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COVID-19 death rates per 100,000 vary widely across the nation. As of September 1, 2020, they range from a low of 4 in Hawaii to a high of 179 in New Jersey. Although academic research has been conducted at the county and metropolitan levels, no research has rigorously examined or identified the demographic and socioeconomic forces that explain state-level differences. This study presents an empirical model and the results of regression tests that help identify these forces and shed light on the role they play in explaining COVID-19 deaths.

A stepwise regression model we tested exhibits a high degree of explanatory power. It suggests that two measures of density explain most of the state-level differences. Less significant variables included the poverty rate and racial/ethnic differences. We also found that variables relating to health, air travel, and government mandates were not significant in explaining COVID-19 deaths at the state level.

This study also examines the elasticities of those variables we found significant. We measured both average and constant elasticities to determine the relationship between changes in COVID-19 deaths and percentage changes in the relevant explanatory variables. In an analysis of residuals, we found that the unexplained variation was found to be related mainly to factors site-specific to individual states.

Unlike the empirical results of several academic studies, our model found that the density of a state is the most important factor explaining COVID-19 deaths. The role that density plays in the transmission of COVID-19 has important policy implications in responding to the challenges posed by the coronavirus and future pandemics.


This scholarship is part of the Chapman University COVID-19 Archives.


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