Brand Name Audit Pricing, Industry Specialization, and Leadership Premiums post‐Big 8 and Big 6 Mergers*
Bibliographic record
Abstract
Abstract This paper investigates brand name, industry specialization, and leadership audit pricing in the wake of the mergers that created the Big 6 and the Big 5 accounting firms. For samples of Australian listed public companies in each of the postmerger years 1990, 1992, 1994, and 1998, we estimate national audit fee premiums for the Big 6/5 auditors and the industry specialists and leaders. We find limited support for the ability of the Big 6/5 to obtain fee premiums over non‐Big 6/5 for those industries not having specialist auditors. Nonspecialist Big 6/5 auditors are able to obtain fee premiums over nonspecialist non‐Big 6/5 auditors for those industries having specialist auditors. However, this result only holds among the smaller half of our sample. We do not find strong support for the presence of industry specialist premiums in the postmerger years, especially after 1990, using various definitions of industry specialist. We find, at best, limited support for the presence of industry leadership premiums. The evidence suggests that after the Big 8/6 audit firm mergers, some caution is required in generalizing the Craswell, Francis, and Taylor 1995 finding of national market industry specialist premiums. More generally, the study raises questions about the tenuous link between the concept of specialization and national market‐share statistics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".