Towards a Multidimensional Model for Evaluating the Sustainable Effect of FDI on the Development of Host Developing Countries: Evidence from Africa
Bibliographic record
Abstract
This study aims to comprehensively evaluate the sustainable impact of FDI on the development of host African countries. Previous empirical studies seem to have overestimated the impact of FDI by limiting its effects to one aspect or sub-aspect of sustainable development. This study focuses on the sustainable/net effect of FDI on development in Africa. To achieve this, a multidimensional model that combines two opposing views (mainstream theory of economic development and dependent theory) was tested. Panel data of 35 African countries with the PMG/ARDL approach were used to probe the sustainable effect of FDI from 1990 to 2020. The key findings of this study reveal that the overall estimated sustainable effect of FDI on real GDP per capita is statistically minuscule for the entire sample. Thus, the effect of FDI on the development of host African countries is not inherently more important. The most striking result that emerged from the data is that environmental degradation is the dominant variable that adversely influences overall development in Africa. Another striking finding that emerged from the data is that income inequality, in general, has a significant negative impact on real GDP per capita in the long run. More importantly, the results of this study confirm that CO2, GINI, and GOV play important roles in the relationship between FDI and African development. Estimates of the error correction term for each specific country are negative and statistically significant. The fastest speed of adjustment was observed in Morocco, while the lowest was recorded in South Africa. Furthermore, this study presents different policy implications based on the long-term 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".