The Impact of Foreign Direct Investment in the Western Balkan Countries - A Panel Data Analysis
Bibliographic record
Abstract
Foreign Direct Investment (FDI) has a significant effect on the economic growth and development of host economies, but also on international economic integration through globalization. Particular aspects of this topic are being extensively addressed by scientific research in recent decades. The purpose of this paper is to determine whether globalization and through it the Foreign Direct Investment (FDI) has an impact on the economic growth (GDPgr) of the Western Balkan countries which are facing a transitional phase. The relation between FDI and economic growth has been analyzed by employing econometric models with panel data approach: linear regression with poled data, the Fixed Effects model, and the Random-Effects model (GLS). The study is based on panel data of six countries for the period between 2004-2018, obtained by the World Bank. The results of the Random Effects model (GLS) shown that lagged FDI has a significant impact on the economic growth (GDPgr) of the Western Balkans (p<0.05%), as well as gross capital formation (Cap) and government expenditure (Gov) whereas export (Ex) has been excluded from the model. The results also shown that there are significant differences in the factors influencing economic growth among countries in the region (LM Method - Breusch-Pagan test; p=0.02455 < 0.05).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".