A Retrospective Look at the Effect of Auditor Specialization and Industry Concentration on the Cost of Audit Services
Bibliographic record
Abstract
The purpose of this paper is to perform a retrospective, pre-merger look at the effect of concentration and specialization on audit fees when there were 6 large accounting firms (i.e. the “Big 6”). The US General Accountability Office (GAO) reviewed the effects of auditor concentration on the market for audit services. The 2008 GAO report includes some discussion of the possibility that one or two of the largest sell off a substantial portion of their business which would revert the Big 4 back to the Big 5 or Big 6. Because of the concern over concentration in the audit market and the future possibility of returning to a market that would consist of more than 4 large accounting firms, we conduct a retrospective look at pricing behavior in the audit market when it was less concentrated.Using a sample of 653 U.S. public companies audited by the Big 6, we find that specialists charged more for their services unless they are in competition with other specialists in concentrated industries.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".