COVID-19 pandemic and economic scenarios for Ontario
Date
2020Version
Acceso abierto / Sarbide irekia
Type
Documento de trabajo / Lan gaiak
Impact
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nodoi-noplumx
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Abstract
To study the efficacy of the public policy response to the COVID-19 pandemic, we develop a model of the rich interactions between epidemiology and socioeconomic choices. Preferences feature a 'fear of death' that lead individuals to reduce their social activity and work time in the face of the pandemic. The aggregate effect of these reductions is to slow the spread of the coronavirus. We calib ...
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To study the efficacy of the public policy response to the COVID-19 pandemic, we develop a model of the rich interactions between epidemiology and socioeconomic choices. Preferences feature a 'fear of death' that lead individuals to reduce their social activity and work time in the face of the pandemic. The aggregate effect of these reductions is to slow the spread of the coronavirus. We calibrate the model, including public policies, to developments in Ontario in spring 2020. The model fits the epidemiological data quite well, including the second wave starting in late 2020. We find that socioeconomic interventions work well in the short term, resulting in a rapid drop off in new cases. The long run, however, is governed chiefly by health developments. Welfare cost calculations point to synergies between the health and socioeconomic measures. [--]
Subject
COVID-19 pandemic,
Public policies,
Socioeconomic measures,
Ontario (Canada)
Serie
Documentos de Trabajo DE - ES Lan Gaiak /
2002
Departament
Universidad Pública de Navarra. Departamento de Economía /
Nafarroako Unibertsitate Publikoa. Ekonomia Saila /
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute for Advanced Research in Business and Economics - INARBE
Sponsorship
Casares acknowledges the support of Ministerio de Educación y Cultura, Spanish Government (PGC2018-09354). Gomme acknowledges the support of SSHRC Grant 435–2016–1388. Khan acknowledges the support of Carleton University Rapid COVID-19 Research Grant 112940.