Did the 1998 Merger of Price Waterhouse and Coopers & Lybrand Increase Audit Quality?
Bibliographic record
Abstract
Abstract We examine the effects of the 1998 merger of Price Waterhouse (PW) and Coopers & Lybrand (CL) on the audit quality of the merged firm PricewaterhouseCoopers (PwC) at both the firm and office levels, where audit quality is surrogated by the auditor's propensity to issue a going‐concern opinion, clients’ likelihood of meeting or beating analysts’ earnings forecasts, and clients’ accrual quality. At the firm level, we find that the merger increased audit quality for PwC relative to the audit quality of the other Big N firms. At the office level, our findings, albeit mixed, collectively suggest that the improvement in firm‐level audit quality was likely driven by the improvement in audit quality at PwC's overlapping offices, that is, offices in cities where both PW and CL had separate offices prior to the merger. Further, our findings suggest that although the PW/CL merger increased auditor concentration in local audit markets with PwC overlapping offices, the merger improved (rather than hurt) audit quality in those markets. Overall, our study contributes to the extant sparse literature on the effect of Big N mergers on audit quality, and is of potential interest to regulators.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".