Sustaining Economic Growth in Sub-Saharan Africa: Do FDI Inflows and External Debt Count?
Bibliographic record
Abstract
The quest for the attainment of economic development is sought after by all global economies, which by effect is expected to transcend to improving livelihoods and standard of living. However, several factors hinder the process of achieving sustained economic development, especially in developing countries. In this regard, assessing the extent of economic expansion orchestrated by foreign direct investment (FDI) inflows in vulnerable economies such as Sub-Saharan Africa (SSA), particularly in the face of the significant fall in global FDI inflow, is worthwhile. In essence, this study ascertains the impact of FDI inflows and external debt on economic growth amidst decline in FDI inflows and excessive foreign borrowings. The mixed order of integration from the stationarity test underpins the adoption of autoregressive distributed lag (ARDL) approach for data covering the period 1990 to 2018. The empirical results found FDI inflows play a crucial role in achieving economic expansion in the region. On average, FDI inflows, external debt, and foreign aids are more useful in expanding the economy compared to trade openness and exchange rate. Thus, this study recommends the need for SSA to open its economic borders for external capital, viz. FDI. A peaceful economic and political environment is a pre-condition to attract and maintain potential foreign investors. Stability in exchange rates is critical in achieving growth in FDI and other foreign resources. However, caution is required, especially in administration of external resources. Particularly, contracting external debt must strictly be driven by economic reasons rather than political motivation. Borrowed funds could be injected mainly into productive streams with the highest investment returns to boost economic development.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".