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COVID-19 and The Macroeconomic Effects of Costly Disasters

Author

Listed:
  • Sydney C. Ludvigson
  • Sai Ma
  • Serena Ng

Abstract

The outbreak of COVID-19 has significantly disrupted the economy. This paper attempts to quantify the macroeconomic impact of costly and deadly disasters in recent US history, and to translate these estimates into an analysis of the likely impact of COVID-19. A costly disaster series is constructed over the sample 1980:1-2020:04 and the dynamic impact of a disaster shock on economic activity and on uncertainty is studied using a VAR. While past natural disasters are local in nature and come and go quickly, COVID-19 is a global, multi-period event. We therefore study the dynamic responses to a sequence of large disaster shocks. Even in a fairly conservative case where COVID-19 is a 5-month shock with its magnitude calibrated by the cost of March 2020 Coronavirus relief packages, the shock is forecast to lead to a cumulative loss in industrial production of 20% and in service sector employment of nearly 39% or 55 million jobs over the next 12 months. For each month that a shock of a given magnitude is prolonged from the base case, heightened macro uncertainty persists for another month.

Suggested Citation

  • Sydney C. Ludvigson & Sai Ma & Serena Ng, 2020. "COVID-19 and The Macroeconomic Effects of Costly Disasters," NBER Working Papers 26987, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:26987
    Note: AP EFG EH ME PE POL
    as

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    References listed on IDEAS

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    1. Alan J. Auerbach & Yuriy Gorodnichenko, 2012. "Measuring the Output Responses to Fiscal Policy," American Economic Journal: Economic Policy, American Economic Association, vol. 4(2), pages 1-27, May.
    2. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    3. Charles S. Gascon, 2020. "COVID-19: Which Workers Face the Highest Unemployment Risk?," On the Economy 87733, Federal Reserve Bank of St. Louis.
    4. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    5. Òscar Jordà, 2005. "Estimation and Inference of Impulse Responses by Local Projections," American Economic Review, American Economic Association, vol. 95(1), pages 161-182, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • I0 - Health, Education, and Welfare - - General
    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty

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