applying the natural disasters vulnerability evaluation model to the march 2011 north-east japan...

Applying the natural disasters vulnerability evaluation model to the March 2011 north-east Japan earthquake and tsunami

Mario Arturo Ruiz Estrada Faculty of Economics and Administration, University of Malaya, Kuala Lumpur, Malaysia, Su Fei Yap Faculty of Economics and Administration, University of Malaya, and Donghyun Park Principal Economist, Asian Development Bank, Manila, the Philippines

Natural hazards have a potentially large impact on economic growth, but measuring their economic impact is subject to a great deal of uncertainty. The central objective of this paper is to demonstrate a model—the natural disasters vulnerability evaluation (NDVE) model—that can be used to evaluate the impact of natural hazards on gross national product growth. The model is based on five basic indicators—natural hazards growth rates (α

i), the national natural

hazards vulnerability rate (ΩT), the natural disaster devastation magnitude rate (Π), the eco-

nomic desgrowth rate (i.e. shrinkage of the economy) (δ), and the NHV surface. In addition, we apply the NDVE model to the north-east Japan earthquake and tsunami of March 2011 to evaluate its impact on the Japanese economy.

Keywords: earthquake, economic desgrowth, gross national product growth, Japan, natural disaster, NDVE model, tsunami

IntroductionInitially, this paper aims to examine the differences that exist between natural haz-ards and natural disasters. According to Okuyama and Chang (2004), natural hazards can be considered physical events (the causes of disasters) and natural disasters are the final effects of natural hazards. Hence, any natural hazard (Sorkin, 1982) can have a potentially large effect on economic growth, but measuring the economic impact of natural disasters is subject to a great deal of uncertainty. This is because they impose both direct and indirect costs that change and evolve over time (Greenberg, Lahr, and Mantell, 2007). Natural hazards adversely affect economic activity in the short run in a number of ways. For example, the north-east Japan earthquake and tsunami of March 2011 severely curtailed manufacturing output by destroying power stations (Rose et al., 1997), production facilities, and transportation and other infrastructure (Rose and Benavides, 1998). Beyond the very short term, however, the negative economic impact of natural hazards tends to fade. For example, in the Kobe earth-quake of January 1995 (Okuyama, 2003) government reconstruction spending spear-headed a robust recovery in private investment and consumption. As a result, macro-economic indicators recovered very quickly after an initial drop (Skidmore and Toya, 2002; Noy, 2009).

doi:10.1111/disa.12069

Disasters, 2014, 38(S2): S206−S229. © 2014 The Author(s). Disasters © Overseas Development Institute, 2014Published by John Wiley & Sons Ltd, 9600 Garsington Road, Oxford, OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA

Applying the natural disasters vulnerability evaluation model S207

Given the potentially large effects of natural hazards on economic growth, it is important for policymakers to have rea-sonably accurate estimates of these effects (Scanlon, 1988). However, such estimates are difficult to calculate, given the high uncertainty surrounding the measurement of these effects. The motivation for this paper comes from the large numbers of natural hazards that seem to be inflict-ing damage on the world economy with growing frequency. Developing coun-tries in particular are more vulnerable to natural hazards due to their weaker infra-structure and lack of anticipatory measures (Cuaresma, Hlouskova, and Obersteiner, 2008). Developing Asia in particular accounted for 61 per cent of global fatali-ties and 9 per cent of all persons affected globally by natural hazards between 1971 and 2011. Table 1 shows the fatali-ties and estimated damage from various types of natural hazards in developing Asia between 2000 and 2010. The esti-mated damage implies a sizable negative economic impact on the region. The central objective of this paper is to demonstrate a model—the natural dis-asters vulnerability evaluation (NDVE) model—that can be used to evaluate the impact of natural hazards on gross national product (GNP) growth. The model is based on five basic indicators: natural hazards growth rates (α

i), the

national natural hazards vulnerability rate (Ω

T), the natural disaster devastation

magnitude rate (Π), the economic des-growth rate (i.e. shrinkage of the economy) (δ), and the NHV surface. Furthermore, the model is also based on elements from an alternative mathematical approach analysis framework from a multidimen-sional perspective. We look at different Ta

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Mario Arturo Ruiz Estrada, Su Fei Yap, and Donghyun Park S208

types of natural hazards that occurred around the world between 1971 and 2011. To illustrate the NDVE model, we use it to assess the economic impact of the earthquake and tsunami that devastated north-east Japan in March 2011 (Shibusawa, 2011). For comparative purposes we also apply the model to an earlier earthquake in Japan that affected the Kobe region in January 1995. We hope that the NDVE model will contribute towards a more systematic and accurate measurement of the economic impact of natural hazards.

Economic modelling in the evaluation of natural disastersClassic economic modelling in the evaluation of natural disasters

Firstly, this paper studies the origins of the economics of natural disasters (Tol and Leek, 1999). Dacy and Kunreuther’s (1969) book entitled The Economics of Natural Disasters: Implications for Federal Policy gives us the first basic economic theoretical framework and policies framework dealing with the analysis of the effects of natu-ral disasters. While this book’s treatment of the issues of policies and implications shows some deficiencies, nonetheless its economic modelling remains the cornerstone of the study of the economics of natural disasters. We believe that the major contribution of this book is the analysis of a long-term recovery model that refers to the need for a recon-struction process that returns the community to its pre-disaster economic level. It is important to mention that the long-term recovery model is based on the use of Solow-Swan’s neoclassical growth model. From a mathematical modelling point of view, the long-term recovery model has a series of basic equations that include the negative effects of natural hazards at differ-ent levels from a macroeconomic perspective. It also uses the long-term recovery model, which applies basic arithmetic operations and calculus—in the latter case using partial differentiation (e.g. first derivatives). The graphical modelling is based on the application of basic geometry using the two-dimensional coordinate system. In our opinion, building a model of this magnitude in 1969 was a significant achieve-ment. If we observe the technological limitations (e.g. the scarcity of computers with large capacity and high speed, and the non-existence of econometrics and statistical software) and the limitations of the database, which was confined to simple obser-vations and interviews, it is clear that this model is major contribution.

