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A tale of three countries: How did Covid-19 lockdown impact happiness?

Author

Listed:
  • Greyling, Talita
  • Rossouw, Stephanie
  • Adhikari, Tamanna
Abstract
Since the start of the Covid-19 pandemic, many governments have implemented lockdown regulations to curb the spread of the virus. Though lockdowns do minimise the physical damage of the virus, there may be substantial damage to population well-being. Using a pooled dataset, this paper analyses the causal effect of mandatory lockdown on happiness in three very diverse countries (South Africa, New Zealand, and Australia), regarding population size, economic development and well-being levels. Additionally, each country differs in terms of lockdown regulations and duration. The main idea is to determine, notwithstanding the characteristics of a country or the lockdown regulations, whether a lockdown negatively affects happiness. Secondly, we compare the effect size of the lockdown on happiness between these countries. We make use of Difference-in-Difference estimations to determine the causal effect of the lockdown and Least Squares Dummy Variable estimations to study the heterogeneity in the effect size of the lockdown by country. Our results show that, regardless of the characteristics of the country, or the type or duration of the lockdown regulations; a lockdown causes a decline in happiness. Furthermore, the negative effect differs between countries, seeming that the more stringent the stay-at-home regulations are, the greater the negative effect.

Suggested Citation

  • Greyling, Talita & Rossouw, Stephanie & Adhikari, Tamanna, 2020. "A tale of three countries: How did Covid-19 lockdown impact happiness?," GLO Discussion Paper Series 584, Global Labor Organization (GLO).
    • Handle: RePEc:zbw:glodps:584
    as

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    File URL: https://www.econstor.eu/bitstream/10419/221748/1/GLO-DP-0584.pdf
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    References listed on IDEAS

    as
    1. Hamermesh, Daniel S., 2020. "Lockdowns, Loneliness and Life Satisfaction," IZA Discussion Papers 13140, Institute of Labor Economics (IZA).
    2. Briscese, Guglielmo & Lacetera, Nicola & Macis, Mario & Tonin, Mirco, 2023. "Expectations, reference points, and compliance with COVID-19 social distancing measures," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 103(C).
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    6. repec:hal:spmain:info:hdl:2441/63csdfkqvu9nfanvuffe3qk8r6 is not listed on IDEAS
    7. Rossouw, Stephanie & Greyling, Talita & Adhikari, Tamanna & Morrison, Phillip S., 2020. "Markov switching models for happiness during a pandemic: The New-Zealand experience," GLO Discussion Paper Series 573, Global Labor Organization (GLO).
    8. Fang, Hanming & Wang, Long & Yang, Yang, 2020. "Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China," Journal of Public Economics, Elsevier, vol. 191(C).
    9. 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.
    10. Guglielmo Briscese & Nicola Lacetera & Mario Macis & Mirco Tonin, 2020. "Compliance with Covid-19 Social-Distancing Measures in Italy: The Role of Expectations and Duration," CESifo Working Paper Series 8182, CESifo.
    11. Lalin Anik & Lara B. Aknin & Michael I. Norton & Elizabeth W. Dunn, 2009. "Feeling Good about Giving: The Benefits (and Costs) of Self-Interested Charitable Behavior," Harvard Business School Working Papers 10-012, Harvard Business School.
    12. Greyling, Talita & Rossouw, Stephanie & Adhikari, Tamanna, 2020. "Happiness-lost: Did Governments make the right decisions to combat Covid-19?," GLO Discussion Paper Series 556, Global Labor Organization (GLO).
    13. Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).
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    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Health > Distancing and Lockdown > Effect on well-being

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    Cited by:

    1. Fernando Díaz & Pablo A Henríquez, 2021. "Social sentiment segregation: Evidence from Twitter and Google Trends in Chile during the COVID-19 dynamic quarantine strategy," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-29, July.
    2. Bertogg, Ariane & Koos, Sebastian, 2021. "Changes of Social Networks during the Covid-19 Pandemic: Who is affected and what are its Consequences for Psychological Strain?," Working Papers 07, University of Konstanz, Cluster of Excellence "The Politics of Inequality. Perceptions, Participation and Policies".
    3. Nanath, Krishnadas & Balasubramanian, Sreejith & Shukla, Vinaya & Islam, Nazrul & Kaitheri, Supriya, 2022. "Developing a mental health index using a machine learning approach: Assessing the impact of mobility and lockdown during the COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    4. Luca Bonacini & Giovanni Gallo & Sergio Scicchitano, 2021. "Working from home and income inequality: risks of a ‘new normal’ with COVID-19," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 303-360, January.

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    More about this item

    Keywords

    Happiness; Covid-19; Big data; Difference-in-Difference;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • J18 - Labor and Demographic Economics - - Demographic Economics - - - Public Policy

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