An Empirical Examination of the Compensation-Dividend Relation to Compare Conflict Resolution Strategies at Public versus Private Firms
Bibliographic record
Abstract
While agency theory predicts that the unification of ownership and control of private family firms reduces agency concerns, some prior studies suggest that the complex family relationships of private, family firms increases agency conflicts. To investigate these conflicting predictions, this study empirically examines with regression analysis how executive total compensation levels relate to dividends at public versus private firms to compare the conflict resolution strategies of public versus private firms. For public firms, this study finds a positive compensation-dividend relation, indicating that public firms increase total compensation levels to reward executives for supporting firms’ dividend policies and realign the interests of owners and managers from the conflict created by dividends. Drawing from special access to Forms 1120, this study examines a large sample of privately held U.S. firms. For private firms, this study finds a negative compensation-dividend relation, indicating that private, family firms do not use compensation to realign the interests of owners and managers and overcome the conflict created by dividends. This new evidence suggests that the ownership structure of private, family firms systematically mitigates agency concerns to some degree. On a practical level, this study indicates that firms can provide compensation arrangements that support firms’ dividend policies, and that regulatory agencies should continue to focus on public firms where the great dispersion of ownership systematically increases agency concerns.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".