The Impact of Mobile Payments on the Internet Inclusive Finance
Bibliographic record
Abstract
Expanding the degree of financial inclusion is one of the important ways to deepen the effect of the Internet. Internet finance in China greatly reduces the information asymmetry and decreases the transaction costs between financial participants, which to some extent promotes the reform of financial systems in China. Besides the elite and center institutional arrangements, some so-called grassroots or civilian financial structures appear. Thus the finance has begun to show a trend of financial disintermediate. This paper selected some variables to build a measure system, based on the 2015 Household Financial Survey. It firstly analyzed the correlation of variables and tested the variance factors through the analysis of use frequency of mobile payment and its transaction amount, meanwhile through the analysis of the impact of family investment structure on the Internet investment transactions. Then it used principal component analysis method to analyze multiple variables in reduced dimensionality. After that it tested ADF unit root and Granger causality of screened indexes, and established a stepwise multivariate linear regression model. Finally, this paper illustrated that the expansion of mobile payment transaction had an impact on the increase in the Internet financial investment transactions based on the empirical analysis and theoretical deduction, and then the paper gave some relevant recommendations.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".