The Effect of R&D Input and Financial Agglomeration on the Growth Private Enterprises: Evidence from Chinese Manufacturing Industry
Bibliographic record
Abstract
Technological innovation is an important factor in the growth of private enterprises, and technological innovation requires strong financial support. How to use financial tools to promote research and development (R&D) input at private enterprises has become an urgent issue. This article analyzes the influence of provincial and prefectural financial agglomeration and R&D input on the growth of private enterprises, using panel data on Chinese private enterprises in manufacturing from 2007 to 2015. We reached the following conclusions. First, the promotion of financial agglomeration and R&D input have a positive impact on the growth of private enterprises. Second, the impact of financial agglomeration on private enterprises is inversely related to the scale of private enterprises—that is, the larger the scale of enterprises, the smaller the impact of financial agglomeration on the growth of private enterprises. Third, financial agglomeration did not promote growth at private enterprises by increasing R&D input. Financial agglomeration can increase the absolute amount of R&D input; however, it will reduce the intensity of R&D input. Financial agglomeration, R&D input, and the growth of enterprises do not create their own virtuous circle, and they fail to provide financial support for technological innovation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".