The Impact of Financial Hoarding on Economic Growth in China
Bibliographic record
Abstract
In recent years, more and more funds circulate internally in the financial field, which is called “financial hoarding”. After calculations, the scale of China’s financial hoarding was 242,178 billion yuan in the first quarter of 2003 and jumped to 1,801,706 billion yuan in the fourth quarter of 2016, which increased by nearly 7.4 times in the past 14 years and accelerated after 2014. The phenomenon that large amounts of money deviate from the real economy to virtual economy is called “shift from real economy to virtual economy”. The large scale of financial hoarding will inevitably influence the economic growth in China. Does financial hoarding promote or inhibit the economy? Does the relationship change with the economic growth rate? To address this issue, this paper first provided theoretical analysis of the relationship between financial hoarding and economic growth. Then, it used the data of the first quarter of 2003 through the fourth quarter of 2016 in China for empirical analysis. The results revealed two facts. Firstly, the simultaneous equations model showed that financial hoarding and economic growth promote each other in the long run and financial hoarding can be conducive to economic growth. Secondly, the MS-VAR model showed that the relationship between financial hoarding and economic growth changed with the economic growth rate. In addition, financial hoarding had a positive effect on the economic growth under both medium and high economic growth regimes, but to a greater extent in high economic growth regimes.
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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 | 0.001 | 0.002 |
| 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) | 0.002 | 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".