Do Boards Reward and Punish CEOs Based on Employee Satisfaction Ratings?
Bibliographic record
Abstract
We investigate whether boards of directors reward and punish chief executive officers (CEOs) based on employee satisfaction ratings. Using data from Glassdoor, we find that CEOs tend to receive larger bonuses when employee satisfaction ratings increase. Similarly, we find a higher rate of CEO dismissal when employees become less satisfied. Further, we investigate three factors that may amplify the role of employee satisfaction ratings in CEO evaluations: the importance of employees to financial performance, the board’s commitment to stakeholders, and the need to preserve firm reputation. We find some evidence that each of these three factors strengthens the relationship between employee satisfaction ratings and CEO evaluations. Finally, we exploit the staggered timing of first-time reviews on Glassdoor and use a difference-in-differences design to strengthen our inferences. Collectively, these findings suggest that boards’ evaluations of CEO compensation and retention incorporate employee satisfaction ratings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".