Off to the Races: the Explanatory Power of Competing Theoretical Perspectives on CEO Compensation
Bibliographic record
Abstract
Despite the ongoing debate among academics and practitioners, there remains little consensus regarding what drives CEO compensation. The academic discussion has principally focused on three theoretical perspectives (the economic, political and social-psychological perspectives) which have been developed based on observations from those involved in the executive compensation determination process. Since there has been no clear winner among these theoretical perspectives, the current paper is designed to empirically compare the strengths and weaknesses of these theories while also identifying their potential overlaps. Overall, we find the economic perspective provides the most consistently valuable insights into observed CEO compensation. The political and social-psychological perspectives provide valuable insights (though less than the economic perspective) with the relative ranking of their explanatory power depending on the specific types and levels of compensation. Regardless of the theoretical perspective, we find corporate governance plays a significant moderating role in the CEO compensation determination process. Diving further into each perspective, we identify a set of factors within each perspective which appear more valuable than others for explaining observed 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 | 0.021 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".