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

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  • Camehl, Annika
  • Rieth, Malte

Abstract

We study the dynamic interaction between COVID-19, economic mobility, and containment policy. We use Bayesian panel structural vector autoregressions with daily data for 44 countries, identified through traditional and narrative sign restrictions. We find that incidence shocks and containment shocks have large and persistent effects on mobility, morbidity, and mortality that last for one to two months. These shocks are the main drivers of the pandemic, explaining between 20 and 60 percent of the average and historical variability in mobility, cases, and deaths worldwide. The policy trade-off associated to nonpharmaceutical interventions is 1 pp less economic mobility per day for 8 percent fewer deaths after 3 months. (JEL C43, H51, I12, I18, O15)

Suggested Citation

  • Camehl, Annika & Rieth, Malte, 2023. "Disentangling COVID-19, Economic Mobility, and Containment Policy Shocks," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 15(4), pages 217-248.
    • Handle: RePEc:zbw:espost:281207
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    References listed on IDEAS

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    1. Raffaella Giacomini & Toru Kitagawa & Matthew Read, 2021. "Identification and Inference Under Narrative Restrictions," Papers 2102.06456, arXiv.org.
    2. repec:zbw:bofrdp:2018_014 is not listed on IDEAS
    3. Roberto Rigobon & Brian Sack, 2003. "Measuring The Reaction of Monetary Policy to the Stock Market," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(2), pages 639-669.
    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. Raffaella Giacomini & Toru Kitagawa, 2021. "Robust Bayesian Inference for Set‐Identified Models," Econometrica, Econometric Society, vol. 89(4), pages 1519-1556, July.
    6. 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.
    7. 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.
    8. 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.
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    More about this item

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • H51 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Health
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration

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