EconPapers    
Economics at your fingertips  
 

Macroeconomic consequences of the COVID-19 pandemic

Terrie Walmsley, Adam Rose, Richard John, Dan Wei, Jakub P. Hlávka, Juan Machado and Katie Byrd

Economic Modelling, 2023, vol. 120, issue C

Abstract: We estimate the economic impacts of COVID-19 in the U.S. using a disaster economic consequence analysis framework implemented by a dynamic computable general equilibrium (CGE) model. This facilitates identification of relative influences of several causal factors as “shocks” to the model, including mandatory business closures, disease spread trajectories, behavioral responses, resilience, pent-up demand, and government stimulus packages. The analysis is grounded in primary data on avoidance behavior and healthcare parameters. The decomposition of the influence of various causal factors will help policymakers offset the negative influences and reinforce the positive ones during the remainder of this pandemic and future ones.

Keywords: Disaster economics; CGE Modeling; COVID-19; Avoidance behavior; Resilience (search for similar items in EconPapers)
JEL-codes: C68 E37 F19 I18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0264999322003844
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:120:y:2023:i:c:s0264999322003844

DOI: 10.1016/j.econmod.2022.106147

Access Statistics for this article

Economic Modelling is currently edited by S. Hall and P. Pauly

More articles in Economic Modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2025-03-19
Handle: RePEc:eee:ecmode:v:120:y:2023:i:c:s0264999322003844