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 Yet many poverty maps are out of date or exist only at very coarse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