five

Aid Fragmentation and Effectiveness: What Do We Really Know?

收藏
NIAID Data Ecosystem2026-03-11 收录
下载链接:
https://data.mendeley.com/datasets/2fdmft845w
下载链接
链接失效反馈
官方服务:
资源简介:
Aid Fragmentation and Effectiveness: What do we Really Know? (With Katja Michaelowa, Axel Dreher, and Franziska Spörri), World Development 99 (11/ 2017) Summary Aid fragmentation is widely recognized as being detrimental to development outcomes. We reinvestigate the impact of fragmentation in the context of growth, bureaucratic policy, and education, focusing on a number of conceptually different indicators of fragmentation, and paying attention to potentially heterogeneous effects across countries, sectors, and channels of influence. Our systematic and detailed reexamination of existing empirical studies shows that this differentiation is crucial. In some sectors—such as primary education—donor concentration or limiting donor numbers appear to be detrimental rather than beneficial for development outcomes. In other areas, we find the expected negative effect, but only when we conceptualize fragmentation as a lack of lead donors (too limited concentration), rather than in terms of donor numbers. In all cases, sufficient initial administrative capacity in recipient countries prevents the negative and reinforces the positive effects of fragmentation. This stresses the importance of questioning the sweeping conclusions drawn by much of the previous literature. Based on what we currently know, generalizing judgments about the effect of aid fragmentation may be misleading.

援助碎片化与援助实效:我们究竟知晓哪些结论?(与卡特亚·米夏洛娃、阿克塞尔·德雷尔、弗兰齐斯卡·斯波里合著),《世界发展》(World Development)第99卷,2017年第11期 研究摘要 援助碎片化普遍被认为对发展成果有害。本研究针对增长、官僚政策与教育场景重新探讨援助碎片化的影响,聚焦多种概念维度不同的碎片化衡量指标,并关注不同国家、部门与影响渠道间可能存在的异质性效应。我们对现有实证研究开展系统性且细致的重新审视,结果表明这种维度区分至关重要。在部分部门(如初等教育领域),捐助方集中度提升或限制捐助方数量,反而似乎对发展成果有害而非有利。在其他领域,仅当我们将碎片化界定为缺乏主导捐助方(即集中度过低)而非以捐助方数量来衡量时,才能观察到预期的负向影响。在所有场景中,受援国具备充足的初始行政能力,均能规避碎片化的负面效应并强化其正面效应。这凸显了对既往大量研究得出的笼统结论进行质疑的重要性。结合当前已有认知,对援助碎片化的影响作出一概而论的判断可能存在误导性。
创建时间:
2019-06-17
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作