P R W P
4963
Impact Estimation of Disasters
A Global Aggregate for 1960 to 2007
Yasuhide Okuyama
Sebnem Sahin
e World Bank
Sustainable Development Network Vice Presidency
Global Facility for Disaster Reduction and Recovery Unit
&
International University of Japan
June 2009
WPS4963
Produced by the Research Support Team
Abstract
e Policy Research Working Paper Series disseminates the ndings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the ndings out quickly, even if the presentations are less than fully polished. e papers carry the
names of the authors and should be cited accordingly. e ndings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. ey do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its aliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
P R W P 4963
is paper aims to estimate the global aggregate of
disaster impacts during 1960 to 2007 using Social
Accounting Matrix (SAM) methodology. e authors
selected 184 major disasters in terms of the size of
economic damages, based on the data available from
the International Emergency Disasters and MunichRe
(NatCat) databases for natural catastrophes. ey
estimate the losses and total impacts including the
higher-order eects of these disasters using social
accounting matrices constructed for this study. Although
the aggregate damages based on the data amount to
US$742 billion, the aggregate losses and total impacts
is paper—a joint product of the Global Facility for Disaster Reduction and Recovery Unit, Sustainable Development
Network Vice Presidency, and the International University of Japan—is part of a larger eort in the Network to disseminate
the emerging ndings of the forthcoming joint World Bank-United Nations’ Assessment of the Economics of Disaster Risk
Reduction. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. e authors may be
contacted at okuyama@iuj.ac.jp and ssahin@worldbank.org, respectively. We are grateful to Apurva Sanghi, Reinhard Mechler
and participants of the seminar at the World Bank held on this topic for their suggestions and constructive comments.
are estimated at US$360 billion and US$678 billion,
respectively. e results show a growing trend of
economic impacts over time in absolute value. However,
once the data and estimates are normalized using global
gross domestic product, the historical trend of total
impacts becomes statistically insignicant. e visual
observation conrms the inverted ‘U’ curve distribution
between total impact and income level, while statistical
analyses indicate negative linear relationships between
them for climatological, geophysical, and especially
hydrological events.
IMPACT ESTIMATION OF DISASTERS: A GLOBAL AGGREGATE FOR 1960
TO 2007
1
YASUHIDE OKUYAMA
2
1
This paper was prepared as a background paper to the joint World Bank - UN Assessment of the
Economics of Disaster Risk Reduction. Funding from the Global Facility for Disaster Reduction and
Recovery is gratefully acknowledged. The authors would like to express gratitude to Apurva Sanghi for
his guidance, encouragement, and patience. We also thank Reinhard Mechler of the IIASA for
providing us the precious disaster data.
Graduate School of International Relations, International University of Japan, Niigata,
Japan
SEBNEM SAHIN
GFDRR - The World Bank, Washington, D.C.
2
Contact author: Yasuhide Okuyama, International University of Japan, 777 Kokusai-cho,
Minami-Uonuma, Niigata – 9497277, Japan, E-mail: okuyama@iuj.ac.jp, Tel: + 81 257 79 1424
2
1. Introduction
More than 7,000 major disasters have been recorded since 1970, causing at least $2
trillion in damage, killing at least 2.5 million people, and adversely affecting societies
(UN, 2008; p. xiii). And some 75% of the world’s population lives in areas affected at
least once by natural disaster between 1980 and 2000 (UNDP, 2004). It is also
reported that the frequency and economic impacts of natural disasters have been
increasing in recent years (UN, 2008). These statistics alone can make natural
disasters one of the major issues and urgent tasks to tackle in the world. However,
little is known about the economic impact of natural disasters, due partly to lack of the a
standardized definition and also to the difficulty in measuring it.
It may be helpful to describe the importance of disaster impacts with some
rhetoric. Masahisa Fujita of the Kyoto University made a comment in 2003
3
The relationship between disaster impacts and development is also a concern.
Most empirical studies with cross-country data investigating the relationship between
development level and disaster impacts conclude that correlation between them is
negative, i.e. “the higher the level of development, the smaller both the number of
that “an
economy is like a tennis ball; the harder you throw the ball against a wall, the harder the
ball bounces back to you.” A natural disaster throws an economy against a wall; then,
how far an economy bounces back depends on the elasticity of the ball, i.e. the
resilience of the economy. Knowing the disaster impacts is analogous to
understanding how hard the ball (economy) is crushed against the wall. Some
researchers, for example Albala-Bertrand (1993), argue that since the ball (economy)
bounces back anyway, it is unimportant to know how hard the ball is crushed.
However, without knowing how the ball (economy) is crushed, the relief efforts may
become inefficient and ineffective and the pace of recovery may turn out to be slower.
At the same time, if the disasters occur frequently and repeatedly, the ball (economy)
accumulates fatigue and the resilience may deteriorate. This will result in the long-run
impacts on the economy.
3
His comments were made to Davis and Weinstein (2004) at the 50th North American Meeting of the
Regional Science Association International at Philadelphia, PA, on November 20, 2003.
3
deaths, injured, and deprived, and the relative material losses” (Albala-Bertrand, 1993;
p.202). This appears consistent with the disaster theory that as countries develop and
grow, they should have sufficient resources, such as financial and/or technological ones,
to better manage disaster risk through the implementation of countermeasures and to
better manage the adverse impact of disasters. However, some recent studies found
somewhat different tendencies. According to Lester (2008), disaster impacts (as % of
GDP) appear to have a negative correlation with GDP per capita; however, as GDP per
capita increases, the complexity of economic system also increases and thus the disaster
impacts have a positive correlation with GDP per capita up to a certain level before
decreasing; as a result, the total impact over GDP per capita has an inverted ‘U’ shape
curve. This implies that the most potentially affected economies by disaster will tend
to be middle-income-level economies. Benson and Clay (1998) also claimed that the
most vulnerable economies are not the most underdeveloped, since least developed
countries tend to have simple economic structures, such as agriculture. While middle
income-level economies with some diversifications seem more secure, because of
intertwined economic activities between industries, however, the economic impacts can
be much greater than in a simple agro-economy, and disaster impacts can be larger than
in a simple economy.
