Exploring the Determinants Attractiveness to Foreign Direct Investments: Do Public Governance, Infrastructure and Macroeconomic Policies Matter?
Bibliographic record
Abstract
The aim of this paper is to determine the factors that attract Foreign Direct Investments (FDIs) to Central and Eastern European Countries (CEECs) and Southern and Eastern Mediterranean Countries (SEMCs). To this end, this paper tested three variables representing public governance, physical infrastructure and macroeconomic quality, over a ten-year period stretching from 2008 to 2017. The results of the regressions estimated on CEE countries show that entrepreneurs are attracted to this region mainly for governance and infrastructure quality. Macroeconomic policy variables seem to attract less FDIs to these countries. Using aggregated  variables, the results of the regressions estimated on SEMCs show that the governance variable becomes statistically significant but retains a low value. The other variables of physical infrastructure and macroeconomic policies seem to be more robust and better explain FDI inflows to this region.
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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 | 0.000 | 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 | 0.001 |
| 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".