Elizabeth Wrigley-Field
Assistant Professor
Sociology, College of Liberal Arts, UMN Twin Cities

This talk explores racial disparities in mortality during U.S. pandemics, using the 1918 and COVID-19 pandemics to develop general frameworks for understanding inequality in pandemic experiences—and what they reveal about inequality during ordinary, non-pandemic times. The first part of the talk considers racial disparities during the most devastating respiratory pandemic of the 20^th century, the 1918 flu; shows that those disparities were surprisingly small; and develops new hypotheses, grounded in social immunology, to account for this anomaly. The second part of the talk pivots from 1918 to 2020. During the 1918 pandemic, U.S. white mortality was still lower than U.S. Black mortality had been nearly every year. Today, during the COVID-19 pandemic, the same pattern holds: despite the pandemic, white mortality in 2020 was likely less than Black mortality has ever been. Using pandemic mortality as a measuring stick for racial disparities offers a new perspective on the measures we do — and do not — embrace in order to combat racial inequality. I use demographic mortality models to make a new, demographically based case for reparations for racism.
 

The University of Minnesota Human in the Data Initiative focuses on the ubiquity of Big Data and algorithmic decision making in every major sector in our world. Human in the Data series events are presented by the Institute for Advanced Study, DASH, the UMN Informatics Institute, and Research Computing.

Elizabeth Wrigley-Field is an Assistant Professor at the University of Minnesota in the Department of Sociology and the Minnesota Population Center. She specializes in racial inequality in mortality and historical infectious disease and co-leads (with J.P. Leider) an ongoing project on COVID-19 mortality in Minnesota. She is also a quantitative methodologist, developing models designed to clarify relationships between micro and macro perspectives on demographic relationships.