In this paper, major disasters in the world during 1960 to 2007 are selected in
order to analyze the historical trends of disaster impacts, and to investigate the
relationship between disaster impacts and development level. In the following section,
the general trends of natural hazard/disaster occurrence are presented and discussed in
connection with development. Section 3 illustrates the data for the cases employed
and the model used in this paper. Then, the impact estimation for global aggregate is
presented and analyzed in Section 4. The final section concludes the paper with some
remarks based on the findings and for the future agenda.
2. Natural Disasters in the World
First, the concept and definition related to disaster are clarified, since unclear
terminology of event has caused confusions about the extent and implications. Several
4
terms, such as disaster, hazard, unscheduled event, catastrophic event, among others,
have been used interchangeably in the literature; however, not all disasters or hazards
lead to catastrophic consequences, and not all hazards or disasters unscheduled events.
In this context, the two terms, “disaster” and “hazard”, include a wider range of events
than the others. The distinction between disaster and hazard can be found in Okuyama
and Chang (2004b, p. 2); “hazard is the occurrence of the physical event per se, and
disaster is its consequence.” Ariyabandu (2001) put this more specifically suggesting
that a disaster is an outcome of a hazard impacting on the vulnerability of a society.
Furthermore, this paper focuses on natural hazards that can be classified into the
following categories: hydro-meteorological origin, such as windstorms, floods, and
drought; and geological origin, such as earthquakes, volcanic eruptions, and landslides.
According to the United Nations’ International Strategy for Disaster Reduction
(UNISDR), the frequency of disasters caused by natural hazard has been increasing.
4
4
http://www.unisdr.org/disaster-statistics/occurrence-trends-century.htm
Figure 2-1 indicates the trends of disaster frequency by type between 1900 and 2005.
All types of disaster are increasing, and especially hydro-meteorological ones have
occurred much more frequent than the other two have. On average, 78 disasters per
year had occurred during the 1970s. This number grew to 351 per year during 2000
and 2006. Meanwhile, the average number of people killed in any single disaster has
been declining, making the total number of casualties per year from disasters fairly
constant (UN, 2008).
5
Figure 2-1. Number of Disasters Registered in EMDAT
5
5
However, economic damages caused by disasters in the world have been also increasing,
especially in the recent years, due partly to more frequent occurrence and also to the
increased complexity of economic structure around the world. Figure 2-2 illustrates
the increasing trends between 1900 and 2008, especially after the mid 1980s, with
several spikes when large-scale disasters occurred. Damages have averaged $83
billion per year since 2000, whereas the average of damages was $12 billion per year
during the 1970s (UN, 2008; constant 2005 US$). These observations lead to the fact
that disasters have become more menacing the well-being of societies, while they have
become less life-threatening.
http://www.unisdr.org/disaster-statistics/occurrence-trends-century.htm; Biological disasters include
epidemics and insect infestations.
6
Figure 2-2. Estimated Economic Damage by Disasters Registered in EMDAT
6
These observations of the relationship between development level and disaster
While more than 60% of the total damages caused by disasters occurred in
high-income countries, the estimated damages of disasters as a share of GDP were
significantly greater in less developed (and small) countries (UN, 2008). Figure 2-3
shows the top 50 disasters with largest damages during 1991 and 2005. The largest
damage in this period was the 2005 Hurricane Katrina in the United States, followed by
the 1995 Hanshin-Awaji (Kobe) Earthquake in Japan. While some developing
countries, like China and Indonesia, are included in the top 50 cases, most of the largest
damages occurred in developed countries with relatively small GDP share. In contrast,
Figure 2-4 presents the top 50 disasters with largest GDP share in the same period. All
the 50 disasters occurred in developing countries, especially in small island countries.
As a matter of fact, no upper middle-income country has been ranked in the top 100 for
most costly disasters as a share of GDP (UN, 2008).
6
http://www.emdat.be/Database/Trends/trends.html
7
damages coincide with the recent studies, such as Benson and Clay (1998), Lester
(2008), and Kellenberg and Mobarak (2008). They found an inverted, non-linear ‘U’
curve relationship between the overall disaster impact and income level of a country
(similar to the Kuznets curve on economic inequality). This is due to the fact that the
complexity of economy increases as it grows and leads to a broader range of impacts;
then after a critical income level is attained, there are sufficient financial and
technological resources available for installing effective countermeasures against
natural disasters
7
7
Please see the further discussion on this point in the companion paper, “Critical Review of
Methodologies on Disaster Impact Estimation”.
. It is still unclear that this inverted U curve relationship can be found
with different measurement of economic impact, based on the empirical data.
However, this point is important to understand how natural hazards become disasters.
In addition, Kellenberg and Mobarak (2008) found that floods, landslides, and
windstorms exhibit the stronger tendency of this inverted U shape non-linearity than
extreme temperature events or earthquakes do. This difference may result from the
characteristics of natural hazards. Albala-Bertrand (1993) suggested the following
seven characteristics of natural hazards: 1) magnitude; 2) frequency; 3) duration; 4)
location extent; 5) spatial dispersion pattern; 6) speed of onset; and 7) regularity.
Hydro-meteorological hazards, such as windstorms, floods, and drought, occur more
frequently, have a wider area of damages, with particularly devastating consequences
for rural economy, have a larger impact on losses, and require a longer recovery time.
On the other hand, geological hazards, such as earthquakes and landslides, are
infrequent events that oftentimes cause considerable damages to assets (UN, 2008).