Modern economic modelling in the evaluation of natural disasters

Since the 1980s the economics of natural disasters has experienced a deep transfor-mation (in form and content) and faster research expansion using sophisticated analytical tools to evaluate the effects of natural disasters such as the implementation of more modern statistical, mathematical and econometric modelling through the use of advanced software (modern econometrics software programs) and hardware


(high-speed computers with large memory storage capacity). Interesting studies in this area are those of Albala-Bertrand (1993), Okuyama and Chang (2004), Hallegate and Przyluski (2010), Kunreuther and Rose (2004), Loayza et al. (2009), Okuyama (2007), Rose and Liao (2005), Sorkin (1982), and Skidmore and Toya (2002). Some of these studies use basic ideas from Dacy and Kunreuther (1969). Additionally, we can observe that the focus of these studies is on real physical infrastructure damage that directly affected consumption and production. According to Okuyama (2007), the most common model employed to study the economics of natural disasters is the input-output model. Okuyama (2004) observes that this model can only show the basic interdependency that exists among different sectors, and leaves out explicit resources constraints, import substitution, and price changes behaviour (Okuyama, 2007). Therefore, many economists have specialised in the study of the economics of disasters. Subsequently, they prefer to use the computable general equilibrium (CGE) model rather than the input-output model, because the CGE model is more flexible in capturing more variables in the process of economic modelling (Yamano, Kajitani, and Shumuta, 2007). However, the CGE model also presented certain lim-itations in that it is based on the use of a large database compared to the input-output model. Another theoretical framework that is widely used in the study of the economics of natural disaster is the social accounting matrix (SAM) (Cole, 2004). The SAM is designed to study different levels of socioeconomic agents and factors simultaneously. It employs a group of coefficients that estimate the impact of natural disasters by evaluating the feasibility of different possible public policies designed to manage natural disasters (reconstruction) under different magnitudes (Okuyama, 2005; 2007). Finally, the econometric models used to analyse natural disasters show some defi-ciencies in their incorporation of non-economic variables and technical indicators into the analysis of the effects of natural hazards as a whole. Therefore, we need to integrate into the study of the economics of natural disasters a new dynamicity and complexity through innovative mathematical and graphical approaches to better understand the behaviour of natural disasters and reconstruction management. The idea in designing the NDVE model is to innovatively access the impacts and con-sequences of a natural hazard. The model tries to evaluate higher order effects of uncertainty after a natural hazard that need to be incorporated into the analysis of the economic impacts of a natural disaster. Here we use the CGE model. Our main objective is to account for this uncertainty and behavioural changes from a multi-dimensional perspective (mathematical and graphically) within the framework of a dynamic imbalanced state (Ruiz Estrada and Yap, 2012) and the Omnia Mobilis assumption (Ruiz Estrada, 2011). The idea is to move on from classical economic modelling such as linear and non-linear models (e.g. the input-output model, the CGE model, the SAM, etc.) to new economic mathematical modelling and mapping of natural hazards (ex-ante—before the natural hazard—and ex-post—after the natural hazard) by using high-resolution mapping software.


The natural disasters vulnerability evaluation model The NDVE model assumes that any country is vulnerable to natural hazards at any time and anywhere. Additionally, each natural hazard has its own level of potential damage and impact on the GNP of any country. Hence, our world is in a constantly dynamic imbalanced state. This means that at any time and anywhere the possibility exists of a natural hazard occurring and that it can generate different magnitudes of natural disasters. When we speak of a natural hazard, we are referring to any event beyond human control that can generate massive destruction at any time, anywhere, without any advance warning. The quantification and monitoring of natural hazards are inherently difficult, and we cannot evaluate and predict them with any degree of accuracy, but we can compute series of natural hazards within a fixed period of time (per year or per decade) (Wilson, 1982). In addition, the NDVE model is useful for demonstrating how the GNP growth rate is directly connected to the presences of natural hazards. In the context of the NDVE model, we would like to propose five new indicators: natural hazards growth rates (αi

), the national natural hazards vulnerability rate (ΩT),

the natural disaster devastation magnitude rate (Π), the economic desgrowth rate (δ) (see below), and the NHV surface. These five indicators simultaneously show the various levels of vulnerability and devastation arising from different natural hazards. They are determined by the collection of historical data on various natural hazards that have impacted any country in terms of which such hazards are defined accord-ing to certain intervals of time and the magnitude of destruction in terms of the loss of material resources (infrastructure) and non-material resources (human lives) (Rose, 1981). According to our model, the analysis of any natural disasters from an eco-nomic point of view must simultaneously take into account the reduction in produc-tion (national output) (Pelling, Özerdem, and Barakat, 2002) and human capital mobility (labour) (Rose and Liao, 2005). In this part of our model we introduce a new concept called the economic des-growth rate (δ) (Ruiz Estrada, 2010), which is defined as a shrinkage of or reduction in economic growth due to any natural hazard. The main function of δ is to deter-mine the ultimate impact of any natural hazard on GNP growth rate behaviour over a particular period of time. The basic data used by the NDVE model is based on the occurrence of 16 possible natural hazard events: earthquakes, tsunamis, floods, volcanic eruptions, typhoons, fire pollution, snow avalanches, landslides, blizzards, cyclones, tornadoes, epidemics, droughts, hailstorms, sandstorms, and hurricanes.

The national natural hazards vulnerability rate (ΩT)According to the NDVE model, we assume a continuous but irregular oscillation in and out of various natural hazard events. We do so by applying the natural hazards growth rate (α

i), which is equal to the total sum of the same type of natural hazard

event in the present year (Σλo) minus the total sum of the same type of natural hazard

event in the past ten years (Σλn-1

) divided by the total sum of the same type of natu-ral hazard event in the past ten years (Σλ

n-1) (see Formula 1).


αi = (Σλo – Σλn-1)/Σλn-1 (1)

This means that our world will be in a permanent dynamic imbalanced state under high risk of experiencing a natural hazard event at any time. The NDVE model allows for different magnitudes of destruction. Therefore, we have different natural hazard events growth rates (α

i), as described in Formula 2. Therefore, we assume

that the national natural hazards vulnerability rate (ΩT) is directly connected to time

(Tj). At the same time, T

j is affected directly by different natural hazards growth

rates (αi). In our case, ‘ j’ is a specific period of time and ‘i’ represents the particular

type of natural hazard according to our classification of 16 different types of natural hazards. Hence, Ω

T includes a total of 16 possible natural hazard events, as follows:

earthquakes (α1), tsunamis (α

2), floods (α

3), volcanic eruptions (α

4), typhoons (α

5), fire

pollution (α6), snow avalanches (α

7), landslides (α

8), blizzards (α

9), cyclones (α

10),

tornadoes (α11), epidemics (α

12), droughts (α

13), hailstorms (α

14), sandstorms (α

15), and

hurricanes (α16

). Each natural hazard has its magnitude of intensity according to its geographical position and related environmental problems. We assume that if any natural hazard event follows another at a great geographical distance from the first then it cannot be predicted with accuracy, as in Formula 4. Hence, the national natural hazards vulnerability rate (Ω

T) is equal to the total sum of all α

i divided by the total

of natural hazards in the analysis (itotal

) (see Formula 3).

