Determining Factors of FDI Flows to Selected Caribbean Countries
Bibliographic record
Abstract
Foreign direct investment (FDI) is a vital ingredient in achieving sustained growth in the Caribbean region. However, FDI inflows have been affected by issues such as market factors, trade barriers, costs factors, investment climate, political and foreign exchange stability. To this end, this paper examines the factors affecting FDI flows into Caribbean countries. We argue that Small Island Developing States in the Caribbean (SIDSC) can be affected by issues such as their small market size, high cost of energy, proneness to exogenous shocks from commodity prices, natural disasters and climate change. A point to note is that countries in the Caribbean with natural resources are expected to have biased FDI inflows. Additionally, countries throughout the Caribbean have different economic and productive structures and unique issues that can affect them based on their individual characteristics. To this end, a panel Autoregressive Distributed Lagged (ARDL) model is used to determine the factors affecting FDI inflows in the Caribbean over the period 2000 to 2019. The findings reveal that GDP growth, natural resource rents, gross capital formation and population growth are significant factors influencing growth in the Caribbean region.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".