Pricing of Initial Audit Engagements by Large and Small Audit Firms*
Bibliographic record
Abstract
Abstract We investigate the extent to which auditors of U.S. companies reduce fees on initial audit engagements (“fee discounting”). We hypothesize that rivalries among sellers, in terms of client turnover and price competition, are more intense among small audit firms. The data support this hypothesis. New clients account for 34 percent of all clients for small audit firms, but only 9 percent of all clients for large audit firms. We theorize that differences in client turnover rates between large and small audit firms can be explained by the market structure of the audit industry, which consists of an oligopolistic segment dominated by a few large audit firms and an atomistic segment composed of many small audit firms. We further hypothesize and confirm that fee discounting is more extensive in the atomistic sector, and our results confirm this hypothesis. Our analysis of audit fee changes indicates that clients who switch auditors within the atomistic sector receive on average a discount of 24 percent over the prior auditor's fee. However, clients who switch auditors within the oligopolistic sector receive on average a discount of only 4 percent. Given that price competition is known to be less intense in oligopolistic markets than in atomistic markets, we believe that market structure theory can explain why fee discounting is lower when larger audit firms compete for clients.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".