The impact of US and China geopolitical risk on foreign direct investment in Latin America
Bibliographic record
Abstract
We examine the impacts of the US and the Chinese geopolitical risk (GPR) on foreign direct investment (FDI) inflows in Latin America (LATAM) from 2000 to 2022, focusing on how bilateral trade and escalating US-China tensions (UCT) moderate these effects. LATAM’s neutrality in major geopolitical conflicts makes it an ideal region for analyzing indirect GPR effects. US and China's GPR may not directly impact LATAM's GPR levels but can create indirect spillovers affecting the FDI flows in the region through economic ties and escalating global tensions, particularly between the US and China. The findings reveal distinct spillover patterns. US GPR negatively impacts FDI in LATAM due to geographic proximity, while the positive impact of China GPR suggests a dynamic of risk-diversification-driven FDI inflows to LATAM. Higher bilateral trade with China turns the impact of Chinese GPR on FDI negative, while trade with the US amplifies the negative effect of US GPR. At high UCT levels, both US and Chinese GPR reduce FDI in LATAM, whereas at low UCT levels, their impact turns positive, highlighting the role of bilateral trade and UCT as key factors in shaping the impact of GPR spillovers from the US and China to FDI inflows in LATAM. Furthermore, GPR deters US FDI but not Chinese FDI, highlighting how firms' responses to geopolitical risk vary based on strategic priorities, risk tolerance, and state involvement. We contribute to the GPR literature by demonstrating that even regions distant from direct geopolitical conflicts remain vulnerable due to economic interconnections and global ripple effects. The findings provide key insights for policymakers and investors, highlighting the need for proactive strategies to mitigate geopolitical risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".