Does FDI Impact the Economic Growth of BRICS Economies? Evidence from Bayesian VAR
Bibliographic record
Abstract
This paper examines the dynamic relationship between foreign direct investment (FDI), economic growth, and trade openness in BRICS countries. Our research aims to address a significant gap in the literature by focusing on this crucial group of emerging nations, given their substantial contribution to the global economy. Annual data for these economies from 1991 to 2020 were collected from various secondary sources. This study employed the Bayesian VAR framework to investigate the panel data. The Pedroni residual cointegration test was used to check the existence of a long-run relationship between FDI and economic growth. The results provided evidence that foreign direct investment (FDI) does exhibit a substantial correlation with economic growth in the short run. However, no long-run relationship was found in the case of BRICS economies. This research contributes to methodological innovation by introducing the Bayesian VAR framework, offering a deeper understanding of the dynamic interactions among these key variables. The incorporation of this framework yields estimates that are both stable and reliable, which is certainly a novelty of this paper. The findings of this study have implications suggesting that policymakers from these emerging economies should establish mechanisms that will monitor the short-term impacts of FDI and adjust policies accordingly to maximize economic gains. The government should tailor policies to the specific circumstances of each country for sustainable economic development.
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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 | 0.001 |
| 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".