Optimal Executive Compensation Dispersion and Product Market Structure
Bibliographic record
Abstract
Executive compensation is considered as one of the most crucial issues for the corporate governance. The proper executive compensation dispersion can be employed to motivate the top managers and then to boost the firm performance, but the definition of “proper” varies in the existing literature. The bigger dispersion is better for firm performance based on Tournament Theory but smaller one is better according to some other theories. In this paper, we try to theoretically study the optimal executive compensation by considering the internal and external situation of the firm at the same time, especially the influence of product market. We find the optimal compensation dispersion will increase (decrease) if more (less) firms enter the market when the cost of sabotage increases more rapidly than the cost of effort, vice versa. The findings imply the firm should increase (decrease) the compensation dispersion if the intensity of competition in product market decreases (increases) when sabotage is expensive and the firm should increase (decrease) the compensation dispersion if the intensity of competition in product market increases (decreases) when sabotage is cheap.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".