# Dynamic, High-Resolution Wealth Measurement in Data-Scarce Environments

Zhuo Zheng,<sup>_a_</sup> Timothy Wu,<sup>_a_</sup> Richard Lee,<sup>_b,c_</sup> David Newhouse,<sup>_d_</sup> Talip Kilic,<sup>_d_</sup> Marshall Burke,<sup>_c,e_</sup> Stefano Ermon,<sup>_a_</sup> and David B. Lobell<sup>_b,c_</sup> _aDepartment of Computer Science, Stanford University, Stanford, 94305, CA, USA bDepartment of Earth System Science, Stanford University, Stanford, 94305, CA, USA_ _cCenter on Food Security and the Environment, Stanford University, Stanford, 94305, CA, USA dDevelopment Economics Data Group, World Bank Group, Washington DC, 20433, DC, USA eDepartment of Environmental Social Science, Stanford University, Stanford, 94305, CA, USA_

## A R T I C L E I N F O

_Keywords_ : Economic well-being High-resolution Poverty mapping Satellite image Deep learning _JEL codes: C45, I32_ Accurate, up-to-date, and highly resolved measurements of economic well-being are essential for monitoring and achieving international goals of poverty alleviation. These goals include the United Nations’ Sustainable Development Goal 1 of “No Poverty," which is nearing its original 2030 deadline, as well as countless other international and regional poverty targets. Granular estimates of household poverty and wealth are critical for understanding whether these goals are being met, as well as for targeting and evaluating anti-poverty interventions in regions where progress is lagging [6].

Official poverty measurement in low- and middle-income countries has long relied on household surveys, an indispensable but time-consuming tool for livelihood measurement. Given the technical capacity needed for reliable survey measurement and the substantial logistical difficulties in carrying out nationally or sub- nationally representative livelihood surveys, such surveys are often infrequently completed in much of the world, rendering com- prehensive and timely measures of poverty and related outcomes unavailable for many periods in many regions [6, 9]. Meanwhile, survey data are typically based on samples meant to be representative at larger spatial scales and are thus usually inadequate for generating reliable estimates at the village or neighborhood level – the level at which anti-poverty interventions often need to be targeted. Consequently, there is a pressing need for more cost-effective and scalable alternatives to local-level livelihood measurement that can complement and scale existing household survey–based efforts.

In recent years, the abundance of publicly available remote sensing data and recent advances in machine learning have transformed the livelihood measurement landscape, progressively shifting from national censuses and related household surveys to efforts to combine this information with information from satellites and other sensors. Early studies used coarse, publicly available satellite imagery and/or mobile phone data, combined with early machine learning and deep learning architectures, to show how these new sources of information could be used to support broad- scale measurement of wealth and poverty [4, 17, 29]. Subsequent studies introduced further refinements that used publicly available or proprietary geospatial data to improve satellite-based wealth measurement [8, 12, 2]. These advances confirmed that leveraging satellite images and machine learning can be an accurate, inexpensive, and scalable solution to estimate wealth [6, 21, 23].

Here we assemble a large-scale, multi-resolution, and multitemporal wealth dataset using national censuses or extracts obtained from National Statistics Offices and multi-spectral satellite imagery from multiple public and private sensors of varying resolutions. Our dataset comprises over 12 million households in four African countries (Malawi, Mozambique, Burkina Faso, and Madagascar) and, uniquely, contains precisely georeferenced measurements within two Malawian cities as well as repeated measurements of the same locations over time – two features lacking in prior studies.

We use these data to make four contributions relative to earlier work. First, we directly test a new type of deep learning model – specifically, vision transformers – against earlier deep learning architectures based on convolutional neural networks (CNNs) that are common in the literature, as well as against simpler models that use geospatial features and a tabular machine learning approach (XGBoost) for prediction. Specifically, we design a conditioning module that enables our transformer model to handle multi-modal inputs, integrating both satellite imagery and geospatial features simultaneously (see “Methods”). We test models that use satellite imagery from Landsat (30m/pixel), PlanetScope (3m/pixel), and/or SkySat (0.5m/pixel) sensors. We then compare these more sophisticated methods and inputs with simpler methods that rely Page 1 of 11