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Disentangling Covid-19, Economic Mobility, and Containment Policy Shocks

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  • Annika Camehl
  • Malte Rieth

Abstract

We study the dynamic impact of Covid-19, economic mobility, and containment policy shocks. We use Bayesian panel structural vector autoregressions with daily data for 44 countries, identified through sign and zero restrictions. Incidence and mobility shocks raise cases and deaths significantly for two months. Restrictive policy shocks lower mobility immediately, cases after one week, and deaths after three weeks. Non-pharmaceutical interventions explain half of the variation in mobility, cases, and deaths worldwide. These flattened the pandemic curve, while deepening the global mobility recession. The policy tradeoff is 1 p.p. less mobility per day for 9% fewer deaths after two months.

Suggested Citation

  • Annika Camehl & Malte Rieth, 2021. "Disentangling Covid-19, Economic Mobility, and Containment Policy Shocks," Discussion Papers of DIW Berlin 1954, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp1954
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    References listed on IDEAS

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    1. Coibion, Olivier & Gorodnichenko, Yuriy & Weber, Michael, 2020. "Labor Markets During the Covid-19 Crisis: A Preliminary View," Department of Economics, Working Paper Series qt7rx7t91p, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    2. Jesus Fernandez-Villaverde & Charles I. Jones, 2020. "Macroeconomic Outcomes and COVID-19: A Progress Report," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 51(3 (Fall)), pages 111-166.
    3. Coibion, Olivier & Gorodnichenko, Yuriy & Weber, Michael, 2025. "The cost of the COVID-19 crisis: Lockdowns, macroeconomic expectations, and consumer spending," Journal of Economic Behavior & Organization, Elsevier, vol. 229(C).
    4. Eric M. Leeper & Todd B. Walker & Shu‐Chun Susan Yang, 2013. "Fiscal Foresight and Information Flows," Econometrica, Econometric Society, vol. 81(3), pages 1115-1145, May.
    5. repec:zbw:bofrdp:2018_014 is not listed on IDEAS
    6. Paul Beaudry & Franck Portier, 2014. "News-Driven Business Cycles: Insights and Challenges," Journal of Economic Literature, American Economic Association, vol. 52(4), pages 993-1074, December.
    7. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, July.
    8. Baumeister, Christiane & Hamilton, James D., 2018. "Inference in structural vector autoregressions when the identifying assumptions are not fully believed: Re-evaluating the role of monetary policy in economic fluctuations," Journal of Monetary Economics, Elsevier, vol. 100(C), pages 48-65.
    9. Glover, Andrew & Heathcote, Jonathan & Krueger, Dirk & Ríos-Rull, José-Víctor, 2023. "Health versus wealth: On the distributional effects of controlling a pandemic," Journal of Monetary Economics, Elsevier, vol. 140(C), pages 34-59.
    10. Baumeister, Christiane & Hamilton, James D., 2020. "Drawing conclusions from structural vector autoregressions identified on the basis of sign restrictions," Journal of International Money and Finance, Elsevier, vol. 109(C).
    11. Baumeister, Christiane & Hamilton, James D., 2018. "Inference in structural vector autoregressions when the identifying assumptions are not fully believed: Re-evaluating the role of monetary policy in economic fluctuations," Journal of Monetary Economics, Elsevier, vol. 100(C), pages 48-65.
    12. Andrew G. Atkeson & Karen A. Kopecky & Tao Zha, 2024. "Four Stylized Facts About Covid‐19," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 65(1), pages 3-42, February.
    13. Juan Antolín-Díaz & Juan F. Rubio-Ramírez, 2018. "Narrative Sign Restrictions for SVARs," American Economic Review, American Economic Association, vol. 108(10), pages 2802-2829, October.
