Managers’ Stock-Based Compensation and Disclosures of High Proprietary Cost Information: An Investigation of US Biotech Firms
Bibliographic record
Abstract
In this study, we examine whether CEOs’ stock-based compensation has any relationship with the disclosure of highly proprietary information. While prior studies suggest that stock-based compensation provides managers with an incentive to enhance their voluntary disclosures in general, we argue that it may not be the case when the proprietary cost is high. By using novel measures capturing the disclosure cost of high proprietary information and focusing on a biotech industry, we find that, on average, managers’ stock-based compensation is not significantly related with their disclosure of high proprietary cost information. More importantly, we find that a larger amount of stock-based compensation motivates managers to reveal less high proprietary cost information when they have a stronger need to protect their proprietary information; specifically, (i) when the stage of product development is earlier, (ii) when the corporate board mainly consists of directors with lack of sufficient knowledge on technology, and (iii) when firms are a leader in an industry. Overall, our study contributes to the literature by documenting that the role of stock-based compensation on managers’ disclosures can differ depending on the level of proprietary cost of information.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".