Assessing Aid—What Works, What Doesn't, and Why summarizes the findings of a multi-year research program on aid effectiveness. Official Development Assistance declined by one-third in real terms in the 1990s. There are a number of reasons for this, but one factor has been a sense that aid does not work very well. Assessing Aid aims to understand when aid works and when it does not, so that the lessons can be used to make aid more effective. A key theme of the report is that aid is a combination of money and ideas. Money has a big impact, but only if countries have good economic institutions and policies. The ideas—or knowledge creation—side of aid is critical for helping countries reform and for helping communities effectively provide public services: education, health, water supply, and others. Authors Assessing Aid—What Works, What Doesn't, and Why was written by David Dollar and Lant Pritchett of the World Bank Development Research Group. It culminates a research program on aid effectiveness initiated and supervised by Lyn Squire. Original research for this report includes work by the authors and by Craig Burnside, Klaus Deininger, Shanta Devarajan, William Easterly, Deon Filmer, Jonathan Isham, Dani Kaufmann, Elizabeth King, Jennie Litvack, Luis Serven, Lyn Squire, Vinaya Swaroop, and Jakob Svensson. Documents Press release (English, Portugues, Francais, Espanol) Full text (English)
Overview (English)
Powerpoint presentation (English, Francais, Espanol) Background Papers Measuring aid flows: a new approach Aid allocation and poverty reduction Does Economic Analysis Improve the Quality of Foreign Assistance? Foreign aid's impact on public spending Aid, the incentive regime, and poverty reduction What explains the success or failure of structural adjustment programs? Civil Liberties, Democracy, and the Performance of Government Projects Aid, policies, and growth The ghost of financing gap: how the Harrod-Domar growth model still haunts development economics Does participation improve project performance: establishing causality with subjective data
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