This tendency also calls for further examination in order to illustrate clearly the
differences in economic impact across types of natural hazard. Furthermore, this line
of research can benefit to understand multi-hazard situations (multiple hazards occur
concurrently or consecutively in the same country or same location), which have
happened increasingly in the recent years.
10
3. Global Aggregation of Disaster Impacts: Data and Methodology
As seen in the previous section, economic damages of disasters have some tendencies
and trends. At the same time, the data for disaster impacts have been still limited and
sometimes confusing due to the use of interchangeable terminologies and to the lack of
standardized definitions. Moreover, while each disaster is unique, economic impacts
of disasters have been analyzed mostly through the case studies of a particular event,
rather than in an aggregated context for the generalized understanding of phenomena.
In this regard, estimating the global aggregate of disaster impacts has been long-sought
in the disaster community. This section presents the data sources for the global
aggregate estimation of disaster impacts in this paper. The data used are mostly
available for public as secondary data, but the definitions and/or extent of disaster
damage data are not standardized to make a direct comparison of the derived impacts
difficult.
3.1. The Case for a Global Aggregate
Natural hazards occur around the world with a wide range of intensities. In order to
set the cases for global aggregate of impact estimation, economic damage, or loss data
of disasters need to be collected. No standardized definitions or frameworks of
economic damage and loss are set so far, except the use of ECLAC methodology (UN
ECLAC, 2003) for recent disasters. Thus, it is difficult to collect the consistent
measurement of economic damage and loss data for past disasters. However, there are
a few sources offer the economic damage or loss data of past disasters: EM-DAT
database by Centre for Research on the Epidemiology of Disasters (CRED) of
Université Catholique de Louvain, NatCat database by Munich Re, and Sigma data base
by Swiss Re.
10
The disaster cases are selected from the ones occurred during 1960 to 2007.
As mentioned above, there is no standard definition of economic impact; furthermore,
In this present study, economic damage data are gathered from
EM-DAT and NatCat databases.
10
Some useful comparison of these databases can be found in Guha-Sapir and Below (2002).
11
economic damage, loss, and impact of disasters are used interchangeably in various
documents, including official ones. In fact, EM-DAT uses ‘estimated damage’
11
while NatCat’s data is labeled as ‘overall losses’. It is then useful to clarify the
terminology: damages are by economics definition the damages on stocks, which
include physical and human capitals; losses are business interruptions, such as
production and/or consumption, caused by damages and can be considered as first-order
losses; higher-order effects, which take into account the system-wide impact based on
first-order losses through inter-industry relationships; and total impacts are the total of
flow impacts, adding losses (first-order losses) and higher-order effects.
12
Then, the disaster cases
Whereas
EM-DAT and NatCat databases used different terms for economic data of disasters, we
consider both of them as damages, i.e. damages on capital stock.
13
are combined between two databases, and are
screened based on the intensity in order to reduce the number of cases by eliminating
smaller cases. The intensity condition is set as: damages should be greater than or
equal to US$ 20 million (current), and either should be greater than 1% of current GDP
for high-income countries or 2% of current GDP for low-income countries. The
number of cases after this screening becomes 184. In order to be used as the input to
estimate total impacts, these damage data were converted first to flow measure, i.e.
losses, using capital-to-output ratio based on the available and estimated capital data
14
11
EM-DAT states the definition of estimated damage as: “Several institutions have developed
methodologies to quantify these losses in their specific domain. However, there is no standard procedure
to determine a global figure for economic impact.”
(http://www.emdat.be/ExplanatoryNotes/explanotes.html)
12
Further discussion of terminology can be found in a companion paper, “Critical Review of
Methodologies on Disaster Impact Estimation.”
13
These cases include climatological, geophysical, hydrological, and meteorological disasters in
EM-DAT definition.
14
Nehru and Dhareshwar (1995), GTAP, and World Bank (2006). Estimation of missing values was
carried out using ‘Gross Capital Formation’ data from the World Bank website.
and the current GDP data. The derived losses are further converted to changes in final
demand through dividing losses by the inverse of diagonal terms in the direct input
coefficient matrix. Then, the total impact of each disaster is estimated by plugging this
final demand changes into the respective accounting multiplier matrix, described below.
12
3.2. Economic Impact Estimation Methodology: Social Accounting Matrix
Various methods can be used to estimate higher-order effect of disasters based on
damages and/or losses data, including input-output (IO) table, social accounting matrix
(SAM), and computable general equilibrium (CGE) model.
15
Since the cases for
global aggregate include a large group of countries in different years, the data
availability of method becomes one of the key issues for the selection of methodology.
SAM is employed in this study because of its data availability of construction and the
familiarity of use in international development community
16
Social accounting matrix (SAM) has been utilized to examine the higher-order
effects across different socio-economic agents, activities, and factors. Notable studies
using a SAM or one of its variants include Cole (1995, 1998, and 2004) among others.
Like IO models, the SAM approach has rigid coefficients and tends to provide upper
bounds of impact estimates. On the other hand, the SAM framework with certain
disaggregation, as well as extended IO model and CGE model, can derive the
distributional impacts of a disaster in order to evaluate equity considerations for public
policies against disasters. In this paper, SAMs were constructed in an aggregated
version for each country and each decade, based on the World Bank data.
.
17
15
A summary and discussion of methodologies on impact estimation can be seen in a companion paper,
“Critical Review of Methodologies on Disaster Impact Estimation.”
16
Please see Appendix 1 for detailed description of SAM, and a companion paper, ‘Impact Estimation
Methodology: Case Studies.’
17
SAM structure draws upon the MAMS model (Lofgren and Diaz-Bonilla, 2008), and Lofgren’s SAM
template is used to construct SAMs in this exercise (see Hans Lofgren’s course material (2008) on
MAMS, for the detail).
