Machine learning and phone data can improve targeting of humanitarian aid.
Machine learning and phone data can improve targeting of humanitarian aid.
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DOI：
10.1038/s41586-022-04484-9
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发表时间：
2022-03
期刊：
影响因子：
64.8
通讯作者：
Blumenstock JE
中科院分区：
文献类型：
--
作者：
Aiken E;Bellue S;Karlan D;Udry C;Blumenstock JE
COVID-19大流行摧毁了许多低收入和中等收入国家，导致普遍的粮食不安全和生活水平急剧下降。为了应对这场危机，世界各国政府和人道主义组织向15亿多人提供了社会援助。确定目标是管理这些方案的一个核心挑战：根据现有数据迅速查明最需要帮助的人仍然是一项艰巨的任务。在这里，我们表明，来自移动的电话网络的数据可以改善人道主义援助的目标。我们的方法使用传统的调查数据来训练机器学习算法，以识别移动的电话数据中的贫困模式;然后，经过训练的算法可以优先向最贫困的移动的用户提供援助。我们通过研究多哥的一个旗舰紧急现金转移计划来评估这种方法，该计划使用这些算法支付了价值数百万美元的COVID-19救济援助。我们的分析比较了结果，包括排除错误，总的社会福利和公平的措施，在不同的目标制度。相对于多哥政府考虑的地理定位选项，机器学习方法将排除错误减少了4- 21%。相对于需要全面社会登记的方法（假设的做法;多哥没有这样的登记），机器学习方法将排除错误增加了9- 35%。这些结果突出表明，新的数据来源有可能补充针对人道主义援助的传统方法，特别是在传统数据缺失或过时的危机环境中。机器学习算法可以利用调查和移动的电话数据来帮助确定最需要援助的人，补充针对人道主义援助的传统方法。
The COVID-19 pandemic has devastated many low- and middle-income countries, causing widespread food insecurity and a sharp decline in living standards. In response to this crisis, governments and humanitarian organizations worldwide have distributed social assistance to more than 1.5 billion people. Targeting is a central challenge in administering these programmes: it remains a difficult task to rapidly identify those with the greatest need given available data. Here we show that data from mobile phone networks can improve the targeting of humanitarian assistance. Our approach uses traditional survey data to train machine-learning algorithms to recognize patterns of poverty in mobile phone data; the trained algorithms can then prioritize aid to the poorest mobile subscribers. We evaluate this approach by studying a flagship emergency cash transfer program in Togo, which used these algorithms to disburse millions of US dollars worth of COVID-19 relief aid. Our analysis compares outcomes—including exclusion errors, total social welfare and measures of fairness—under different targeting regimes. Relative to the geographic targeting options considered by the Government of Togo, the machine-learning approach reduces errors of exclusion by 4–21%. Relative to methods requiring a comprehensive social registry (a hypothetical exercise; no such registry exists in Togo), the machine-learning approach increases exclusion errors by 9–35%. These results highlight the potential for new data sources to complement traditional methods for targeting humanitarian assistance, particularly in crisis settings in which traditional data are missing or out of date. Machine-learning algorithms can take advantage of survey and mobile phone data to help to identify people most in need of aid, complementing traditional methods for targeting humanitarian assistance.
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影响因子：
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作者：
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DOI：
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发表时间：
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影响因子：
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DOI：
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发表时间：
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期刊：
The American economic review
影响因子：
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