The spread of the coronavirus (COVID-19) puts into stark relief the disparities in our nation’s infrastructure system. All the social and economic benefits that infrastructure provides--such as proximity to goods, services, and each other--become liabilities in times of pandemics. The systems that enhance our physical connectivity also make social distancing more difficult. The aim of this proposal is to characterize how these infrastructure disparities influence the spread of COVID-19 and who are made most vulnerable as a result. Prior studies argue that density and close physical proximity are crucial to positive social and economic outcomes. These studies also argue physical infrastructure is important in health outcomes, namely quicker access to critical medical and emergency services. However, the coronavirus makes closer physical proximity a liability as it makes engaging in social distancing more difficult. The contribution of this proposal is to understand and characterize this tension that the coronavirus makes salient in our physical infrastructure systems. By identifying “hot spots†of dense and proximate physical infrastructure, we can better target our nation’s limited testing capacity around vulnerable areas in the hopes of maintaining the benefits of physical infrastructure, while also mitigating its observed downsides in the face of a pandemic. Prior studies suggest that technology generates inequality due to the fact it is “skill-biasedâ€. However, the coronavirus pandemic reveals a different possible mechanism. In this case, individuals may be similarly-skilled, but have dissimilarities in terms of access to digital infrastructure. This proposal seeks to assess this alternative mechanism in the hopes of targeting our limited economic stimulus resources to ease the economic burden on the pandemic's most vulnerable.
The project will leverage demographic, economic, and infrastructure data and employ spatial diffusion and difference-in-difference models. In particular, the work will aggregate COVID-19 and healthcare utilization data from the CDC, federal, state and local health agencies, and academic institutions such as Johns Hopkins and compare this to data from the National Bridge Inventory, the FCC, Quarterly Census of Employment and Wages, National Historical Geographic Information System, and O*Net, by the North Carolina Department of Commerce. This work pairs two types of infrastructure—physical and digital—to examine how the construction and availability of this infrastructure affects cities, both in terms of health and individual economic prosperity. The work is novel in that it problematizes physical infrastructure as being both a contributor to the spread of disease as well as a mechanism for combating it, and that it will assess how pandemics can generate wage inequality due to unequal access to digital infrastructure, even between individuals with similar skills. The research provides a relatively quick way to leverage variation in physical and digital infrastructure to a) identify those communities that are most in need of currently available, and highly limited funds and b) craft a rapid response and deployment strategy to help the nation to slow the transmission and economic impacts of the virus in the future. The work will have impacts across all of society, but is particularly targeted at underserved populations and those in lower wage industries that may not be able to easily take advantage of digital infrastructure. All of the data utilized are publicly available and the PI will make available the code on the CMU website. Furthermore, this project will also educate a group of engineers who can combine both their technical training with social science research methods and training to ensure our technical systems are robust and resilient to health crises.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
