Working Paper: NBER ID: w27741
Authors: Marina Azzimonti; Alessandra Fogli; Fabrizio Perri; Mark Ponder
Abstract: We develop an ECON-EPI network model to evaluate policies designed to improve health and economic outcomes during a pandemic. Relative to the standard epidemiological SIR set-up, we explicitly model social contacts among individuals and allow for heterogeneity in their number and stability. In addition, we embed the network in a structural economic model describing how contacts generate economic activity. We calibrate it to the New York metro area during the 2020 COVID-19 crisis and show three main results. First, the ECON-EPI network implies patterns of infections that better match the data compared to the standard SIR. The switching during the early phase of the pandemic from unstable to stable contacts is crucial for this result. Second, the model suggests the design of smart policies that reduce infections and at the same time boost economic activity. Third, the model shows that reopening sectors characterized by numerous and unstable contacts (such as large events or schools) too early leads to fast growth of infections.
Keywords: COVID-19; Epidemiology; Economic Policy; Social Networks
JEL Codes: D85; E23; I18
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| econepi network model (D85) | infection patterns (I12) |
| unstable to stable contacts (C62) | infection dynamics (C69) |
| type of social contacts severed (Z13) | rate of infection spread (J11) |
| type of social contacts severed (Z13) | economic impact (F69) |
| targeted shutdowns in high-contact sectors (L52) | lower infection rates (I14) |
| targeted shutdowns in high-contact sectors (L52) | limiting economic losses (F69) |
| premature reopening of sectors with unstable contacts (F41) | rapid increase in infections (F44) |
| timing of reopening (C41) | growth of infections (O42) |