Due to the
large number of SAMs that needed to be constructed, and in order to maintain the
consistency of the structure and features among them, the SAMs were constructed in the
most aggregated way—one sector (one value) for each principal account (see the Figure
3-1). This simple structure is also necessary to suit with the aggregation level of input
data, total damages, for each case.
13
Figure 3-1. Structure of SAM for the Global Aggregate Estimation
18
During 1960 to 2007, based on the sampled 184 disasters, total damages on capital
stock were about US$ 742 billion (in 2007 constant value). Estimated losses and total
impacts in this period were US$ 360 billion and US$ 678 billion, respectively. The
impact multiplier from these figures becomes 1.88 (ratio between total impacts and
losses), implying that on average losses from a disaster can be nearly doubled via
4. Analysis of Global Aggregate Disaster Damages, Losses, and Higher-Order
Effects
The economic impact of 184 disasters for the last 50 years are estimated and analyzed in
this section. As described in the previous section, the higher-order effects of these
disasters are derived based on the data from EM-DAT and NatCat and the constructed
SAMs for this study. The historical trends, differences in types of disaster, and the
relationship between disaster impact and development level are investigated below.
4.1. Historical Trends of Impact
18
The highlighted cells are treated as endogenous, and other cells are set as exogenous in this paper.
Production
Activities
Factors Households
Other
Institutions
Rest of the
World
Total
Production
Activities
Factors
Households
Other
Institutions
Rest of the
World
Total
14
interdependencies in an economy. Table 4-1 shows the distribution of impact across
the types
19
19
Climatological disasters include droughts, extreme temperatures, and wildfires; geophysical disasters
are earthquakes and volcano eruptions; hydrological disasters are floods and landslides; and
meteorological disasters include storms.
of disaster. Over all, geophysical disasters have the largest portion of all
the economic impacts, i.e. damages, losses, and total impacts, with around 40% of the
total impacts. This implies that geophysical disasters cause significant damages on
stock, as well as losses and total impacts. This tendency may result from the fact that
geographical disasters cause the destructions of not only production facilities and
houses but also infrastructure including road networks and lifelines. These damages to
infrastructure propagate economic impacts to a wider extend through economic
interdependencies and may prolong the recovery and reconstruction. Meanwhile,
hydrological and meteorological disasters have the similar shares of economic impacts,
with around 25% of the total. Since these types of disasters have a wider range of
location extent but less destruction of physical assets, the significance of economic
impacts is moderate comparing to geophysical disasters.
15
Table 4-1. Economic Impacts by Type of Disaster (1960 – 2007)
Source: values of damages are based on the data of EM-DAT and Munich Re.
Remark: values are in constant 2007 US$ million.
The relationships among damages, losses, and total impacts of each disaster type
show some interesting features. The loss-damage ratio, dividing the value of estimated
losses by the damage value, looks very similar across the different disaster types, at
around 0.5. On the other hand, total impact-damage ratios, dividing the value of
estimated total impacts by damage value, have noticeable differences among them:
geophysical disasters have the largest ratio (0.96), followed closely by meteorological
disasters (0.96), while climatological and hydrological disasters have relatively smaller
values, 0.86 and 0.84, respectively. At the same time, the impact multipliers, total
impact-loss ratios, have a slightly different order: meteorological disasters have the
largest impact multiplier (2.02), followed by geophysical (1.88), hydrological (1.80),
and climatological (1.78). Because highly aggregated SAMs for each country are used
for the estimation in this study, the interpretation of these results requires some caution.
Nonetheless, in general, geophysical disasters seem the most costly in absolute value,
and the impacts may become large (larger total impact-damage ratio and impact
multiplier) than other types of disaster. Meteorological disasters are not so
straightforward: in total, their economic impacts in absolute value are about average
(around 25% for damages, losses, and total impacts); however, their total
Damages
total value total value loss-damage ratio total value
total impact-damage
ratio
share across column share across column share across column total impact-loss ratio
Climatological 84,910 40,837 0.48 72,604 0.86
11.4% 11.4% 10.7% 1.78
Geophysical 282,987 144,196 0.51 271,489 0.96
38.1% 40.1% 40.1% 1.88
Hydrological 188,360 87,994 0.47 158,678 0.84
25.4% 24.5% 23.4% 1.80
Meteorological 186,098 86,717 0.47 175,073 0.94
25.1% 24.1% 25.8% 2.02
Total 742,356 359,744 0.48 677,844 0.91
1.88
Estimated Losses
Estimated Total Impacts
16
impact-damage ratio and impact multiplier is rather larger, and even the largest. These
imply again that meteorological disasters wipe out a large extent of areas and a large
range of activities resulting in greater total impacts than other types of disasters. In
fact, out of the top ten events with largest impact multipliers, seven events are
meteorological ones (in Madagascar and Guatemala), two are hydrological (in
Madagascar and Bangladesh), and one is geophysical (in Guatemala).
Figure 4-1. Historical Trends of Economic Impacts
20
The historical trends of economic impacts for the 184 cases mimic the one in
figure 2-2 based on EM-DAT data. Figure 4-1 illustrates the trends of aggregated
damages, losses, and total impacts for each year. A gradual increase of all the three
economic impacts is observed until year 2000, with an exception of 1995 including the
Hanshin-Awaji (Kobe) Earthquake in Japan. Between 2001 and 2004, a lull of
economic impacts was observed; and then, year 2005 becomes another exception with
Hurricane Katrina in the U.S. This lull is not found in figure 2-2; the difference
between figures 2-2 and 4-1 results from the screening of events, in which the cases in
figure 4-1 are selected with some certain size in damages (grater than US$ 20 million;
larger than 1% of GDP for developed countries or 2% of GDP for developed countries).
For instance, multi-country disasters, such as the 2004 Indian Ocean Earthquake and
20
Values are in constant 2007 US$ million. Lines in the figure are the linear regression line for each
item, and all three lines are statistically significant with 5% level (please see the details in Appendix 2).
