Payout differences between family and nonfamily listed firms: a socioemotional wealth perspective
Bibliographic record
Abstract
Purpose The objective of this study is to explain family firm payout decisions based on socioemotional wealth (SEW) considerations. Design/methodology/approach A sample of publicly listed Canadian companies is examined for the period from 2003 to 2008. Distinguishing family firms from nonfamily firms, a Probit regression is used to analyze the likelihood of making a payout. For payout firms, regressions are used to analyze the relationship between payout level (dividends and share repurchases) and payout mix and family firms. Findings Results indicate that family firms are more likely to make a payout than nonfamily firms. Among payout firms, the level of payout among payout firms is lower for family firms than for nonfamily firms and their portion of payout in the form of dividends is higher. Lone founder family firms have a lower likelihood of making payouts than other family firms. However, among payout firms, they pay out more than other family firms and have a smaller percentage of their total payout in dividends than other family firms. Research limitations/implications Results are impacted by the definition of what constitutes a family firm. Family ownership was used as a proxy for the underlying SEW considerations. Future research could involve interviews with family firm representatives to investigate the relative importance of SEW considerations in their payout decisions. Originality/value In providing an alternative theoretical framing of family firms’ payout policies, the study suggests that payout differences between family and nonfamily firms may be driven in part by SEW considerations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".