ΩT = (Σαi)/itotal Є[0 < Σαi < 1] itotal = 16 (2)

ΩTe = Ln[(αi)Tj – (αi)Tj-1]/(αi)Tj] ΩTe ≠ 0 (3)

ΩTp = Ln[(αimax)Tj] – [(αimin)Tj)] 0 > αimax ≤ 1 or 0 ≥ αimin < 1 (4)

ΩTe ‡ ΩTp (5)

Formulas 3 and 4 show the effective national natural hazards vulnerability rate (Ω

Te) and the potential national natural hazards vulnerability rate (Ω

Tp). Ω

Tp is based

on a comparison of the past and present natural hazards events growth rates. We assume that the present national natural hazards vulnerability rate (Ω

T) cannot be equal

to zero (see Formula 3). However, ΩTp is based on the use of a maximal and minimal

natural hazard events growth rate in a determinate period of time (Tj) (see Formula 4).

Additionally, we need to assume that for the purposes of ΩTp a random database exists

that makes it possible for the NDVE model to analyse unexpected results from differ-ent natural disaster events that cannot be predicted and monitored with traditional methods of linear and non-linear mathematical modelling. The effective natural hazard events growth rate is identified in Formula 3. Finally, the potential natural hazard event growth rate cannot be equal to the effective natural hazard events growth rate in either the short run or long run (see Formula 5). This is because we assume at the very outset that our world is in a dynamic imbalanced state. Thus the Ω

T cal-

culation can be observed in Table 3 for different countries by using different αi and

a single ΩT. The evaluation of the national natural hazards vulnerability rate (Ω

T)

is applied to three different levels of vulnerability (see Formula 6).


Table 2. Calculation of the natural disaster devastation magnitude rate (Π) and the

economic desgrowth rate (δ)

