The economic determinants of compensation committee quality
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the economic determinants of compensation committee quality. Design/methodology/approach Sample firms were selected from the IRRC Directors' database. Compensation committee quality is measured as the factor score from a principal component analysis of six compensation committee characteristics. Regression analyses are conducted to test the hypotheses. Findings It was found that firms with lower CEO influence, less institutional shareholders, fewer growth opportunities, and that are smaller in size are more likely to have high quality compensation committees. Practical implications The results imply that even in the presence of a requirement to have only independent directors on the compensation committee, the quality of compensation committees can vary cross‐sectionally depending on the firm's economic circumstances. Thus, a one‐size fits all solution for compensation committee quality might not be optimal as different firms have different incentives in composing their compensation committees. Originality/value This paper adds to the limited literature on compensation committees by using a new measure of compensation committee quality to examine the economic factors that affect the governance quality of independent compensation committees. This paper also complements the board and audit committee research by examining whether the same factors that affect board and audit committee quality might also affect compensation committee quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".