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Happiness-lost: Did Governments make the right decisions to combat Covid-19?

Author

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  • Greyling, Talita
  • Rossouw, Stephanie
  • Adhikari, Tamanna

Abstract

Amidst the rapid global spread of Covid-19, many governments enforced country-wide lockdowns, with likely severe well-being consequences. The actions by governments triggered a debate on whether the well-being and economic costs of a lockdown surpass the benefits perceived from a lower infection rate. In this regard, South Africa is an extreme case: enforcing very stringent lockdown regulations, while amid an economic crisis. We analyse the impact of both Covid-19 and the lockdown on happiness. We use the Gross National Happiness Index to compare the determinants of happiness before and after the Covid-19 lockdown regulations. Further, we estimate the likelihood of happiness levels in 2020, reaching the average levels in 2019 using two models; one predicting the likelihood after the lockdown was enforced and the other if no lockdown regulations were in place. The results shed light on happiness outcomes in a scenario of lockdown versus no lockdown.

Suggested Citation

  • 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).
  • Handle: RePEc:zbw:glodps:556
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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. 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).
    2. Tiziana Carpi & Airo Hino & Stefano Maria Iacus & Giuseppe Porro, 2020. "On a Japanese Subjective Well-Being Indicator Based on Twitter data," Papers 2012.14372, arXiv.org.
    3. Tiziana Carpi & Airo Hino & Stefano Maria Iacus & Giuseppe Porro, 2021. "Twitter Subjective Well-Being Indicator During COVID-19 Pandemic: A Cross-Country Comparative Study," Papers 2101.07695, arXiv.org.
    4. 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).
    5. Rossouw, Stephanie & Greyling, Talita & Adhikari, Tamanna, 2021. "New Zealand's happiness and COVID-19: a Markov Switching Dynamic Regression Model," GLO Discussion Paper Series 573 [rev.], Global Labor Organization (GLO).
    6. 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).

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    More about this item

    Keywords

    Happiness; Covid-19; Big data; Regulations; Probabilities; South Africa;
    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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