Micro-Estimates of Wealth for all Low- and Middle-Income Countries

TL;DR

This paper introduces high-resolution, machine learning-based micro-estimates of wealth and poverty for all low- and middle-income countries, integrating diverse data sources to support policy and development goals.

Contribution

It develops the first comprehensive, high-resolution wealth estimates covering 135 LMICs using innovative data integration and machine learning calibration methods.

Findings

Coverage of 135 LMICs at 2.4km resolution

Validated estimates with multiple independent surveys

Provides confidence intervals for each estimate

Abstract

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 Yet many poverty maps are out of date or exist only at very coarse levels of granularity.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