Ownership dynamics within founder teams: The role of external financing
Bibliographic record
Abstract
Abstract Research Summary This paper examines how founders within start‐up teams dynamically readjust their relative ownership stakes. It leverages a unique dataset from British Columbia, Canada, which contains detailed information on founder ownership over time. Two trade‐offs between efficiency and fairness are identified, one at the time of founding, the other as the venture develops. Teams with a preference for fairness at the start, as revealed by an equal division of the founder shares, also exhibit a dynamic preference for fairness, as witnessed by their reluctance to change the ownership structure over time. Relative founder stakes are more likely to change when a company raises investments. Larger rounds and lower valuations are associated with bigger changes in relative founder stakes. Managerial Summary Splitting the equity stakes among founders involves a delicate trade‐off between efficiency and fairness. This trade‐off is made when founders determine their initial division of equity, and also as the venture develops. We find that teams with a preference for fairness, as revealed by an equal split of their original founder equity, are also unlikely to change their relative stakes over time. We also find that changes in the division of founder ownership often coincide with external financing rounds, suggesting that renegotiations within teams are more easily settled in the presence of outside investors. Overall, the evidence suggests that although notions of fairness inhibit changes to the relative founder equity stakes, the stakes are not set in stone, and financing rounds provide opportunities for recalibration.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".