The Role of Deferred Equity Pay in Retaining Managerial Talent*
Bibliographic record
Abstract
ABSTRACT We examine the extent to which deferred vesting of stock and option grants (deferred pay) helps firms retain executives. To the extent an executive forfeits all deferred pay if they leave the firm, deferred vesting will increase the cost (to the executive) of an early exit. The impact of deferred pay on executive retention, a key ingredient for firms to create shareholder value is hence an important empirical issue. Using pay duration proposed in Gopalan et al. (2014) as a measure of the extent of deferred equity, we find that CEOs and non‐CEO executives with longer pay duration are less likely to leave the firm voluntarily. The talent retention role of deferred pay is mitigated by performance‐vesting provisions and signing bonuses offered by industry peers. Moreover, we also find that voluntary turnover is less sensitive to pay duration for executives who are perceived to be more talented and have more firm‐specific skills. Overall, our study highlights a strong link between compensation design and turnover of top executives. It suggests that firms take into account the need for retaining managerial talent in designing executive compensation.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".