The Effect of Education Expenditure on Per Capita GDP in Developing Countries
Bibliographic record
Abstract
Is further public and private investment in education warranted in developing countries that is economically efficient? Does an increase in education expenditure, generate a positive impact on per capita GDP in developing countries? If so, is the impact different from that of Sub-Saharan African (SSA) countries? Education is one of the key factors of promoting economic growth because of its role in enhancing human capital thus productivity. However, adverse macroeconomic conditions and increased competition for scarce public funds have reduced governments’ capacity to expand education expenditure to improve labor productivity. I use the ‘system’ GMM estimator to estimate the effect of an increase in education expenditure on per capita GDP. The uniqueness of this paper is that unlike other studies where only one country or region is considered, this paper examines the impact of increased spending on education on per capita GDP in developing countries. The findings indicate that expansion in education expenditure in developing countries affects per per capita GDP positively, and the effect is not different from that of SSA countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".