Do Alma Mater Ties Between the Auditor and Audit Committee Affect Audit Quality?<sup>*</sup>
Bibliographic record
Abstract
ABSTRACT We examine whether audit firm alma mater ties between the auditor and the audit committee (AC) are associated with significantly greater nonaudit services (NAS) provided by the auditor. We further examine whether greater NAS in the presence of such alma mater ties are associated with audit quality. Since the AC is responsible for approving and monitoring the services provided by the auditor, the presence of AC and auditor alma mater ties underscores the controversies surrounding such ties' undermining audit quality. Predicating our hypotheses on social ties theory, we find a positive association between the presence of an audit firm alumnus on the AC and NAS acquired from the alma mater auditor. We further find that this association becomes stronger as the tenure of the alumnus increases. Next, using multiple measures of audit quality, we find that, when the alumnus on the AC is associated with significantly more NAS provided by the alma mater audit firm, the quality of the audit suffers. Collectively, our results suggest that audit firm alma mater ties between the AC and auditor engender economic ties that adversely affect audit quality. Our study provides new evidence on the channels through which the quality of the audit is affected and raises important implications for the composition of the AC, auditor‐provided NAS, and client assignment to engagement partners.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".