The Adoption of Voluntary Say-on-Pay
Bibliographic record
Abstract
Say-on-Pay (SOP) gives shareholders the right to vote on executive compensation. Unlike in a number of other western countries, SOP is not prescribed by regulation in Canada, although more and more firms are now adopting this practice voluntarily. We take advantage of this legal context to first examine the characteristics of firms that have voluntarily adopted SOP. Secondly, we examine whether shareholders' votes are linked to the extent and specifics of the compensation granted; and thirdly, we determine whether the votes cast affect executive compensation growth in the next year. The statistical analyses are based on a sample of 744 observations covering the years from 2013 to 2017. Results suggest that the firms that have voluntary adopted SOP differ significantly in several ways from those that have not. SOP vote results appear to be negatively related to the level and growth of total CEO compensation and its components involving immediate cash outflows. The vote does not seem to impact executive compensation and its components in the following year.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".