Contribution of Non Oil Exports to Economic Growth in Nigeria (1985-2015)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it