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Damages Estimated Losses Estimated Total Impacts
17
Tsunami, are separated by affected country and screened; thus, some countries affected
by the events are not included. Therefore, some years in figure 4-1 have much smaller
total damages than in figure 2-2.
The relationships among damages, losses, and total impacts appear different
each year. For example, in 1990
21
, the aggregated total impacts are the largest,
followed by the aggregated losses and the aggregated damages. In 1999
22
It is a common practice to normalize data in current value with time varying
factors. The analysis above is based on the constant value (in 2007 US$), controlling
inflation over time. In disaster impact analysis, some other factors may need to be
controlled. For instance, Pielke et al. (2008) used the changes in inflation and wealth
at the national level and the changes in population and housing units at coastal county
level for analyzing the trends of hurricane damage in the United States between 1900
, for instance,
the aggregated total impacts are the largest, followed by the aggregated damages and
the aggregated losses. On the other hand, in many years, aggregated damages are the
largest, followed by aggregated total impacts and aggregated losses. Since the
estimation of losses (and of higher-order effects based on losses, and the construction of
SAMs) relies a great deal on capital stock data, which are rarely available and thus are
estimated based mostly on the available data in recent years, and thus the estimated
results are sensitive to capital stock estimation, the above observations of relationship
among these economic impacts cannot be easily generalized. In addition, the
relationship between losses and total impacts needs further attention, because the
damages and/or losses to specific industries can cause different higher-order effects: for
example, damages and/or losses of manufacturing industry may result in a production
bottleneck via forward linkage (supply chain) and backward linkage (demand chain)
and can cause effects in a broader range of industries, depending on how the domestic
(or international) interindustry relationships are intertwined. This kind of
disaggregated analysis of higher-order effects for the recent disasters can be found in a
companion paper, ‘Impact Estimation of Higher-Order Effects: Case Studies’.
21
In 1990, five events are included: floods in Honduras; earthquake in Iran; drought in Mozambique;
drought in Namibia; and earthquake in Philippines.
22
Nine events are included in 1999: earthquakes in Columbia, Greece, and Turkey; storms in Denmark
and St. Kitts and Nevis; droughts in Iran, Mauritius, and Morocco; and, flood in Venezuela.
18
and 2005. For a cross-country and time-series analysis like this research, it is difficult
to control all the factors; therefore, the Gross World Production (World GDP) is
employed to normalize the disaster impact data by controlling the size of economy in
question. Figure 4-2 illustrates the trends of disaster impacts as the share of world
GDP. The trends appear very similar to the ones in absolute value (figure 4-1).
However, the statistical analysis
23
Figure 4-2. Historical Trends of Normalized Disaster Impacts
indicates that only the trends of damages and losses
are statistically significant, but being rather weak at 10% level, with a linear trend line
between time and GDP share of total impacts, while the trend of total impacts is not
statistically significant. Not significant trend in total impacts is in fact consistent with
other studies using normalized disaster impact, including abovementioned Pielke et al.
(2008). However, the inconsistency between statistically significant trends of damages
and losses and insignificant total impacts needs to be further investigated, perhaps
including smaller intensity of disasters.
24
The historical trends of normalized economic impacts appear quite different
across the types of disaster (see Figure 4-3). Climatological disasters were increasing
the economic impacts until the early 1980s; and then there was a lull for the remaining
23
Please see the details of statistical analysis with normalized data in Appendix 2.
24
Lines in the figure are the linear regression line for each item, and both lines are statistically
significant with 10% level (please see the details in Appendix 2).
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
0.35%
0.40%
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
Damages Estimated Losses Estimated Total Impacts
19
of 1980s; the significant economic impacts were observed in 1994 again, but became
decreasing afterwards. Geophysical disasters seem not to exhibit a particular trend due
to their mechanism of occurrence. As for hydrological disasters, the economic impacts
have an increasing trend until 1998, and show a lull afterwards, corresponding to the
trends of overall economic impacts. Meteorological disasters display the similar trend
to the hydrological events, having somewhat increasing trends until 1998, then a lull,
with an exception of Hurricane Katrina in 2005. The relationships among damages,
losses, and total impacts appear not particularly different across the types of disasters,
indicating that the relationships among economic impacts depend solely on the
economic structure of each country, i.e. SAM.
20
Figure 4-3 (a). Historical Trends of Climatological Disasters (normalized)
Figure 4-3 (b). Historical Trends of Geophysical Disasters (normalized)
0.00%
0.03%
0.06%
0.09%
0.12%
0.15%
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
Damages Estimated Losses Estimated Total Impacts
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
0.35%
0.40%
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
Damages Estimated Losses Estimated Total Impacts
21
Figure 4-3 (c). Historical Trends of Hydrological Disasters (normalized)
Figure 4-3 (d). Historical Trends of Meteorological Disasters (normalized)
0.00%
0.03%
0.06%
0.09%
0.12%
0.15%
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
Damages Estimated Losses Estimated Total Impacts
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
0.35%
0.40%
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
Damages Estimated Losses Estimated Total Impacts
22
4.2. Analysis on Development
As discussed in Section 2, recent studies found an inverted ‘U’ curve relationship
between the overall disaster impact and income level of a country. While damages to
assets can be reduced with the installation of countermeasures against natural hazards as
income level increases and thus sufficient financial resources can be used, losses can
increase as economy becomes developed and complex. Then, the overall impact,
adding damages and losses, becomes the inverted ‘U’ shape curve. However, the use
of overall impact as the sum of damages and losses can be considered as
double-counting of economic impacts, according to Rose (2004). Damages and losses
are measurements on different variables—stock and flow, implying two sides of the
same phenomenon. On the other hand, total impacts of a disaster include losses and
the ripple effect of initial losses. This total impact should be used to investigate the
inverted ‘U’ curve relationship between economic impact and income level.
