Pandemic precarity: COVID-19 is exposing and exacerbating inequalities in the American heartland

Abstract

Crises lay bare the social fault lines of society. In the United States, race, gender, age, and education have affected vulnerability to COVID-19 infection. Yet, consequences likely extend far beyond morbidity and mortality. Temporarily closing the economy sent shock waves through communities, raising the possibility that social inequities, preexisting and current, have weakened economic resiliency and reinforced disadvantage, especially among groups most devastated by the Great Recession. We address pandemic precarity, or risk for material and financial insecurity, in Indiana, where manufacturing loss is high, metro areas ranked among the hardest hit by the Great Recession nationally, and health indicators stand in the bottom quintile. Using longitudinal data (n = 994) from the Person to Person Health Interview Study, fielded in 2019-2020 and again during Indiana's initial stay-at-home order, we provide a representative, probability-based assessment of adverse economic outcomes of the pandemic. Survey-weighted multivariate regressions, controlling for preexisting inequality, find Black adults over 3 times as likely as Whites to report food insecurity, being laid off, or being unemployed. Residents without a college degree are twice as likely to report food insecurity (compared to some college), while those not completing high school (compared to bachelor's degree) are 4 times as likely to do so. Younger adults and women were also more likely to report economic hardships. Together, the results support contentions of a Matthew Effect, where pandemic precarity disproportionately affects historically disadvantaged groups, widening inequality. Strategically deployed relief efforts and longer-term policy reforms are needed to challenge the perennial and unequal impact of disasters.

Keywords: COVID-19 pandemic; disparities; economic insecurity; racial and ethnic inequality; socioeconomic inequality.

Fig. 1.

Racial and educational disparities in COVID-19 precarity, P2P (n = 994). Sample means are estimated with survey weights. Stars represent statistical significance at *P < 0.05, **P < 0.01, and ***P < 0.001 via sample-weighted t tests, with racial reference = White and educational reference = bachelors. Economic precarity variables dichotomized into 1 = agreed or strongly agreed and 0 = disagreed or strongly disagreed.

Fig. 2.

Adjusted parameter estimates for disparities in COVID-19 precarity, P2P (n = 994). Tables of models with parameters for intercepts are provided in SI Appendix, Tables S2–S5. Dots represent parameter estimates, and lines represent 95% CI. Colored dots indicate statistically significant parameter estimates; gray dots indicate nonsignificant parameter estimates. All models are estimated with ordinal logistic regression, with the exception of Fired/Unemployed, which is estimated with logistic regression. Models are adjusted by sample weights and SEs clustered by shared county. Race reference category = White. Education reference category = bachelors.