Firm-level litigation risk and CEO equity incentives
Bibliographic record
Abstract
• We find a positive association between firm-level litigation risk and CEO equity incentives, robust across various methodologies. • The relationship is stronger in firms with weaker governance, and the market responds more favorably to CEO risk incentives in firms with stronger governance. • Regulators should focus on strengthening corporate governance frameworks to enhance the effectiveness of CEO risk incentives. Using Kim and Skinner's (2012) framework, we document a positive association between firm-level litigation risk and CEOs’ equity incentives. This relationship remains robust when using an entropy-balanced sample, alternative regression specifications, a Granger causality test, and a difference-in-differences analysis leveraging Obama's election as an exogenous shock. Our results are also consistent across various restricted samples and alternative proxies for litigation risk and CEO pay. Additional tests indicate that this association is stronger (weaker) in firms with weaker (stronger) corporate governance. Furthermore, we find that the market responds more (less) favorably to risk-to-pay incentives in firms with strong (weak) governance. These findings suggest that regulators should strengthen corporate governance frameworks.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".