Causality between Financial Development and Foreign Direct Investment in Asian Developing Countries
Bibliographic record
Abstract
This study investigated the linkages between foreign direct investment (FDI) and financial development measured by banks and stock markets in 30 Asian developing countries from 1986 to 2019. We used a bivariate model with Granger causality tests to test the reverse causality between FDI and financial development and multivariate models with the system generalized method of moments (GMM) estimator to identify how one factor affected the other. Our Granger test results showed a bidirectional linkage between FDI and financial development. Using the system GMM estimator, we showed that greater financial development drew more inward FDI to host countries. Similarly, local financial markets benefited from FDI by improving capital mobilization and financial services and products to intensify economic activity. Our findings suggest that, to attract FDI, policymakers should improve local banks and the stock market environment with strong institutional backgrounds to enhance foreign investors’ confidence and provide incentives to increase cross-border investments in host economies.
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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 | 0.001 | 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".