Data citizenship: Quantifying structural racism in COVID-19 and beyond

Abstract

Data-driven public health policies were widely implemented to mitigate the uneven impact of COVID-19. In the United States, evidence-based interventions are often employed in “racial equity” initiatives to provide calculable representations of racial disparities. However, disparities in working or living conditions, germane to public health but outside the conventional scope of epidemiology, are seldom measured or addressed. What is the effect of defining racial equity with quantitative health outcomes? Drawing on qualitative analysis of 175 interviews with experts and residents in Chicago during the emergence of COVID-19, we find that these policies link the distribution of public resources to effective participation in state projects of data generation. Bringing together theories of quantification and biosocial citizenship, we argue that a form of data citizenship has emerged where public resources are allocated based on quantitative metrics and the variations they depict. Data citizenship is characterized by at least two mechanisms for governing with statistics. Data fixes produce better numbers through technical adjustments in data collection or analysis based on expert assumptions or expectations. Data drag delays distribution of public relief until numbers are compiled to demonstrate and specify needs or deservingness. This paper challenges the use of racial statistics as a salve for structural racism and illustrates how statistical data can exacerbate racial disparities by promising equity.