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A global perspective
2015
Institute of
Health and
Society (IRSS)
THE
A global perspective
2015
H
UMAN
COST OF
NATURAL
DISASTERS
THE
List of
contents
Foreword 03
Who we are 05
Executive
Summary 07
Chapter 1
Global natural disasters:
patterns & trends 09
Chapter 2
Human impacts of
natural disasters 17
Chapter 3
Disaster damage
to housing
& infrastructure 31
Chapter 4
Counting the economic
costs of disasters 37
Chapter 5
Spotlight on
Small States 43
Chapter 6
The power of maps:
georeferencing
EM-DAT data 48
Technical Notes 51
Annexes 53
Acknowledgements 55
The Human cost of Natural Disasters |03
Foreword
Over the last ten years, following the adoption by UN Member States of the Hyogo
Framework for Action, the first comprehensive global blueprint for disaster risk reduction,
much progress has been made in disaster risk management. Efforts at raising awareness,
promoting prevention, preparedness and mitigation have been stepped up around the world
and some countries have had significant success in reducing mortality from weather-
related disasters notably Cuba, Bangladesh, India and the Philippines.
What this 20-year review of disaster impacts reveals is that there is still much progress
to be made on tackling the underlying drivers of risk such as poverty and more proximal
factors. We need to have better understanding of the specific risks that link negative
impacts to disaster events. Sound studies that provide such convincing evidence on ways
in which disasters affect individuals, families and communities are badly needed.
This report helps frame the debate on disaster risk reduction in the Post-2015
Development Agenda. It also underlines that climate-related disasters have come to
dominate the risk landscape to the point where they now account for more than 80% of
all major internationally reported disasters.
As resources get tighter, objective evidence is needed to fine tune the focus of our
investments in preparedness and mitigation. For this, we need sound and convincing data.
We also need equally sound analyses. We need to know which disasters are priorities for
which countries, who are most likely to be affected and why, are disasters losses really
increasing and where.
More countries than ever before now have national disaster loss databases which, combined
with the research and data collection expertise of insurance companies and organizations
like CRED and its EMDAT database, means that policy-makers and decision-takers have a
wealth of evidence on which to base investment decisions which will reduce existing levels
of risk and avoid the creation of new risk.
Now, increasing attention is placed on improving the quality and the accuracy of data on
the impact of the catastrophes. The next important step to be taken remains the
development of appropriate methodology and technical guidelines to enable countries and
region to collect and analyze their own data.
This is necessary to set meaningful, manageable and measurable targets for disaster risk
reduction over the next decade.
Margareta Wahlstrom
Head of the UN Office for Disaster Risk Reduction,
Special Representative of the Secretary-General
for Disaster Risk Reduction
Debarati Guha-Sapir
Director of the Centre for Research
on the Epidemiology of Disasters (CRED)
The Human cost of Natural Disasters | 05
Who we are
The Centre for Research on the Epidemiology of Disasters (CRED) was established in Brussels in 1973 at the School of Public Health
of the Université catholique de Louvain as a non-profit-making institution. In 1980, CRED became a World Health Organization
(WHO) collaboration centre as part of the Global Programme for Emergency Preparedness and Response. Since then CRED has
increased its international network substantially and works closely with numerous United Nations agencies, inter-governmental
and governmental institutions, non-governmental organizations (NGOs), research institutes and other universities.
Our goals
With a special focus on public health, epidemiology, structural and socio-economic issues, CRED promotes research, training,
information dissemination and technical services in the field of international disaster and conflict health studies.
Our scope
CRED's activities focus on all emergencies with a major human impact. These include sudden, natural and technological
disasters including hurricanes, earthquakes and industrial accidents as well as longer-term disasters and complex emergencies
such as famines and armed conflicts. CRED focuses primarily on public health and sanitary aspects of mass disasters as well
as socio-economic and developmental effects. Disasters preparedness, mitigation and prevention for vulnerable populations
have also gained a higher profile within CRED's activities.
Our staff
The centre is headed by Professor Debarati Guha-Sapir. The multinational, multidisciplinary team includes experts in medicine
and public health, geography, biostatistics, economics, informatics and database management, international relations and
nutritional sciences. The working languages are English and French.
EM-DAT
Since 1988, CRED has maintained an Emergency Events Database (EM-DAT). Initially created with the support of the WHO and
the Belgian government, the main objective of EM-DAT is to inform humanitarian action at the national and international levels
in order to improve rational decision-making in disaster preparedness, provide objective data for assessing communities’
vulnerability to disasters and to help policy-makers set priorities.
EM-DAT contains core data on the occurrence and effects of more than 21,000 technological and natural disasters from 1900 to
the present day. It is compiled from various sources, including UN agencies, the US Office of Foreign Disaster Assistance (OFDA),
national governments, the International Federation of Red Cross and Red Crescent Societies (IFRC), NGOs, insurance companies,
research institutes and the media, according to a priority list.
