Microestimates of wealth for all low- and middle-income countries

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 Yet many poverty maps are out of date or exist only at very coarse levels of granularity.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.

Many critical decisions require accurate, quantitative data on the local distribution of wealth and poverty. Governments and nonprofit organizations rely on such data to target humanitarian aid and design social protection systems (1, 2); businesses use this information to guide marketing and investment strategies (3); these data also provide the foundation for entire fields of basic and applied social science research (4).Yet reliable economic data are expensive to collect, and only half of all countries have access to adequate data on poverty (5). In some cases, the data that do exist are subject to political capture and censorship (6, 7) and often cannot be disaggregated below the largest administrative level (8). The scarcity of quantitative data impedes policymakers and researchers interested in addressing global poverty and inequality and hinders the broad international coalition working toward the Sustainable Development Goals, in particular toward the first goal of ending poverty in all its forms everywhere (9).To address these data gaps, researchers have developed approaches to construct poverty maps from nontraditional data. These include methods from small area statistics that combine household sample surveys with comprehensive census data (10), as well as more recent use of satellite “nightlights” (11–13), mobile phone data (14, 15), social media (16), high-resolution satellite imagery (17–21), or a combination of these (22, 23). But to date these efforts have focused on a single continent or a select set of countries, limiting their relevance to development objectives that require a global perspective.