The Impact of the Interest Rate Liberalization on Both Banks and Small Firms: Evidence from China
Bibliographic record
Abstract
More and more commercial banks have developed their business with small firms rapidly in China since the interest rate liberation reform in 1996. Many scholars have investigated how the rate liberalization influences the risk taking behaviors of banks. Meanwhile, some researchers have exploded from another perspective that how the reform would affect the firms especially its financing business. However, few of them have put two of the effects together under one shared model to find out how the liberalization affect both of the suppliers and buyers in this financial market. Thus, this article makes an empirical analysis on the issue above by using the data of five biggest commercial banks in China from 2004 to 2015, trying to find out the interactive effect it has on both of the market players. We put a multiplication factor into the analysis model and use GMM regression method. The results show that under the situation of interest rate liberalization, the bank loans of small firms will not be exposed under great non-performing risks. On the contrary, this will encourage more banks to develop business with small firms, which could be viewed as a win-win result.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".