The Impact of Say‐On‐Pay on Firm Efficiency in Anglo‐Saxon Economies—Do <scp>CEO</scp> Personal Traits and <scp>CG</scp> Mechanisms Matter?
Bibliographic record
Abstract
ABSTRACT In this study, we explore how the personal traits of CEOs and corporate governance mechanisms moderate the link between say‐on‐pay (SOP) votes and various aspects of firm efficiency. Our sample consists of 1931 firms listed in four Anglo‐Saxon economies (i.e., USA, UK, Canada and Australia) during a period of notable regulatory changes. Our findings reveal a significant and positive impact of SOP votes on firm efficiency. This suggests that company executives recognise that lower efficiency leads to lower pay or even job loss. Interestingly, our analysis indicates that younger managers can contribute more to creating value and improving business performance compared with their older counterparts. However, the relationship between gender and firm efficiency remains inconclusive. Furthermore, our study highlights the limited involvement of the board of directors in driving firm efficiency. This could be attributed to inadequate monitoring, cooperation and communication among board members, particularly in the case of audit committees, which seem to have less skilled members. Alternatively, this lack of board engagement may be due to the influence of powerful managers within the company. This paper also offers practical implications to policymakers and practitioners and suggests avenues for future research that can build upon our evidence.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".