Woojin Jung, Rutgers University

Abstract

Aid policy has the potential to alleviate global poverty by targeting areas of concentrated need. A critical question remains, however, over whether aid is reaching the areas of most need. Often little ground-truth poverty data is available at a granular level (e.g., village) where aid interventions take place. This research explores remote sensing techniques to measure poverty and target aid in data-sparse contexts. Our study of Myanmar examines i) the performance of different methods of poverty estimation and ii) the extent to which poverty and other development characteristics explain community aid distribution. This study draws from the following sources of data: georeferenced community-driven development projects (n=12,504), daytime and nighttime satellite imagery, the Demographic and Health Survey, and conflict data. We first compare the accuracy of four poverty measures in predicting ground-truth survey data. Using the best poverty estimation in the first step, we investigate the association between village characteristics and aid per capita per village. Our results show that daytime features perform the best in predicting poverty as compared to the analysis of RSG color distribution, Kriging, and nighttime-based measures. We use a Convolutional Neural Network, pre-trained on ImageNet, to extract features from the satellite images in our best model. These features are then trained on the DHS wealth data to predict the DHS wealth index/poverty for villages receiving aid. The linear and non-linear estimator indicate that development assistance flows to low-asset villages, but only marginally. Aid is more likely to be disbursed to those villages that are less populous and farther away from fatal conflicts. Our study concludes that the nuances captured in satellite-based models can be used to target aid to impoverished communities.

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Joint with Computer Science Department Colloquium

Presented in association with the DATA-INSPIRE TRIPODS Institute.