ΩT ΩT – 1 √ΩT – 1 Ln √ΩT – 1 ψL Φk Π δ

0.00 1 1 0.0000 0.000 0.00 0.000 0.000

0.01 0.99 0.99 -0.0050 0.001 0.01 0.000 0.000

0.02 0.98 0.99 -0.0101 0.002 0.02 0.000 0.000

0.03 0.97 0.98 -0.0152 0.003 0.03 0.000 0.000

0.04 0.96 0.98 -0.0204 0.004 0.04 0.000 0.000

0.05 0.95 0.97 -0.0256 0.005 0.05 0.000 0.000

0.06 0.94 0.97 -0.0309 0.006 0.06 0.000 0.000

0.07 0.93 0.96 -0.0363 0.007 0.07 0.000 0.000

0.08 0.92 0.96 -0.0417 0.008 0.08 0.001 0.000

0.09 0.91 0.95 -0.0472 0.009 0.09 0.001 0.000

0.10 0.90 0.95 -0.0527 0.010 0.10 0.001 0.000

0.11 0.89 0.94 -0.0583 0.011 0.11 0.001 0.000

0.12 0.88 0.94 -0.0639 0.012 0.12 0.001 0.000

0.13 0.87 0.93 -0.0696 0.013 0.13 0.002 0.000

0.14 0.86 0.93 -0.0754 0.014 0.14 0.002 0.000

0.15 0.85 0.92 -0.0813 0.015 0.15 0.002 0.000

0.16 0.84 0.92 -0.0872 0.016 0.16 0.003 0.000

0.17 0.83 0.91 -0.0932 0.017 0.17 0.003 0.000

0.18 0.82 0.91 -0.0992 0.018 0.18 0.003 0.000

0.19 0.81 0.90 -0.1054 0.019 0.19 0.004 0.000

0.20 0.80 0.89 -0.1116 0.020 0.20 0.004 0.000

0.21 0.79 0.89 -0.1179 0.021 0.21 0.004 -0.001

0.22 0.78 0.88 -0.1242 0.022 0.22 0.005 -0.001

0.23 0.77 0.88 -0.1307 0.023 0.23 0.005 -0.001

0.24 0.76 0.87 -0.1372 0.024 0.24 0.006 -0.001

0.25 0.75 0.87 -0.1438 0.025 0.25 0.006 -0.001

0.26 0.74 0.86 -0.1506 0.026 0.26 0.007 -0.001

0.27 0.73 0.85 -0.1574 0.027 0.27 0.007 -0.001

0.28 0.72 0.85 -0.1643 0.028 0.28 0.008 -0.001

0.29 0.71 0.84 -0.1712 0.029 0.29 0.008 -0.001

0.30 0.70 0.84 -0.1783 0.030 0.30 0.009 -0.002

0.31 0.69 0.83 -0.1855 0.031 0.31 0.010 -0.002

0.32 0.68 0.82 -0.1928 0.032 0.32 0.010 -0.002

0.33 0.67 0.82 -0.2002 0.033 0.33 0.011 -0.002



0.34 0.66 0.81 -0.2078 0.034 0.34 0.012 -0.002

0.35 0.65 0.81 -0.2154 0.035 0.04 0.001 0.000

0.36 0.64 0.80 -0.2231 0.036 0.36 0.013 -0.003

0.37 0.63 0.79 -0.2310 0.037 0.37 0.014 -0.003

0.38 0.62 0.79 -0.2390 0.038 0.38 0.014 -0.003

0.39 0.61 0.78 -0.2471 0.039 0.39 0.015 -0.004

0.40 0.60 0.77 -0.2554 0.040 0.40 0.016 -0.004

0.41 0.59 0.77 -0.2638 0.041 0.41 0.017 -0.004

0.42 0.58 0.76 -0.2724 0.042 0.42 0.018 -0.005

0.43 0.57 0.75 -0.2811 0.043 0.43 0.018 -0.005

0.44 0.56 0.75 -0.2899 0.044 0.44 0.019 -0.006

0.45 0.55 0.74 -0.2989 0.045 0.45 0.020 -0.006

0.46 0.54 0.73 -0.3081 0.046 0.46 0.021 -0.007

0.47 0.53 0.73 -0.3174 0.047 0.47 0.022 -0.007

0.48 0.52 0.72 -0.3270 0.048 0.48 0.023 -0.008

0.49 0.51 0.71 -0.3367 0.049 0.49 0.024 -0.008

0.50 0.50 0.71 -0.3466 0.050 0.50 0.025 -0.009

0.51 0.49 0.70 -0.3567 0.051 0.51 0.026 -0.009

0.52 0.48 0.69 -0.3670 0.052 0.52 0.027 -0.010

0.53 0.47 0.69 -0.3775 0.053 0.53 0.028 -0.011

0.54 0.46 0.68 -0.3883 0.054 0.54 0.029 -0.011

0.55 0.45 0.67 -0.3993 0.055 0.55 0.030 -0.012

0.56 0.44 0.66 -0.4105 0.056 0.56 0.031 -0.013

0.57 0.43 0.66 -0.4220 0.057 0.57 0.032 -0.014

0.58 0.42 0.65 -0.4338 0.058 0.58 0.034 -0.015

0.59 0.41 0.64 -0.4458 0.059 0.59 0.035 -0.016

0.60 0.40 0.63 -0.4581 0.06 0.60 0.036 -0.016

0.61 0.39 0.62 -0.4708 0.061 0.61 0.037 -0.018

0.62 0.38 0.62 -0.4838 0.62 0.62 0.384 -0.186

0.63 0.37 0.61 -0.4971 0.063 0.63 0.040 -0.020

0.64 0.36 0.60 -0.5108 0.064 0.64 0.041 -0.021

0.65 0.35 0.59 -0.5249 0.065 0.65 0.042 -0.022

0.66 0.34 0.58 -0.5394 0.066 0.66 0.044 -0.023



0.67 0.33 0.57 -0.5543 0.067 0.67 0.045 -0.025

0.68 0.32 0.57 -0.5697 0.068 0.68 0.046 -0.026

0.69 0.31 0.56 -0.5856 0.069 0.69 0.048 -0.028

0.70 0.30 0.55 -0.6020 0.070 0.700 0.049 -0.029

0.71 0.29 0.54 -0.6189 0.071 0.71 0.050 -0.031

0.72 0.28 0.53 -0.6365 0.072 0.72 0.052 -0.033

0.73 0.27 0.52 -0.6547 0.073 0.73 0.053 -0.035

0.74 0.26 0.51 -0.6735 0.074 0.74 0.055 -0.037

0.75 0.25 0.50 -0.6931 0.075 0.75 0.056 -0.039

0.76 0.24 0.49 -0.7136 0.076 0.76 0.058 -0.041

0.77 0.23 0.48 -0.7348 0.077 0.77 0.059 -0.044

0.78 0.22 0.47 -0.7571 0.078 0.78 0.061 -0.046

0.79 0.21 0.46 -0.7803 0.079 0.79 0.062 -0.049

0.80 0.20 0.45 -0.8047 0.080 0.80 0.064 -0.052

0.81 0.19 0.44 -0.8304 0.081 0.81 0.066 -0.054

0.82 0.18 0.42 -0.8574 0.082 0.82 0.067 -0.058

0.83 0.17 0.41 -0.8860 0.083 0.83 0.069 -0.061

0.84 0.16 0.40 -0.9163 0.084 0.84 0.071 -0.065

0.85 0.15 0.39 -0.9486 0.085 0.85 0.072 -0.069

0.86 0.14 0.37 -0.9831 0.086 0.86 0.074 -0.073

0.87 0.13 0.36 -1.0201 0.087 0.87 0.076 -0.077

0.88 0.12 0.35 -1.0601 0.088 0.88 0.077 -0.082

0.89 0.11 0.33 -1.1036 0.089 0.89 0.079 -0.087

0.90 0.10 0.32 -1.1513 0.090 0.90 0.081 -0.093

0.91 0.09 0.30 -1.2040 0.091 0.91 0.083 -0.100

0.92 0.08 0.28 -1.2629 0.092 0.92 0.085 -0.107

0.93 0.07 0.26 -1.3296 0.093 0.93 0.086 -0.115

0.94 0.06 0.24 -1.4067 0.094 0.94 0.088 -0.124

0.95 0.05 0.22 -1.4979 0.095 0.95 0.090 -0.135

0.96 0.04 0.20 -1.6094 0.096 0.96 0.092 -0.148

0.97 0.03 0.17 -1.7533 0.097 0.97 0.094 -0.165

0.98 0.02 0.14 -1.9560 0.098 0.98 0.096 -0.188

0.99 0.01 0.10 -2.3026 0.099 0.99 0.098 -0.226

Source: authors.


Level 1: High vulnerability (dark grey cells in Table 2): 1 – 0.75

Level 2: Average vulnerability (light grey cells in Table 2): 0.74 – 0.34

Level 3: Low vulnerability (white cells in Table 2): 0.33 – 0.0 (6)

However, in Figure 1 it is possible to observe diminishing returns between the economic desgrowth rate (δ) and the national natural hazards vulnerability rate (Ω

T).

There are three possible scenarios for this relationship between δ and ΩT. In the first

scenario, if ΩT is very high, then δ will be high. In the second scenario, if Ω

T is very

low, then δ will be low (see Figure 1). Finally, we assume that ΩT can intercept δ,

because we are using the ‘dynamic imbalanced state’ (DIS), which never stays static, but constantly changes. Hence, we suggest the application of the Omnia Mobilis assumption to keep the DIS in the long run. It changes according to changes in Ω

T.

Natural disaster devastation magnitude rate (Π)

We use two main variables to calculate the natural disaster devastation magnitude rate (Π). The first main variable is capital devastation (Φk), which we compute by dividing the area of infrastructure destroyed by the natural hazard (km2) by the total infrastructure area (km2) in the same geographical space. The second main variable is human capital devastation (ΨL), which we compute by dividing the number of people killed by or missing due to a natural disaster by the total population in the

Figure 1. Relationship between the national natural hazards vulnerability rate (ΩT) and

the economic desgrowth rate (δ)

Source: authors; see Table 2.

ΩT

δ


same geographical space. After calculating both main variables, we can then mul-tiply the results to get our natural disaster devastation magnitude rate (Π), which is equal to the product of the capital devastation rate (Φk) and the human capital dev-astation (ΨL) (see Formula 7). Finally, we generate the natural logarithm.

Π = ƒ(Φk ,ΨL) = Ln [(Φk) x (ΨL)] (7)

We decided to apply the product rule of differentiation in Formula 7 to obtain the first derivative test to find the relative maximum and minimum of the capital devastation (Φk) and capital devastation rates (Φk) (see Formulas 8, 9, and 10).

∂ƒ/∂(Φk) = Φ’(k)ΨL/Φ(k) ΨL (8)

∂ƒ/∂(ΨL) = Ψ’(L)Φ(k)/Ψ(L)Φ(k) (9)

∂Π = Φ’(k) Ψ(L) + Φ(k) Ψ’(L) (10)


Figure 2. How the national natural hazards vulnerability rate (ΩT) can affect the natural

disaster devastation magnitude rate (Π)

ΩT

Π


Moreover, we can also observe that the natural disaster devastation magnitude rate (Π) is directly proportional to the national natural hazards vulnerability rate (Ω

T).

Refer to Table 2 and Figure 2.

Economic desgrowth rate (δ)We define economic desgrowth (δ) (Ruiz Estrada, 2010) as a macroeconomic indi-cator that shows the final impact of a natural hazard on GNP. We can say that the final GNP post-natural hazard effect is a function of the natural disaster devastation magnitude rate (Π) (see Formula 11). At the same time, Π is directly dependent on the national natural hazards vulnerability rate (Ω

T) (see Formula 11) according to

Figures 1 and 2. In Formula 12 we calculate the preliminary GNP post-natural hazard effect (Q’). Hence, Q’ is a function of Π.

