How do audit fees change? Effects of firm size and section 404(b) compliance
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the effect of the size of the audit firm and compliance with Section 404(b) on how audit fees change over time. Design/methodology/approach This study uses panel data and an OLS regression to examine the relationship between audit fee changes, firms’ size and Section 404(b) compliance. Findings Section 404(b)-compliant companies experience a larger change in audit fees if they are audited by Big 4 firms than second-tier firms. Second-tier audit firms increase the fees primarily for the companies which do not comply with Section 404(b). Practical implications Regulators have been concerned with the Big 4 fee premium for four decades. This study informs regulators that the Big 4 continue increasing their fees at a higher rate than second-tier firms for their Section 404(b)-compliant clients (even though recent research shows that second-tier firms have increased quality to match the Big 4). This suggests that the Big 4 fee premium increases for this subset of clients, adding to the regulatory concerns. Originality/value While prior research has established the existence of the Big 4 fee premium, little is known about how this premium changes over time. Prior research shows that audit fees increase when internal controls are weak; however, little is known about how Section 404(b) compliance (once control effectiveness is controlled) affects fee changes. This paper addresses these voids in research.
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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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".