An Empirical Investigation into the Impact of FDI on Domestic Investments in East, Central and Southern Africa Region
Bibliographic record
Abstract
This study examines the impacts of FDI inflows on domestic investments for the East, Central and Southern Africa region. The main study problem is whether FDI inflows into the region leads to a crowding-out or a crowding-in impact on domestic investments and the mechanisms through which such impacts happens. The data was obtained from the World Development Indicators, World Bank, International Monetary Fund and Heritage Foundation for the period 1995 to 2021. Initially, the study targeted all the 25 member countries affiliated to the East, Central and Southern Africa region. However, the final sample dropped to 12 countries due to lack of data. The main empirical estimation model for the study is the fixed effects regression model that is applied for the panel data. From the main findings, the study concludes that: one, there exists a crowding-out effect on domestic investments as a result of FDI inflows into the East, Central and Southern Africa region; and two, the impacts seems to be happening through the real market as opposed to the financial market channels. Notably, the study finds the natural resource curse to be an important factor for FDI impacts for the region. Based on the foregoing conclusions, the policy implications are that reform interventions that prioritize real market may sustain benefits of FDI as the region works towards financial market reforms. Further, the region may consider prioritizing joint reform initiatives as well as national level reforms to become competitive in attracting FDI. Enhancing member states absorptive capacity, addressing problem of human capital flight and increasing investments in technology may accelerate FDI benefits innumerably for the region. Finally, there were limitations on data for some of the countries. That notwithstanding, the 12 countries analyzed offer a sufficient panel data for a credible and robust estimation results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".