, in multivariate tests, when controlling for other fraud determinants and endogeneity among the fraud, NAS , and audit fee variables, we find that while NAS fees and total fees are positively and significantly related to the likelihood of being sanctioned by the SEC for fraud, audit fees are not. These findings suggest that higher NAS fees may cause economic bonding, thereby leading to reduced audit quality. Our findings of significantly higher NAS fees and total fees in fraud firms hold after controlling for latent size effects and other rigorous testing. These results contribute to the literature that examines the SEC 's concerns regarding NAS and can be used by policy makers for additional consideration.
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How this classification was reachedexpand
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".