Investment Motives in Africa: What Does the Meta-Analytic Review Tell?
Bibliographic record
Abstract
Abstract Over the past two decades, Africa has witnessed a dramatic increase in foreign direct investment (FDI) despite a lack of significant changes in infrastructure and the host country’s policies. What are the motives to invest in Africa? How do these investment motives differ for firms from developed and emerging markets? Several studies empirically tested these questions, however, provided inconclusive results. By taking 735 estimates extracted from 51 studies and applying advanced meta-analysis techniques, this study examines the motives of FDI in Africa. We found that compared to market-seeking motive, the effect size of resource seeking and efficiency seeking is larger (smaller) on FDI attractiveness in Africa. In terms of effect size, the impact of asset-seeking motive on FDI is statistically comparable to that of market-seeking motive. Contrary to general perceptions, the impact of natural resources on FDI attractiveness in Africa is not different from market seeking for developed countries’ firms. Our results show that compared to GDP per capita, the effect size of accessing minerals and oil reserves on FDI attractiveness in Africa is positive and significant for global and emerging market firms. Our research shows that there is more likelihood of type I and type II publication selection bias in this research field.
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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 | 0.046 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".