IDEAS home Printed from https://ideas.repec.org/p/diw/diwwpp/dp1954.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.diw.de/documents/publikationen/73/diw_01.c.820425.de/dp1954.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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. 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.
    4. 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).
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    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. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, October.
    11. Canova, Fabio & Paustian, Matthias, 2011. "Business cycle measurement with some theory," Journal of Monetary Economics, Elsevier, vol. 58(4), pages 345-361.
    12. Baumeister, Christiane & Hamilton, James, 2018. "Inference in Structural Vector Autoregressions When the Identifying Assumptions are Not Fully Believed: Re-evaluating the Role," CEPR Discussion Papers 12911, C.E.P.R. Discussion Papers.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. Andrew Atkeson, 2020. "What Will Be the Economic Impact of COVID-19 in the US? Rough Estimates of Disease Scenarios," NBER Working Papers 26867, National Bureau of Economic Research, Inc.
    21. 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.
    22. repec:zbw:bofrdp:2018_014 is not listed on IDEAS
    23. 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.
    24. 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.
    25. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Atsushi Inoue & Lutz Kilian, 2020. "The Role of the Prior in Estimating VAR Models with Sign Restrictions," Working Papers 2030, Federal Reserve Bank of Dallas.
    2. Kilian, Lutz, 2022. "Facts and fiction in oil market modeling," Energy Economics, Elsevier, vol. 110(C).
    3. Robin Braun & Ralf Brüggemann, 2017. "Identification of SVAR Models by Combining Sign Restrictions With External Instruments," Working Paper Series of the Department of Economics, University of Konstanz 2017-07, Department of Economics, University of Konstanz.
    4. Hristov, Nikolay & Hülsewig, Oliver & Wollmershäuser, Timo, 2020. "Capital flows in the euro area and TARGET2 balances," Journal of Banking & Finance, Elsevier, vol. 113(C).
    5. Ferreira, Leonardo N., 2022. "Forward guidance matters: Disentangling monetary policy shocks," Journal of Macroeconomics, Elsevier, vol. 73(C).
    6. 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).
    7. Barrot, Jean-Noël & Bonelli, Maxime & Grassi, Basile & Sauvagnat, Julien, 2024. "Causal effects of closing businesses in a pandemic," Journal of Financial Economics, Elsevier, vol. 154(C).
    8. Sam Ouliaris & Adrian Pagan, 2022. "Three Basic Issues that Arise when Using Informational Restrictions in SVARs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(1), pages 1-20, February.
    9. Miescu, Mirela & Rossi, Raffaele, 2021. "COVID-19-induced shocks and uncertainty," European Economic Review, Elsevier, vol. 139(C).
    10. David Berger & Kyle Herkenhoff & Chengdai Huang & Simon Mongey, 2022. "Testing and Reopening in an SEIR Model," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 43, pages 1-21, January.
    11. Brinca, Pedro & Duarte, Joao B. & Faria-e-Castro, Miguel, 2021. "Measuring labor supply and demand shocks during COVID-19," European Economic Review, Elsevier, vol. 139(C).
    12. Kong, Edward & Prinz, Daniel, 2020. "Disentangling policy effects using proxy data: Which shutdown policies affected unemployment during the COVID-19 pandemic?," Journal of Public Economics, Elsevier, vol. 189(C).
    13. Inoue, Atsushi & Kilian, Lutz, 2022. "Joint Bayesian inference about impulse responses in VAR models," Journal of Econometrics, Elsevier, vol. 231(2), pages 457-476.
    14. Garriga, Carlos & Manuelli, Rody & Sanghi, Siddhartha, 2022. "Optimal management of an epidemic: Lockdown, vaccine and value of life," Journal of Economic Dynamics and Control, Elsevier, vol. 140(C).
    15. Liang, Yousha & Shi, Kang & Tang, Junjie & Xu, Juanyi, 2022. "Pandemic and containment policies in open economy," Journal of International Money and Finance, Elsevier, vol. 125(C).
    16. Giacomini, Raffaella & Kitagawa, Toru & Read, Matthew, 2022. "Robust Bayesian inference in proxy SVARs," Journal of Econometrics, Elsevier, vol. 228(1), pages 107-126.
    17. Martin Geiger & Jochen Güntner, 2019. "How are oil supply shocks transmitted to the U.S. economy?," Economics working papers 2019-13, Department of Economics, Johannes Kepler University Linz, Austria.
    18. Drago Bergholt & Francesco Furlanetto & Etienne Vaccaro-Grange, 2023. "Did monetary policy kill the Phillips Curve? Some simple arithmetics," Working Paper 2023/2, Norges Bank.
    19. Matthew Read, 2022. "The Unit-effect Normalisation in Set-identified Structural Vector Autoregressions," RBA Research Discussion Papers rdp2022-04, Reserve Bank of Australia.
    20. Cerqueti, Roy & Tramontana, Fabio & Ventura, Marco, 2022. "The complex interplay between COVID-19 and economic activity," Mathematical Social Sciences, Elsevier, vol. 119(C), pages 97-107.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:diw:diwwpp:dp1954. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Bibliothek (email available below). General contact details of provider: https://edirc.repec.org/data/diwbede.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.