A Study of Private Equity Rounds of Entrepreneurial Finance in EU: Are Buyout Funds Uninvited Guests for Startup Ecosystems?
Bibliographic record
Abstract
This paper studies the difference between startup investments by private equity funds (buyout funds; PE) and venture capital funds (VC). PEs, which have traditionally invested in mature companies, have been increasingly investing in later-stage startups in recent years. Based on Crunchbase’s data on EU startup investments from 2011 to the first half of 2021, we find that: (1) later-stage VC-backed startups and PE-backed startups differ in terms of the industry domain, (2) PE-backed startups tend to have higher revenue when they receive investments, and (3) VC-backed startups are more likely to exit via Initial Public Offering (IPO) and slightly less likely to exit via Mergers and Acquisitions (M&A) than PE-backed startups. These results connect previous studies on VC and PE and deepen our understanding of later-stage startup investment. It also suggests that PE invests differently than VCs and provides new added value to the startup ecosystem. In addition, it adds insights into corporate behavior in new business domain expansion.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".