Litigation Risk and the Financial Reporting Credibility of Big 4 versus Non-Big 4 Audits: Evidence from Anglo-American Countries
Bibliographic record
Abstract
Prior research suggests that Big 4 auditors provide higher quality audits in the U.S. in order to protect the firm's brand name reputation and to avoid costly litigation. In this study, we examine whether the perceived higher quality of a Big 4 audit is related to auditor litigation exposure or to reputation concerns. Specifically, we utilize an estimable proxy for financial reporting credibility—the ex ante cost of equity capital—to examine whether Big 4 auditors are perceived as providing higher quality audits (relative to non-Big 4 auditors) in the U.S., and in the less litigious (but economically similar) environments in other Anglo-American countries during the 1990–99 period. We find that a Big 4 audit is associated with a lower ex ante cost of equity capital for auditees in the U.S. but not in Australia, Canada, or the U.K. Our findings suggest that it is litigation exposure rather than brand name reputation protection that drives perceived audit quality.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".