Founder Versus Family Owners’ Impact on Pay Dispersion Among Non-CEO Top Managers: Implications for Firm Performance
Bibliographic record
Abstract
Emerging evidence suggests that pay dispersion among non-CEO top management team (TMT) members harms firm performance, which raises questions about why firms’ owners tolerate or even support it. Prior research shows that the key distinction between founder and family owners is that in addition to firm performance and growth goals, family owners pursue socioemotional goals. On the basis of this distinction, we develop and test theory linking founders’ and families’ ownership to TMT pay dispersion. Consistent with our theory, a Bayesian panel analysis of Standard & Poor’s 500 firms shows that founder owners use less TMT pay dispersion and that family owners, relative to founder owners, use more, although that declines across generations. We also provide evidence that TMT pay dispersion harms firm performance. Our theory and results are significant because they help to explain why some owners favor compensation practices that cause TMT pay dispersion, despite evidence that this harms firm performance.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".