Different strokes for different folks: The moderating effect of top managers’ political ideologies on the efficacy of top management team vertical pay disparities
Bibliographic record
Abstract
A debate surrounds the utility of tournament theory prescription for the pay arrangements of top executives, based on competing perspectives on the relationship between vertical pay disparities and important firm outcomes. In this study, we attempt to reconcile the competing perspectives by offering a contingency view of the utility of tournament theory prescriptions. We integrate insights from the person-pay interaction theory with research on political ideology to show how top executive’s individual and top management team’s team-level political ideology shapes the relationship between vertical pay disparities and top executive departure and firm performance. Using data on US public firms, we find that liberal-leaning top executives are more likely to exit the firm at higher levels than at lower levels of vertical pay disparity, whereas conservative-leaning top executives are more likely to exit the firm at lower levels than at higher levels of vertical pay disparity. Furthermore, liberal-leaning top management teams perform better at lower levels than at higher levels of vertical pay disparity, whereas conservative-leaning top management teams perform better at higher levels than at lower levels of vertical pay disparity. We discuss the implications of these findings for the literature on executive compensation, corporate governance, and executive values.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".