Micro-Estimates of Wealth for all Low- and Middle-Income Countries
This enables targeted policy responses, such as for the COVID-19 pandemic, and supports insights into economic development for policymakers and researchers, though it is incremental in applying existing ML methods to new data sources.
The paper tackles the lack of up-to-date, high-resolution wealth and poverty data in low- and middle-income countries by developing the first micro-estimates covering all 135 such countries at 2.4km resolution, validated with independent survey data from 18 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 Yet many poverty maps are out of date or exist only at very coarse levels of granularity.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 the first micro-estimates of wealth and poverty that cover the populated surface of all 135 low and middle-income countries (LMICs) at 2.4km resolution. The estimates are built by applying machine learning algorithms to vast and heterogeneous data from satellites, mobile phone networks, topographic maps, as well as aggregated and de-identified connectivity data from Facebook. We train and calibrate the estimates using nationally-representative household survey data from 56 LMICs, then validate their accuracy using four independent sources of household survey data from 18 countries. We also provide confidence intervals for each micro-estimate 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 new insights into the causes and consequences of economic development and growth, and promote responsible policymaking in support of the Sustainable Development Goals.