Π = ƒ(ΩT) (11)

Q’ = ƒ(Π) (12)

Therefore, the economic desgrowth rate (δ) depends on these two functions in our model, as shown in Formula 13 (i.e. a function of a function). Therefore, δ can only obtain values between 0 and -∞ . . .

δ = ƒ(Π(ΩT)) (13)

In the last instance, the final GNP preliminary hazard effect (Q’) directly depends on the changes between the natural disaster devastation magnitude past rate (∂Π o) and the natural disaster devastation magnitude present rate (Π o+1

) (see Formula 14).

Q’ = ∂Πo/∂Πo+1 (14)

Finally, the economic desgrowth rate (δ) is equal to the preliminary GNP post-natural hazard effect (Q’) minus the final GNP pre-natural hazard effect (Q

o) (see

Formula 15).

δ = Q’ – Qo (15)

Figures 1 and 2 show that a strong relationship exists between the economic des-growth rate (δ), on the one hand, and Π and Ω

T, on the other. The empirical results

show that if Π and ΩT are higher, then δ shows the same behaviour. Our experi-

ment is based on the uses of different rates from 0.00 to 0.99 in the case of ΩT. The

finals results calculated for δ show that when the Π and ΩT are high, the effect on δ

is magnified. Hence, δ is directly proportional to Π and ΩT in the long run (see Table 2).

Finally, we assume that δ, Π, and ΩT are moving significantly together (see Formulas 16

and 17). According to our model, δ always starts from zero and retains negative values.

↑δ = ƒ↑Π (↑ΩT) (16)

↓δ = ƒ↓Π (↓ΩT) (17)


The natural hazards vulnerability surface (NHV surface)

The construction of the NHV surface is based on the natural hazard growth rate (α

i) results and the mega-surface coordinate space (see Formula 18 and Figure 3).

The NHV surface is a four-by-four matrix that contains the individual results of all 16 variables (taken from Table 3). However, the 16 variables are plotted in a four-by-four array with the vertical value on the NHV surface. The idea is to produce a surface for a quick pictorial representation of the overall propensities for any one country. The underlying idea here is to use the results of 16 variables in α

i to build

a symmetrical surface. When the NHV coordinate system (η) has strictly the same number of rows as the number of columns, then the natural hazards growth rates can always be perfectly symmetrical.

α1 α5 α9 α13

η = α2 α6 α10 α14

α3 α7 α11 α15 α4 α8 α12 α16 (18)

The final analysis of the NHV surface depends on any changes that this surface can experience in a fixed period of time.1

Applying the NDVE model: JapanApplying the NDVE model to the Japanese economy will give us a much better idea of how the model works. Before we do so, it is useful to have a look at general data

Figure 3. The mega-surface coordinate space

Source: authors.


about Japan such as the contribution of each region to the country’s final GNP and the geographical distribution of Japanese industry. In terms of the geographical distribution of Japanese GNP, we find that Hokkaido contributes around 15 per cent of Japan’s GNP, while the Honshu region contributes 43 per cent, the highest share. The region with the second highest contribution to Japan’s GNP is the Shikoku region with 27 per cent. Therefore, the major contributors to Japanese GNP are the Honshu and Shikoku regions, which collectively account for 70 per cent of Japanese output. Finally, the Kyushu region contributes 15 per cent to Japanese output (see Figure 4). Honshu and Shikoku also account for about 70 per cent of Japanese industrial output,

with the remaining output divided among the other regions (see Figure 5).

Natural hazards growth rates (αi)

In this section we first examine the nat-ural disaster vulnerability propensity rates for countries around the world and then take a closer look at Japan’s natural disas-ter vulnerability propensity rate.

Global natural hazards growth rates (αi)

Table 3 shows the natural hazards growth rates in 59 countries around the world. These countries show a wide range of probability of natural disaster event based on their historical data. We use three different shades of grey to classify coun-tries according to their α

i: the dark grey

shading represents high vulnerability, medium grey represents medium vul-nerability and light grey represents low vulnerability. We can observe in Table 3 that the ten countries with the highest risk of natural disasters are China, Japan, the USA, Indonesia, the Philippines, Australia, South Korea, Taiwan, Chile, and Guatemala. Therefore, Japan is among the top ten countries with the highest natural hazard growth rates (α

i); spe-

cifically, second highest, according to Table 3. On the other hand, countries such as Mongolia, Hungary, South Africa, Denmark, Belgium, and Luxemburg have

Figure 4. Contribution of each Japanese

region to GNP

Source: METI (2011a; 2011b; 2011c); JETRO (2011a; 2011b; 2011c; 2011d).

Figure 5. Concentration of industries in

Japan

Source: METI (2011a; 2011b; 2011c); JETRO (2011a; 2011b; 2011c; 2011d).


Tab

le 3

. Nat

ura

l haz

ard

s g

row

th r

ates

(α i)

and

nat

ion

al n

atu

ral h

azar

ds

vuln

erab

ility

rat

e (Ω

T),

197

1–20

11

No.

Coun

try

α1α2

α3α4

α5α6

α7α8

α9α1

0α1

1α1

2α1

3α1

4α1

5α1

6Ω

T

1Ch

ina

0.95

0.55

0.99

0.05

0.95

0.65

0.55

0.65

0.30

0.20

0.35

0.95

0.65

0.30

1.00

0.85

0.62

1

2Ja

pan

1.00

1.00

0.35

0.75

0.85

0.00

0.65

0.55

0.35

0.65

0.45

0.85

0.00

0.10

0.85

0.90

0.58

1

3U

SA0.

650.

250.

850.

250.

750.

650.

500.

200.

70.

650.

700.

500.

350.

40.

250.

650.

513

4In

done

sia

0.95

0.95

0.85

1.00

0.95

0.65

0.00

0.85

0.00

0.25

0.00

0.65

0.00

0.00

0.00

0.95

0.50

3

5Ph

ilipp

ines

0.99

0.98

0.85

0.95

0.95

0.00

0.00

0.85

0.00

0.75

0.00

0.65

0.00

0.00

0.00

0.98

0.49

7

6Au

stra

lia0.

350.

351.

000.

000.

501.

000.

000.

000.

000.

550.

650.

900.

650.

000.

650.

750.

459

7So

uth

Kore

a0.

800.

750.

700.

000.

850.

000.

250.

350.

250.

550.

850.

650.

000.

000.

350.

890.

453

8Ta

iwan

0.80

0.75

0.55

0.35

0.95

0.00

0.00

0.55

0.00

0.55

0.65

0.45

0.00

0.00

0.35

0.99

0.43

4

9Ch

ile0.

850.

750.

650.

