A Drop in an Empty Pond: Canadian Public Policy towards Venture Capital
Bibliographic record
Abstract
This paper presents evidence about the shortfall of venture capital in Canada relative to comparable regions in the United States, despite massive government spending on governmental venture capital programs in Canada. The Government of Canada committed $500 million towards venture capital in 2013 through the Venture Capital Action Plan. The Government of Ontario committed $29 million to the Investment Accelerator Fund in 2007, $105 million to the Ontario Venture Capital Fund in 2008, and up to $50 million per year through the Ontario Emerging Technologies Fund in 2009. We present data that shows Ontario’s expenditures would have to be higher by $4.4 billion per year to achieve levels of VC/GDP that are comparable to Massachusetts. Similarly, federal expenditures would have to be higher by $1.6 billion per year higher to achieve levels of VC/GDP that are comparable to the U.S. We attribute the shortfall in Canadian venture capital to two major policy failures. First, there is a persistent government venture capital support program that crowds out private investment. Second, other government programs favor established businesses. In Ontario in 2012, $4.1 billion in expenditures were allocated towards businesses, and the vast majority of these expenses are targeted towards the largest and oldest companies and the companies with the greatest revenues. We discuss the impact of such policies on the venture capital ecosystem in Canada.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".