IDEAS home Printed from https://ideas.repec.org/a/gam/jecomi/v10y2022i8p202-d892378.html
   My bibliography  Save this article

Uncertainty Analysis of Business Interruption Losses in the Philippines Due to the COVID-19 Pandemic

Author

Listed:
  • Joost R. Santos

    (Department of Engineering Management and Systems Engineering, George Washington University, Washington, DC 20052, USA)

  • John Frederick D. Tapia

    (Department of Chemical Engineering, De La Salle University, Manila 0922, Philippines)

  • Albert Lamberte

    (School of Economics, De La Salle University, Manila 0922, Philippines)

  • Christine Alyssa Solis

    (School of Economics, De La Salle University, Manila 0922, Philippines)

  • Raymond R. Tan

    (Department of Chemical Engineering, De La Salle University, Manila 0922, Philippines)

  • Kathleen B. Aviso

    (Department of Chemical Engineering, De La Salle University, Manila 0922, Philippines)

  • Krista Danielle S. Yu

    (School of Economics, De La Salle University, Manila 0922, Philippines)

Abstract

In this study, we utilize an input–output (I–O) model to perform an ex-post analysis of the COVID-19 pandemic workforce disruptions in the Philippines. Unlike most disasters that debilitate physical infrastructure systems, the impact of disease pandemics like COVID-19 is mostly concentrated on the workforce. Workforce availability was adversely affected by lockdowns as well as by actual illness. The approach in this paper is to use Philippine I–O data for multiple years and generate Dirichlet probability distributions for the Leontief requirements matrix (i.e., the normalized sectoral transactions matrix) to address uncertainties in the parameters. Then, we estimated the workforce dependency ratio based on a literature survey and then computed the resilience index in each economic sector. For example, sectors that depend heavily on the physical presence of their workforce (e.g., construction, agriculture, manufacturing) incur more opportunity losses compared to sectors where workforce can telework (e.g., online retail, education, business process outsourcing). Our study estimated the 50th percentile economic losses in the range of PhP 3.3 trillion (with telework) to PhP 4.8 trillion (without telework), which is consistent with independently published reports. The study provides insights into the direct and indirect economic impacts of workforce disruptions in emerging economies and will contribute to the general domain of disaster risk management.

Suggested Citation

  • Joost R. Santos & John Frederick D. Tapia & Albert Lamberte & Christine Alyssa Solis & Raymond R. Tan & Kathleen B. Aviso & Krista Danielle S. Yu, 2022. "Uncertainty Analysis of Business Interruption Losses in the Philippines Due to the COVID-19 Pandemic," Economies, MDPI, vol. 10(8), pages 1-18, August.
    • Handle: RePEc:gam:jecomi:v:10:y:2022:i:8:p:202-:d:892378
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7099/10/8/202/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7099/10/8/202/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Krista Danielle S. Yu & Kathleen B. Aviso & Joost R. Santos & Raymond R. Tan, 2020. "The Economic Impact of Lockdowns: A Persistent Inoperability Input-Output Approach," Economies, MDPI, vol. 8(4), pages 1-14, December.
    2. Neil M. Ferguson & Derek A. T. Cummings & Christophe Fraser & James C. Cajka & Philip C. Cooley & Donald S. Burke, 2006. "Strategies for mitigating an influenza pandemic," Nature, Nature, vol. 442(7101), pages 448-452, July.
    3. Albala-Bertrand, J. M., 1993. "Political Economy of Large Natural Disasters: With Special Reference to Developing Countries," OUP Catalogue, Oxford University Press, number 9780198287650.
    4. Yasuhide Okuyama & Joost R. Santos, 2014. "Disaster Impact And Input--Output Analysis," Economic Systems Research, Taylor & Francis Journals, vol. 26(1), pages 1-12, March.
    5. Jalal Ali & Joost R. Santos, 2015. "Modeling the Ripple Effects of IT‐Based Incidents on Interdependent Economic Systems," Systems Engineering, John Wiley & Sons, vol. 18(2), pages 146-161, March.
    6. Yasuhide Okuyama & Krista D. Yu, 2019. "Return of the inoperability," Economic Systems Research, Taylor & Francis Journals, vol. 31(4), pages 467-480, October.
    7. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    8. Jansen, Pieter S. M. Kop, 1994. "Analysis of multipliers in stochastic input-output models," Regional Science and Urban Economics, Elsevier, vol. 24(1), pages 55-74, February.
    9. Adam Rose, 2004. "Economic Principles, Issues, and Research Priorities in Hazard Loss Estimation," Advances in Spatial Science, in: Yasuhide Okuyama & Stephanie E. Chang (ed.), Modeling Spatial and Economic Impacts of Disasters, chapter 2, pages 13-36, Springer.
    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. Elena B. Zavyalova & Vera A. Volokhina & Marija A. Troyanskaya & Yulia I. Dubova, 2023. "A humanistic model of corporate social responsibility in e-commerce with high-tech support in the artificial intelligence economy," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-10, December.

