How Do Audit Offices Respond to Audit Fee Pressure? Evidence of Increased Focus on Nonaudit Services and their Impact on Audit Quality
Bibliographic record
Abstract
ABSTRACT We investigate whether audit offices respond to audit fee pressure by increasing their focus on nonaudit services (NAS), as well as the combined effect of audit fee pressure and an increased focus on NAS on audit quality. We find a positive association between audit fee pressure and changes in NAS at the audit office level. We also find increased rates of client misstatement among audit offices that increase focus on NAS in the presence of audit fee pressure compared to audit offices that do not, suggesting a joint effect on audit quality. We find that the reduction in audit quality occurs in large audit offices. Overall, we provide evidence that audit offices’ provision of additional NAS in the presence of fee pressure is an important dimension to consider when examining the effects of declining audit fees on audit quality.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".