The Multidimensional Poverty Index (MPI) is a measure of poverty that goes beyond income, incorporating dimensions such as education, health, and living standards for a more comprehensive view. The Human Development Report Office of the United Nations Development Programme (HDRO/UNDP) and Oxford Poverty and Human Development Initiative (OPHI) release the global multidimensional poverty index estimates annually. The MPI typically relies on Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), however, these sources have limitations due to data gaps. In this seminar, we present our paper which uses both daytime and nighttime satellite imagery data to develop machine-learning models that allow us to train the data at a local level (e.g., cluster level) to predict multidimensional poverty. Our analysis shows that satellite imagery at the cluster level has strong predictive power, explaining significant variations in poverty within countries. The model shows stronger predictive capabilities in rural areas compared to urban areas. Additionally, standard of living indicators such as electricity, housing, and cooking fuel are predicted with high accuracy. Overall, these findings highlight the potential of satellite imagery as a valuable tool for predicting poverty and informing policies in regions with limited ground-level survey data.
Join in person (refreshments provided) or register to join the Zoom webinar: bit.ly/OPHIseminar
Join in person (refreshments provided) or register to join the Zoom webinar: bit.ly/OPHIseminar