    14. Sumedha Gupta & Kosali I. Simon & Coady Wing, 2020. "Mandated and Voluntary Social Distancing During The COVID-19 Epidemic: A Review," NBER Working Papers 28139, National Bureau of Economic Research, Inc.
    15. Mario Forni & Luca Gambetti, 2010. "Fiscal Foresight and the Effects of Government Spending," UFAE and IAE Working Papers 851.10, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    16. Andrew Atkeson, 2020. "What Will be the Economic Impact of COVID-19 in the US? Rough Estimates of Disease Scenarios," Staff Report 595, Federal Reserve Bank of Minneapolis.
    17. Canova, Fabio & Paustian, Matthias, 2011. "Business cycle measurement with some theory," Journal of Monetary Economics, Elsevier, vol. 58(4), pages 345-361.
    18. Christiane Baumeister & James D. Hamilton, 2015. "Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information," Econometrica, Econometric Society, vol. 83(5), pages 1963-1999, September.
    19. Martin S Eichenbaum & Sergio Rebelo & Mathias Trabandt, 2021. "The Macroeconomics of Epidemics [Economic activity and the spread of viral diseases: Evidence from high frequency data]," The Review of Financial Studies, Society for Financial Studies, vol. 34(11), pages 5149-5187.
    20. Jeffrey E. Harris, 2020. "The Subways Seeded the Massive Coronavirus Epidemic in New York City," NBER Working Papers 27021, National Bureau of Economic Research, Inc.
    21. Jonas E. Arias & Juan F. Rubio‐Ramírez & Daniel F. Waggoner, 2018. "Inference Based on Structural Vector Autoregressions Identified With Sign and Zero Restrictions: Theory and Applications," Econometrica, Econometric Society, vol. 86(2), pages 685-720, March.
    22. Daron Acemoglu & Victor Chernozhukov & Iván Werning & Michael D. Whinston, 2021. "Optimal Targeted Lockdowns in a Multigroup SIR Model," American Economic Review: Insights, American Economic Association, vol. 3(4), pages 487-502, December.
    23. Roel Beetsma & Massimo Giuliodori, 2011. "The Effects of Government Purchases Shocks: Review and Estimates for the EU," Economic Journal, Royal Economic Society, vol. 121(550), pages 4-32, February.
    24. Scott R. Baker & Nicholas Bloom & Steven J. Davis & Kyle J. Kost & Marco C. Sammon & Tasaneeya Viratyosin, 2020. "The Unprecedented Stock Market Impact of COVID-19," NBER Working Papers 26945, National Bureau of Economic Research, Inc.
    25. repec:rza:wpaper:826 is not listed on IDEAS
    26. Baek, ChaeWon & McCrory, Peter B & Messer, Todd & Mui, Preston, 2020. "Unemployment Effects of Stay-at-Home Orders: Evidence from High Frequency Claims Data," Institute for Research on Labor and Employment, Working Paper Series qt042177j7, Institute of Industrial Relations, UC Berkeley.
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    Cited by:

    1. Famiglietti, Matthew & Leibovici, Fernando, 2022. "The impact of health and economic policies on the spread of COVID-19 and economic activity," European Economic Review, Elsevier, vol. 144(C).
    2. Holtemöller, Oliver & Rieth, Malte, 2021. "Wirtschaftliche Mobilität dürfte nach Lockerung deutlich steigen – aber auch die Zahl der COVID-19-Fälle," IWH Policy Notes 3/2021, Halle Institute for Economic Research (IWH).
    3. Aquilante, Tommaso & Di Pace, Federico & Masolo, Riccardo M., 2022. "Exchange-rate and news: Evidence from the COVID pandemic," Economics Letters, Elsevier, vol. 213(C).
    4. Holtemöller, Oliver & Rieth, Malte, 2021. "Economic mobility likely to increase significantly after relaxation - but also number of COVID-19 cases," IWH Policy Notes 3/2021 (en), Halle Institute for Economic Research (IWH).

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    More about this item

    Keywords

    Epidemics; general equilibrium; non-pharmaceutical interventions; structural vector autoregressions; coronavirus; Bayesian analysis; panel data;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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