Does ‘The Belt and Road Initiative’ Promote the Economic Growth of Key Participating Provinces? Evidence from a Quasi-Natural Experiment Design
Bibliographic record
Abstract
As a new Chinese national strategy, ‘The Belt and Road (B&R) Initiative’ has attracted substantial interest with its broad development prospect and huge economic impetus. However, existing research is not comprehensive due to the limitation of specific industry and there are few empirical studies. This paper adopted the panel data of key participating provinces in China from 2008 to 2017 according to the China City Statistical Yearbook to fill in the research gap. Around the issue of whether the B&R initiative promotes the economic development of Chinese key participating provinces along the routes, this paper proposed three hypotheses and used DID, PSM-DID and DDD methods to study relevant factors and the influencing mechanism. The empirical results show that the B&R policy could promote the overall economic development of participants on the whole, despite a certain degree of policy effect lag. Moreover, after the division of geographical location and administrative level in the eastern and west-central regions of China, the study found that the B&R initiative has significantly facilitated the advancement of the economy in the west-central areas, especially the less developed regions. In addition, after the classification of marketization degree, it is found that the B&R policy has a greater promoting effect on regions with high marketization degree than those with low marketization degree. Through the mechanism test, it is discovered that the tertiary industry is the main economic accelerator for growth in central and western China. Finally, based on the empirical results, this paper provided policy suggestions to further implement the B&R strategy.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".