The Frequency of Say-on-Pay Vote, Shareholder Value, and Corporate Governance: Initial Evidence from the U.S. Firms
Bibliographic record
Abstract
In this study, I examine (1) the market reaction to the shareholders’ decision on the frequency of the say-on-pay vote, and (2) the relation between such decision and firms’ existing corporate governance structures. When firms released the results of shareholders’ frequency vote in Form 8-K, I find that the market reaction was significantly positive for firms with excess CEO equity pay, and for firms whose shareholders preference of the frequency is the same as that recommended by the board. This positive market reaction is more profound at firms where shareholders “correct” the recommendations of the boards by demanding more frequent votes on the executive compensation practices. When examining the relation between shareholders’ frequency vote and corporate governance, I develop and test two hypotheses. The “substitute” hypothesis holds that shareholders at firms with less effective corporate governance are more likely to vote for an annual voting on the executive compensation programs. Under this hypothesis, the say-on-pay is used as a substitute to the existing monitoring mechanism of the executive compensation. In contrast, the “complement” hypothesis posits that shareholders prefer more frequent voting rights on executive compensation at firms where the current level of governance is considered to be effective. In this case, the say-on-pay is considered as a complement to the current level of monitoring of the executive compensation. The results in this paper indicate that, consistent with the “complement” hypothesis, shareholders opt for more frequent voting on the executive compensation at firms where the existing corporate governance structures are considered to be effective.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".