Does Economic Growth Attract FDI Inflows? A Dynamic Panel Analysis
Bibliographic record
Abstract
Economic growth is deemed to be a conducive factor in attracting foreign direct investment (FDI) as it often confers location advantage to host countries and fosters business confidence. This paper examines the short-run and the long-run effects of economic growth on FDI inflows. The empirical analysis is conducted through the Generalized Method of Moments (GMM) System estimator for dynamic panel models. The main results show significant positive effects of economic growth on FDI inflows, and they indicate that the magnitudes of these effects are statistically comparable over time and do not diminish with higher economic growth levels. They also reveal important variations in the magnitude of these effects across geo-economic regions and over pertinent economic variables such as economic development level, international trade and foreign investment openness, and endowment in natural resources. These findings underscore the significance of developing growth-enhancing policies that are designed on the basis of the economic and geo-economic characteristics of host countries. Such policies could be coupled with international trade and foreign investment openness directions to stimulate stronger responses of FDI inflows to economic growth and mitigate the implications of unfavorable global and regional political conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".