The distribution of 184 cases, seen in Figure 4-4, displays the tendency of
inverted ‘U’ curve relationship between the natural log of GDP per capita and the share
of total impacts over GDP (hereafter GDP impact). While both tails, lower and higher
GDP per capita countries, indicate relatively low GDP impact, many middle level GDP
per capita countries have higher GDP impact values. Two outliers, with extremely
large GDP impact rate, are 1963 storm in Haiti and 1988 storm in St. Lucia, small island
nations hit by hurricanes. This observation appears to prove that the theory of inverted
‘U’ curve relationship can be found with total impact of disasters. It should be noted,
however, that the 184 cases used in this study are screened and have some certain
intensity in terms of damages. Therefore, further research might be necessary to
include all the disaster cases reported in order to test this tendency.
23
Figure 4-4. Relationship between GDP per capita and Impact on GDP
25
Categories of disaster show differences in the relationship between disaster
impact and income level (see Figure 4-5). Climatological, geophysical, and
meteorological disasters appear to have the inverted ‘U’ curve relationship as in Figure
4-4; however, their distributions look a bit diverse. The distribution of climatological
disasters seems relatively flat with a small bulge in the middle, indicating less
significant GDP impact across income levels. Geophysical disasters exhibit a large
protuberance in the middle and longer right tail, implying the characteristics of
geophysical hazards (unpredictable and less frequent) and the effectiveness of counter
measures in richer countries. And, meteorological disasters show a wider variance
over GDP per capita and a larger range of GDP impact. This may result from the facts
that most of the countries affected are small island countries or located on the coast with
a mixture of income levels and that damages from hazards depend heavily on the route
of storms. On the other hand, the distribution of hydrological disasters appears
considerably skewed to left, indicating that the most vulnerable countries against
25
X-axis indicates natural log of GDP per capita; Y-axis shows the share of higher-order effect over
GDP; in constant 2007 US$ million.
0%
20%
40%
60%
80%
100%
120%
4 5 6 7 8 9 10 11 12
24
hydrological disasters are low-income countries. This is because hydrological
disasters, such as floods and landslides, “have a wider impact, with particularly
devastating consequences for rural economy (p. 80)” (UN, 2008). Those countries
having lowest income countries with higher GDP impact are Bangladesh, Mozambique,
and Nepal.
A series of statistical analyses is performed to see whether or not the inverted U
curve relationship actually exists for the above cases
26
These results, to some extent, may contradict with Kellenberg and Mobarak’s
(2008) study, in which with the data of 133 countries over 28 years they found the
. The results show that inverted
U curve relationship (non-linear function form) in either all events case or any type of
disasters is not statistically significant, contrary to the above visual inspections. On
the other hand, negative linear relationships are statistically significant with
climatological, geophysical, and hydrological events. For the cases with all the 184
events and with meteorological events, neither linear nor non-linear form is statistically
significant. The negative linear relationship is, in fact, consistent with the traditional
disaster theory, in which as development level, i.e. income level, increases, the risk for
disaster impacts decreases. A striking finding of the statistical analysis is that the
slope of the statistically significant regression line is much steeper in geophysical
disasters than in the other two cases (climatological and hydrological): around two
times steeper. This also signifies the characteristics of disaster. Geophysical
disasters damage mostly the structure of built environment; therefore, as national
income rises, the better structure of buildings and housing can become affordable and
utilized and the total impacts may become relatively small, and the efficacy of such
solid structure appear effective to reduce the impacts of geophysical disasters. On the
other hand, climatic and hydrological disasters may damage the functions of society and
economy in a wide area; thus, national income increase might not have such a direct
improvement. In addition, while statistically insignificant, the results of non-linear
form display some interesting findings. Among four types of disasters, climatological,
geophysical, and meteorological events indicate an inverted U curve relationship, while
hydrological events show a U curve relationship.
26
Please see Appendix 3 for detailed results and discussions.
25
stronger tendency of this inverted ‘U’ shape non-linearity between the number of
disaster casualties and income level for floods, landslides, and windstorms than for
extreme temperature events or earthquakes. However, they use the number of
casualties as disaster intensity, whereas this study employs the higher-order effects and
total impacts of disasters. These factors, number of casualties and total impacts may
represent different aspects of a disaster, and may not have a perfect correlation with
each other. An important common implication between this and their studies is that
type of disaster plays a major role for this inverted ‘U’ curve relationship between
disaster intensity and development level. And, further extending this line of analysis,
inclusion of smaller events, which are excluded in the present study, might potentially
reveal more concrete tendencies of the relationship between impact on GDP and GDP
per capita. Or, since the global aggregate estimation of disaster impacts is carried out
based on the aggregated economic damage data, with no sector disaggregation, the
estimated results did not take into account inter-industry relationships, which is
considered as the basis for increased vulnerability in middle-income countries.
Disaggregating sectors to some extent, for example at least to primary, secondary, and
tertiary industries, is necessary to see the intricacy of higher-order effects and their
differences among different development levels.
