Economic Policy Uncertainty, Bilateral Investment Treaties, and Chinese Outward Foreign Direct Investment
Bibliographic record
Abstract
This paper examines the effect of bilateral investment treaties (BITs) in promoting Chinese outward foreign direct investment (COFDI) in the presence of rising economic policy uncertainty in China’s partner countries. We postulate that the signing of BITs should help stimulate COFDI because the treaties send a credible signal to foreign investors about the host country’s intent to protect Chinese investment, and make it more difficult for the host country to violate its treaty obligations. BITs that contain rigorous investment protection and liberalization provisions, in particular, should be more likely to encourage COFDI as they directly influence Chinese investors’ expectations about the stability, predictability, and security of the host market. However, while BITs generally promote COFDI, host country economic policy uncertainty may also limit their effectiveness. This is because uncertainty tends to undermine investor confidence, trigger capital flows from high- to low-risk countries, and dampen commercial activities. Poisson pseudo-maximum likelihood (PPML) estimation models of the determinants of COFDI to 188 countries between 2003 and 2017 lend substantial support to our conjectures.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".