Cross-Border Venture Capital Investments: What Is the Role of Public Policy?
Bibliographic record
Abstract
(1) Background: Cross-border venture capital (VC) investments play an important role in the scaling up of high-growth companies. However, policymakers worry that foreign VC investments transfer the majority of economic activity to the investor country. On the one hand, start-ups welcome the foreign capital, expertise, and networks that accompany cross-border investments. On the other hand, policymakers are concerned that cross-border investments predominantly benefit foreign economies and fail to develop the local entrepreneurial ecosystem. This paper describes a framework for how policymakers can develop a set of policies toward cross-border VC investments. (2) Methods: The paper examines available data and trends about the role of cross-border investing, focusing on Europe, Israel, and Canada. Then, the paper explains the underlying economic challenges and develops a policy framework. (3) Results: The analysis shows that in addition to policies that aim to attract foreign investors, there are also important policies for the development of the domestic VC market. The analysis encompasses policies that are both financial and non-financial in nature. (4) Conclusions: A core insight for policymakers is to retain a balance of initiatives, attracting foreign investors while simultaneously making sure to strengthen the country’s domestic VC industry and innovation ecosystem. The mix of policies will adjust as the domestic ecosystem matures.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".