Do Accounting Firm Consulting Revenues Affect Audit Quality? Evidence from the Pre‐ and Post‐SOX Eras
Bibliographic record
Abstract
ABSTRACT In recent years, public accounting firms have experienced a steady increase in the proportion of their revenues generated from consulting services. Although growth in consulting revenue following the Sarbanes‐Oxley Act (SOX) has been generated primarily from services provided to nonaudit clients, regulators have expressed concerns about the potential implications of this increase for audit quality. In contrast, accounting firms assert that the expertise developed by their consulting professionals helps them to provide better quality audits. We examine the relation between the proportion of accounting firm consulting revenue to total revenue and audit quality and investor perceptions of audit quality. Because SOX drastically altered the source of consulting revenues for public accounting firms, we also separately examine these relations in the pre‐ and post‐SOX eras. We find evidence suggesting that before SOX, higher proportions of audit firm consulting revenues negatively impacted both audit quality and investor perceptions of audit quality. However, we do not find a statistically significant association between audit firm consulting revenues and either audit quality or investor perceptions of audit quality following SOX. Our analyses suggest that even if these relations exist following SOX, the potential economic magnitude of the effect is small.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".