Aid and investment in LDCs: A robust approach
Why this work is in the frame
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Bibliographic record
Abstract
This paper uses panel data and the Local Linear Kernel Estimator (LLKE), to investigate the effects of aid on physical capital investment in developing countries. Specifically, we investigate the robustness of the relationship between aid and physical capital investment in Less Developed countries (LDCs) using two different measures of aid and five measures of the policy environment. We find that external aid has a positive and significant impact on physical capital investment given the support of the sample data we use. This effect is robust to the measurement of aid as well as the policy environment. However, the character of the positive relationship between aid and investment varies with the combination of the aid measure and the policy environment. We find that conditional on inflows, the better the policy environment, the higher the investment rate, all things being equal. The results have implications for aid research and aid policy.
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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 | 0.001 | 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 | 0.000 | 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