Minding the Gap
This paper applies novel techniques to long-standing questions of aid effectiveness. It first replicates findings that donor finance is discernibly but weakly associated with sector outcomes in recipient countries. It then shows robustly that donors' own ratings of project success provide limited information on the contribution of those projects to development outcomes. By training a machine learning model on World Bank projects, the paper shows instead that the strongest predictor of these projects’ contribution to outcomes is their degree of adaptation to country context, and the largest differences between ratings and actual impact occur in large projects in institutionally weak settings.
| Main Authors: | , , |
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| Format: | Working Paper biblioteca |
| Language: | English English |
| Published: |
World Bank, Washington, DC
2023-08-02
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| Subjects: | DEVELOPMENT OUTCOME, WORLD BANK PROJECTS, IMPACT EVALUATION, AID EFFECTIVENESS, MACHINE LEARNING METHOD, |
| Online Access: | http://documents.worldbank.org/curated/en/099434007312336860/IDU0fc5d219f0d389045880bfb20d9cc719ef84a https://openknowledge.worldbank.org/handle/10986/40143 |
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