Rainy season is just beginning. Memories of typhoons such as Ondoy and Pepeng literally flood our minds as we delve into the unknown. Over the years, the incidence of natural disasters has grown into a significant number. Statistically speaking, six out of the ten deadliest typhoons happened in the last ten years, while seven out of the ten costliest typhoons, in the last five years based on data from the National Disaster Risk Reduction and Management Council (NDRRMC). In an article from Mindanews, Arguillas (2014) points out that there is an unusual spike in the number of typhoons that passed through Mindanao. From 1996 to 2010, only six typhoons made landfall in Mindanao. However, since 2011, there have been nine typhoons that made landfall, four of which were in 2014.
Indeed, climate change has transformed the landscape of agricultural production and other economic activities, and electricity supply, bringing about consequences such as reduction of productivity, destruction of property and loss of human life, leading to grave repercussions on the global economy. Aside from the cost of physical destruction of these extreme events, the interdependent nature of economic systems guarantees that this will trickle down to initially unaffected industries, ultimately leading to increased levels of damage than is immediately evident.
Recently, Yolanda, the strongest typhoon to make landfall in world history, ravaged the Visayan Region, leaving around 6,200 reported deaths, close to 1,800 missing persons, millions of households displaced, weeks without electricity, water, and communication, and almost P50 million in estimated damages to infrastructure and agriculture. Relief and aid from all over the world poured into the Philippines during the aftermath, with hopes of improving the conditions of the affected population. However, it still took weeks before issues were addressed. Oftentimes, post-disaster management becomes a challenging feat as a result of the lack of a method to quantify key sector prioritization, which would enhance the quality of disaster risk policies.
Hence, my colleagues and I developed an index grounded on the foundations of Leontief’s Nobel Prize winning work on input-output (IO) analysis. The IO model consists of a system of linear equations that shows the inter-industry relationship between the different sectors of the economy, providing a general structure of the commodities that will either be further processed by a sector or consumed by end-users. The framework accounts for the following components:
• Economic impact. We can estimate economic impact of changes in final demand through the output multiplier. For example, if a sector has an output multiplier of 1.66, a PhP 1 million increase in its final demand will increase the economy’s total output by P1.66 million due to linkages. On another note, Prof. Joost Santos, along with Prof. Yacov Haimes, developed an extension of the IO model, more commonly known as the inoperability input-output model (IIM), which accounts for inoperability in the various sectors as a result of a perturbation such as a natural disaster. They defined inoperability as a dimensionless number with 0 corresponding to the normal state of a system, 1 being total failure (i.e., complete loss of output), and intermediate values reflecting partial failure. In effect, the inoperability multiplier serves as a measure of risk that cascades through the economy in response to an external shock. These two measures are consolidated into a ratio of the gain to the risks of inoperability cascading through the economy.
• Diversity of reach. In addition to economic impact, the index assesses the ability of a sector to influence other sectors. We use the propagation length index derived from the concept of Prof. Erik Dietzenbacher and Prof. Isidoro Romero’s average propagation length. It measures the scope and extent of the sector’s linkages across the economic network as a supplier of inputs as well as a purchaser of output from other sectors of the economy, and thus reflects the “diversity of reach” through interconnectivity.
• Sector size. Lastly, sector size is considered to measure the sector’s significance relative to the rest of the economy.
Given that our model assumes that these components determine sector vulnerability, we do not discount the subjectivity of the decision maker. In fact, the index is flexible enough to allow policy makers to adjust their preferences towards a specific determinant. For example, if a policy maker thinks that the number of sectors influenced through an increased level of demand is more important, then he can assign a higher weight on the diversity of reach component relative to economic impact and sector size in the assessment process.
Scientific approaches towards managing disasters and risk are needed to efficiently allocate the limited resources available in developing countries. The vulnerability index we developed can still be improved through implementing systematic procedures for elucidating weights based on policy priorities such as fuzzy set theory or the analytical hierarchical process. Furthermore, the inclusion of welfare implications to various social clusters is also relevant. Clearly, there is more work to be done for us.
This work is based on a journal article entitled “A Vulnerability Index for Post-Disaster Key Sector Prioritization” co-authored with Dr. Raymond R. Tan (DLSU), Dr. Kathleen B. Aviso (DLSU), Dr. Michael Angelo B. Promentilla (DLSU), and Dr. Joost R. Santos (GWU) published in Economic Systems Research (Impact Factor: 2.098) (2014) Volume 26 Number 1, pp. 81-97.
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Krista Danielle S. Yu is an assistant professor at the School of Economics in De La Salle University (DLSU), Manila, Philippines. Her main areas of research are input-output modelling, computable general equilibrium analysis and disaster risk analysis. She received her Ph.D. in Economics from De La Salle University. She has also served as a visiting researcher at The George Washington University (GWU), Washington D.C., U.S.A. Currently, she is chairing a research program workshop that promotes an interdisciplinary approach towards quantitative modeling of disaster risk management. Email her at krista.yu@dlsu.edu.ph.