The Influence of ‘Say on Pay’ on Excessive Executive Compensation in the UK and the US
Bibliographic record
Abstract
Along with the separation of ownership and control in modern companies, the agency problem between shareholders and managers has become a core issue in corporate law. In recent decades, there was a trend of increasing executive compensation in many countries, which led to shareholders’ dissatisfaction and social concerns about the income gap. Since directors did not effectively solve the problem of excessive executive remuneration, many countries introduced the advisory shareholder vote on the remuneration report (‘Say on Pay’). It is a new mechanism that allows shareholders to vote on executive remuneration. After it was first introduced in the UK, many other countries including the US adopted ‘Say on Pay’ to relieve the problem of excessive executive remuneration. However, there is an ongoing debate about whether ‘Say on Pay’ has a meaningful influence on excessive executive compensation. Some believe that shareholder voting results lead directors to create better executive remuneration plans. Others argue that ‘Say on Pay’ contributes little to solving this problem. It is therefore essential to analyse the effects of ‘Say on Pay’ on solving the excessive executive remuneration problem in the UK and the US. This essay will analyse several arguments related to the influence of ‘Say on Pay’ on excessive executive compensation in order to demonstrate the reasons why ‘Say on Pay’ contributes little to solving the excessive executive remuneration problem in the UK and the US.
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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 | 0.005 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".