The Moderation Role of Corruption in the Relationship between Foreign Direct Investment and Economic Growth in Sub-Saharan African Countries
Bibliographic record
Abstract
Foreign direct investment has recently become a major source of external financing among the developing countries. The Sub-Saharan Africa has become a major investment destination by the foreign investors. The literature shows that foreign direct investment contributes to economic growth through technological spillovers from developed countries to developing countries. In addition, FDI leads to the human capital development and employment creation. It also promotes international trade integration thus creating a competitive environment for local enterprises. Despite the increase in FDI in the Sub-Saharan Africa region, proportionate economic growth has not been realized. Corruption levels are also high in the region thus necessitating the need to investigate its role in the FDI economic growth nexus. Many studies have investigated the direct relationship between FDI and economic growth. There is also quite a number of studies that have studied the direct relationship between corruption and economic growth. However, a study on the moderating role of corruption on the relationship between FDI and economic growth is yet to be carried out. This study therefore investigates the moderating role of corruption on the relationship between FDI and economic growth in the Sub-Saharan Africa using data from 46 countries. The study uses fixed effects model. The study finds a negative and significant coefficient of the interaction term between FDI and corruption. This finding reveals that a corruption distorts the effectiveness of FDI in realizing economic growth. The study recommends the need for government to put in place strong institutions that deter corruption in the region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".