Poverty is a multifaceted and dynamic phenomena impacting billions of people worldwide. Despite its prevalence, there remains much to be understood about what makes families susceptible to experiencing economic distress. In recent years, income shocks — which constitute unexpected expenses or interruptions to one’s income flow — have garnered increased public attention as being intricately intertwined with poverty. Despite a vast body of empirical work showing the impact of shocks on welfare, they do not play a correspondingly central role in the design of assistance programs.
In this talk, we present a mathematical and computational analysis of shocks. We pose a set of algorithmic questions about allocation of subsidies in the presence of shocks and present optimal and near-optimal solutions for various general settings. We computationally analyze the impact of shocks on poverty using a longitudinal, survey-based dataset, revealing insights about the interactions of different types of shocks. We discuss how these insights can inform the design and deployment of assistance programs and highlight new directions at this emerging interface between algorithms, public finance, and social work.
Rediet Abebe is a Junior Fellow at the Harvard Society of Fellows and an incoming Assistant Professor of Computer Science at the University of California, Berkeley. Abebe holds a Ph.D. in computer science from Cornell University and graduate degrees in mathematics from Harvard University and the University of Cambridge. Her research is in artificial intelligence and algorithms, with a focus on equity and justice concerns. Abebe is a co-founder and co-organizer of the multi-institutional, interdisciplinary research initiative Mechanism Design for Social Good (MD4SG). Her dissertation received the 2020 ACM SIGKDD Dissertation Award for offering the foundations of this emerging research area. Abebe’s work has informed policy and practice at the National Institutes of Health (NIH) and the Ethiopian Ministry of Education. She has been honored in the MIT Technology Reviews’ 35 Innovators Under 35 and the Bloomberg 50 list as one to watch. Abebe also co-founded Black in AI, a non-profit organization tackling representation issues in AI. Her research is influenced by her upbringing in her hometown of Addis Ababa, Ethiopia.