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理解社会安全网与家庭内部食物分配:来自孟加拉国的实验证据

Understanding social safety nets and intra-household food allocation: Experimental evidence from Bangladesh
Journal of Development Economics · 2025 · [{"name": "Fiona Coleman", "affiliation": ["Cornell University"]}, {"name": "Akhter Ahmed", "affiliation": []}, {"name": "Shalini Roy", "affiliation": ["International Food Policy Research Institute"]}, {"name": "John Hoddinott", "affiliation": ["International Food Policy Research Institute", "Cornell University"]}]

中文摘要

证据表明,社会保护能够改善膳食,但人们对以下问题知之甚少:其影响在家庭内部如何存在差异、转移支付的形式在多大程度上影响其在所有家庭成员间的分配、与单纯提供转移支付相比,加入关于营养与膳食重要性的培训是否会改变转移资源在家庭内部的分配方式,以及分配差异是否由生计机会的差异所塑造。我们使用在孟加拉国农村开展的两项随机对照试验(RCT)中收集的个人食物摄入数据来回答这些问题。结果压倒性地表明,无论家庭获得的转移支付类型(现金、食物或二者组合)、是否包含营养培训、地区背景如何,或衡量的具体膳食结果为何,食物收益均被平等分配。在考虑以下若干扩展分析时,这些发现模式依然成立:(1)分析更为聚合的人口群体;(2)采用替代性的膳食衡量指标;(3)分析份额而非水平;(4)考察相对于基线匮乏程度的影响;(5)分析可按人口特征归属的非食物结果所受的影响;(6)使用替代样本和替代估计模型重新估计影响。在发现少数显著差异之处,这些差异的幅度通常较小,且多有利于儿童。

Abstract

Evidence shows that social protection can improve diets, but little is known about how impacts vary within households, the extent to which the modality of the transfer affects how it is distributed across all household members, whether adding training on the importance of nutrition and diets alters the way transfer resources are allocated within the household, relative to a transfer alone, and if differences in allocations are shaped by differences in livelihood opportunities. We use individual food intake data from two randomized control trials fielded in rural Bangladesh to address these questions. Our results overwhelmingly demonstrate that food gains are distributed equally, regardless of the type of transfers households received (cash, food, or combination), inclusion of nutrition training, regional context, or specific dietary outcome measured. These patterns of findings hold when we consider several extensions: (1) analyzing more aggregated demographic groups; (2) considering alternative measures of diet; (3) analyzing shares rather than levels; (4) considering impacts relative to deprivation at baseline; (5) analyzing impacts on non-food outcomes that can be assigned demographically; (6) re-estimating impacts using alternate samples and alternate estimation models. Where the few significant differences are found, they are often small in magnitude and in favor of children.
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