Contribution of Non Oil Exports to Economic Growth in Nigeria (1985-2015)
Bibliographic record
Abstract
This study examines the contribution of non oil export to the growth of the Nigerian economy for the period 1985-2015. The economy is experiencing a fall in exchange earning, a fall in GDP, depletion of external reserve, scarcity of foreign exchange, and high cost of goods. This is as a result of the sudden fall in international oil price. Thus, this forms the motivation for the study. Augmented Dickey Fuller was used to test for unit root and to ascertain the stationarity of the variables. The result showed non oil exports to be stationary at level while economic growth proxied by Gross Domestic Product (GDP) and exchange rate were stationary at first difference. Auto-regressive distributed lag (ARDL) model was then employed to ascertain the relationship between non oil exports and GDP. The Bound test conducted showed the presence of cointegration which means a long run relationship among the variables existed. The ARDL regression result indicated a positive and significant relationship between non oil exports and GDP. This means non oil exports contributed significantly to economic growth in Nigeria. The result also revealed that exchange rate had a negative though not significant relationship with GDP which is in line with economic theory. The study recommended making legislation that makes participation in non oil sectors like agriculture, solid minerals and manufacturing easy by both local and foreign investors, provision of credit at lower interest rate to the non oil sectors and direct participation in developing these sectors by the government.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".