Financial Deepening, OFDI and Economic Growth: Based on the Perspectives of Both China and Host Countries
Bibliographic record
Abstract
Based on the perspectives of both China (home country) and host countries (economies), a financial deepening indicator system and an economic growth indicator system are constructed, the mutual influence mechanism between financial deepening (FD), outward foreign direct investment flows (OFDI) and economic growth (EG) are studied. Firstly, from the perspective of China, based on Error Correction Model (ECM) and Vector Error Correction Model (VECM), the conclusions received are as follows. (i) China's financial deepening and OFDI have a long-run positive impact on China's economic growth. (ii) Further analysis also confirms that reforms related to financial deepening have positive policy effects on promoting OFDI in China. Secondly, from the perspective of the interaction between China and host countries (economies), based on Time-varying Spatial Durbin Model (TVSDM), the conclusions received are as follows. (i) In general, China's OFDI to host countries plays a positive intermediary role in the process of financial deepening for economic development in host countries. (ii) Three aspects (FD, OFDI, EG) have different degrees of spatial autocorrelation and spatial spillover effects on each other, which are positive in general. (iii) Further analysis also found heterogeneity in the above conclusions for high-income and low-income host countries individually. In a word, a comprehensive analysis framework of three aspects (financial deepening, OFDI and economic growth) is finally constructed, which has important implications for overseas investments and financial support to the real economy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".