Is U.S. CEO Equity and Cash Compensation Aligned with Agency Theory to Maximize Shareholder Returns?
Bibliographic record
Abstract
Recent international studies on CEO pay in Europe, Japan, and South Korea reveal significant differences from the U.S. in the use and effectiveness of equity-based CEO compensation, raising questions about the ability of conventional contracts based on agency theory to align with actual CEO compensation practices. Our study contributes to this debate by evaluating nine hypotheses from an extended principal–agent framework in which CEO equity and cash incentives are jointly determined in the shareholder return-maximizing contract. The extended model also incorporates the noisy market valuation relationship between firm income and its market equity value, and distinguishes between firm ‘business risk’ and ‘equity risk’. Our empirical results show that CEO cash incentives increase with firm growth prospects and equity risk and decline with firm business risk and firm scale as predicted by the model; meanwhile, CEO equity incentives are partially consistent. Overall, given the dominance of equity compensation in U.S. CEO pay, our results show that cash pay tied to firm business performance (e.g., operating cash flow) is efficient and plays an important role in aligning CEO and shareholder interests and reducing corporate governance risks associated with agency misalignment.
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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 | 0.006 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".