The Impact of Infrastructure Quality on China’s OFDI: A Study Based on RCEP Partners
Bibliographic record
Abstract
Infrastructure plays a crucial role in facilitating economic development and attracting foreign direct investment (FDI) in a country. With the implementation of the Regional Comprehensive Economic Partnership (RCEP) Agreement, trade and investment ties between China and its RCEP partners have been further strengthened. This study investigates the influence of host country infrastructure development on China’s outward FDI (OFDI) within RCEP partners. By analyzing China’s OFDI to RCEP partners and the infrastructure characteristics of member countries, we explore the impact of host country infrastructure quality on China’s OFDI. Using panel data on China’s direct investment stock in 12 RCEP countries from 2008 to 2020, in conjunction with host country infrastructure quality indicators, we apply a fixed-effect regression model to examine the effects of different types of infrastructure quality on China’s OFDI. Our results reveal that transportation, communications, and energy infrastructure in host countries significantly promote Chinese OFDI. Furthermore, we find that a larger market size, population, and higher trade openness in the host country effectively attract FDI from China. Based on these empirical findings, we provide recommendations to optimize the capital flow and enhance the efficiency of China’s OFDI.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".