Foreign Direct Investment and Employment Creation in Nigeria
Bibliographic record
Abstract
This study investigated the impact of foreign direct investment on employment creation in Nigeria for the period of 35years (1985-2019). The study used five regressors (foreign direct investment, trade openness, government expenditure, infrastructural development, and exchange rate) and one explained variable (employment rate). The data were culled from the World Bank Development Indicators and analysis was carried out using unit root test, ordinary least square and granger causality test. The findings revealed that there is negative and insignificant relationship between trade openness, government expenditure, infrastructures and employment rate. However, positive relationship exists between foreign direct investment, exchange rate and employment but statistically insignificant at 5% level of significance. Based on the f-statistic result, the study concluded that foreign direct investment played a crucial role in creating employment for the citizens of Nigeria. It was therefore recommended among others that government should improve the state of infrastructures and security in the country as the present economy is characterized by terrorisms, kidnapping and robbery and this may drive out the investors in the country and discourage the potential ones.
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 | 0.000 | 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".