550.

350.

350.

850.

750.

30.

250.

200.

250.

300.

30.

200.

100.

431

10G

uate

mal

a0.

950.

250.

551.

000.

350.

850.

000.

900.

000.

250.

000.

850.

350.

000.

000.

550.

428

11El

Sal

vado

r0.

950.

250.

551.

000.

350.

850.

000.

900.

000.

250.

000.

850.

000.

000.

000.

550.

406

12H

ondu

ras

0.95

0.25

0.55

1.00

0.35

0.85

0.00

0.90

0.00

0.25

0.00

0.85

0.00

0.00

0.00

0.55

0.40

6

13N

icar

agua

0.95

0.25

0.55

1.00

0.35

0.85

0.00

0.90

0.00

0.25

0.00

0.85

0.00

0.00

0.00

0.55

0.40

6

14Co

sta

Rica

0.95

0.25

0.55

1.00

0.35

0.85

0.00

0.90

0.00

0.25

0.00

0.85

0.00

0.00

0.00

0.55

0.40

6

15Pa

nam

a0.

950.

250.

551.

000.

350.

850.

000.

900.

000.

250.

000.

850.

000.

000.

000.

550.

406

16M

exic

o0.

950.

200.

700.

750.

550.

350.

000.

450.

000.

200.

200.

750.

450.

000.

050.

550.

384

17Ru

ssia

0.85

0.15

0.75

0.20

0.10

0.85

0.95

0.35

0.95

0.00

0.00

0.75

0.10

0.00

0.05

0.00

0.37

8

18Vi

etna

m0.

650.

901.

000.

000.

950.

000.

000.

550.

000.

000.

000.

950.

000.

000.

000.

950.

372

19Ca

ribbe

an0.

950.

850.

450.

000.

990.

000.

000.

200.

000.

950.

000.

550.

000.

000.

000.

990.

371

20In

dia

0.65

0.55

1.00

0.00

0.85

0.75

0.00

0.10

0.00

0.00

0.00

0.85

0.45

0.00

0.15

0.55

0.36

9

21Ca

mbo

dia

0.70

0.85

1.00

0.00

0.95

0.00

0.00

0.65

0.00

0.00

0.00

0.98

0.00

0.00

0.00

0.75

0.36

8


No.

Coun

try

α1α2

α3α4

α5α6

α7α8

α9α1

0α1

1α1

2α1

3α1

4α1

5α1

6Ω

T

22Th

aila

nd0.

350.

850.

850.

000.

900.

000.

000.

650.

000.

000.

000.

980.

000.

000.

000.

900.

343

23Ba

ngla

desh

0.10

0.90

1.00

0.00

0.99

0.00

0.00

0.55

0.00

0.00

0.00

0.90

0.03

0.00

0.00

0.85

0.33

3

24Co

lom

bia

0.95

0.10

1.00

0.00

0.55

0.20

0.00

0.85

0.00

0.00

0.00

0.65

0.00

0.00

0.00

0.55

0.30

3

25Eg

ypt

0.85

0.15

0.90

0.00

0.00

0.10

0.00

0.25

0.00

0.00

0.00

0.85

0.65

0.00

1.00

0.00

0.29

7

26Ve

nezu

ela

0.95

0.20

1.00

0.00

0.65

0.25

0.00

0.75

0.00

0.00

0.00

0.45

0.00

0.00

0.00

0.45

0.29

4

27Si

ngap

ore

0.03

0.95

0.95

0.00

0.90

0.00

0.00

0.00

0.00

0.00

0.00

0.95

0.00

0.00

0.00

0.90

0.29

3

28Ec

uado

r0.

950.

101.

000.

000.

350.

150.

000.

900.

000.

000.

000.

550.

000.

000.

000.

650.

291

29Ita

ly0.

980.

200.

950.

950.

000.

000.

650.

250.

150.

000.

000.

300.

000.

000.

050.

150.

289

30M

alay

sia

0.03

0.75

0.75

0.00

0.85

0.00

0.00

0.35

0.00

0.00

0.00

0.85

0.00

0.00

0.05

0.75

0.27

4

31Pa

kist

an0.

950.

050.

850.

000.

000.

550.

100.

650.

000.

000.

000.

550.

150.

450.

000.

000.

269

32Br

azil

0.35

0.85

0.85

0.00

0.00

0.35

0.00

0.00

0.00

0.25

0.00

0.65

0.25

0.00

0.00

0.65

0.26

3

33N

ew Z

eala

nd0.

950.

350.

350.

550.

250.

000.

200.

000.

000.

350.

000.

300.

000.

000.

000.

850.

259

34G

reec

e0.

950.

900.

250.

050.

200.

000.

000.

550.

000.

000.

000.

850.

000.

000.

000.

350.

256

35Ic

elan

d0.

650.

200.

010.

350.

000.

000.

750.

000.

950.

000.

000.

030.

000.

850.

000.

000.

237

36H

olla

nd0.

150.

851.

000.

000.

550.

000.

000.

000.

000.

000.

000.

850.

000.

000.

000.

250.

228

37M

oroc

co0.

900.

850.

250.

000.

000.

000.

000.

250.

000.

000.

000.

350.

600.

000.

000.

100.

206

38Bo

livia

0.95

0.00

0.55

0.35

0.00

0.00

0.00

0.65

0.00

0.00

0.00

0.55

0.15

0.10

0.00

0.00

0.20

6

39Cy

prus

0.25

0.85

0.45

0.00

0.00

0.00

0.00

0.50

0.00

0.00

0.00

0.45

0.00

0.00

0.00

0.55

0.19

1

40Ca

nada

0.10

0.10

0.10

0.00

0.00

0.00

0.65

0.35

0.6

0.00

0.00

0.25

0.00

0.75

0.05

0.00

0.18

1

41Ke

nya

0.55

0.00

0.10

0.15

0 .00

0.15

0.00

0.00

0.00

0.00

0.00

0.90

0.65

0.00

0.15

0.00

0.16

6

42Sw

eden

0.00

0.15

0.05

0.00

0.00

0.00

0.45

0.00

0.55

0.30

0.00

0.05

0.00

0.75

0.00

0.00

0.14

4

43N

orw

ay0.

000.

150.

050.

000.

000.

000.

450.

000.

550.

300.

000.

050.

000.

750.

000.

000.

144


No.

Coun

try

α1α2

α3α4

α5α6

α7α8

α9α1

0α1

1α1

2α1

3α1

4α1

5α1

6Ω

T

44Is

rael

0.85

0.05

0.01

0.00

0.05

0.00

0.00

0.00

0.00

0.00

0.00

0.60

0.55

0.00

0.00

0.10

0.13

8

45Sp

ain

0.75

0.10

0.35

0.00

0.00

0.00

0.00

0.00

0.00

0.10

0.10

0.40

0.35

0.00

0.00

0.00

0.13

4

46La

os0.

