An Analysis of Trends in Foreign Direct Investment Inflows to Africa
Bibliographic record
Abstract
This study examines the trends in Foreign Direct Investment (FDI) inflows to Africa, with the ultimate aim of proposing implications for policy action. The main source of data for this study is the UNCTAD (2018) database which at the time of the study contained FDI data from 1990 to 2016. The findings show that, although Africa is in dire need for FDI due to scarcity of capital, it is not able to attract as much FDI compared to advanced countries and other developing regions. The little FDI that comes to Africa is concentrated sub-regionally and country-wise. Region-wise, most FDI is concentrated on Southern Africa followed by Northern Africa with East Africa and Central Africa at the bottom. Country-wise, the two main recipients of FDI in each sub-region are Angola and South Africa (Southern Africa); Egypt and Morocco (North Africa); Nigeria and Ghana (West Africa); Tanzania and Ethiopia (East Africa) and Congo and the Democratic Republic of Congo (Central Africa). The FDI that comes into the continent is further concentrated in the primary (extractive) sector. As a result the benefits to the region have not been as significant as in East Asia where FDI was mainly into the secondary (manufacturing) sector. It is concluded that, FDI is a growth point that countries can count on as a source of resources for development, however, Africa need to change the approach adopted in promoting FDI, which focuses on providing incentives to creating a domestic environment conducive to entrepreneurship and business in general.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".