Determinants and Growth Effect of FDI in South Asian Economies: Evidence from a Panel Data Analysis
Bibliographic record
Abstract
This empirical study investigates the determinants and growth effect of FDI in case of four South Asian countries over the period of 1995-2000. It comprises two major analytical parts. Firstly, we incorporate a gravity model equation using panel data in order to investigate potential determinants of foreign direct investment in these countries. Secondly, we use a growth model equation to investigate growth effect of foreign direct investment in the countries. In both analyses, we employ Arellano-Bond dynamic panel system method of moment estimation method. The results derived from this study suggest that: (1) the pulling, pushing and cyclical factors are crucially important in determining FDI in South Asian countries; (2) foreign direct investment in South Asian countries are significant and positively associated with growth rate, but seem to be having average supportive to the growth rate in these countries.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".