Auditor Conservatism, Asymmetric Monitoring, and Earnings Management*
Bibliographic record
Abstract
Abstract In this paper, we investigate whether, and how, audit effectiveness differentiation between Big 6 and non‐Big 6 auditors is influenced by a conflict or convergence of reporting incentives faced by corporate managers and external auditors. In so doing, we incorporate into our analysis the possibility that managers self‐select both external auditors and discretionary accruals, using the two stage “treatment effects” model. Our results show that only when managers have incentives to prefer income‐increasing accrual choices are Big 6 auditors more effective than non‐Big 6 auditors in deterring/monitoring opportunistic earnings management. Contrary to conventional wisdom, we find Big 6 auditors are less effective than non‐Big 6 auditors when both managers and auditors have incentives to prefer income‐decreasing accrual choices and thus no conflict of reporting incentives exists between the two parties. The above findings are robust to different proxies for opportunistic earnings management and different proxies for the direction of earnings management incentives.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".