Pay for Outsiders: Incentive Compensation for Nonfamily Executives in Family Firms*
Bibliographic record
Abstract
ABSTRACT We use a hand‐collected sample of 1,628 S&P 1500 firms and more than 12,000 executives to examine how family firms compensate nonfamily executives. Family firms comprise a large percentage of firms around the world, and most of their executives are not members of the founding family. Moreover, the founding family's engagement in the firm alters agency conflicts, which in turn should influence the design of incentive compensation. However, there is no empirical evidence on whether and how the incentive compensation of nonfamily executives differs between family and nonfamily firms. Our study intends to fill this gap in the literature. Consistent with our predictions, nonfamily executives in family firms receive significantly less performance‐based pay and equity‐based pay. Family monitoring, risk aversion, and a reluctance to dilute family ownership all contribute to the pay differences. Although incentive pay and total pay are lower in family firms, nonfamily executives receive safer pay and enjoy greater job stability. An analysis of executives' moves across firms suggests that ownership structure, not executives' preferences, is more likely the driver of pay differences between family and nonfamily firms. Our findings suggest that researchers should consider founding family's engagement to avoid misleading inferences with regard to the determinants of incentive compensation, and our findings should help compensation consultants better understand and implement pay packages for family firms and nonfamily firms. The results also imply that uniform compensation regulations intended to improve the monitoring of executives in widely held firms may not be as effective in family firms.
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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 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".