Institutions and Foreign Direct Investment: Evidence from Sub-Saharan Africa Regions
Bibliographic record
Abstract
The paper set out to investigate the nexus between institutional quality and inward FDI and how the presence of liberalization and financial development influence this linkage. We build on Dunning’s eclectic paradigm that focuses on locational advantages. A fixed effects approach is employed and the estimation results confirm the crucial role of institutional quality in attracting FDI inflows. However the impact varies with the particular group. In particular, apart from SADC, institutional quality seems to matter significantly in all the other groups especially in EAC and ECOWAS. Additional findings reveal a mixed impact regarding the presence of financial development and liberalization in the institution-FDI nexus: While Trade liberalization policies seem to be at the forefront in ECOWAS and SADC groups, it is credit depth and capital account openness that appear to matter most in EAC. We confirm the resilience of inward FDI during the global crisis and document a positive significant relationship between FDI inflows on the one hand and host market size and infrastructure development on the other. While a one-size-fits-all-policy should be discouraged due to the heterogeneous nature of SSA countries, overall, a comprehensive set of policies designed with caution to improve the institutional quality, the financial system, trade openness and capital account liberalization would be valuable for attracting FDI inflows to 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".