Research on the Abnormal Growth of Chinese Household Savings Under the Effect of “Negative Interest Rate”
Bibliographic record
Abstract
At present, China has once again entered the era of negative interest rate, but China’s residents’ savings have shown a phenomenon of extraordinary growth. The coexistence of negative interest rate and high savings makes the effect of savings rate weaken the effect of monetary policy. This paper conducts an empirical quantitative analysis of the nominal interest rate, real interest rate and the growth rate of Chinese residents’ savings from 1978 to 2017, in order to and studies the influence and change relationship between the nominal interest rate and real interest rate on Chinese savings. The results show that the nominal interest rate and Chinese household savings change in the same direction, while the real interest rate changes in the opposite direction. In the long run, the sensitivity of both nominal and real interest rates to savings decreases and tends to be stable. It is concluded that savings are still the means of maintaining and increasing the value of the public residents when the inflation rate is relatively obvious. The author analyzes the reasons and disadvantages on this basis. Finally, it points out that we should stick to the general direction of interest rate liberalization reform, improve residents’ consumption ability, and deal with the negative interest rate effect with “Chinese characteristics” flexibly.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".