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Understanding the impact of travel on wellbeing: evidence for Great Britain during the pandemic

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  • MAMATZAKIS, emmanuel
  • MAMATZAKIS, E

Abstract

This study examines the impact of the COVID-19 on the wellbeing of individuals in Great Britain, as measured by life satisfaction and happiness, by analysing the dramatic drop in travel during this time. The Bayesian VAR model considers a range of exogenous and endogenous variables, including COVID-19, modes of transportation, and wellbeing variables. Results indicate that shocks in COVID-19 have a negative impact on travel, which subsequently affects wellbeing. However, there is limited evidence to suggest that COVID-19 responses to shocks in various forms of transportation have a significant impact on COVID-19 outcomes. Additionally, the study provides forecasts for key endogenous variables, which can inform evidence-based policymaking during the pandemic. The study emphasizes the importance of considering the relationship between travel and wellbeing amidst the pandemic and highlights the need for policies that balance the public health risks of travelling with the benefits of mobility and travel for wellbeing.

Suggested Citation

  • MAMATZAKIS, emmanuel & MAMATZAKIS, E, 2022. "Understanding the impact of travel on wellbeing: evidence for Great Britain during the pandemic," MPRA Paper 121782, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:121782
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    References listed on IDEAS

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    1. Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
    2. Kock, Florian & Nørfelt, Astrid & Josiassen, Alexander & Assaf, A. George & Tsionas, Mike G., 2020. "Understanding the COVID-19 tourist psyche: The Evolutionary Tourism Paradigm," Annals of Tourism Research, Elsevier, vol. 85(C).
    3. Helmut Lütkepohl, 2005. "New Introduction to Multiple Time Series Analysis," Springer Books, Springer, number 978-3-540-27752-1, March.
    4. Dong Hwan Oh & Andrew J. Patton, 2021. "Better the Devil You Know: Improved Forecasts from Imperfect Models," Finance and Economics Discussion Series 2021-071, Board of Governors of the Federal Reserve System (U.S.).
    5. Liu, Anyu & Kim, Yoo Ri & O'Connell, John Frankie, 2021. "COVID-19 and the aviation industry: The interrelationship between the spread of the COVID-19 pandemic and the frequency of flights on the EU market," Annals of Tourism Research, Elsevier, vol. 91(C).
    6. Sun, Xiaoqian & Wandelt, Sebastian & Zhang, Anming, 2020. "How did COVID-19 impact air transportation? A first peek through the lens of complex networks," Journal of Air Transport Management, Elsevier, vol. 89(C).
    7. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    8. Dieppe, Alistair & van Roye, Björn & Legrand, Romain, 2016. "The BEAR toolbox," Working Paper Series 1934, European Central Bank.
    9. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    10. Kadiyala, K Rao & Karlsson, Sune, 1997. "Numerical Methods for Estimation and Inference in Bayesian VAR-Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 99-132, March-Apr.
    11. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
    12. Anna Zabai, 2020. "How are household finances holding up against the Covid-19 shock?," BIS Bulletins 22, Bank for International Settlements.
    13. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    14. Ghysels, Eric & Wright, Jonathan H., 2009. "Forecasting Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
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    Keywords

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    JEL classification:

    • I0 - Health, Education, and Welfare - - General
    • M0 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - General
    • Z0 - Other Special Topics - - General

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