Should Size Matter When Regulating Firms? Implications from Backdating of Executive Options
Bibliographic record
Abstract
This paper presents a data point relevant to significant issues of policy concerning areas of law where small firms have either been granted exemption from regulations or not investigated for violations of laws that, on their face, apply to them. Whether small firms should be exempted is an empirical question the answer to which depends on the likelihood of such firms violating regulations. Researchers, however, face a problem when obtaining data on violations because violations are typically observed only when they are investigated. The selection of firms for investigation is under the discretion of enforcement officials who may select larger firms for investigation, passing over smaller firms, to either promote societal welfare or to further various career aspirations. The stock options backdating scandal provides a unique opportunity to examine the likelihood that a firm is engaging in illicit activity by observing stock price behavior regardless of whether the firm is ever investigated. Our data set thus enables us to compare the size of the firms likely to have engaged in illegal backdating of executive stock options with those firms that have been investigated or prosecuted for these frauds. Our results show that smaller firms are overly represented in the sample of firms that have engaged in illegal activity, but spared the bulk of law enforcement efforts. Thus, these firms have essentially been given a free pass to engage in illicit behavior – raising significant issues for public policy.
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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 | 0.011 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".