Stock-based compensation and performance – does compensation that leans more toward executives create better ROE performance?
Bibliographic record
Abstract
While executives are often seen as the centre of corporate governance, making important decisions, impacting firm operations, they are often compensated generously by stock-based compensation. The cost of compensating them is significant and material. At the same time, whether such high compensation is worthwhile is questionable, especially when the compensation is unequally high compared to that of ordinary employees. This study examines the impact of stock-based compensation disparity between executives and average employees on firm performance. Utilizing data from publicly traded North American firms between 2015 and 2019 from Compustat’s Fundamental Annual and Execucomp, this paper applies statistical methodologies to analyze the causality between compensation structure and Return on Equity (ROE). This paper provides insights into the effectiveness of compensation strategies by developing a model that projects firm performance based on the CEO-to-employee stock-based compensation ratio. The findings indicate a statistically significant relationship between compensation disparity and firm performance, with a positive correlation suggesting that higher CEO compensation relative to employees may contribute to improved ROE. Along with the model, this study highlights the importance of understanding compensation structures in optimizing firm outcomes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".