Effects of Regulation and Internal Monitoring on Earnings Quality: Evidence from the 2002 SOX
Bibliographic record
Abstract
In response to a string of highly publicized corporate scandals, the US Congress passed the Sarbanes-Oxley Act (SOX) on July 30, 2002, with the intention to restore investors' confidence in financial and public reporting. This study addresses the question of whether any improvement in earnings quality (proxied by unsigned discretionary accruals) following SOX depends on the strength of the reporting entity's internal monitoring (proxied by financial expertise or independence of an audit committee) surrounding SOX. We find that the reporting gap between Big4 clients with strong internal monitoring and non-Big4 clients with weak internal monitoring widens shortly after the introduction of SOX. Further analyses indicate that non-Big4 clients with strong internal monitoring can widen the reporting gap over non-Big4 clients with weak internal monitoring from the pre- to the post-SOX period. However, there is no change to the reporting gap between firms with strong versus weak monitoring when they retain a Big4 auditor. These findings highlight potential cross-sectional variations in the value of comprehensive regulations over internal monitoring, such as SOX.
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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 | 0.008 | 0.051 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".