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Predicting the Impact of COVID-19 on Australian Universities

Author

Listed:
  • Arran Thatcher

    (Business School, University of Sydney, Sydney 2006, Australia)

  • Mona Zhang

    (Business School, University of Sydney, Sydney 2006, Australia)

  • Hayden Todoroski

    (Business School, University of Sydney, Sydney 2006, Australia)

  • Anthony Chau

    (Business School, University of Sydney, Sydney 2006, Australia)

  • Joanna Wang

    (Business School, University of Sydney, Sydney 2006, Australia)

  • Gang Liang

    (Faculty of Finance, Guangxi University of Economics, Nanning 530003, China
    Faculty of Education, University of British Columbia, Vancouver, BC V6T1Z4, Canada)

Abstract

This article explores the impact of the novel coronavirus (COVID-19) upon Australia’s education industry with a particular focus on universities. With the high dependence that the revenue structures of Australian universities have on international student tuition fees, they are particularly prone to the economic challenges presented by COVID-19. As such, this study considers the impact to total Australian university revenue and employment caused by the significant decline in the number of international students continuing their studies in Australia during the current pandemic. We use a linear regression model calculated from data published by the Australian Government’s Department of Education, Skills, and Employment (DESE) to predict the impact of COVID-19 on total Australian university revenue, the number of international student enrolments in Australian universities, and the number of full-time equivalent (FTE) positions at Australian universities. Our results have implications for both policy makers and university decision makers, who should consider the need for revenue diversification in order to reduce the risk exposure of Australian universities.

Suggested Citation

  • Arran Thatcher & Mona Zhang & Hayden Todoroski & Anthony Chau & Joanna Wang & Gang Liang, 2020. "Predicting the Impact of COVID-19 on Australian Universities," JRFM, MDPI, vol. 13(9), pages 1-20, August.
    • Handle: RePEc:gam:jjrfmx:v:13:y:2020:i:9:p:188-:d:400908
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    References listed on IDEAS

    as
    1. Hedegaard, Esben & Hodrick, Robert J., 2016. "Estimating the risk-return trade-off with overlapping data inference," Journal of Banking & Finance, Elsevier, vol. 67(C), pages 135-145.
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    Cited by:

    1. Bingbing Wang, 2021. "How Does COVID-19 Affect House Prices? A Cross-City Analysis," JRFM, MDPI, vol. 14(2), pages 1-15, January.
    2. Samet Gunay & Walid Bakry & Somar Al-Mohamad, 2021. "The Australian Stock Market’s Reaction to the First Wave of the COVID-19 Pandemic and Black Summer Bushfires: A Sectoral Analysis," JRFM, MDPI, vol. 14(4), pages 1-19, April.
    3. Ping Qiao & Xiaoman Zhu & Yangzhi Guo & Ying Sun & Chuan Qin, 2021. "The Development and Adoption of Online Learning in Pre- and Post-COVID-19: Combination of Technological System Evolution Theory and Unified Theory of Acceptance and Use of Technology," JRFM, MDPI, vol. 14(4), pages 1-17, April.
    4. Mariia Rizun & Artur Strzelecki, 2020. "Students’ Acceptance of the COVID-19 Impact on Shifting Higher Education to Distance Learning in Poland," IJERPH, MDPI, vol. 17(18), pages 1-19, September.
    5. Shakeel Ahmad & Ahmad Shukri Mohd Noor & Ali A. Alwan & Yonis Gulzar & Wazir Zada Khan & Faheem Ahmad Reegu, 2023. "eLearning Acceptance and Adoption Challenges in Higher Education," Sustainability, MDPI, vol. 15(7), pages 1-18, April.

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