Investigating the Motivations of VC Syndication in China --- Do Chinese Leading VC Firms Make a Difference in Terms of Syndication Decisions
Bibliographic record
Abstract
The venture capital industry in China is quickly evolving and becoming more and more important in the development of small and medium-size companies in China. Venture capital firms usually invest in young private transactions which are usually involved with high risk. In addition, the legal and political environments in China are significantly different from those in the developed markets and at the same time, China is undergoing significant changes of business environments, which brings even more challenges to the VC firms in China’s market. Under these challenges, syndication has become a very popular investment method for the VC companies to diversify their investment risks. In this paper, we explore the various factors that might influence the motivation of VC firm’s syndication decisions in China’s market and especially focus on the impact of the firm’s Chinese ownership. We believe that VC firms’ Chinese ownership has a significant influence on the firm’s decision for syndication investment and our empirical analysis confirms this. We find that Chinese VC firms have a significantly lower likelihood to make syndicated investment than their foreign counterparties. We also explore the interactions between the firms’ Chinese ownership and other influencing factors to investigate their joint impacts on the syndication likelihood. We believe our study will provide a better and thorough understanding about the VC firms’ syndication behavior in China’s market and thus will offer significant values to Chinese policy makers in terms of their efforts to promoting VC development in China.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".