250.

000.

550.

000.

000.

000.

000.

650.

000.

000.

000.

550.

000.

000.

050.

000.

128

43Pa

ragu

ay0.

950.

000.

450.

000.

000.

000.

000.

000.

000.

050.

000.

350.

100.

000.

000.

000.

119

48A

rgen

tina

0.35

0.10

0.35

0.10

0.00

0.00

0.15

0.15

0.10

0.00

0.00

0.25

0.05

0.10

0.00

0.15

0.11

6

49Fr

ance

0.05

0.10

0.10

0.00

0.00

0.00

0.35

0.00

0.35

0.00

0.00

0.55

0.00

0.25

0.00

0.00

0.10

9

50U

rugu

ay0.

550.

350.

250.

000.

000.

000.

000.

000.

000.

150.

000.

250.

000.

000.

000.

000.

097

51En

glan

d0.

250.

350.

250.

000.

000.

000.

000.

000.

000.

250.

000.

350.

000.

050.

000.

000.

094

52G

erm

any

0.10

0.05

0.05

0.00

0.00

0.00

0.35

0.00

0.25

0.00

0.00

0.20

0.00

0.25

0.00

0.00

0.07

8

53Cz

ech

Rep.

0.10

0.00

0.15

0.00

0.00

0.00

0.25

0.00

0.00

0.00

0.00

0.45

0.00

0.25

0.00

0.00

0.07

5

54M

ongo

lia0.

000.

000.

000.

000.

000.

000.

000.

000.

000.

000.

100.

030.

000.

001.

000.

000.

071

55H

unga

ry0.

000.

000.

150.

000.

000.

000.

200.

000.

250.

000.

000.

050.

000.

250.

000.

000.

056

53So

uth

Afric

a0.

000.

250.

100.

000.

000.

000.

000.

000.

000.

350.

000.

150.

000.

000.

000.

000.

053

57D

enm

ark

0.00

0.15

0.05

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.05

0.00

0.35

0.00

0.00

0.03

8

58Be

lgiu

m0.

100.

050.

150.

000.

000.

000.

000.

000.

000.

000.

000.

150.

000.

030.

000.

000.

030

59Lu

xem

burg

0.00

0.00

0.00

0.00

0.00

0.00

0.25

0.05

0.00

0.00

0.00

0.05

0.00

0.00

0.00

0.00

0.02

2

TOTA

L34

22.4

3113

.35

19.8

312

.18.

5520

.86.

59.

154.

2533

6.83

6.2

6.25

22.4

0.27

1

α i = n

atu

ral h

azar

ds

gro

wth

rat

es

α 1 =

ear

thq

uak

es α

2 =

tsu

nam

is α

3 =

flo

od

s α 4

= v

olc

anic

eru

pti

on

s α 5

= t

yph

oo

ns

α 6 =

fire

po

lluti

on

α7

= s

no

w a

vala

nch

es α

8 =

lan

dsl

ides

α9

= b

lizza

rds

α 10 =

cyc

lon

es α

11 =

to

rnad

oes

α12

= e

pid

emic

s α 13

= d

rou

gh

ts α

14 =

hai

lsto

rms

α 15 =

Nu

clea

r p

ollu

tio

n α

16 =

hu

rric

anes

Hig

h le

vel o

f ri

sk: 1

. Ear

thq

uak

es; 2

. Flo

od

s; 3

. Ep

idem

ics

No

te: W

e ap

plie

d p

rob

abili

ties

in t

erm

s o

f th

e re

cord

of

all n

atu

ral d

isas

ters

eve

nts

men

tio

ned

in t

his

tab

le.

Sou

rce

: EM

-DA

T (n

.d.)

.


No.

Coun

try

α1α2

α3α4

α5α6

α7α8

α9α1

0α1

1α1

2α1

3α1

4α1

5α1

6Ω

T

44Is

rael

0.85

0.05

0.01

0.00

0.05

0.00

0.00

0.00

0.00

0.00

0.00

0.60

0.55

0.00

0.00

0.10

0.13

8

45Sp

ain

0.75

0.10

0.35

0.00

0.00

0.00

0.00

0.00

0.00

0.10

0.10

0.40

0.35

0.00

0.00

0.00

0.13

4

46La

os0.

250.

000.

550.

000.

000.

000.

000.

650.

000.

000.

000.

550.

000.

000.

050.

000.

128

43Pa

ragu

ay0.

950.

000.

450.

000.

000.

000.

000.

000.

000.

050.

000.

350.

100.

000.

000.

000.

119

48A

rgen

tina

0.35

0.10

0.35

0.10

0.00

0.00

0.15

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the lowest αi. This means that according to historical data, they face lower risk of

natural disaster than the other countries in our sample.

Japan’s natural hazards vulnerability rate (ΩT): max. and min.

In the case of Japan, we find large differences between the maximum and minimum of the natural hazards vulnerability rate (Ω

T). According to the historical data of

natural disasters, Hokkaido has the lowest vulnerability, with a ΩTmin

of only 0.15 and a Ω

Tmax of 0.25. In the rest of Japan the natural disaster vulnerability propensity rates

are higher. More specifically, the vulnerability rate ranges from 0.45 to 0.95 in Honshu, from 0.35 to 0.95 in the Shikoku region, and from 0.25 to 0.85 in the Kyushu region (see Figure 6).

Natural disaster devastation magnitude rate (Π)In addition, we would like to compare the natural disaster devastation magnitude rate (Π) between the Kobe earthquake of 1995 and the north-east Japan earthquake of 2011. According to our results the devastation resulting from the 1995 Kobe earth-quake was quite limited at -6.68. But according to our computations the devastation caused by the 2011 north-east Japan earthquake and tsunami was much larger at -12.22. In Figure 7 we can observe

Figure 6. Japan’s natural hazards vulnerability rate (ΩT) by region

Source: EM-DAT (n.d.); FDMA (2011).


Table 4. Natural disaster devastation magnitude rates of the Kobe earthquake (Π1995)

and the 2011 earthquake and tsunami (Π2011)

Natural disaster devastation magnitude rate of Kobe earthquake (Π1995) (Π)

Φk150

552

0.271739

ΨL 5500

1200000

0.004583333

0.00124547

-6.68

Natural disaster devastation magnitude rate of 2011 earthquake and tsunami (Π2011)

Φk35000

227962

0.15353436

ΨL16000

50000000

0.00032

4.9131E-05

-12.22


Figure 7. Natural disaster devastation magnitude rates (Π) of the 1995 Kobe earthquake

(left-hand cone) and the 2011 earthquake and tsunami (right-hand cone)

Note: final result from the NDVE model.


more clearly from a graphical perspective that the north-east Japan earthquake and tsunami (right-hand cone) caused devastation several times greater than the Kobe earthquake (left-hand cone).


