The Effect of Compensation Committee Quality on the Association between CEO Cash Compensation and Accounting Performance
Bibliographic record
Abstract
ABSTRACT Manuscript Type: Empirical Research Question/Issue: We examine the effect of compensation committee quality on the association between CEO cash compensation and accounting earnings and the moderating effects of growth opportunities and earnings status. Research Findings/Insights: Using a sample of 812 US firms, we find that CEO cash compensation is more positively associated with accounting earnings when firms have high compensation committee quality. We also find that the positive effect of compensation committee quality on the association between CEO cash compensation and accounting earnings is less for high growth firms or loss‐making firms. Theoretical Implications: We contribute to the agency‐based research on CEO compensation by: 1) directly examining the impact of compensation committee quality on the sensitivity of CEO cash compensation to accounting earnings; 2) examining whether the role of compensation committee quality varies across firms; and 3) developing a broader and richer measure of compensation committee quality. Practical Implications: Our findings imply that shareholders and directors should be concerned about the composition of compensation committees as we find that compensation committee quality varies depending on compensation committee size and other characteristics of the committee members. Our findings also imply that for compensation committee members, there are greater challenges in monitoring CEO compensation contracts for firms with high growth or that incur losses. Further, our findings imply that even when all compensation committees are regulated to be fully independent, there are still quality differences among these independent compensation committees.
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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 | 0.014 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".