The curious case of one-dollar CEO salaries: evidence from market reaction to salary decision announcements
Bibliographic record
Abstract
Purpose This study investigates the motivations for and compensation structure changes behind $1 CEO salary decisions. Design/methodology/approach Using a hand-collected sample, we relied on an event study framework and regression analysis to decipher the informational content of $1 CEO salary announcements. Findings The results show that the market reacts positively to $1 CEO salary announcements that indicate aligning the interests of CEOs and shareholders. Practical implications A lot of academic and professional attention has been given to the components of executive compensation packages as tools for incentivizing managers. Our findings will help executive board members tasked with determining CEO compensation packages. Originality/value This study adds to the literature on CEO compensation by deciphering the market reaction to $1 salary decision announcements. Our study contributes to the literature on executive compensation by providing evidence consistent with efficient contracting.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".