Corporate Monitoring and Misreporting: The Role of Rules-Based and Principles-Based Accounting Standards
Bibliographic record
Abstract
SUMMARY Prior research provides some evidence that strict corporate monitoring constrains financial misreporting. We examine whether the efficacy of various corporate monitoring mechanisms hinges on the nature of accounting standards—rules-based standards (RBS) versus principles-based standards (PBS)—in place. We generally document that the negative association between the likelihood of misstatements and tough monitoring by audit committees, boards, external auditors, and the SEC is more pronounced under RBS than under PBS. This evidence collectively suggests that most corporate gatekeepers fulfill their monitoring obligations primarily through ensuring better compliance with detailed standards when the applicable standards are more specific and leave less room for discretion. Although some prior studies document higher financial reporting quality under PBS, our results imply that it is important for regulators to also consider the potentially higher monitoring efficacy under RBS when setting accounting standards. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: M40; M42.
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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 | 0.056 | 0.290 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".