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Should I stay or should I go? Embracing causal heterogeneity in the study of pandemic policy and citizen behavior

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  • Lorena G Barberia
  • Maria Leticia Claro Oliveira
  • Andrea Junqueira
  • Natália de Paula Moreira
  • Guy D. Whitten

Abstract

Objective To test for multicausality between government policy, health outcomes, economic performance, and citizen behavior during the COVID‐19 global pandemic. Methods We perform Granger‐causality tests to explore the interrelationship between four endogenous variables, social distancing policy, home isolation, balance rate, and average weekly COVID‐19 deaths, in the 26 states of Brazil. As exogenous variables, we included a linear time trend and a dummy for the week in which the World Health Organization (WHO) declared COVID‐19 a global pandemic. Results Our analysis of Granger causal ordering between the four variables demonstrates that there is significant heterogeneity across the Brazilian federation. These findings can be interpreted as underscoring that there is no common model applicable to all states, and that the dynamics are context‐dependent. Conclusion Our suggested approach allows researchers to account for the complex interrelationship between government policy, citizen behavior, the economy, and COVID‐related health outcomes.

Suggested Citation

  • Lorena G Barberia & Maria Leticia Claro Oliveira & Andrea Junqueira & Natália de Paula Moreira & Guy D. Whitten, 2021. "Should I stay or should I go? Embracing causal heterogeneity in the study of pandemic policy and citizen behavior," Social Science Quarterly, Southwestern Social Science Association, vol. 102(5), pages 2055-2069, September.
    • Handle: RePEc:bla:socsci:v:102:y:2021:i:5:p:2055-2069
    DOI: 10.1111/ssqu.13037
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    References listed on IDEAS

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    1. Charles F. Manski, 2013. "Response to the Review of ‘Public Policy in an Uncertain World’," Economic Journal, Royal Economic Society, vol. 0, pages 412-415, August.
    2. Granger, C. W. J., 1980. "Testing for causality : A personal viewpoint," Journal of Economic Dynamics and Control, Elsevier, vol. 2(1), pages 329-352, May.
    3. John Y. Campbell & Pierre Perron, 1991. "Pitfalls and Opportunities: What Macroeconomists Should Know about Unit Roots," NBER Chapters, in: NBER Macroeconomics Annual 1991, Volume 6, pages 141-220, National Bureau of Economic Research, Inc.
    4. Oscar Jorda & Sanjay R. Singh & Alan M. Taylor, 2022. "Longer-Run Economic Consequences of Pandemics," The Review of Economics and Statistics, MIT Press, vol. 104(1), pages 166-175, March.
    5. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    6. Nicholas W. Papageorge, 2021. "Modeling Behavior during a Pandemic: Using HIV as an Historical Analogy," NBER Working Papers 28898, National Bureau of Economic Research, Inc.
    7. Stasavage, David, 2020. "Democracy, Autocracy, and Emergency Threats: Lessons for COVID-19 From the Last Thousand Years," International Organization, Cambridge University Press, vol. 74(S1), pages 1-17, December.
    8. Megan Arnot & Eva Brandl & Olk Campbell & Yuan Chen & Juan Du & Mark Dyble & Emily Emmott & Erhao Ge & Luke Kretschmer & Ruth Mace & Alberto J. C. Micheletti & Sarah Nila & Sarah Peacey & Gul Deniz Sa, 2020. "How evolutionary behavioural sciences can help us understand behaviour in a pandemic," Post-Print hal-03081477, HAL.
    9. Pulejo, Massimo & Querubín, Pablo, 2021. "Electoral concerns reduce restrictive measures during the COVID-19 pandemic," Journal of Public Economics, Elsevier, vol. 198(C).
    10. John D. Levendis, 2018. "Time Series Econometrics," Springer Texts in Business and Economics, Springer, number 978-3-319-98282-3, October.
    11. Jonathan Jay & Jacob Bor & Elaine O. Nsoesie & Sarah K. Lipson & David K. Jones & Sandro Galea & Julia Raifman, 2020. "Neighbourhood income and physical distancing during the COVID-19 pandemic in the United States," Nature Human Behaviour, Nature, vol. 4(12), pages 1294-1302, December.
    12. Manski, Charles F., 2013. "Public Policy in an Uncertain World: Analysis and Decisions," Economics Books, Harvard University Press, number 9780674066892, Spring.
    13. Carlitz, Ruth D. & Makhura, Moraka N., 2021. "Life under lockdown: Illustrating tradeoffs in South Africa’s response to COVID-19," World Development, Elsevier, vol. 137(C).
    14. Toda, Hiro Y. & Yamamoto, Taku, 1995. "Statistical inference in vector autoregressions with possibly integrated processes," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 225-250.
    15. David A. Armstrong II & Matthew J. Lebo & Jack Lucas, 2020. "Do COVID-19 Policies Affect Mobility Behaviour? Evidence from 75 Canadian and American Cities," Canadian Public Policy, University of Toronto Press, vol. 46(S2), pages 127-144, August.
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