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The Impact of Domestic Travel Bans on COVID-19 is Nonlinear in Their Duration

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  • Fiona Burlig
  • Anant Sudarshan
  • Garrison Schlauch

Abstract

Domestic mobility restrictions to control the spread of COVID-19 are widespread in developing countries, and have trapped millions of migrant workers in hotspot cities. We show that bans can increase cumulative infections relative to a counterfactual sans restrictions. A SEIR model shows bans’ impacts are nonlinear in duration. We empirically test this hypothesis using a natural experiment in India as well as data from China, Indonesia, the Philippines, South Africa, and Kenya. Although very short and long restrictions limit the spread of disease, moderately lengthy restrictions substantially increase infections. This underscores the importance of considering duration in mobility-restricting policy decisions in developing countries.

Suggested Citation

  • Fiona Burlig & Anant Sudarshan & Garrison Schlauch, 2021. "The Impact of Domestic Travel Bans on COVID-19 is Nonlinear in Their Duration," NBER Working Papers 28699, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:28699
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    References listed on IDEAS

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    1. Priya Deshingkar & Shaheen Akter, 2009. "Migration and Human Development in India," Human Development Research Papers (2009 to present) HDRP-2009-13, Human Development Report Office (HDRO), United Nations Development Programme (UNDP), revised Apr 2009.
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    3. Giovanni Bonaccorsi & Francesco Pierri & Matteo Cinelli & Andrea Flori & Alessandro Galeazzi & Francesco Porcelli & Ana Lucia Schmidt & Carlo Michele Valensise & Antonio Scala & Walter Quattrociocchi , 2020. "Economic and social consequences of human mobility restrictions under COVID-19," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(27), pages 15530-15535, July.
    4. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    5. Su, Yaqin & Tesfazion, Petros & Zhao, Zhong, 2018. "Where are the migrants from? Inter- vs. intra-provincial rural-urban migration in China," China Economic Review, Elsevier, vol. 47(C), pages 142-155.
    6. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    7. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(1), pages 249-275.
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    9. World Bank, 2020. "Global Economic Prospects, June 2020," World Bank Publications - Books, The World Bank Group, number 33748, December.
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    Cited by:

    1. Ahmed Mushfiq Mobarak & Edward Miguel, 2022. "The Economics of the COVID-19 Pandemic in Poor Countries," Annual Review of Economics, Annual Reviews, vol. 14(1), pages 253-285, August.

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    More about this item

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • J60 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - General
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development

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