Economic desgrowth rate (δ)Finally, to measure the impact of earthquakes and tsunamis on economic growth, we use the new concept of ‘economic desgrowth’ (δ) introduced by Ruiz Estrada (2010). Using this concept, we try to discover possible leakages that can adversely affect GNP performance. Basically, this new concept assumes that in the process of GNP formation, leakages may arise due to various factors, in our case natural disasters. According to our estimates, the economic desgrowth caused by the Kobe earth-quake had an impact of -1.51 on Japan’s economic desgrowth. Our estimates indicate that economic desgrowth caused by the north-east Japan earthquake and tsunami of 2011 was much larger, at -4.6 in 2011. Therefore, the economic desgrowth difference between the Kobe earthquake and the north-east Japan earthquake and tsunami of 2011 is -3.1 according to our final result in Table 5.

Concluding observations and policy implicationsNatural disasters can have a significant negative impact on economic performance, but measuring this impact with any degree of certainty is inherently challenging. In this paper we propose a new model for evaluating the impact of natural disasters on economic performance. The natural disaster vulnerability evaluation (NDVE) model is based on five indicators: (i) natural hazards growth rates (α

i); (ii) the national

natural hazards vulnerability rate (ΩT); (iii) the natural disaster devastation magnitude

rate (Π); (iv) the economic desgrowth rate (δ); and (v) the NHV surface. The under-lying intuition is that the economic impact of natural disasters depends on a country’s vulnerability to natural disasters and the devastation caused by natural disasters, which jointly determines the leakage from economic growth and hence the impact on growth. We hope that our model will contribute to a better and deeper understanding of how to measure the economic impact of natural disasters. A more useful measurement of impact is conducive to the drawing up of appro-priate policies, both for dealing with the effects of natural disasters and for anticipa-tory policy measures that seek to lessen the impact of natural disasters before they occur. For example, underestimating the impact may lead to the government allocat-ing too few resources for addressing the impact of disasters—e.g. public investment in physical infrastructure and income support for households most affected by the disaster. On the other hand, overestimating the impact may cause the allocation of too many resources, raising the risk of inefficiency and waste. By the same token, determining the appropriate level of anticipatory investments to limit the impact of future disasters would benefit from an accurate ex-ante assessment of their impact. The NDVE model can also help in determining the appropriate mix of disaster man-agement and prevention policies. For example, the model may allow policymakers to better estimate and compare the impact of different types of natural disasters. The application of our model to two natural disasters in Japan—the Kobe earth-quake of January 1995 and the north-east Japan earthquake and tsunami of March


Table 5. Japan’s GNP growth rate, 1971–2011

1 1971 4.4

2 1972 8.4

3 1973 8.0

4 1974 -1.2

5 1975 3.1

6 1976 4.0

7 1977 4.4

8 1978 5.3

9 1979 5.5

10 1980 3.2

11 1981 4.2

12 1982 3.4

13 1983 3.1

14 1984 3.1

15 1985 4.5

16 1986 2.8

17 1987 4.1

18 1988 7.1

19 1989 5.4

20 1990 5.6

21 1991 3.3

22 1992 0.8

23 1993 0.2

24 1994 0.9

25 1995 0.7 δ = -1.51 ΩT = 0.95 Π = -6.68

26 1996 1.7

27 1997 1.6

28 1998 -2.0

29 1999 -0.1

30 2000 2.9

31 2001 0.2

32 2002 0.3

33 2003 1.4

34 2004 2.7

35 2005 1.9

36 2006 2.0

37 2007 2.4

38 2008 -1.2

39 2009 -5.2

40 2010 1.2

41 2011 -2.8 δ = -4.6 ΩT = 0.99 Π = -12.22

Variables:

δ = GNP desgrowth rate ΩT = national natural hazards vulnerability rate Π = natural disaster devastation magnitude rate

Source: IMF (2011).


2011—indicates that the 2011 disaster will have a bigger impact on the Japanese economy than the Kobe earthquake. Nevertheless, the immediate implication for Japanese policymakers is that they need to support growth with stronger measures than they implemented in 1995. In particular, they need to provide more fiscal resources for reconstruction efforts to rebuild the region’s devastated physical infra-structure, which in turn will lay the foundation for the recovery of the region’s produc-tive activities, in particular manufacturing. In addition to rebuilding infrastructure, the government should provide income support for residents whose homes and live-lihoods have been destroyed by disasters. While Japan’s high public debt level con-strains the government’s fiscal space, concerted fiscal support is nevertheless vital for north-east Japan’s recovery. At a broader level, our results confirm that natural disasters can have a significant economic impact even in advanced countries with good infrastructure and high levels of preparedness. The inescapable policy implication for developing countries, which tend to suffer the bulk of natural disasters, is that investing in anticipatory measures may yield sizable benefits in the medium and long term, even though they can be costly in the short run. Anticipatory measures can reduce the extent of damage, loss of life, and disruption to economic activity. Such measures include: (1) good design of and adherence to rigorous building codes, earthquake and storm proofing of buildings, floodplain and drainage systems, hillside stabilisation, and other meas-ures related to the natural and manmade environments; (2) early warning system for floods, storms, epidemics, typhoons, tsunamis, and others; and (3) emergency response plans such as evacuation systems, emergency response drills, equipment readiness, and the storage of essential supplies like medicine and water. Given the high opportunity costs of using fiscal resources to mitigate the effects of natural dis-asters in developing countries, the NDVE model’s more accurate measurement of the economic impact of natural disasters is all the more valuable. Better measure-ment allows for the more efficient and better-targeted use of fiscal resources. One interesting direction for future research is to examine the importance of effective communication in mitigating the adverse impact of natural disasters. It is widely believed that more effective communication by the Japanese government to the general public, for example about the magnitude and nature of the damage, could have limited the damage from the 2011 tsunami. The failure of authorities to quickly and reliably inform the public led to widespread concerns and fear, which further dented consumer and business confidence. Therefore, more and better information is likely to reduce the impact of natural disasters, and looking at the role of informa-tion would contribute to a more accurate measurement of the impact of a disaster.

Correspondence Dr Mario Ruiz Estrada, Faculty of Economics and Administration, University of Malaya, Kuala Lumpur 50603, Malaysia. E-mail: [email protected]


Endnotes1 Initially it was intended to provide examples of NHV surfaces for Japan, the USA, China, Luxemburg,

Guatemala, and South Korea to illustrate the points made here. However, due to technical problems during design this was not possible. The author will be happy to provide such examples on request; see his e-mail address under ‘Correspondence’.

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applying the natural disasters vulnerability evaluation model to the march 2011 north-east japan...

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