Is Aid Allocation Consistent with Global Poverty Reduction?: A Cross-donor comparison (Japanese)
Bibliographic record
Abstract
In this paper, we investigate the gap between the first target of the Millennium Development Goals (MDGs) and the actual allocation of grant aid in the late-1990s and the early-2000s in order to identify necessary policy adjustments to achieve the goal. As a theoretical framework, we extend the poverty-targeting model of Besley and Kanbur (1988) by considering multiple donors and possible strategic interactions among them. To test theoretical predictions, we employ detailed data on grant aid allocation of eleven major aid donor countries and on aid disbursement of six international institutions including the IBRD, IDA, and UN organizations. Four main empirical results emerged. First, both in the late-1990s and the early-2000s, grant allocations from Canada, France, Japan, the Netherlands, and UK are consistent with the necessary conditions of optimal poverty targeting. Second, we found that there is a negative population scale effect for aid allocation, suggesting that strategic motives may also exist. Third, the overall results for multilateral donors indicate that allocation patterns are consistent with the theory of poverty targeting. Finally, there has been a recent improvement in coordination among major donors in reducing global poverty.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".