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Learning or habit formation? Optimal timing of lockdown for disease containment

Author

Listed:
  • Siddhartha Bandyopadhyay

    (University of Birmingham)

  • Kalyan Chatterjee

    (Penn State University)

  • Kaustav Das

    (University of Leicester)

  • Jaideep Roy

    (University of Bath)

Abstract

We analyse a model where the government has to decide whether to impose a lockdown in a country to prevent the spread of a possibly virulent disease. If the government decides to impose a lockdown, it has to determine its intensity, timing and duration. We find that there are two competing effects that push the decision in either direction. An early lockdown is beneficial not only to slow down the spread of the disease, but to create beneficial habit formation (such as social distancing, developing hygienic habits) that persists even after the lockdown is lifted. Against that, an early lockdown in addition to damaging the economy, leads to a loss of information and impedes learning about the nature and the dynamics of the disease. Based on the prior probability of the disease being virulent, we characterise the timing, intensity and duration of a lockdown with the above mentioned tradeoffs. Specifically, we show that as the precision of learning goes up, a government tends to delay the imposition of lockdown. Conversely, if the habit formation parameter is very strong, a government is likely to impose an early lockdown.

Suggested Citation

  • Siddhartha Bandyopadhyay & Kalyan Chatterjee & Kaustav Das & Jaideep Roy, 2020. "Learning or habit formation? Optimal timing of lockdown for disease containment," Discussion Papers 20-17, Department of Economics, University of Birmingham.
  • Handle: RePEc:bir:birmec:20-17
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    References listed on IDEAS

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    Cited by:

    1. Elena Gubar & Laura Policardo & Edgar J. Sanchez Carrera & Vladislav Taynitskiy, 2021. "Optimal Lockdown Policies driven by Socioeconomic Costs," Papers 2105.08349, arXiv.org.
    2. Pongou, Roland & Tchuente, Guy & Tondji, Jean-Baptiste, 2021. "Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States," GLO Discussion Paper Series 957, Global Labor Organization (GLO).
    3. Roland Pongou & Guy Tchuente & Jean-Baptiste Tondji, 2021. "Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States," Papers 2110.10230, arXiv.org.

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    More about this item

    Keywords

    COVID-19; Lockdown; Learning; Habit formation.;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • I10 - Health, Education, and Welfare - - Health - - - General

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