Empirical Analysis on the Sustainable Development of China’s Outward Foreign Direct Investment from the Perspective of Economic Institution
Bibliographic record
Abstract
The countries along the Belt and Road (B&R) are important destinations of China's outward foreign direct investment (OFDI). Based on the panel data (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)) on China's OFDI in 65 B&R countries, this paper sets up a Heckman two-stage model, and then empirically analyzes how China's OFDI is affected by the difference between China and the host country in economic institution. In addition, the authors explored whether China has institutional preference in the OFDI with different investment motives. The empirical test shows that: investment selection and investment scale of China's OFDI are promoted to different degrees by the economic institution of the host country, and the absolute distance between China and the host country in economic institution; China has different institutional preferences in market-seeking OFDI between the selection stage and the investment stage; In terms of technology-seeking OFDI, host countries with short economic institutional distance are preferred in the selection stage, and host countries with good economic institution and long economic institutional distance are preferred in the investment stage. The research results provide empirical evidence for China to continuously implement OFDI in B&R countries and create a green investment environment.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".