Evaluating the health and economic impacts of COVID-19 and policy responses

Principal Investigator

Bruce Weinberg

Eric Byron Fix-Monda Endowed Professor, Department of Economics, The Ohio State University

Co-Investigators

 

  • Adibah Abdulhadi, Postdoctoral Research Fellow, The Ohio State University
  • Meta Brown, Associate Professor, Department of Economics, The Ohio State University
  • Hanbat Jeong, Postdoctoral Research Fellow, Department of Economics, The Ohio State University
  • Kurt Lavetti, Associate Professor, Department of Economics, The Ohio State University
  • Yanli Lin, PhD Candidate, Department of Economics, The Ohio State University
  • Rebecca McKibbin, Lecturer, University of Sydney
  • Guanting Yi, PhD Candidate, Department of Economics, The Ohio State University

Funded By

National Institute on Aging (NIA)

The problem:

Many public health emergencies caused by communicable diseases have common properties:

  1. they spread geosocially,
  2. their incidence is often underestimated,
  3. they have economic impacts, which can have their own health effects, and
  4. they have disparate impacts on population subgroups.

Given the broad impact of these public health emergencies, it is important to identify policies that are likely to result in “best-case” scenarios. Specifically, identifying optimal policy combinations with regard to morbidity, mortality, and economic outcomes can allow researchers to evaluate the effectiveness of federal, state, and local pandemic response and identify areas where it may be improved.

The approach:

Using COVID case data from January 2020 to November 2020 from the 369 largest U.S. counties, the study team developed spatial models of the health and economic outcomes from the COVID-19 pandemic. The model considered policies, geospatial linkages, bidirectional interactions between health and economic outcomes, and disparities across different groups.

This allowed the researchers to identify the “optimal” set of policy interventions from both the public health and economic perspectives.

The findings:

The figure shows estimates from a simplified version of the model that focuses on health outcomes and estimates how an additional case in a focal county propagates, generating cases in that and in other counties. The figure considers a case in a given county and traces mortality over the next six weeks in the originating county, in other counties within the same state, and in counties in other states.

Twenty-two percent of cases occur in the originating county over the next six weeks, 26 percent in other counties within the same state, and the remaining 52 percent are in counties in other states. Intuitively, as more people live outside than inside any given county, spread tends to be geographically mediated.

These results show that policies in one county have implications for other counties and other states.

Pie chart “Share of COVID-19 cases originating from a focal county in a focal week on cases in that county, other counties in the same state, and other states after six weeks.”
Counties in different states: 55%
Counties within same state: 26%
Originating county: 22%

Selected Publications & Presentations

Gupta, S., Montenovo, L., Nguyen, T., Lozano‐Rojas, F., Schmutte, I., Simon, K., Weinberg, B. A., & Wing, C. (2022). Effects of social distancing policy on labor market outcomes. Contemporary Economic Policy, coep.12582. https://doi.org/10.1111/coep.12582

Montenovo, L., Jiang, X., Lozano-Rojas, F., Schmutte, I., Simon, K., Weinberg, B. A., & Wing, C. (2022). Determinants of disparities in early COVID-19 job losses. Demography, 59(3), 827–855. https://doi.org/10.1215/00703370-9961471