Does China’s Outward Direct Investment Improve the Institutional Quality of the Belt and Road Countries?
Bibliographic record
Abstract
This article investigates the effects of China’s outward direct investment (ODI) on the institutional quality of the Belt and Road (B&R) countries. Based on a panel data set of 63 B&R countries during the period 2003 to 2016, we find that China’s ODI improves the institutional quality of B&R countries not only in the short run but also in the long run. Further, although China’s ODI exerts no differential impacts on host country institutional dimensions of “control of corruption,” “government effectiveness,” and “political stability” in countries with different natural resource endowments, it improves their institutional dimensions of “regulatory quality” and “rule of law,” implying that China’s ODI may help the host B&R countries minimize the “resource curse”. As one of the most important strategies for China’s opening-up development in the current era, the B&R initiative serves as means to promote sustainable development of B&R countries. The article therefore contributes to existing scholarship on the institutional effects of China’s ODI and sheds light on the mechanisms that drive sustainable development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".