The impact of the China-U.S. trade war on net short-term capital flows in emerging markets
Bibliographic record
Abstract
This paper explores the impact of the China–U.S. trade war on net short-term capital flows in emerging markets. Despite rising global financial uncertainty, this topic has received limited attention in empirical research. Using quarterly panel data from 20 emerging economies spanning from the third quarter of 2011 to the fourth quarter of 2024, the study applies Random Effects (RE) regression, System GMM estimation, and Rolling VAR models to compare capital flow dynamics before and during the trade war. Based on the push–pull theoretical framework, the analysis distinguishes between external (push) and domestic (pull) driving forces, including the Federal Reserve interest rate, Global Economic Policy Uncertainty (GEPU), the Volatility Index (VIX), and domestic macroeconomic indicators. The results reveal a structural shift in the determinants of short-term capital flows: prior to the trade war, domestic factors such as exchange rates played a dominant role; during the trade war, GEPU and VIX emerged as more influential drivers. The study also identifies strong path dependence in capital flows, underscoring the persistent influence of previous capital movements. These findings highlight the growing sensitivity of emerging markets to external shocks and suggest the need to strengthen early warning systems and macroprudential policy tools.
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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 | 0.000 |
| 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".