Are Resource-Rich Countries More Attractive than Countries with Good Institutions to Foreign Direct Investors in Sub-Saharan Africa?
Bibliographic record
Abstract
Africa is no longer behind in the race of acquiring global share of foreign direct investment (FDI) compared to other developing regions. This study uses FDI dataset of 27 sub-Saharan African (SSA) countries and examines what drives the recent trend of higher FDI flows to Africa. A variety of empirical techniques (e.g. cross-section OLS, panel fixed effects and dynamic GMM) are employed for identifying main drivers of FDI in African countries. The finding of this research suggests that resource endowment is the main driver attracting FDI to SSA countries. More specifically, empirical estimates suggest that a one-standard deviation increase in resource endowment in the SSA countries is associated with an increase in FDI ranging from 34% to 83%. Empirical result also suggests that between institutions and resource endowment, resource endowment is the most robust determinant of FDI in SSA countries.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".