The moderating effects of <scp>CEO</scp> power and personal traits on say‐on‐pay effectiveness: Insights from the <scp>Anglo‐Saxon</scp> economies
Bibliographic record
Abstract
Abstract This study investigates the efficacy of say‐on‐pay (SOP) regulation in mitigating excessive CEO compensation and how it is affected by CEO personal traits and the power distribution inside a corporation. Using IV‐GMM method and a sample of 1,931 firms from Australia, Canada, the UK, and the USA, we find that shareholder voices are successful in reducing the pay gap between CEOs and the median employee, regardless of the exact nature of the regulation. In addition, older CEOs are associated with lower pay ratios and there are some evidences suggesting that older or female CEOs enhance SOP effectiveness. Further, power distribution manifested through corporate governance mechanisms matters, as increasing board size and director and audit committee independence reduce pay ratio. A measure of CEO power, CEO pay slice, has a significant and large positive explanatory power for the model and its exclusion can greatly exaggerate the estimated impact of SOP on pay ratio. Another measure of CEO power, CEO duality, appears to enhance the potency of SOP slightly. There is also some evidence indicating that ownership concentration enhances SOP effectiveness. Our findings have implications for companies, investors, and regulators concerning the importance of power balance structure within corporations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".