Foreign Direct Investment, Export Performance and Sustainable Development in Nigeria
Bibliographic record
Abstract
This study sets out to examine the role of manufacturing sector Foreign Direct Investment (FDI) in the quest for export sector diversification in Nigeria for sustainable development. This objective was achieved by estimating the effects of manufacturing sector FDI on manufactured goods export from Nigeria using the Autoregressive Distributed Lag estimating technique. The study discovered that FDI inflows into the country’s manufacturing sector impacted negatively on manufactured exports in the short run. The short run result nevertheless gave way to a positive and significant influence of FDI on manufactured exports in the long run, indicating that this form of foreign capital is important for manufactured export promotion in Nigeria. The resulting long run positive FDI- spillovers on export performance in Nigeria is in tandem with the neoliberal theoretical viewpoint that developing countries can rely on FDI as ladder to sustainable development. The findings suggest that sustainable development can be enhanced in Nigeria by exploiting the channel of positive spillovers from sector specific FDI inflows. The study concludes that with appropriate policy stance, one important way of pursuing the long run goal of sustainable development is to route FDI inflows in the direction of the country’s manufacturing sector.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".