Performance measure skewness and the structure of <scp>CEO</scp> compensation: Theory and evidence
Bibliographic record
Abstract
Abstract While research has analyzed how the structure of incentive pay relates to the dispersion of the performance measure distribution, as measured by its variance or volatility, we examine how it relates to the asymmetry of the distribution, as measured by its skewness. In contrast to the variance, skewness affects the relative informativeness of high and low performance about the agent's effort, which determines the relative efficiency of providing rewards and punishments for incentive purposes. Therefore, skewness is an important determinant of compensation convexity, which is determined by the relative holdings of stock and options. Consistent with our analytical and numerical results, we find that the skewness of expected earnings is negatively associated with the convexity of CEO compensation. Our results are economically significant, robust to alternative specifications, and do not appear to be driven by reverse causality. In addition, we find that earnings skewness is negatively associated with total CEO compensation and that this association is driven by lower options‐based compensation. These findings are consistent with CEOs preferring positively skewed performance metrics. Overall, we provide theoretical, numerical, and empirical evidence suggesting that skewness is a more important determinant of the convexity and structure of CEO compensation than volatility.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".