The Spatial Spillover Effect of Financial Agglomeration on China’s Regional Economic Growth
Bibliographic record
Abstract
With the development of economic globalization and economic integration, the regional capital flow accelerated, the flow of resources to expand the scope of the financial industry agglomeration effect is most obvious, leading to form a financial center in some areas highly concentrated. The paper analyzes the agglomeration of China’s current banking industry, securities industry and the insurance industry three big financial pillar industries, through the establishment of comprehensive evaluation index system of financial agglomeration, of China’s provinces (municipalities and autonomous regions) of the financial agglomeration level determination. The relevant panel data collected from 2006-2015 in 31 provinces in China, combined with the geographical position, building spatial econometric model, to study China’s financial agglomeration on the spatial spillover effect of economic growth. The empirical results show that the provincial financial agglomeration has a significant impact on the economy and the surrounding provinces, and has a significant spatial spillover effect. At the same time, the financial agglomeration has different characteristics on the economic development of the eastern, central and western regions. The paper puts forward some policy suggestions on the development of the financial industry under the new situation of the supply side reform in different regions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".