Third-Party Payments Impact on Commercial Banks’ Non-Interest Income: Evidence from China
Bibliographic record
Abstract
This study aims to explore the effects of Chinese third-party payments on commercial banks’ non-interest income over the period 2008-2017. In China, third-party payment is a digital payment provided by private non-bank firms that consist of desktop payment and mobile payment. More people prefer to use third-party payment, especially the mobile payment, instead of cash and bank card as the payment can be transacted easily and safely on the mobile phone by scanning QR code. To find whether this new digital payment trend impacts on commercial banks’ non-interest income or not, this paper first employs the random effects panel data technique. The regression results show that for overall banks, higher desktop payment yields higher non-interest income, while the mobile payment deters the non-interest income. Then in order to investigate whether third-party payment exerts the effects differently across bank types, we include interaction terms and dummy variables in the regression. Findings show that from the perspective of bank types, small-medium commercial banks reap the positive spillover effects. But for large state-owned commercial banks, the non-interest income suffers a loss when desktop payment and mobile payment are growing. Base on the findings, the insightful policy implications are put forth for commercial banks’ non-interest income expansion and profitability enhancement.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".