Education Expenditure-Led Growth: Evidence from Nigeria (1980-2018)
Bibliographic record
Abstract
This study examines the belief that education fosters economic growth by analyzing the impact of Government education expenditures at different levels on economic growth using Nigerian data for the period 1980-2018. Time series econometrics tests like Unit Root, cointegration, Error Correction Model and Granger Causality were employed to test the hypothesis of education expenditure-led growth strategy. The outcomes of the studies showed that that there is cointegration between total government education expenditures, primary, secondary and tertiary education expenditure and economic growth. The outcomes of the study also revealed that all levels of education expenditure contribute to economic growth positively (tertiary education exerting more positive impact) and are statistically significant (except primary education expenditure that is not significant) at 5%level. The study equally revealed bi-directional causality between t all levels public expenditure on education and economic growth. The study therefore, recommends improved funding for education at all levels given their interconnections. It also recommends that funding of primary education should by supported Federal Government as weak primary school funding will impact on quality of pupils that graduate to secondary school. Again policies aimed at diversifying and broadening the Nigerian economy be rekindled as economic growth have the potential of increasing education spending.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".