Are the Big 4 audit firms homogeneous? Further evidence from audit pricing
Bibliographic record
Abstract
We provide new evidence on audit pricing differences within the Big 4 audit firms in the U.S. market. Industry expertise research argues that an audit firm with greater competencies can differentiate itself from competitors in terms of within‐industry market share and charge an audit fee premium for its services. We show that while KPMG's average fee premium is smaller than those of other Big 4 audit firms, PricewaterhouseCoopers consistently earns an above‐average fee premium and has remained the market share leader across most U.S. industries. More importantly, the supposed effects of industry specialization on audit fees become statistically insignificant after controlling for individual pricing differences within the Big 4. Overall, we conclude that the Big 4 firms are not homogeneous in audit pricing, and that the literature has apparently confounded an individual audit firm reputational effect (as first observed by Simunic, 1980) with an industry specialist fee premium in the U.S. audit market.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".