Brand Name Audit Pricing, Industry Specialization, and Leadership Premiums post‐Big 8 and Big 6 Mergers*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it