Foreign Direct Investment Flow to Africa: Does Natural Resources Matter？
Bibliographic record
Abstract
In this study, it explored connections between FDI inflows and natural resource. The paper is an effort to investigate a sample of 10 most resourced sub-Sahara African countries and examine the influence of natural resources on FDI inflow. Further, natural resource wealth is reflected to weaken the FDI inflow. This study discovers if the natural resource overflow affects the FDI inflows. By means of panel data for a sample dated 1990-2017, the paper employed fixed effects method and settles that natural resource slows down FDI inflow of the host nation. The results indicate that economic growth, labor force, trade openness and financial development framework promote FDI inflow in Sub-Sahara Africa countries. The study proposes that FDI inflow to SSA is not only driven by the availability of natural resources in a country but by some exogenous factors, countries with non-existence of natural resources and can obtain FDI by cultivating their bodies and policy environment. Second, multifaceted organizations like the IMF and the World Bank can play a significant role in assisting FDI by encouraging good organisations in SSA.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".