Does CEO compensation suppress employee wages?
Bibliographic record
Abstract
Purpose This study aims to examine the impact of CEO equity-based compensation (EBC) on employee wages. It also examines the impact of EBC on average employee wages in different industries and business cycles. Design/methodology/approach The authors use pay-performance sensitivity (PPS) to measure for CEO EBC and run OLS models with year and industry dummies. As many firms do not report labor expenses, the authors conduct the two-step analysis as in Heckman (1979) to overcome the potential selection bias. Findings The authors find that CEOs with higher EBC tend to pay their employees lower wages. They also find that such an impact is more evident in non-technology firms than in technology firms. Finally, they find that CEOs with higher PPS are more likely to depress employee wages when the business cycle shows a downturn. Originality/value No study examines the impact of EBC on employee wages directly to date. The authors add to the existing stream of literature regarding employee wages and managerial compensation. Hence, they purport that the findings support existing literature suggesting EBC contributes to, rather than alleviates, the classic agency conflict. Finally, the evidence suggests an unexplored manifestation of that agency conflict and an additional source of CEO rent extraction.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".