A0712
Title: Poverty from Space: Using High Resolution Satellite Imagery for Estimating Economic Well-being and Geographic Targeting
Authors: Jonathan Hersh - Boston University (United States) [presenting]
David Newhouse - World Bank Group (United States)
Ryan Engstrom - George Washington University (United States)
Abstract: Measuring poverty is important for targeting aid and formation of policy in developing economies. This paper investigates the ability of high spatial resolution satellite images to accurately predict poverty and economic well-being. We extract both object and texture features from satellite images of Sri Lanka. These data are then used to train models of local area poverty and economic well-being. The important features include the number and density of buildings, shadow area (a proxy for building height), number of cars, density and width of roads, type of farmland, and roof material. These variables are used to estimate poverty rates and average log consumption for 1,287 Gram Niladhari (GN) Divisions, which are on average 2 square km in area. Predictions from a baseline binomial logit model, using only these satellite features as explanatory variables, explain sixty percent of poverty and sixty-five percent of average log consumption. We control for overfitting by using Lasso regularization. Our policy simulations find that these poverty estimates perform as well as official estimates for geographic targeting. In contrast to a popular, low-cost alternative measure of poverty, night time lights, our measures are two to eight times as efficient for geographic targeting. We conclude that the use of satellite data has the potential to revolutionize poverty measurement, reducing survey costs and improve efficiency of geographic targeting.
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