Fool Me Once, Shame on You; Fool Me Twice, Shame on Me: The <scp>Long‐Term</scp> Impact of Arthur Andersen's Demise on Partners' Audit Quality*
Bibliographic record
Abstract
ABSTRACT Although recent evidence suggests that individual audit partners explain a substantial portion of the variation in audit quality proxies, much less is known about what determines an audit partner's quality. Psychology and behavioral economics theories hold that an individual's experiences can have enduring impacts on subsequent behavior. We examine whether auditors' direct exposure to Arthur Andersen's collapse has a long‐term impact on the quality of their audits. Our evidence implies that audit partners who directly experienced Andersen's demise impose stricter monitoring evident in their clients exhibiting a lower propensity for misstatements and small profits, and paying higher audit fees. Importantly, these findings reconcile with research in finance and economics implying that firsthand experiences matter more to subsequent behavior than general economic conditions or secondhand or thirdhand experiences. Collectively, the results shed light on one facet of how partners' audit quality evolves over time. Our findings suggest that major failures associated with the audit firm in which an auditor works can ultimately result in these affected individuals later delivering higher audit quality, which should benefit audit committees in partner selection decisions and audit firms in designing partner assignment policies.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".