Passive Versus Active Growth: Evidence from Founder Choices and Venture Capital Investment
Bibliographic record
Abstract
This paper develops a novel approach for assessing the role of passive learning versus a proactive growth orientation in the entrepreneurial growth process.We develop a simple model linking early-stage founder choices, venture capital investment and skewed growth outcomes such as the achievement of an IPO or significant acquisition.Using comprehensive business registration data from 34 US states from 1995-2004, we observe that firms that register in Delaware or obtain intellectual property such as a patent or trademark are far more likely to ultimately realize significant equity growth, and these choices also predict early-stage venture capital investment.Moreover, the estimated probability of receiving venture capital as reflected in early-stage founder choices predicts growth even for firms that do not receive venture capital.We use these findings to estimate bounds on the fraction of proactive versus passive firms among firms that ultimately achieve significant equity growth.While nearly half of all firms that achieve modest equity growth (> $10M) are consistent with passive learning (as they neither make early-stage founder choices nor receive venture capital), 78% of firms experiencing an equity growth event greater than $100M are associated with active founder choices and/or venture capital investment, and these firms are concentrated in geographic hubs such as Silicon Valley.Finally, our approach offers a novel approach for estimating the private returns to venture capital, matching on founder choices rather than demographics; consistent with prior studies, venture-backed firms are approximately 5X more likely to grow, with heterogeneity across location and time period.
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.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".