Do Clients Get What They Pay For? Evidence from Auditor and Engagement Fee Premiums
Bibliographic record
Abstract
ABSTRACT Despite the intuitive appeal, prior research finds mixed evidence on whether higher audit fees translate to superior audit quality. Under the assumption that product differentiation between auditors is based, in large part, on the level of financial statement assurance, we propose more refined measures of excess audit fees that separate auditor premiums from other fee premiums. Consistent with our conjecture, we identify significant variation in audit pricing across auditors (i.e., auditor premiums) that relates positively to audit quality. Conversely, we find no evidence that higher engagement‐specific fee premiums (i.e., fee model residuals) are positively related to proxies for audit quality. Additional tests indicate that our results do not simply reflect premiums attributable to auditor characteristics evaluated in prior research (e.g., Big 4 membership, office size, and industry expertise). In fact, our findings suggest that the positive association between auditor premiums and audit quality is better captured at the auditor level than it is at the auditor “tier,” office, auditor‐industry, or engagement levels. In sum, our results suggest that auditors charging higher fees, on average, deliver superior levels of financial statement assurance, but engagement‐specific fee premiums do not reflect quality‐enhancing audit effort. These contrasting results provide a possible explanation for the mixed findings in prior 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 | 0.013 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".