全球智库信息集成服务系统(G2TT): DP16385 Machine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance

G2TT

来源类型	Discussion paper
规范类型	论文
来源ID	DP16385
	DP16385 Machine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance
	Emily Aiken; Suzanne Bellue; Christopher Udry; Dean Karlan; Joshua Blumenstock
发表日期	2021-07-21
出版年	2021
语种	英语
摘要	The COVID-19 pandemic has devastated many low- and middle-income countries (LMICs), causing widespread food insecurity and a sharp decline in living standards. In response to this crisis, governments and humanitarian organizations worldwide have mobilized targeted social assistance programs. Targeting is a central challenge in the administration of these programs: given available data, how does one rapidly identify the individuals and families with the greatest need? This challenge is particularly acute in the large number of LMICs that lack recent and comprehensive data on household income and wealth. Here we show that non-traditional “big” data from satellites and mobile phone networks can improve the targeting of anti-poverty programs. Our approach uses traditional survey-based measures of consumption and wealth to train machine learning algorithms that recognize patterns of poverty in non-traditional data; the trained algorithms are then used to prioritize aid to the poorest regions and mobile subscribers. We evaluate this approach by studying Novissi, Togo’s flagship emergency cash transfer program, which used these algorithms to determine eligibility for a rural assistance program that disbursed millions of dollars in 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 at the time, the machine learning approach reduces errors of exclusion by 4-21%. Relative to methods that require 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to contribute to humanitarian response efforts, particularly in crisis settings when traditional data are missing or out of date.
主题	Development Economics ; Public Economics
关键词	Targeting Machine learning Poverty Mobile phone data
URL	https://cepr.org/publications/dp16385
来源智库	Centre for Economic Policy Research (United Kingdom)
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/545348
推荐引用方式 GB/T 7714	Emily Aiken,Suzanne Bellue,Christopher Udry,et al. DP16385 Machine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance. 2021.