Aid and Economic Growth: A Robust Approach
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it