The Effects of Geopolitical Risk on Foreign Direct Investment in a Transition Economy: Evidence from Vietnam
Bibliographic record
Abstract
Foreign direct investment (FDI) is a key driver of economic development of both developed and developing countries. Understanding and having insights into the factors that motivate increased FDI arevery important for both academics and policy makers. A key factor that multinationals incorporate in their decisions on FDI is geopolitical risk (GPR). Therefore, this study is devotedto investigating the short-term and long-term effects of GPR on FDI in Vietnam. Data used in this study are the yearly geopolitical risk index, FDI, and other control variables covering the period from 1986 to 2021. Using the autoregressive distributed lag (ARDL) bounds testing approach, the empirical results confirm that geopolitical risk (GPR) has a significantly negative effect on FDI in Vietnam in the longterm. Specifically, in the longterm, 1 percent increase in the GPR index is associated with 5.7983 percent decrease in Vietnam’s FDI. In addition, the results derived from the ARDL model indicate that in the shortterm, GPR has a significantly positive effect on the FDI for the one-year lag, meaning that an increase in the GPR index leads to an increase in FDI. Moreover, the results derived from the error correction model (ECM) indicate that 42.89% of the disequilibria from the previous year are converged and corrected back to the long-run equilibrium in the current year. Based on the findings, some policy implications are drawn for policymakers to mitigate the negative effects of GPR on FDI.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".