The Internationalization of Venture Capital and Private Equity
Bibliographic record
Abstract
This paper investigates the internationalization of venture capital (VC) and private equity (PE) investments. We derive flows between countries of VC and PE investments worldwide, relying on comprehensive firm-level data sources, covering three decades and about 100 countries. A gravity analysis indicates that distance, common language, and colonial ties are significant factors in directing these flows. Additionally, the presence of high-end human capital, a better business environment, high levels of military expenditure, and deeper financial markets are important local factors that attract international venture capital. There is also evidence of path dependency and persistence in VC and PE flows, indicating network effects and fixed costs of entry may be at work. Further analysis suggests the internalization of VC and PE is an ongoing story. Prior to the 1990s, VC was primarily a US-only phenomenon. The globalization of IT activities induced the US venture capital industry to mature, and to start exporting its unique skills as VC managers. The US is now a dominant net exporter of deals, though most crossborder deals are still either to or from the US. China has emerged as the dominant net importer, followed by Sweden, Canada, the UK, India and France. For deals outside the US, cross-border participation has been the norm, while US-located deals have been almost exclusively domestic, involving a higher percent of international participation only after 2001. In the past few years, domestic VC and PE capacity has begun to emerge in many countries where it did not exist previously.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".