发表机构
Columbia Business School; School of Information, UC Berkeley; Department of Economics, UC San Diego; Graduate School of Business, Stanford University（哥伦比亚商学院; 加州大学伯克利分校信息学院; 加州大学圣地亚哥分校经济系; 斯坦福大学商学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究将贫困最小化构建为统计学习问题，利用34个国家的家庭消费调查数据，估算出将全球极端贫困率降至1%的年成本为2110亿美元，仅为全民基本收入成本的19%，占全球GDP的0.28%。
AI 中文摘要
我们通过直接转移支付研究贫困最小化问题，将其构建为统计学习问题，同时保留现实项目面临的信息约束。使用来自34个国家的全国代表性家庭消费调查（这些国家的贫困人口占全球的76%），我们估计将贫困率从基准的13%降至1%，每年名义成本为2110亿美元。这是总贫困缺口相应减少额的4.0倍，但仅为全民基本收入成本的19%。全球推算结果显示，消除极端贫困的成本为全球GDP的0.28%。
英文摘要
We study poverty minimization via direct transfers, framing this as a statistical learning problem while retaining the information constraints faced by real-world programs. Using nationally representative household consumption surveys from 34 countries that together account for 76% of the world's poor, we estimate that reducing the poverty rate to 1% (from a baseline of 13%) would cost $211 B nominal per year. This is 4.0 times the corresponding reduction in the aggregate poverty gap, but only 19% of the cost of universal basic income. Extrapolated globally, the results imply a cost of 0.28% of global GDP to (approximately) end extreme poverty.