26
Figure 4-5 (a). Relationship between GDP per capita and Impact on GDP
(Climatological Disasters; red line is the statistically significant regression line)
Figure 4-5 (b). Relationship between GDP per capita and Impact on GDP
(Geophysical Disasters; red line is the statistically significant regression line)
0%
20%
40%
60%
80%
100%
4 5 6 7 8 9 10 11
0%
20%
40%
60%
80%
100%
4 5 6 7 8 9 10 11 12
27
Figure 4-5 (c). Relationship between GDP per capita and Impact on GDP
(Hydrological Disasters; red line is the statistically significant regression line)
Figure 4-5 (d). Relationship between GDP per capita and Impact on GDP
(Meteorological Disasters)
0%
20%
40%
60%
80%
100%
4 5 6 7 8 9 10 11
0%
20%
40%
60%
80%
100%
120%
4 5 6 7 8 9 10 11 12
28
5. Summary and Conclusions
This paper estimated the global aggregate of the economic impact of major disasters
during 1960 to 2007 using SAM methodology and examined the trends of estimated
disaster impact. The results indicate, in total, the global aggregate of damages is about
US$742 billion, losses are US$360 billion, and total impacts are estimated close to
US$680 billion, in 2007 value. While geophysical disasters are most costly in terms of
absolute value for damages, losses, and total impacts, meteorological disasters have the
highest impact multiplier of 2.02, indicating that damages and losses can spread to a
wider extent through interdependency of economic activities. Moreover, the analysis
indicates a growing trend of economic impacts, such as damages, losses, and total
impacts, over time using all the 184 events, whereas the trends of damages and losses
are statistically significant with linear regression lines. The impact multiplier of the
globally aggregated results over the period becomes nearly two, implying that on
average the losses caused by a disaster can become doubled through the
interdependencies of an economy. Furthermore, the investigation of economic impacts
and development level was carried out to see whether or not an inverted ‘U’ curve
relationship between total impacts and income level can be observed. The statistical
analyses, however, found that inverted U curve relationship, or more generally quadratic
relationship, is not statistically significant for total and each type, while climatological,
geophysical, and hydrological disasters show a negative linear relationship, confirming
the traditional disaster theory, indicating lower-income countries are more vulnerable to
higher-order effects than in middle- or higher- income countries. These results conflict
with Kellenberg and Mobarak’s (2008) study, in which they use the number of
casualties as the disaster impact.
The results in this paper were derived from the damage data of EM-DAT and
NatCat database and using SAM for each country in each decade. These damage data
and SAMs are highly aggregated without having any sector-level information. For an
analysis of historical trends and for international comparison, the aggregation level in
this study is acceptable due to the data availability. On the other hand, further detailed
analysis, based on disaggregated sectors and/or space, can reveal a more thorough and
comprehensive figure of disaster impacts, as presented in a companion paper ‘Impact
29
Estimation Methodology: Case Studies.’ While more sophisticated analysis requires
further precise numerical input data (West and Lenze, 1994), some standardized
framework, such as the ECLAC methodology (UN ECLAC, 2003), can guide us on
how to gather the more detailed data in a consistent way for future disasters. And, if
some common economic model of nations, such as SAM with some level of
disaggregation, becomes available, the estimation and examination of disaster impacts
will provide not only a clearer and more complete picture, but also a broader and more
robust picture of what happens during a disaster. In this regard, the role of
international organizations is particularly important.
30
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MDG Country Strategies – an application to Latin America and the Caribbean”, mimeo,
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(2008) “Normalized Hurricane Damage in the United States: 1900-2005,” Natural
Hazards Review, Vol. 9 (1): pp. 29-42.
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Pyatt, G. and Thorbecke, E. (1976) Planning Techniques for a Better Future, Geneve,
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Rose, A. (2004) Economic principles, issues, and research priorities in hazard loss
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32
Appendices
Appendix 1. Description of Social Accounting Matrix
Social accounting matrix (SAM) was developed by Stone (1961) and further
formalized by Pyatt and Thorbecke (1976) and Pyatt and Roe (1977) for policy and
planning purpose. SAM is an extended version of IO (and more closely to Miyazawa
formulation above), and the structure of a typical SAM includes IO accounts as
production activities. Similar to IO analysis, the accounting multiplier matrix can be
derived in the following way. The relationships in SAM can be transformed into the
equation below:
1 11 13 1
2 21 2
3 32 33 3
x X 0X f
x =X 0 0 +f
x 0X X f
where x
1
is gross output, x
2
is income of factors, x
3
is income of private sector
(including household and companies), X
11
is transaction between production activities
(input-output relationships), X
13
is private consumption, X
21
is value added payments,
X
32
is income to private sector, X
33
is inter-institution transfer, f
1
is final demand for
production activities, f
2
is final demand for factor, and f
3
is final demand for private
sector. Then, equation (8) can be rewritten with direct input coefficient matrix as
follows:
1 11 13 1 1
2 21 2 2
3 32 33 3 3
x A 0A x f
x =A 0 0 x +f
x 0A A x f
Solving this yields the accounting multiplier matrix:
( )
-1
n n n an
x = I-A f =M f
where
1
n2
3
x
x=x
x
,
11 13
n 21
32 33
A 0A
A=A 0 0
0A A
,
1
n2
3
f
f=f
f
, and M
a
is the accounting
multiplier matrix. Use of SAM for impact analysis is similar to IO, changes in final
demand lead to changes in output through accounting multiplier matrix.
33
Appendix 2. Statistical Analysis of Disaster Impact Trends
The trend analysis of disaster impacts, such as damages, losses, and total impacts, is
performed with the following model:
01tt
DI t
ββ ε
= + ⋅+
where
t
DI
is the disaster impact (in constant 2007 US$ million) at t, t is year. The
results of regression analysis are summarized in Table A2-1. All the coefficients are
statistically significant at 5% level. While the values for goodness of fit (R-squared)
are relatively small, the sign of
1
β
for all three cases are positive, as expected,
indicating the increasing trends of disaster impacts. However, these results change
with the normalization of disaster impacts.
Table A2-1: Time Series Analysis of Disaster Impacts (All Events)
**: Significant at 5% level.
Standard errors in parentheses.
Table A2-2 displays the results with normalized disaster impacts data.
Normalization was carried out through dividing the value of disaster impact by the
world GDP at respective year. Thus, the dependent variable is now the share (%) of
disaster impact over world GDP. The results indicate that damages and losses have
statistically significant linear trends over years with 10% level, whereas total impacts
appear showing no linear trend with the coefficients and the model (F-value) indicating
statistically not significant at any level.
