The Value of Mandatory Certification: A Real Effects Perspective
Bibliographic record
Abstract
ABSTRACT We study the real effects of certification to demonstrate the value of mandatory certification over and above mandatory disclosure in enhancing investment efficiency. In our model, a firm's manager selects a project to maximize the firm's short‐term stock price, which is a function of her certification and disclosure decisions about the outcome of the project. Although the manager might be either forthcoming or strategic with regard to the disclosure of her private information, she can strategically choose whether to incur a cost or not to certify her disclosure, unless mandated. The manager always selects the first‐best project when both certification and disclosure are mandatory. However, when certification is voluntary, project selection is inefficient. In addition, mandating disclosure without mandating certification can lead to lower investment efficiency than mandating neither. In justifying why mandatory certification is beneficial for public firms, our results offer a note of caution regarding the contemplated regulatory moves for increased disclosures by public firms without corresponding certification requirements, for example, the recent SEC proposal requiring extensive climate‐related disclosure.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".