Higher Education and Economic Growth of Nigeria: Evidence from Co-integration and Granger Causality Examination
Bibliographic record
Abstract
This study investigates the level of co-integration between education and economic growth in Nigeria and the causality effect of education on economic growth. The study employs secondary form of data spanning from 2000 to 2018 and are sourced from UNESCO, World Bank and CBN statistical bulletin. The data are collected on GDP, education expenditure and gross enrolment ratio of higher education for the period under review. The study uses Johansen co-integration and Granger causality tests for analysis and the findings show that education and economic growth in Nigeria have a long term co-integration while Granger causality test reveals that education and gross enrolment ratio of higher education are not affecting economic progress and the GDP is not influencing both of them too. The implication is that if Nigeria’s educational system continues the way it is presently, it will remain a long term problem and will continue to negatively affect economic growth. Other countries will be benefiting from modern technologies through improvement on their educational system, but Nigeria may not be at the same pace if immediate policy changes in favor of education are not embraced. Thus, the study suggests major improvement on government’s annual budgets for education in order to decrease the population of out-of-school children and increase the stock of skilled human capital in the country.
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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 | 0.001 |
| 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".