Damages Losses Total Impacts
Intercept -1325340** -635467** -1165020**
(567477) (269310) (511800)
Year 676.165** 324.245** 594.648**
(286.026) (135.741) (257.963)
Observations 46 46 46
Durbin-Watson
Statistics
2.111 2.094 2.181
F-value 5.588** 5.706** 5.314**
R-squared 0.11 0.11 0.11
34
TableA2-2: Time Series Analysis of Normalized Disaster Impacts (All Events)
*: Significant at 10% level.
Standard errors in parentheses.
Damages Losses Total Impacts
Intercept -0.0265* -0.0122* -0.0195
(0.014) (0.007) (0.013)
Year 1.36E-05* 6.30E-06* 1.01E-05
(0.00001) (0.000003) (0.00001)
Observations 46 46 46
Durbin-Watson
Statistics
1.77503 1.78751 1.81422
F-value 3.542* 3.419* 2.411
R-squared 0.074 0.072 0.052
35
Appendix 3. Statistical Analysis of Inverted U Curve Relationship
The relationship between GDP impacted (total impacts divided by GDP) and GDP per
capita at respective year is analyzed with the following linear and non-linear (quadratic)
functions:
01
_ ln( _ _ )
i ii
GDP impact GDP per capita
αα ε
= +⋅ +
; and
( ) ( )
2
01 2
_ ln _ _ ln _ _
i i ii
GDP impact GDP per capita GDP per capita
ββ β ε
= +⋅ +⋅ +
If an inverted U curve relationship existed, the signs of coefficient should be
1
0
β
<
and
2
0
β
>
in the second form. The results of regression analysis are summarized in
Table A3-1 for all the events. While in the linear model the value of intercept is
statistically significant, the t-value of slope coefficient and F-value are not, indicating
no specific trend is found. As for the non-linear model, all the coefficients are
statistically insignificant, implying that the inverted U curve relationship cannot be
found. While statistically insignificant, the signs of coefficient in the non-linear model
suggests inverted U curve, with the turning point at US$ 1,281.
36
Table A3-1. Regression Results of Relationship between Impact on GDP and Income
Level (all events)
*** Significant at 1% level.
Standard errors in parentheses.
Table A3-2 through Table A3-5 show the results for different disaster types. While no
statistically significant trends are found with all events, climatological, geophysical, and
hydrological events found the linear relationship with a negative slope between disaster
impact and income level. Meteorological events do not have any statistically
significant results either with linear or non-linear model. While any of disaster types
fond no statistically significant results with non-linear model, their values for goodness
of fit (R-square) are always slightly larger (better) than the linear model counterpart.
This implies that non-linear model has greater explanation power over observations
than linear model does, for each type of disaster and for overall. Adding more
observations might bring more concrete results for this type of analysis.
Linear Model Non-Linear Model
Intercept 0.210*** -0.416
(0.079) (0.414)
[ln(GDP_per_capita)]^2 - -0.010
- (0.007)
ln(GDP_per_capita) -0.014 0.150
(0.010) (0.107)
Observations 184 184
F-Value 1.846 2.117
R-squared 0.010 0.023
Implied Turning Point
(GDP per capita;
2007 US$)
- 1,281
Curve Shape - Inverted U
All Events
37
Table A3-2. Regression Results of Relationship between Higher-Order Effects and
Income Level (climatological events)
* Significant at 10% level.
** Significant at 5% level.
Standard errors in parentheses.
Linear Model Non-Linear Model
Intercept 0.204** -0.289
(0.079) (0.420)
[ln(GDP_per_capita)]^2 - -0.008
- (0.007)
ln(GDP_per_capita) -0.019* 0.107
(0.010) (0.106)
Observations 27 27
F-Value 3.328* 2.409
R-squared 0.117 0.167
Implied Turning Point
(GDP per capita;
2007 US$)
- 925
Curve Shape - Inverted U
Climatological Events
38
Table A3-3. Regression Results of Relationship between Impacts on GDP and Income
Level (geophysical events)
* Significant at 10% level.
** Significant at 5% level.
Standard errors in parentheses.
Linear Model Non-Linear Model
Intercept 0.427** -0.213
(0.172) (0.925)
[ln(GDP_per_capita)]^2 - -0.010
- (0.014)
ln(GDP_per_capita) -0.040* 0.121
(0.021) (0.228)
Observations 35 35
F-Value 3.401* 1.923
R-squared 0.093 0.107
Implied Turning Point
(GDP per capita;
2007 US$)
- 472
Curve Shape - Inverted U
Geophysical Events
39
Table A3-4. Regression Results of Relationship between Impacts on GDP and Income
Level (hydrological events)
* Significant at 10% level.
** Significant at 5% level.
*** Significant at 1% level.
Standard errors in parentheses.
Linear Model Non-Linear Model
Intercept 0.212*** 0.598*
(0.056) (0.320)
[ln(GDP_per_capita)]^2 - 0.007
- (0.006)
ln(GDP_per_capita) -0.022*** -0.126
(0.008) (0.085)
Observations 50 50
F-Value 8.14*** 4.862**
R-squared 0.145 0.171
Implied Turning Point
(GDP per capita;
2007 US$)
- 10,380
Curve Shape - U
Hydrological Events
40
Table A3-5. Regression Results of Relationship between Impacts on GDP and Income
Level (meteorological events)
Standard errors in parentheses.
Linear Model Non-Linear Model
Intercept 0.212 -0.638
(0.180) (0.950)
[ln(GDP_per_capita)]^2 - -0.014
- (0.0160)
ln(GDP_per_capita) -0.008 0.216
(0.023) (0.247)
Observations 72 72
F-Value 0.119 0.475
R-squared 0.002 0.014
Implied Turning Point
(GDP per capita;
2007 US$)
- 1,735
Curve Shape - Inverted U
Meteorological Events
