文章基本信息

标题：Microestimates of wealth for all low- and middle-income countries
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作者：Guanghua Chi ; Han Fang ; Sourav Chatterjee 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2022
卷号：119
期号：3
DOI：10.1073/pnas.2113658119
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Significance Many critical policy decisions rely on data about the geographic distribution of wealth and poverty, yet only half of all countries have access to adequate data on poverty. This paper creates a complete and publicly available set of microestimates of the distribution of relative poverty and wealth across all 135 low- and middle-income countries. We provide extensive evidence of the accuracy and validity of the estimates and also provide confidence intervals for each microestimate to facilitate responsible downstream use. These methods and maps provide a set of tools to study economic development and growth, guide interventions, monitor and evaluate policies, and track the elimination of poverty worldwide. Many critical policy decisions, from strategic investments to the allocation of humanitarian aid, rely on data about the geographic distribution of wealth and poverty. Yet many poverty maps are out of date or exist only at very coarse levels of granularity. Here we develop microestimates of the relative wealth and poverty of the populated surface of all 135 low- and middle-income countries (LMICs) at 2.4 km resolution. The estimates are built by applying machine-learning algorithms to vast and heterogeneous data from satellites, mobile phone networks, and topographic maps, as well as aggregated and deidentified connectivity data from Facebook. We train and calibrate the estimates using nationally representative household survey data from 56 LMICs and then validate their accuracy using four independent sources of household survey data from 18 countries. We also provide confidence intervals for each microestimate to facilitate responsible downstream use. These estimates are provided free for public use in the hope that they enable targeted policy response to the COVID-19 pandemic, provide the foundation for insights into the causes and consequences of economic development and growth, and promote responsible policymaking in support of sustainable development.
关键词：enpovertymachine learninglow- and middle-income countriespoverty mapssustainable development