Does family ownership create or destroy value: evidence from Canada
Bibliographic record
Abstract
This study examines whether and how family ownership enhances or damages firm value using a sample of Canadian companies listed on the Toronto Stock Exchange (TSX) 1999 to 2007. We identify family companies as firms in which the founder or founding family hold more than 20 percent of outstanding shares and are the largest shareholders, or firms in which family members work as CEOs and/or serve as Chairman of the board of directors. In addition, we construct a sample of matching firms which are in the same industry, have a similar size as the family companies, and whose sales range within +/- 25 percent of the sales of family companies. We use Tobin's Q and return on asset (ROA), measured by either net income or EBITDA divided by total assets, as proxies of firm value. Our results suggest that family companies are generally superior to non-family companies. In addition, we find that control-enhancing mechanisms which are often employed by family companies add values to companies. Furthermore, we find that agency conflicts between ownership and management are more costly than those between majority and minority shareholders, suggesting that family ownership helps resolve the agency conflicts between ownership and management and in turn enhances firm value. Finally, we find that family companies with founders as CEOs outperform those with descendants as CEOs.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".