Boards of Directors, CEO Ownership, and the Use of Non‐Financial Performance Measures in the CEO Bonus Plan
Bibliographic record
Abstract
ABSTRACT Manuscript Type: Empirical Research Question/Issue: This study examines the associations between the board of director's choice to integrate non‐financial performance measures into the CEO bonus plan and two other governance mechanisms – board independence and CEO ownership – in a sample of publicly traded Canadian firms. Research Findings/Results: The results provide evidence that the use of non‐financial performance measures in the CEO bonus plan varies predictably. Growth opportunities are positively associated with the firm's choice to integrate non‐financial information into the CEO bonus plan. The results are also sensitive to our proxy for board independence and CEO ownership in firms with high growth opportunities. Theoretical Implications: Agency theory states that any costless performance measure providing incremental information about the agent's effort will improve the efficiency of the contract with the agent. In contrast with most of the literature in this area, which investigates pay‐performance sensitivity and governance structure, we examine an important component of pay‐for‐performance plans used to align and compensate executive actions that might not be reflected in traditional financial performance measures. Practical Implications: This study documents that boards choose performance measures that best reflect the CEO's contribution to firm value, taking into account the firm's monitoring environment. This study therefore has policy implications regarding the need for enhanced disclosure of CEO compensation to improve investor understanding of the alignment between executive pay and firm performance.
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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 | 0.003 | 0.022 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".