The deterrent effect of the <scp>SEC</scp> Whistleblower Program on financial reporting securities violations
Bibliographic record
Abstract
Abstract The stated goal of the SEC Whistleblower Program introduced as part of the Dodd‐Frank Act was to deter securities violations and thereby to strengthen investor protection. We document significant reductions in the likelihood of financial reporting fraud by US firms following the introduction of this program. The reductions are robust to controlling for other regulatory changes in the Dodd‐Frank Act and economic trends. Given that employees of firms with weaker internal compliance and reporting programs are more likely to report irregularities directly to the SEC rather than internally, we predict and find that these firms are more likely to change their reporting behavior. We also show that the observed reductions are attributable to an improvement in internal whistleblower programs and the hiring of more capable audit committee members after the program's inception. Collectively, these findings provide important large‐sample evidence of significant benefits of the SEC Whistleblower Program for deterring financial reporting fraud and of the efficacy of bounty‐type whistleblower programs.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".