Impact of Macroeconomic, Governance and Risk Factors on FDI Intensity—An Empirical Analysis
Bibliographic record
Abstract
The study analyzes the impact of macroeconomic, governance and risk factors on foreign direct investment (FDI) intensity with respect to the US market during the period 1960–2019. The study adopted regression methodology. The FDI, macroeconomic and risk data were sourced from the Federal Reserve Economic Data (FRED) database. The governance data were collected from the World Bank Governance Database. The study suggests that infrastructural investments lead to higher FDI. A stronger Euro leads to higher FDI activity in the United States. Research & Development investments is a significant factor which contributes towards enhanced FDI activity. The higher the corporate profitability, the greater the FDI inflows. Exports and imports are significant factors which determine FDI in markets like USA. Inflation has a negative impact on FDI flow regulations, which are aimed to promote private sector development is negatively related to FDI intensity. FDI activity by firms tend to be lower when corruption levels are higher in the country. The higher the governance perception in terms of voice and accountability of citizens, the greater the propensity to attract FDI. The perception of the effectiveness of a government’s commitment towards the quality of public and civil services is directly related to FDI investment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".