Essays on International Venture Capital
Bibliographic record
Abstract
Venture Capital firms (VCs), compared with other sources of financing, are known to be a value-adding source of finance for high-growth entrepreneurial firms. Venture capital has transitioned from a local to an international subject in recent years. In this thesis , I address three important aspects of the international venture capital research area. \n \nIn the first essay, I answer these questions: do venture capital firms decide to invest in a cross-border company based solely on their own international experience, or do they also decide based on other venture capital firms’ behaviour in investing in that country? I address these questions by investigating vicarious and experiential learning in the venture capital context, focusing on US cross-border venture capital investment data from 2000 to 2013. The analysis indicates that, on average, venture capital firms use both experiential and vicarious learning strategies in making their cross-border investment decisions. Moreover, the effect of experiential learning is greater than that of vicarious learning, and a venture capital firm’s size moderates this effect. \nIn the second essay, I answer this question: do government venture capital funds crowd-in or crowd-out international private venture capital investment? The crowding-in effect arises when international private venture capital benefits from government subsidies through the enhancement of an entrepreneurial ecosystem and investment syndication. The crowding-out effect arises when government venture capital competes with private venture capital, bidding up deal prices and lowering returns, thereby spurring local private venture capitalists to invest internationally. I examine data from 26 countries from 1998 to 2013. The analysis indicates that, on average, more mixed-structured government venture capital investments than pure-structured government investments in a country crowds-in domestic and foreign private venture capitalists internationally. Moreover, the effect of both structures is greater on domestic private venture capitalists than on foreign ones. \nIn the third essay, I investigate whether government venture capital practices in Canada promote a robust entrepreneurial ecosystem, by analyzing the effect of these practices on domestic and cross-border venture capital investments by private venture capital firms separately. I research the following two questions in parallel: a) Does Canadian government venture capital investment attract private venture capital firms to invest in the domestic market? b) Does Canadian government venture capital investment lead to, or prevent, domestic private venture capital firms from investing in other countries? I find that Canadian government venture capital investment has no measurable impact on private venture capital firms’ decisions to invest in the domestic market. I also find that certain of the Canadian government’s venture capital programs have displaced private venture capital, although with negligible impact, towards cross-border VC markets, primarily to the United States.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".