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The Effect of Control Measures on COVID-19 Transmission and Work Resumption: International Evidence

Author

Listed:
  • Meng, Lina
  • Zhou, Yinggang
  • Zhang, Ruige
  • Ye, Zhen
  • Xia, Senmao
  • Cerulli, Giovanni
  • Casady, Carter
  • Härdle, Wolfgang Karl

Abstract

Many countries have taken non-pharmaceutical interventions (NPIs) to contain the spread of the coronavirus (COVID-19) and push the recovery of national economies. This paper investigates the effect of these control measures by comparing five selected countries, China, Italy, Germany, the United Kingdom, and the United States. There is evidence that the degree of early intervention and efficacy of control measures are essential to contain the pandemic. China stands out because its early and strictly enforced interventions are effective to contain the virus spread. Furthermore, we quantify the causal effect of different control measures on COVID-19 transmission and work resumption in China. Surprisingly, digital contact tracing and delegating clear responsibility to the local community appear to be the two most effective policy measures for disease containment and work resumption. Public information campaigns and social distancing also help to flatten the peak significantly. Moreover, material logistics that prevent medical supply shortages provide an additional conditioning factor for disease containment and work resumption. Fiscal policy, however, is less effective at the early to middle stage of the pandemic.

Suggested Citation

  • Meng, Lina & Zhou, Yinggang & Zhang, Ruige & Ye, Zhen & Xia, Senmao & Cerulli, Giovanni & Casady, Carter & Härdle, Wolfgang Karl, 2020. "The Effect of Control Measures on COVID-19 Transmission and Work Resumption: International Evidence," IRTG 1792 Discussion Papers 2020-011, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    • Handle: RePEc:zbw:irtgdp:2020011
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    1. repec:hum:wpaper:sfb649dp2016-057 is not listed on IDEAS
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    Cited by:

    1. Weining Wang & Jeffrey M. Wooldridge & Mengshan Xu & Cuicui Lu & Chaowen Zheng, 2025. "Using generalized estimating equations to estimate nonlinear models with spatial data," Econometric Reviews, Taylor & Francis Journals, vol. 44(2), pages 214-242, February.
    2. Wang, Weining & Yu, Lining & Wang, Bingling, 2020. "Tail Event Driven Factor Augmented Dynamic Model," IRTG 1792 Discussion Papers 2020-022, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    3. Weining Wang & Jeffrey M. Wooldridge & Mengshan Xu, 2025. "Improved estimation of dynamic models of conditional means and variances," Journal of Time Series Analysis, Wiley Blackwell, vol. 46(3), pages 458-490, May.

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    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General

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