Are Referred-To Auditors Associated with Lower Audit Quality and Efficiency?
Bibliographic record
Abstract
SUMMARY Inadequate supervision by lead auditors of “other” (component) auditors contributing to audit engagements has been a recent regulatory concern. However, uniquely in the United States, the lead auditor is required to conduct only minimal supervision of the other auditor and refer to the other auditor in its audit report, when it divides responsibility with the latter. Our sample of “referred-to” (RT) firm-years is divided, about equally, between audits of consolidated subsidiaries and equity-method investees. We document two findings. First, supervision challenges drive the use of RT auditors for consolidated subsidiaries while the component’s materiality drives the use of RT auditors in both settings. Second, there is some evidence that RT auditors in both settings are associated with lower audit quality and efficiency compared with control samples, and this negative effect is stronger for consolidated subsidiaries. Our research is relevant to the Public Company Accounting Oversight Board’s proposed changes in auditing standards for other auditors.
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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 | 0.009 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".