Bibliographic record
Abstract
After many years in which directors have used directors ’ and officers ’ (D&O) coverage to shield themselves from personal responsibility for corporate failure, directors at Enron and WorldCom have been forced to pay $31 million out of their own pockets to settle securities class action lawsuits stemming from two of the largest corporate governance scandals in U.S. history. These settlements have brought the question of director's liability to the foreground, generating interest in the extent to which a firm’s D&O liability coverage reveals valuable information regarding its quality of corporate governance. Since insurers are expected to perform a thorough examination of the directors for whom insurance protection is sought, there is reason to believe that poor governance increases litigation risk and thus the size of the insurance premium. Using a sample of D&O premiums gathered from the proxy statements of firms cross-listed in the U.S. and Canada, I find a significant negative relationship between D&O premiums and variables that proxy for the quality of firms ’ governance structures. This association is robust to a number of alternate specifications. As further evidence that the D&O premium reflects the quality of a firm’s corporate governance, the proxies for weak governance are positively related to excess CEO compensation. Overall, these results suggest that the D&O premium can be a useful measure of the quality of a firm’s governance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.570 | 0.321 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".