Government Intervention in Venture Capital in Canada: Toward Greater Transparency and Accountability
Bibliographic record
Abstract
This study asesses government venture capital policy in Canada, providing for the first time an overview of policies and how they have evolved over time. The purpose of the study is three-fold: (i) to demonstrate that governments at the federal and provincial levels are extensively involved in venture capital; (ii) to examine the reasons behind this activity and the consequences that flow from the plethora of agencies, programs and tax incentives that have mushroomed over the years; and (iii) to recommend ways in which government might better organize its approach to handling the venture capital file. Undergirding the study is the conviction that getting venture capital policy right is a vital task for all governments that pursue a successful innovation agenda. Improving the quality of venture capital policy will provide a win-win outcome for the wider public, government and even the venture capital industry itself. The breadth and depth of governments’ venture capital initiatives are not fully appreciated by either the general public, the media or legislatures. This inattention stems from the very nature of governments’ approaches to venture capital wherein policy is multi-faceted and relatively hidden from scrutiny, involving as it does, units within Crown corporations, programs managed by private-sector third parties and tax credits for retail investors. The study provides a comprehensive suite of actionable recommendations that, if adopted, will better position Canada to take advantage of the technological revolution that is underway throughout the economy. Those recommendations include: • Taking the steps necessary to improve accountability, transparency and reporting of government activities in the venture capital arena, including a mandated 10-year policy and programs review. Venture capital policy in Canada displays two central characteristics that need to be addressed: there is a certain policy stickiness, with policy tools remaining in place for decades, coupled with policy experimentation, with governments trying various techniques to improve the flow of capital to innovative firms. • Establishing a more robust analytical framework for understanding the dynamics of venture capital by forming a national public-private Venture Capital Research Institute. • Ensuring a better handoff of investments by venture capital funds into the portfolios of private-equity or buyout funds. • Improving the social contract with the venture capital industry by instituting measures to enhance professional development in the industry and by encouraging the industry to adopt international best practices standards for responsible investing. • Developing a formal federal-provincial coordinating mechanism to ensure that governments across the country don’t act at cross-purposes to one another. Finally, the study looks ahead to the emerging public policy challenges facing government venture capital programs as a result of developing trends within and without the industry.
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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 | 0.000 | 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 | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".