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COVID-19 and Mental Health: Natural Experiments of the Costs of Lockdowns

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  • Quintana-Domeque, Climent

    (University of Exeter)

  • Zeng, Jingya

    (University of Exeter)

Abstract

The COVID 19 pandemic has profoundly impacted the world, affecting not only physical health and the economy but also mental well being. This chapter provides an investigation of the causal link between lockdown measures a significant public health intervention and mental health. Our examination begins with an overview of the mental health landscape across various countries prior to the COVID 19 pandemic. We then summarize key insights from a range of surveys, reviews, and meta analyses concerning the pandemic's effect on mental health. Further, we delve into a detailed analysis of three noteworthy studies that employ natural experiments to investigate the effects of lockdowns on mental health in different countries. Despite their differing research designs, these studies converge on the conclusion that lockdowns have had a detrimental impact on mental health. The intensity of this effect, however, varies among different population groups. This suggests that lockdown measures have affected certain segments of the population more profoundly than others.

Suggested Citation

  • Quintana-Domeque, Climent & Zeng, Jingya, 2023. "COVID-19 and Mental Health: Natural Experiments of the Costs of Lockdowns," IZA Discussion Papers 16532, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp16532
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    1. Mahler,Daniel Gerszon & Yonzan,Nishant & Lakner,Christoph, 2022. "The Impact of COVID-19 on Global Inequality and Poverty," Policy Research Working Paper Series 10198, The World Bank.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    3. Oreffice, Sonia & Quintana-Domeque, Climent, 2021. "Gender inequality in COVID-19 times: evidence from UK prolific participants," Journal of Demographic Economics, Cambridge University Press, vol. 87(2), pages 261-287, June.
    4. Staneva, Anita & Carmignani, Fabrizio & Rohde, Nicholas, 2022. "Personality, gender, and age resilience to the mental health effects of COVID-19," Social Science & Medicine, Elsevier, vol. 301(C).
    5. Onur Altindag & Bilge Erten & Pinar Keskin, 2022. "Mental Health Costs of Lockdowns: Evidence from Age-Specific Curfews in Turkey," American Economic Journal: Applied Economics, American Economic Association, vol. 14(2), pages 320-343, April.
    6. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    7. Bubonya, Melisa & Cobb-Clark, Deborah A. & Wooden, Mark, 2017. "Mental health and productivity at work: Does what you do matter?," Labour Economics, Elsevier, vol. 46(C), pages 150-165.
    8. Manuel Serrano‐Alarcón & Alexander Kentikelenis & Martin Mckee & David Stuckler, 2022. "Impact of COVID‐19 lockdowns on mental health: Evidence from a quasi‐natural experiment in England and Scotland," Health Economics, John Wiley & Sons, Ltd., vol. 31(2), pages 284-296, February.
    9. Quintana-Domeque Climent & Proto Eugenio, 2022. "On the Persistence of Mental Health Deterioration during the COVID-19 Pandemic by Sex and Ethnicity in the UK: Evidence from Understanding Society," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 22(2), pages 361-372, April.
    10. Fabrice Kämpfen & Iliana V Kohler & Alberto Ciancio & Wändi Bruine de Bruin & Jürgen Maurer & Hans-Peter Kohler, 2020. "Predictors of mental health during the Covid-19 pandemic in the US: Role of economic concerns, health worries and social distancing," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-13, November.
    11. David S. Lee & Thomas Lemieux, 2010. "Regression Discontinuity Designs in Economics," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 281-355, June.
    12. World Bank, 2021. "Global Economic Prospects, January 2021," World Bank Publications - Books, The World Bank Group, number 34710, December.
    13. Abi Adams-Prassl & Teodora Boneva & Marta Golin & Christopher Rauh, 2022. "The impact of the coronavirus lockdown on mental health: evidence from the United States," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 37(109), pages 139-155.
    14. Francisco Perez-Arce & Marco Angrisani & Daniel Bennett & Jill Darling & Arie Kapteyn & Kyla Thomas, 2021. "COVID-19 vaccines and mental distress," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-11, September.
    15. World Bank, 2021. "Global Economic Prospects, June 2021," World Bank Publications - Books, The World Bank Group, number 35647, December.
    16. Eugenio Proto & Climent Quintana-Domeque, 2021. "COVID-19 and mental health deterioration by ethnicity and gender in the UK," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-16, January.
    17. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
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    More about this item

    Keywords

    COVID 19; mental distress; natural experiments;
    All these keywords.

    JEL classification:

    • I1 - Health, Education, and Welfare - - Health
    • J1 - Labor and Demographic Economics - - Demographic Economics

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