Government Sponsored versus Private Venture Capital: Canadian Evidence
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
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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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 | 0.002 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it