A Hierarchical Study on the Efficiency of the City Commercial Banks in China
Bibliographic record
Abstract
City commercial banks have become an important pillar of the banking system in China. Data Envelopment Analysis (DEA) is used to study the efficiency of city banks at this paper, and evaluate the effects of four indicators profitability, liquidity, security and sustainability on bank efficiency. This paper divides Chinese City Commercial Banks into three categories according to their asset scale and selects those ranking top for analysis. After the regression for all observers, two dummies representing different capital scale are significant, which indicates the effect of data category. Liquidity is significant and higher liquidity leads to lower efficiency. After that, regressions are conducted on banks in different categories respectively. For banks with asset size larger than 100 billion, sustainability indicator is significant. For banks with asset size between 50 billion and 100 billion, liquidity and sustainability are significant. For banks with asset size between 30 billion and 50 billion, profitability is more important in determining efficiency. The paper puts forward some policy suggestions also.
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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 | 0.055 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".