Economic Policy Uncertainty and China’s FDI Inflows: Moderating Effects of Financial Development and Political Stability
Bibliographic record
Abstract
This paper investigates the impact of global EPU and China’s EPU on China’s FDI inflows, examining whether financial development and political stability moderate these relationships. Using panel data from 212 countries spanning 2009 to 2022, we first establish causal direction through Granger causality tests, then employ instrumental variable estimation to address endogeneity concerns, while conducting heterogeneity analysis across development levels and Belt and Road Initiative participation. We find that both global and domestic EPU significantly reduce China’s FDI inflows, with a 1% increase in China’s EPU leading to a 0.083% decrease in FDI inflows. However, political stability and financial development serve as effective moderators, reducing EPU’s negative impact by up to 60% and 70%, respectively. The effects vary substantially across investor countries: non-developed countries show ten times stronger sensitivity to EPU than developed countries, while Belt and Road Initiative countries demonstrate 86% lower sensitivity than non-BRI countries. This research advances EPU–FDI theory by demonstrating how institutional quality creates “policy buffers” against uncertainty and provides policymakers with evidence that strengthening political stability and financial development can maintain investor confidence during uncertain periods, while strategic international partnerships can insulate investment flows from policy volatility.
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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 | 0.004 |
| 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 | 0.001 | 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".