When do corporate penalties for financial misreporting enhance long-term firm value?
Bibliographic record
Abstract
Securities regulators frequently punish firms for their managers’ misreporting. They argue that this would enhance firms’ long-term value by mitigating underinvestment in compliance mechanisms, such as internal controls over financial reporting. Opponents of corporate penalties argue that the penalties would harm the very same investors already harmed by misreporting. We evaluate these arguments in a model with a capital market-oriented misreporting manager and a board of directors that invests in internal control quality. We identify governance transparency and board dependence as key factors that moderate the firm-value effects of corporate penalties. Internal control underinvestment occurs only if the board is severely dependent and if its choice of internal controls is opaque. Then corporate penalties curb internal control underinvestment, but they only improve long-term firm value if, additionally, internal control costs are sufficiently small (e.g., in small and less complex firms). Overall, our differentiated results have implications for regulatory enforcement policies and empirical studies on the firm-value effects of public enforcement.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".