Research on the Influence of Shadow Banking Development on the Scale of Social Financing in China
Bibliographic record
Abstract
Background: With the continuous improvement and innovation of China’s market-oriented economy, some traditional financial institutions have released new financial products through financial innovation, and the transaction leverage level of most financial institutions and some non-financial institutions is also rising year by year. The latest Quarterly Monitoring Report of China’s Shadow Banks issued by Moody’s, an international rating agency, shows that the assets of China’s broad shadow banks in the first quarter decreased by about 540 billion yuan to 58.7 trillion yuan, continuing the downward trend in recent years. At the same time, China’s economy rebounded rapidly, and the proportion of shadow bank assets in nominal GDP fell to 55.4% from 58.3% at the end of 2020, the lowest level in eight years. The report shows that in the first quarter of 2021, the overall economic leverage ratio measured by the proportion of the adjusted total social financing to the nominal GDP has declined, because the nominal GDP growth in the quarter was 21.2%, far exceeding the growth of 10.1% of the total social financing. Even so, China’s shadow banks are still huge. Also, some non-financial enterprises began to operate shadow banking business, the number of shadow banking enterprises coming into the financial market is growing fast, which is the background and environment of the emergence and development of enterprise shadow banking studied in this paper. In 2020, due to the impact of the epidemic, the Chinese government will actively adjust its macro policies and release liquidity, which will further drive the development of shadow credit business. In the first quarter of 2020, the overall shadow bank assets increased by 100 billion yuan, rebounding compared with 2019. Among them, financial products and asset management products accounted for the main part, with a total increase of 350 billion yuan. Objective: This paper focuses on using different kinds of products operated by shadow banking enterprises to represent the shadow banking scale of enterprises, and explores its impact on the total amount of social financing, based on which a series of policy recommendations are given. Methods: Collect the operation data of shadow banking enterprises in the ten years from 2010 to 2019, use ols regression analysis and intermediary effect test to analyze the development of enterprises’ shadow banking scale and China’s social financing scale. Results: The development of shadow banking will affect the total scale of social financing. There is a non-linear correlation between the development of shadow banking and the total scale of social financing. Conclusion: The improvement of enterprises’ shadow banking scale will promote the expansion of social financing scale as a whole, and will have a negative effect on the productive investment of various enterprises themselves.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".