Foreign Aid Effectiveness in the Education Sector: A Dynamic Panel Analysis
Bibliographic record
Abstract
In the realm of development economics, foreign aid and economic development are interconnected concepts, both in theory and practice. Education, a fundamental human right, plays a pivotal role in shaping human capital and driving economic progress. With this in mind, the primary objective of this study is to explore the relationships between education aid and the various levels of schooling, namely primary, secondary, and tertiary, in developing countries. The effect of the primary, secondary, and tertiary level education aid of fifty developing countries with 19 years of panel data was investigated to determine the relationship with Primary Completion Rate, Secondary School Net Enrolment Rate, and Tertiary Gross Enrolment Rate, respectively. The study used the system GMM (One-step GMM and Two-step GMM). The findings suggest that a statistically significant relationship exists between education aid and various levels of education, and education aid effectively enhances the education outcome in developing countries. The findings also underline the importance of establishing sound economic foundations, addressing corruption, maintaining optimal Pupil-Teacher Ratio, and emphasizing female teachers. These factors collectively contribute to fostering an enabling environment for enhancing education outcomes in developing 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".