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Projecting the Economic Consequences of the COVID-19 Pandemic

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  • Guenette,Justin Damien
  • Yamazaki,Takefumi

Abstract

The highly uncertain evolution of the COVID-19 pandemic, influenced in part by government actions, social behavior, and vaccine-related developments, will play a critical role in shaping the global recovery’s strength and durability. This paper develops a modeling approach to embed pandemic scenarios and the rollout of a vaccine in a macroeconometric model and illustrates the impact of different pandemic- and vaccine-related assumptions on growth outcomes. The pandemic and the measures to contain it, including vaccine deployment, are assumed to be represented by consumption shocks in a macroeconometric model. In the baseline scenario, social distancing and a gradual vaccination process allow policy makers to make significant inroads in containing the pandemic. In a downside scenario, insufficient pandemic control efforts accompanied by delayed vaccination leads to persistently higher infection levels and a materially worse growth outcome. In contrast, in an upside scenario, effective management of the pandemic combined with rapid vaccine deployment would set the stage for stronger growth outcomes.

Suggested Citation

  • Guenette,Justin Damien & Yamazaki,Takefumi, 2021. "Projecting the Economic Consequences of the COVID-19 Pandemic," Policy Research Working Paper Series 9589, The World Bank.
  • Handle: RePEc:wbk:wbrwps:9589
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    References listed on IDEAS

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    1. Coibion, Olivier & Gorodnichenko, Yuriy & Weber, Michael, 2020. "The Cost of the COVID-19 Crisis: Lockdowns, Macroeconomic Expectations, and Consumer Spending," Department of Economics, Working Paper Series qt4jn1x65h, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    2. Liu, Laura & Moon, Hyungsik Roger & Schorfheide, Frank, 2021. "Panel forecasts of country-level Covid-19 infections," Journal of Econometrics, Elsevier, vol. 220(1), pages 2-22.
    3. Seth Flaxman & Swapnil Mishra & Axel Gandy & H. Juliette T. Unwin & Thomas A. Mellan & Helen Coupland & Charles Whittaker & Harrison Zhu & Tresnia Berah & Jeffrey W. Eaton & Mélodie Monod & Azra C. Gh, 2020. "Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe," Nature, Nature, vol. 584(7820), pages 257-261, August.
    4. World Bank, 2021. "Global Economic Prospects, January 2021," World Bank Publications - Books, The World Bank Group, number 34710, December.
    5. Dureau, Joseph & Kalogeropoulos, Konstantinos & Baguelin, Marc, 2013. "Capturing the time-varying drivers of an epidemic using stochastic dynamical systems," LSE Research Online Documents on Economics 41749, London School of Economics and Political Science, LSE Library.
    6. Schorfheide, Frank & Liu, Laura & Moon, Hyungsik Roger, 2020. "Panel Forecasts of Country-Level Covid-19 Infectionsliu," CEPR Discussion Papers 14790, C.E.P.R. Discussion Papers.
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