Bibliographic record
Abstract
ABSTRACT Using the timeliness of misstatement discovery as a proxy for audit quality, we examine the association between audit firm tenure and audit quality in a setting that alleviates the endogeneity problem endemic to this line of research. We find that longer audit firm tenure leads to less timely discovery and correction of misstatements, which is consistent with a negative effect of long auditor tenure on audit quality. In addition, using the non-voluntary auditor change following the demise of Arthur Andersen in 2002 as a natural experiment, we show that the misstatements of its former clients were discovered faster than those of comparable companies that retained their auditors throughout the misstatement. This finding speaks to the benefit of a fresh look by a new auditor. An extended analysis shows that longer auditor tenure also leads to misstatements of greater magnitudes, and that the Sarbanes-Oxley Act has mitigated, but not eliminated, the negative effect of long auditor tenure. Last, we show that the negative association between auditor tenure and timely discovery of misstatements is mainly present in the first ten years of an audit engagement. Our study has implications for regulators who continue to express concern regarding lengthy auditor-client engagement. JEL Classifications: K22; K23; L51; M41; M42; M48.
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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 | 0.010 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".