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Poverty maps derived from satellite imagery are increasingly used to informhigh-stakes policy decisions, such as the allocation of humanitarian aid andthe distribution of government resources. Such poverty maps are typicallyconstructed by training machine learning algorithms on a relatively modestamount of ``ground truth" data from surveys, and then predicting poverty levelsin areas where imagery exists but surveys do not. Using survey and satellitedata from ten countries, this paper investigates disparities in representation,systematic biases in prediction errors, and fairness concerns insatellite-based poverty mapping across urban and rural lines, and shows howthese phenomena affect the validity of policies based on predicted maps. Ourfindings highlight the importance of careful error and bias analysis beforeusing satellite-based poverty maps in real-world policy decisions.

Fairness and representation in satellite-based poverty maps: Evidence of urban-rural disparities and their impacts on downstream policy