Private Information in Executive Compensation: The Information Role vs. The Monitoring Role of the Board
Bibliographic record
Abstract
Abstract Manuscript Type Empirical Research Question/Issue This paper examines the effect of boards of directors’ characteristics on the use of private information in executive compensation. Research Findings/Insights We predict and find that boards’ competence both in information acquisition and in monitoring influences the extent to which boards use private performance measures in CEO compensation. Specifically, smaller and more independent boards with their CEOs as the board chair are found to rely more heavily on private performance measures. The documented effects of board characteristics disappear after the passage of SOX, likely due to the homogenized composition of boards and compensation committees brought by the legislated changes. Theoretical/Academic Implications The paper extends the literature on the board's role in executive compensation. Although prior evidence is abundant on how boards affect the alignment of CEO compensation with public performance measures, little is known about boards’ use of private information to compensate CEOs. It also extends the literature on the role of corporate boards by examining both their monitoring and information roles. Practitioner/Policy Implications This study offers insights to regulators regarding the role of boards of directors in monitoring and their role in private information acquisition. It highlights the importance of considering the tradeoff between these two roles when regulating corporate governance rules.
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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 | 0.015 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".