Microestimates of wealth for all low- and middle-income countries.
Microestimates of wealth for all low- and middle-income countries.
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对所有低收入和中等收入国家财富的微观估计。
DOI：
10.1073/pnas.2113658119
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发表时间：
2022-01-18
影响因子：
11.1
通讯作者：
Blumenstock JE
中科院分区：
文献类型：
--
作者：
Chi G;Fang H;Chatterjee S;Blumenstock JE
许多关键的政策决策依赖于有关财富和贫困的地理分布的数据，但只有一半的国家可以访问足够的贫困数据。本文创建了一套完整的公开可用的微观估计，以分配相对贫困和财富在所有135个低收入和中等收入国家中的分布。我们提供了估计值的准确性和有效性的广泛证据，并为每个微观估计的置信区间提供了促进负责的下游使用的置信区间。这些方法和地图提供了一组研究经济发展和增长，指导干预，监控和评估政策的工具，并跟踪全球贫困的消除。
从战略投资到人道主义援助的分配，许多关键的政策决策都取决于有关财富和贫困的地理分配的数据。然而，许多贫困地图已过时或仅在非常粗糙的粒度水平上存在。在这里，我们开发了135个低收入国家（LMIC）（2.4公里）分辨率的135个低收入和中等收入国家（LMIC）的相对财富和贫困的微观估计。估计值是通过将机器学习算法应用于来自卫星，手机网络和地形图的庞大而异质数据的大量数据，以及来自Facebook的汇总和更识别的连接数据。我们使用来自56个LMIC的全国代表性家庭调查数据训练和校准估计值，然后使用来自18个国家的四个独立的家庭调查数据来验证其准确性。我们还为每个微观估计提供置信区间，以促进负责任的下游使用。这些估计值是免费提供的，以便他们希望它们能够对COVID-19-19大流行的有针对性的政策做出反应，为经济发展和增长的原因和后果奠定基础，并促进负责支持可持续发展的负责任决策。
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 Yet many poverty maps are out of date or exist only at very coarse levels of granularity.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.
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