Is Framing More Effective Than Regulating Disclosures? The Effects of Risk Disclosure Frame and Regime on Managers' Disclosure Choices*
Bibliographic record
Abstract
ABSTRACT I conduct an experiment with senior executives (CEOs, CFOs, controllers) to examine how their risk disclosure quality, with respect to disclosure volume and specificity, is influenced by three factors: first, whether the disclosure behavior is framed internally by the firm as obtaining a gain or avoiding a loss from disclosure; second, whether the external disclosure regime mandates risk mitigation disclosures that explain how a risk is handled; and third, whether the risk under consideration for disclosure is weakly or strongly mitigated. This research question is important because high‐quality risk disclosures are challenging to regulate and changing how disclosure behavior is framed could substitute for costly disclosure regulations. I predict and find that a gain frame prompts managers to make more detailed risk disclosures than a loss frame, regardless of the disclosure regime. I also predict and find that a loss frame leads to less detailed and more boilerplate disclosure of weakly mitigated risks when risk mitigation plans are mandated. Given that the SEC is considering mandating risk mitigation disclosures similar to the practice in other regimes, my findings provide insights on the limitations of mandating these disclosures. My results suggest that changing managers' disclosure frame internally through firm initiatives could be more effective in prompting higher‐quality risk disclosures.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".