Investor Reactions to Company Disclosure of High CEO Pay and High CEO-to-Employee Pay Ratio: An Experimental Investigation
Bibliographic record
Abstract
ABSTRACT There is significant debate about the usefulness of disclosing the CEO-to-median employee pay ratio, as required under Section 953(b) of the Dodd-Frank Act in the United States. Using an experiment, we find that disclosing higher-than-industry CEO pay (versus comparable-to-industry CEO pay) marginally decreases perceived CEO pay fairness and perceived workplace climate, which is counteracted by a significant positive effect on perceived CEO attraction/retention ability, although there are no significant indirect effects through these perceptions on perceived investment potential. However, incrementally disclosing a higher-than-industry pay ratio (versus disclosing only higher-than-industry CEO pay) significantly decreases perceived CEO pay fairness and marginally deceases perceived workplace climate, and we find a significant indirect negative effect on perceived investment potential through perceived CEO pay fairness. If companies are concerned about negative public perceptions, then our results suggest that pay ratio disclosures may be better able than current CEO pay disclosures at shaming companies into restraining CEO pay. Data Availability: Contact the authors.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".