Reparations for Black American Descendants of Persons Enslaved in the U.S. and Their Estimated Impact on SARS-CoV-2 Transmission
Abstract
Background
In the United States, Black Americans are suffering from significantly disproportionate incidence and mortality rates of COVID-19. The potential for racial-justice interventions, including reparations payments, to ameliorate these disparities has not been adequately explored.
Methods
We compared the COVID-19 time-varying R t curves of relatively disparate polities in terms of social equity (South Korea vs. Louisiana). Next, we considered a range of reproductive ratios to back-calculate the transmission rates β i → j for 4 cells of the simplified next-generation matrix (from which R 0 is calculated for structured models) for the outbreak in Louisiana. Lastly, we modeled the effect that monetary payments as reparations for Black American descendants of persons enslaved in the U.S. would have had on pre-intervention β i → j .
Results
Once their respective epidemics begin to propagate, Louisiana displays R t values with an absolute difference of 1.3 to 2.5 compared to South Korea. It also takes Louisiana more than twice as long to bring R t below 1. We estimate that increased equity in transmission consistent with the benefits of a successful reparations program (reflected in the ratio β b→b / β w→w ) could reduce R 0 by 31 to 68%.
Discussion
While there are compelling moral and historical arguments for racial injustice interventions such as reparations, our study describes potential health benefits in the form of reduced SARS-CoV-2 transmission risk. As we demonstrate, a restitutive program targeted towards Black individuals would not only decrease COVID-19 risk for recipients of the wealth redistribution; the mitigating effects would be distributed across racial groups, benefitting the population at large.
Funding
ETR and LW are supported by NIGMS MIDAS grant R01 GM130900. ETR is also supported by NIAID K08 AI139361. WAD is supported by NIMHD R01 MD011606, NSF SES 1851845, and IES R305A190484. MMM is supported by the Ethics and Governance of Artificial Intelligence Fund.
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