Bibliographic record
Abstract
Abstract I provide evidence on the demand for auditor reputation by examining the defections of Arthur Andersen LLP's clients following the accounting scandals and criminal conviction marring the auditor's reputation in 2002. About 95 percent of clients in my sample did not switch auditors until after Andersen was indicted for criminal misconduct regarding its failed audit of Enron Corp. I test whether the timing of client defections and the choice of a new auditor are consistent with managers' incentives to mitigate potentially costly information and agency problems. I find that clients defected sooner, mostly to another Big 5 auditor, if they were more visible in the capital markets; such clients attracted more analysts and press coverage, had larger institutional ownership and share turnover, and raised more cash in recent security issues. However, my proxies for agency conflicts — managerial ownership and financial leverage — are not associated with the timing of defections or the choice of new auditor. Overall, my study suggests that firms more visible in the capital markets tend to be more concerned about engaging highly reputable auditors, consistent with such firms trying to build and preserve their own reputations for credible financial reporting.
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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 | 0.008 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".