Does the Disclosure of an Audit Engagement Partner’s Name Improve the Audit Quality? A Difference-in-Difference Analysis
Bibliographic record
Abstract
On 15 December 2015, the Public Company Accounting Oversight Board (PCAOB) passed Rule 3211, requiring audit firms registered with PCAOB in the U.S. to disclose the audit engagement partner’s name in the Form AP, effective 31 January 2017. The regulation aims to improve the transparency and quality of audits, thereby increasing investors’ confidence in financial statements. Using the audit firms registered with the PCAOB and their clients as the treated sample, we employed a difference-in-difference analysis to investigate whether and the extent to which implementing Rule 3211 impacts audit quality and audit costs. We compared the audit quality (proxied by the abnormal discretionary accruals quality, the probability of restating the financial statements, and the ratio of the audit fees to the total fees) and audit costs (proxied by the total audit fees) from one year (up to three years) pre- to one year (up to three years) post-Rule 3211, to a control sample (comprised of U.K. audit firms, which were not subject to such regulation during the sample period). The empirical results generally indicate that there was an increase in the audit quality and in the audit costs from the pre- to the post-Rule 3211 period and also suggest that auditor independence increased in the post-regulation period compared to the pre-regulation period. Our empirical results are new and contribute to the research on the PCAOB and audits.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".