IDEAS home Printed from https://ideas.repec.org/p/zbw/glodps/573r.html
   My bibliography  Save this paper

New Zealand's happiness and COVID-19: a Markov Switching Dynamic Regression Model

Author

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

Abstract

Happiness levels (states) are volatile and often fluctuate between a happy and unhappy state from one day to the next. The reasons for these shifts are mostly unobservable and not predictable. In this paper, we fit a Marko Switching Dynamic Regression Model (MSDR) to better understand the dynamic patterns of happiness levels before and during a pandemic. The estimated parameters from the MSDR model include each state's mean and duration, volatility and transition probabilities. Once these parameters have been estimated, we predict the unobserved states' evolution over time using the one-step method. This gives us unique insights into the evolution of happiness. Furthermore, as maximising happiness is a policy priority, we determine the factors that can contribute to the probability of increasing happiness levels. We empirically test these models using New Zealand's daily happiness data for May 2019 - November 2020. The results show that New Zealand seems to have two regimes, an unhappy and happy regime. In 2019 the happy regime dominated; thus, the probability of being unhappy in the next time period (day) occurred less frequently, whereas the opposite is true for 2020. The higher frequencies of time periods with probabilities to be unhappy in 2020 mostly correspond to the pandemic events. Lastly, we find the factors positively and significantly related to the probability of being happy after lockdown to be jobseeker support payments and international travel. On the other hand, mobility is significantly and negatively related to the probability of being happy.

Suggested Citation

  • 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).
    • Handle: RePEc:zbw:glodps:573r
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/233931/1/GLO-DP-0573rev.pdf
    Download Restriction: no
    ---><---

    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. Marcin Piekałkiewicz, 2017. "Why do economists study happiness?," The Economic and Labour Relations Review, , vol. 28(3), pages 361-377, September.
    3. Yann Algan & Fabrice Murtin & Elizabeth Beasley & Kazuhito Higa & Claudia Senik, 2019. "Well-being through the lens of the internet," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-23, January.
    4. Bryson, Alex & Clark, Andrew E. & Freeman, Richard B. & Green, Colin P., 2016. "Share capitalism and worker wellbeing," Labour Economics, Elsevier, vol. 42(C), pages 151-158.
    5. Daniel W. Sacks & Betsey Stevenson & Justin Wolfers, 2010. "Subjective Well-Being, Income, Economic Development and Growth," NBER Working Papers 16441, National Bureau of Economic Research, Inc.
    6. John Helliwell & Shun Wang, 2014. "Weekends and Subjective Well-Being," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 116(2), pages 389-407, April.
    7. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    8. repec:hal:spmain:info:hdl:2441/63csdfkqvu9nfanvuffe3qk8r6 is not listed on IDEAS
    9. Rossouw, Stephanie & Greyling, Talita, 2020. "Big Data and Happiness," GLO Discussion Paper Series 634, Global Labor Organization (GLO).
    10. repec:hal:pseose:halshs-01383778 is not listed on IDEAS
    11. A. I. McLeod & W. K. Li, 1983. "Diagnostic Checking Arma Time Series Models Using Squared‐Residual Autocorrelations," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(4), pages 269-273, July.
    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. Granger, Clive W. J. & Terasvirta, Timo, 1993. "Modelling Non-Linear Economic Relationships," OUP Catalogue, Oxford University Press, number 9780198773207.
    14. Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Greyling, Talita & Rossouw, Stephanié, 2022. "Re-examining adaptation theory using Big Data: Reactions to external shocks," GLO Discussion Paper Series 1129, Global Labor Organization (GLO).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. 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).
    3. 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).
    4. Philip S. Morrison & Stephanié Rossouw & Talita Greyling, 2022. "The impact of exogenous shocks on national wellbeing. New Zealanders’ reaction to COVID-19," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 17(3), pages 1787-1812, June.
    5. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    6. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, September.
    7. Talita Greyling & Stephanie Rossouw & Tamanna Adhikari, 2021. "A Tale of Three Countries: What is the Relationship Between COVID‐19, Lockdown and Happiness?," South African Journal of Economics, Economic Society of South Africa, vol. 89(1), pages 25-43, March.
    8. Theodore Panagiotidis, 2010. "Market efficiency and the Euro: the case of the Athens stock exchange," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 37(3), pages 237-251, July.
    9. Álvaro Escribano & Oscar Jordá, 2001. "Testing nonlinearity: Decision rules for selecting between logistic and exponential STAR models," Spanish Economic Review, Springer;Spanish Economic Association, vol. 3(3), pages 193-209.
    10. Singh, Tarlok, 2014. "On the regime-switching and asymmetric dynamics of economic growth in the OECD countries," Research in Economics, Elsevier, vol. 68(2), pages 169-192.
    11. Rubén Arrondo & Ana Cárcaba & Eduardo González, 2021. "Drivers of Subjective Well-being in Spain: Are There Gender Differences?," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 16(5), pages 2131-2154, October.
    12. Christian Melzer & Thorsten Neumann, 2005. "Changing Effects of Monetary Policy in the U.S. –Evidence from a Time-Varying Coefficient VAR," Computing in Economics and Finance 2005 144, Society for Computational Economics.
    13. Sibel Cengiz & Afsin Sahin, 2014. "Modelling nonlinear behavior of labor force participation rate by STAR: An application for Turkey," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 7(1), pages 113-127, April.
    14. Martinez Oscar & Olmo Jose, 2012. "A Nonlinear Threshold Model for the Dependence of Extremes of Stationary Sequences," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(3), pages 1-39, September.
    15. Psaradakis Zacharias & Spagnolo Nicola, 2002. "Power Properties of Nonlinearity Tests for Time Series with Markov Regimes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 6(3), pages 1-16, November.
    16. Dick van Dijk & Philip Hans Franses & Michael P. Clements & Jeremy Smith, 2003. "On SETAR non-linearity and forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(5), pages 359-375.
    17. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers 1210, University of Nevada, Las Vegas , Department of Economics.
    18. Yiwei Wang & Shuwang Yang & Canmian Liu & Shiying Li, 2018. "How Would Economic Development Influence Carbon Productivity? A Case from Hubei in China," IJERPH, MDPI, vol. 15(8), pages 1-13, August.
    19. Das, Mahamitra & Kundu, Srikanta & Sarkar, Nityananda, 2019. "Mean and Volatility Spillovers between REIT and Stocks Returns A STVAR-BTGARCH-M Model," MPRA Paper 94707, University Library of Munich, Germany.
    20. Jonathan Manton & Anton Muscatelli & Vikram Krishnamurthy & Stan Hurn, "undated". "Modelling Stock Market Excess Returns by Markov Modulated Gaussian Noise," Working Papers 9806, Business School - Economics, University of Glasgow.

    More about this item

    Keywords

    Happiness; COVID-19; Big data; Markov switching dynamic regression model; New Zealand;
    All these keywords.

    JEL classification:

    • 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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:glodps:573r. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/glabode.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.