Sources of Funds and Investment Activities of Venture Capital Funds: Evidence from Germany, Israel, Japan and the UK
Bibliographic record
Abstract
We compare sources of funds and investment activities of venture capital (VC) funds in Germany, Israel, Japan and the UK using a newly constructed data set.The data provide a rare opportunity to evaluate relations between funds' sources of finance and activities.We find that sources of VC funds differ significantly across countries, e.g.banks are particularly important in Germany, corporations in Israel, insurance companies in Japan, and pension funds in the UK.VC investment patterns also differ across countries in terms of the stage, sector of financed companies and geographical focus of investments.These differences in investment patterns are related to the variations in funding sources -for example, bank and pension fund backed VC's invest in later stage activities than individual and corporate backed funds.The relations differ across countries; for example, bank backed VC funds in Germany and Japan are as involved in early stage finance as other funds in these countries, whereas they tend to invest in relatively late stage finance in Israel and the UK.We consider the implication of this for the influence of financial systems on relations between finance and activities.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".