Ownership structure, large inside/outside shareholders, and firm performance: evidence from Canada
Bibliographic record
Abstract
This study gathers additional evidence on the association between ownership concentration and firm performance, as measured by the firm’s Q ratio. Using panel data from a sample of 159 Canadian public firms over a three-year period, I focus on the distinction between large inside shareholders, who directly participate in the management of the firm, and large outside shareholders, who do not. I examine whether direct and indirect monitoring on the part of large shareholders has an impact on the association between ownership concentration and firm performance. Along with the distinction between large inside and outside shareholders, this study also investigates whether concentration of voting rights is associated to firm performance, and whether the identity of the owner affects this association. The findings suggest that large inside shareholdings tend to be negatively associated to firm performance, while no association is found in firms with a majority of large outside shareholdings or firms combining large inside and outside shareholdings in its ownership structure. Concentration of voting rights is negatively associated to firm performance only in firms with a majority of large outside shareholders, suggesting that the market may not discriminate between voting rights and ownership concentration in owner-managed firms. Although the results for the identity of large shareholders are not conclusive, there is evidence that family and institutional large shareholders wield different performance impacts
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".