Research on the Influence of Government-Guided VC Funds on Regional Economic Development
Bibliographic record
Abstract
Using data from the Qingsike Private Equity Database, in this paper, we systematically examine how government policy-guiding funds impacted regional economic development in China from 2010 to 2021. An empirical analysis confirms that government-guided funds have a significant positive effect on regional economic growth, particularly in less affluent areas. Additionally, we found that the level of venture capital marketization and industrial structural upgrading mediate the relationship between policy-guiding funds and regional economic growth. These findings suggest that government policy-guiding funds foster regional economic advancement by enhancing market dynamism in the venture capital sector and optimizing industrial structures. A further analysis of moderating effects reveals that the effectiveness of policy-guiding funds is significantly influenced by government intervention and reginal marketization levels. In highly marketized regions, government-guided funds demonstrate a stronger economic stimulus effect. However, excessive government intervention can disrupt efficient market operations, thereby weakening the positive impact of the funds. These findings underscore the importance for policymakers to design and implement policy-guiding funds while carefully balancing the interplay between marketization and government intervention to achieve optimal outcomes.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".