Compensation Committees' Treatment of Earnings Components in CEOs' Terminal Years
Bibliographic record
Abstract
ABSTRACT Compensation committees face special difficulties when setting pay in the last years of a CEO's tenure. For example, incentives to manipulate earnings for the purpose of enhancing earnings-based compensation are greater in CEOs' terminal years. We predict that compensation committees are aware of these incentives and adjust the relative weights placed on earnings components in the cash compensation function to mitigate the problem. Consistent with our prediction, we find that in CEOs' terminal years, positive changes in discretionary accruals receive significantly less weight than other income components in determining cash compensation. This provides new evidence that not all gains flow through to compensation. We also find that in non-terminal years, managers' compensation is partially shielded from the negative effects of selling, general, and administrative expenditures (SG&A), but this effect reverses in the terminal period, consistent with the compensation committee discouraging investment in legacy assets by outgoing CEOs. Overall, our findings suggest that compensation committees treat components of earnings differently when setting pay in the terminal period. JEL Classifications: M41; J33.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".