Can features extracted from high spatial resolution satellite imagery accurately estimate
poverty and economic well-being? The present study investigates this question by extracting …

Poverty is a glaring issue in the twenty-first century, even after concerted efforts of
organizations to eliminate the same. Predicting poverty using machine learning can offer …

Several methods have been proposed for using satellite imagery to model poverty. These
include poverty mapping using convolutional neural networks applied either directly or using …

Big Data refers to data sets of much larger size, higher frequency, and often more
personalized information. Examples include data collected by smart sensors in homes or …

Measuring empowerment is both complicated and time consuming. A number of recent
efforts have focused on how to better measure this complex multidimensional concept such …

Abstract This paper uses Sri Lankan data to investigate the ability of features derived from
high spatial resolution satellite images to accurately predict spatial variation in poverty …

International best practice on survey design recommends using a complete listing to
develop the second-stage sampling frame for a household survey. In certain contexts …

The collection of survey data from war zones or other unstable security situations is
vulnerable to error because conflict often limits the implementation options. Although there …

This paper implements a machine learning approach to estimate intra-generational
economic mobility using cross-sectional data. A Least Absolute Shrinkage and Selection …

This chapter provides a hopefully complete 'ecosystem'of the literature on the use of
machine learning (ML) methods for poverty, inequality, and development (PID) studies. It …