Do Social Ties between External Auditors and Audit Committee Members Affect Audit Quality?
Bibliographic record
Abstract
ABSTRACT We examine whether social ties between engagement auditors and audit committee members shape audit outcomes. Although these social ties can facilitate information transfer and help auditors alleviate management pressure to waive correction of detected misstatements, close interpersonal relations can undermine auditors' monitoring of the financial reporting process. We measure social ties by alma mater connections, professor-student bonding, and employment affiliation, and audit quality by the propensity to render modified audit opinions, financial reporting irregularities, and firm valuation. Our evidence implies that social ties between engagement auditors and audit committee members impair audit quality. In additional results consistent with expectations, we generally find that this relation is concentrated where social ties are more salient, or firm governance is relatively poor and agency conflicts are more severe. Implying reciprocity stemming from social networks, we also report some suggestive evidence that audit fees are higher in the presence of social ties between an engagement auditor and the audit committee. Collectively, our analysis lends support to the narrative that the negative implications—namely, worse audit quality and higher audit fees—of these social ties may outweigh the benefits.
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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 | 0.005 | 0.044 |
| 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 | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".