Board of directors' independence and executive compensation disclosure transparency
Bibliographic record
Abstract
Purpose This paper aims to investigate the relationship between board of directors' independence and executive compensation disclosures transparency. Design/methodology/approach The paper examines compensation disclosure practices of a sample of 181 firms listed on the Toronto Stock Exchange. Board independence from management is assessed through an aggregate score which takes into account the proportion of independent directors, board leadership structure (i.e. CEO is the board chairperson), and the existence and independence of board committees. A cross‐sectional regression analysis is used to examine the relationship between board independence and the extent of compensation disclosure. Findings The paper finds that board independence from management is positively related to the transparency of executive compensation‐related information. In addition, this study documents a positive (negative) relation between firm size, US cross‐listing, growth opportunities (leverage) and the extent of executive compensation disclosure. Research limitations/implications The study's results provide support to the managerial opportunism hypothesis in executive compensation. These findings highlight the importance of the board of directors as an effective governance mechanism which limits managerial rent‐seeking in the design as well as the disclosure of executive compensation practices. Originality/value This paper extends prior disclosure studies by examining the impact of board characteristics on the transparency of executive compensation disclosures in a principles‐based governance regime. Furthermore, executive compensation disclosure provides an interesting setting in which to examine the ability of the directors to act independently from managers in a conflict of interests situation.
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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 | 0.005 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".