The Relationship Between Education Funding and Student Performance in Unity Schools in Nigeria
Bibliographic record
Abstract
Developing countries need to significantly increase education funding in order to meet the Sustainable Development Goal 4 (SDG 4) targets. The question remains as to whether increased funding leads to improved learning outcomes and whether allocated scarce resources are used efficiently to maximize learning outcomes. Using ordinary least squares (OLS) regression and stochastic frontier analysis (SFA), we investigate the relationship between education funding and student performance using a representative sample of secondary schools in Nigeria and analyze school efficiency in utilizing the funding (budget). The OLS analysis robustly indicates that an increase in the released budget—in real terms—is consistently associated with a higher pass rate, while the SFA shows that after controlling for past budgets the average inefficiency is approximately 47–60%. Our correlation findings support the argument for more funding to improve performance. However, the SFA shows that more can still be achieved with current levels of funding if schools become more efficient. We observe differences across school type and school geographical location, concluding that these factors also influence performance and efficiency. Future research should examine the performance and efficiency differences between all-girls schools and mixed schools, and the higher efficiency of schools in low socioeconomic status (SES) and conflict-affected states.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".