Ownership dynamics within founder teams: The role of external financing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it