Resilience of Bruneian economy amidst Covid-19 based on the United Nations Disaster Risk Reduction (UNDRR) framework

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The outbreak of Covid-19 is the second most devastating event over a century. The pandemic, alongside deep health crises, has ushered the largest economic shocks, which require governments’ attention to ameliorate to avoid an economic downturn. The aim of this study is to measure the economic impacts of Covid-19 in Brunei by estimating the exposure, vulnerability, and resilience of the economy. This study deployed the United Nations Disaster Risk Reduction framework to examine the economic impact empirically. The data related to variables of gross domestic product, oil prices, international merchandise trade, tourism, unemployment, consumer price index, money supply, and national accounts were collected from September 2019 to July 2020 and analyzed through the fixed effects panel regression technique. The findings show that the news of the Covid-19 outbreak has exposed the weaknesses in energy sectors by having a significant negative impact. Additionally, analysis discloses that the energy and tourism sectors are vulnerable to the shocks of Covid-19. During the peak of the pandemic outbreak, unemployment in Brunei has also escalated. Additionally, the energy and tourism sectors are less resilient to pandemic shocks. The findings indicated that the consumer price index has significantly escalated during the economic recovery process. The findings elucidate that the overall GDP growth rate, international merchandise trade, and the financial sector continue exhibiting better performance amid Covid-19. The findings of this study contribute to developing policy implications for the emerging economies concerned with the economic recovery process during the pandemic.

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    • Table 1. Estimation of the variables used in this study
    • Table 2. Results of mean, median, and standard deviation
    • Table 3. Results of panel regression analysis (exposure scenario)
    • Table 4. Mean, median, standard deviation, and t-tests results
    • Table 5. Panel regression analysis (vulnerability scenario)
    • Table 6. Results of mean, median, and t-test
    • Table 7. Results of panel regression analysis (resilience scenario)
    • Conceptualization
      Hakimah Yaacob
    • Formal Analysis
      Hakimah Yaacob
    • Writing – review & editing
      Hakimah Yaacob, Zaki Zaini
    • Software
      Qaisar Ali
    • Validation
      Qaisar Ali
    • Visualization
      Qaisar Ali, Zaki Zaini
    • Methodology
      Nur Anissa Sarbini
    • Resources
      Nur Anissa Sarbini
    • Writing – original draft
      Nur Anissa Sarbini, Abdul Nasir Rani
    • Investigation
      Abdul Nasir Rani
    • Project administration
      Abdul Nasir Rani
    • Supervision
      Zaki Zaini