Government Sponsored versus Private Venture Capital: Canadian Evidence
Bibliographic record
Abstract
¸This paper investigates the relative performance of enterprises backed by government-sponsored venture capitalists and private venture capitalists.While previous studies focus mainly on investor returns, this paper focuses on a broader set of public policy objectives, including value-creation, innovation, and competition.A number of novel data-collection methods, including web-crawlers, are used to assemble a near-comprehensive data set of Canadian venture-capital backed enterprises.The results indicate that enterprises financed by government-sponsored venture capitalists underperform on a variety of criteria, including value-creation, as measured by the likelihood and size of IPOs and M&As, and innovation, as measured by patents.It is important to understand whether such underperformance arises from a selection effect in which private venture capitalists have a higher quality threshold for investment than subsidized venture capitalists, or whether it arises from a treatment effect in which subsidized venture capitalists crowd out private investment and, in addition, provide less effective mentoring and other value-added skills.We find suggestive evidence that crowding out and less effective treatment are problems associated with government-backed venture capital.While the data does not allow for a definitive welfare analysis, the results cast some doubt on the desirability of certain government interventions in the venture capital market.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".