Audit Committee Incentive Compensation and Accounting Restatements*
Bibliographic record
Abstract
This study investigates whether incentive-based compensation for audit committee members is associated with accounting restatements. We use an agency framework to predict that short-term (long-term) incentive compensation for audit committee members will increase (decrease) the likelihood of accounting restatements due to error or fraud. Using a matched-sample logistic regression with 153 restatement and 153 nonrestatement companies, we find the predicted positive relation between short-term incentive compensation (short-term stock option grants) for audit committee members and likelihood of restatement. However, the long-term incentive compensation results contradict prediction and indicate a significant positive relation between audit committee member long-term incentive compensation {long-term stock option grants) and restatement likelihood. Supplemental testing provides evidence that the findings generally are robust to numerous alternative measures and models. The results raise questions about stock option grants for audit committee members and suggest the need for additional theoretical and empirical research to clarify the audit committee's role and incentives in agency frameworks.
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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 | 0.011 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".