    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. Yasuhide Okuyama, 2016. "Long-Run Effect Of A Disaster: Case Study On The Kobe Earthquake," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 61(01), pages 1-18, March.
    2. Masato Yamazaki & Atsushi Koike & Yoshinori Sone, 2018. "A Heuristic Approach to the Estimation of Key Parameters for a Monthly, Recursive, Dynamic CGE Model," Economics of Disasters and Climate Change, Springer, vol. 2(3), pages 283-301, October.
    3. Iman Rahimi Aloughareh & Mohsen Ghafory Ashtiany & Kiarash Nasserasadi, 2016. "An Integrated Methodology For Regional Macroeconomic Loss Estimation Of Earthquake: A Case Study Of Tehran," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 61(04), pages 1-24, September.
    4. Otto, Christian & Willner, Sven Norman & Wenz, Leonie & Frieler, Katja & Levermann, Anders, 2017. "Modeling loss-propagation in the global supply network: The dynamic agent-based model acclimate," OSF Preprints 7yyhd, Center for Open Science.
    5. Baghersad, Milad & Zobel, Christopher W., 2015. "Economic impact of production bottlenecks caused by disasters impacting interdependent industry sectors," International Journal of Production Economics, Elsevier, vol. 168(C), pages 71-80.
    6. Hallegatte, Stephane, 2012. "Modeling the roles of heterogeneity, substitution, and inventories in the assessment of natural disaster economic costs," Policy Research Working Paper Series 6047, The World Bank.
    7. Balbi Stefano & Giupponi Carlo & Mojtahed Vahid & Olschewski Roland, 2015. "The Total Cost of Water-Related Disasters," Review of Economics, De Gruyter, vol. 66(2), pages 225-252, August.
    8. Lopez, Ramon, 2009. "Natural disasters and the dynamics of intangible assets," Policy Research Working Paper Series 4874, The World Bank.
    9. Naqvi, Asjad, 2017. "Deep Impact: Geo-Simulations as a Policy Toolkit for Natural Disasters," World Development, Elsevier, vol. 99(C), pages 395-418.
    10. Selerio, Egberto & Maglasang, Renan, 2021. "Minimizing production loss consequent to disasters using a subsidy optimization model: a pandemic case," Structural Change and Economic Dynamics, Elsevier, vol. 58(C), pages 112-124.
    11. Wenzel, Lars & Wolf, André, 2013. "Protection against major catastrophes: An economic perspective," HWWI Research Papers 137, Hamburg Institute of International Economics (HWWI).
    12. Loayza, Norman V. & Olaberría, Eduardo & Rigolini, Jamele & Christiaensen, Luc, 2012. "Natural Disasters and Growth: Going Beyond the Averages," World Development, Elsevier, vol. 40(7), pages 1317-1336.
    13. Ayaz Hyder & David L Buckeridge & Brian Leung, 2013. "Predictive Validation of an Influenza Spread Model," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-20, June.
    14. Oosterhaven, Jan, 2015. "On the doubtful usability of the inoperability IO model," Research Report 15008-EEF, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    15. Otto, C. & Willner, S.N. & Wenz, L. & Frieler, K. & Levermann, A., 2017. "Modeling loss-propagation in the global supply network: The dynamic agent-based model acclimate," Journal of Economic Dynamics and Control, Elsevier, vol. 83(C), pages 232-269.
    16. Suman K SHARMA, 2010. "Socio-Economic Aspects of Disaster’s Impact: An Assessment of Databases and Methodologies," Economic Growth Centre Working Paper Series 1001, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
    17. Kuhla, Kilian & Willner, Sven N & Otto, Christian & Levermann, Anders, 2023. "Resilience of international trade to typhoon-related supply disruptions," Journal of Economic Dynamics and Control, Elsevier, vol. 151(C).
    18. Stefano Balbi & Carlo Giupponi & Roland Olschewski & Vahid Mojtahed, 2013. "The economics of hydro-meteorological disasters: approaching the estimation of the total costs," Working Papers 2013-12, BC3.
    19. Giovanni Marin & Marco Modica, 2016. "Mapping the exposure to natural disaster losses for Italian municipalities," SEEDS Working Papers 0916, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Oct 2016.
    20. Logan C Brooks & David C Farrow & Sangwon Hyun & Ryan J Tibshirani & Roni Rosenfeld, 2015. "Flexible Modeling of Epidemics with an Empirical Bayes Framework," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-18, August.

    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:gam:jecomi:v:10:y:2022:i:8:p:202-:d:892378. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.