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A Vulnerability Index For Post-Disaster Key Sector Prioritization

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  • Krista Danielle S. Yu
  • Raymond R. Tan
  • Kathleen B. Aviso
  • Michael Angelo B. Promentilla
  • Joost R. Santos

Abstract

Input--output-based techniques have proven to be effective in modeling how disasters lead to economic disruptions, while taking into account the structural connectivity of economic systems. In particular, through the inoperability input--output model (IIM), the degree of failure in an economic system can be quantified on a scale from 0 (normal state) to 1 (complete failure). This paper develops a vulnerability index that builds upon the foundations of the Leontief input--output model and the IIM, which is capable of identifying and prioritizing the key sectors in the aftermath of disasters. The key sector prioritization framework proposed in this paper is expected to contribute to the domain of disaster preparedness planning, such as enhancing the efficiency of resource allocation across various sectors. The proposed vulnerability index is formulated in terms of three underlying components: (1) economic impact, (2) propagation length, and (3) sector size. The vulnerability index captures the impact of investments to various sectors in times of disaster in order to yield the maximum benefits to the entire economy. This paper considers a baseline scenario that assumes that the decision-maker has an equal preference for all index components. Using Monte Carlo simulation and sensitivity analysis, we investigated the extent to which the key sector rankings could fluctuate with respect to variations in the decision-maker preferences. Key sectors tend to be sensitive to the weight assignments across the three vulnerability index components; nevertheless, some sectors are less sensitive to such weight variations and may persist on their level of priority, independent of the scenario. Using the Philippine input--output data, we found that the private services sector is consistently a high-priority sector, the trade sector is a mid-priority sector while the real estate and ownership of dwellings sector tend to be a low-priority sector.

Suggested Citation

  • Krista Danielle S. Yu & Raymond R. Tan & Kathleen B. Aviso & Michael Angelo B. Promentilla & Joost R. Santos, 2014. "A Vulnerability Index For Post-Disaster Key Sector Prioritization," Economic Systems Research, Taylor & Francis Journals, vol. 26(1), pages 81-97, March.
    • Handle: RePEc:taf:ecsysr:v:26:y:2014:i:1:p:81-97
    DOI: 10.1080/09535314.2013.872603
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    2. Daryn Joy Go & Michael Angelo Promentilla & Kathleen Aviso & Krista Danielle Yu, 2019. "The Evolution of the Key Sectors in the Philippine Economy Using an AHP-Based Sector Prioritization Index," Economies, MDPI, vol. 7(3), pages 1-20, August.
    3. Hoa Thi Nguyen & Naoya Kojima & Akihiro Tokai, 2019. "An input–output linear programming model for assessing climate policy considering economic growth," Environment Systems and Decisions, Springer, vol. 39(1), pages 34-48, March.
    4. Chopra, Shauhrat S. & Khanna, Vikas, 2015. "Interconnectedness and interdependencies of critical infrastructures in the US economy: Implications for resilience," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 865-877.
    5. Krista Danielle S. Yu & Kathleen B. Aviso & Michael Angelo B. Promentilla & Joost R. Santos & Raymond R. Tan, 2016. "A weighted fuzzy linear programming model in economic input–output analysis: an application to risk management of energy system disruptions," Environment Systems and Decisions, Springer, vol. 36(2), pages 183-195, June.

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