Litigation Risk and the Financial Reporting Credibility of Big Four vs. Non-Big Four Audits: Evidence from Anglo-American Countries
Bibliographic record
Abstract
Prior research suggests that Big Four auditors provide higher quality audits in the US 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 Four 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 Four auditors are perceived as providing higher quality audits (relative to non-Big Four auditors) in the US, and in the less litigious (but economically similar) environments in other Anglo-American countries during the 1990-99 period. We find that a Big Four audit is associated with a lower ex ante cost of equity capital for auditees in the US but not in Australia, Canada, or the UK. 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 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 | 0.009 | 0.053 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".