Panacea, placebo, or poison? The impact of development aid on growth
Bibliographic record
Abstract
This article holistically evaluates the impact of developmental aid on economic growth. Although most scholars find that aid has no direct impact on economic development (for example, Easterly Citation2001) and some even question the value of aid altogether (Moyo Citation2009), we highlight that these claims are premature and unsupported by empirical evidence. While our structural equation model confirms that there is no direct impact of aid on growth, we find that aid has an indirect positive effect on economic development. Developmental aid leads to a decrease in a country's level of corruption, which then positively affects growth. The implications of this finding are that most aid is effective in that it leads to better governing practices and more transparency. These improved governing practices then positively impact a country's level of development. Résumé Cet article évalue globalement l'impact de l'aide au développement sur la croissance économique. Bien que la plupart des spécialistes (par exemple Easterly, Citation2001) montrent que l'aide n'a pas d'impact direct sur le développement économique et certains (par exemple, Moyo. Citation2009), en questionnent même sa valeur, nous devons souligner que ces allégations sont sous-développées et non basées sur des preuves empiriques. Bien que notre modèle d'équation structurelle confirme qu'il n'y a pas d'impact direct de l'aide sur la croissance, nous constatons que l'aide a un effet positif indirect sur le développement économique. L'aide au développement mène à une diminution du niveau de la corruption d'un pays, ce qui a ensuite une influence positive sur la croissance. Les implications de ces résultats sont que l'aide, dans la plupart des cas, est efficace dans le cas où elle conduit à de meilleures pratiques de gouvernance et à une plus grande transparence en général. L'amélioration de ces pratiques de gouvernance a ensuite un impact positif sur le niveau du développement d'un pays.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".