The Mechanism of Foreign Strategic Investment Affecting Efficiency of Chinese Banks
Bibliographic record
Abstract
Foreign financial institutions’ strategic investment in China’s banking sector is to acquire certain equity stake of Chinese banks and provide business assistance and cooperation to them, and it is one of the important foreign bank entry modes in China. This paper explores the impacting mechanism of foreign strategic investment on efficiency of different types of Chinese banks. Foreign strategic investments have several characterisitics: foreign equity ownership should be less than 20% for one strategic investor; foreign financial institutions could send foreign directors to Chinese bank board; they must provide business cooperation with Chinese banks; their equity share must be locked up at least in 3 years. We propose that foreign financial institutions have more incentive to improve the efficiency of smaller Chinese banks which include city commercial banks, rural commercial banks, and most joint-owned commercial banks; foreign directors also have more incentive to improve the corporate governance of smaller Chinese banks; foreign financial institutions would likely to transfer more technology to smaller Chinese banks than that to big Chinese banks, which mainly are the state-owned commercial banks. The longer holding periods of Chinese banks equity also make foreign financial institutions have more incentive to improve the efficiency of Chinese banks. In addition, the interest conflict between state-owned commercial banks and their foreign investors is larger than that between smaller Chinese banks and their foreign investors.
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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 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".