Does Governance Matter for Foreign Direct Investment? A Comparative Analysis
Bibliographic record
Abstract
This study attempts to determine how important governance quality is for attracting foreign direct investment (FDI). To do so, using a combination of entropy weighting and TOPSIS methods, we assess the governance quality in 171 countries and correlate country rankings provided by the TOPSIS method with the FDI inflows performance of these economies. The findings from the analysis indicate that Voice and Accountability, Control of Corruption, and Regulatory Quality are the most important attributes of governance quality. Furthermore, Finland, Denmark, New Zealand, Sweden, Switzerland, Netherlands, Norway, Luxembourg, Canada, and Australia, are the best ten performing countries in terms of governance quality, respectively, while the worst-performing ten countries of governance quality are Myanmar, Zimbabwe, Eritrea, Chad, Uzbekistan, Tajikistan, Venezuela, Equatorial Guinea, Turkmenistan, and Angola, respectively. The findings also imply a close correlation between governance quality and inward FDI performance. The countries with higher governance performance are also mostly the economies attracting more FDI.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".