The importance of audit partners' risk tolerance to audit quality
Bibliographic record
Abstract
Abstract Relying on their history of legal infractions to measure individuals' risk tolerance, we examine the association between engagement partners' risk appetites and audit quality in the United States. Criminology and economics research links infraction activity with enduring personality traits that capture an individual's risk tolerance. Our evidence supports the prediction that partners known to engage in risky off‐the‐job behaviors conduct lower quality audits. Specifically, we find that clients of partners with prior legal infractions exhibit a higher likelihood of material misstatements revealed through subsequent restatements, greater propensity to misstate based on the F‐score, more instances of “missed” material weaknesses, and less timely loss recognition, while also paying lower audit fees. In cross‐sectional results consistent with expectations, we generally find that the impact of partners' risk tolerance on audit quality is more heavily concentrated in clients of non–Big 4 firms and offices without industry expertise. Collectively, our analysis contributes to emerging research on the role that individual partner characteristics play in shaping audit outcomes.
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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 | 0.004 | 0.038 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".