Aid and Economic Growth: A Robust Approach
Bibliographic record
Abstract
Abstract This paper uses panel data and the Local Linear Kernel Estimator (LLKE) to investigate the effects of aid on economic growth in developing countries. Specifically, we investigate the robustness of a popular parametric specification of the aid/economic growth relationship in Less Developed countries (LDCs). First, we find that aid has a significant impact on economic growth given the support of the sample data we use. However, the effect depends on how aid is measured. We find a positive growth effect when aid is measured as aidgni but no significant growth effect when aid is measured as aidpercap. Second, we find some evidence of increasing returns to aidgni. Finally, we find that a “good” policy environment increases the effectiveness of aid in LDCs, all things equal. The impact of the policy environment on growth varies according to how the policy environment is measured. Our results generally support the popular quadratic parametric specification of the aid/growth relationship. Our results have implications for aid policy and for research on the effectiveness of aid.
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".