The list does not reflect the quality or value of the data. Instead it recognizes that few reporting sources cover all disasters and
some may have political limitations that could affect their figures.
Since 2014, EM-DAT also georeferences natural disasters, adding geographical values to numeric data which is essential for deeper
analysis. The increasing value of georeferencing for disaster preparedness and response is discussed further in Chapter 6 of this report.
Methodology
When a disaster occurs, related information is entered at three different levels (see below):
•The event/disaster level.
•The country/countries level (EM-DAT is currently developing a project to increase the precision of location of a disaster
at the 2nd administrative level).
•The sources level (the chosen sources are shown in orange frames).
DISASTER EVENT
Flood
COUNTRY 1 - Brazil
Georeferencing:
2
nd
administrative level
Source 1
REDLAC (UN)
Source 2
Munich RE
Source 3
Dartmouth Flood
Observatory (DFO)
Source 4
Press Source 1
REDLAC (UN)
Source 2
UNETE (UN)
Source 3
Dartmouth Flood
Observatory (DFO)
COUNTRY 2 - Uruguay
Georeferencing:
2
nd
administrative level
➔
➔
➔
➔
➔
➔
➔
➔
➔
06 | The Human cost of Natural Disasters
UN agencies •IRIN (Integrated Regional Information Networks)
•OCHA (Office for the Coordination of Humanitarian Affairs)
•WFP (World Food Programme)
•WHO (World Health Organization)
Governments •Official country figures
US governmental sources •NASA (National Aeronautics and Space Administration)
•NOAA (National Oceanic and Atmospheric Administration
•OFDA (Office of US Foreign Disaster Assistance)
•USGS (United States Geological Survey)
Non-governmental institutions •IFRC (International Federation of the Red Cross and Red Crescent)
Private sector •AON Benfield
•Lloyd Casualty Week magazine
•Munich RE
•Swiss RE
Research centres and academic sector •DFO (Dartmouth Flood Observatory)
•GVP (Global Volcanism Programme)
•IRIS (Incorporated Research Institutions for Seismology)
Sources /Partners
UN agencies •GRIP (Global Risk Identification Programme)
•IRDR (Integrated Research on Disaster Risk)
•UNDP (United Nations Development Programme)
•UNISDR (Secretariat for the International Strategy for Disaster Reduction)
Government and multilateral agencies •ADRC (Asian Disaster Reduction Centre)
•European Union (Joint Research Centre, Humanitarian Aid and Civil Protection)
•USAID (US Agency for International Development)
•World Bank
Non-governmental institutions •IDMC (International Displacement Monitoring Centre)
•ODI (Overseas Development Institute)
Other Partners
The Human cost of Natural Disasters | 07
The frequency of geophysical disasters (earthquakes, tsunamis,
volcanic eruptions and mass movements) remained broadly
constant throughout this period, but a sustained rise in
climate-related events (mainly floods and storms) pushed
total occurrences significantly higher. Since 2000, EM-DAT
recorded an average of 341 climate-related disasters per
annum, up 44% from the 1994-2000 average and well over
twice the level in 1980-1989.
From a disasters analysis point of view, population growth
and patterns of economic development are more important
than climate change or cyclical variations in weather when
explaining this upward trend. Today, not only are more people
in harm’s way than there were 50 years ago, but building in
flood plains, earthquakes zones and other high-risk areas has
increased the likelihood that a routine natural hazard will
become a major catastrophe.
EM-DAT data show that flooding caused the majority of
disasters between 1994 and 2013, accounting for 43% of all
recorded events and affecting nearly 2.5 billion people.
Storms were the second most frequent type of disaster, killing
more than 244,000 people and costing US$936 billion in
recorded damage. This makes storms the most expensive type
of disaster during the past two decades and the second most
costly in terms of lives lost.
Earthquakes (including tsunamis) killed more people than all
other types of disaster put together, claiming nearly 750,000
lives between 1994 and 2013. Tsunamis were the most deadly
sub-type of earthquake, with an average of 79 deaths for
every 1,000 people affected, compared to four deaths per
1,000 for ground movements. This makes tsunamis almost
twenty times more deadly than ground movements.
Drought affected more than one billion people between 1994
and 2013, or 25% of the global total. This is despite the fact
that droughts accounted for just 5% of disaster events in this
period. Some 41% of drought disasters were in Africa,
indicating that lower-income countries are still being
overwhelmed by drought despite effective early warnings
being in place.
In absolute numbers, the USA and China recorded the most
disasters between 1994 and 2013, due mainly to their size,
varied landmasses and high population densities. Among the
continents, Asia bore the brunt of disasters, with 3.3 billion
people affected in China and India alone. If data are
standardized, however, to reflect the numbers of people
affected per 100,000 head of population, then Eritrea and
Mongolia were the worst-affected countries in the world. Haiti
suffered the largest number of people killed both in absolute
terms and relative to the size of its population due to the
terrible toll of the 2010 earthquake.
While disasters have become more frequent during the past
20 years, the average number of people affected has fallen
from one in 23 in 1994-2003 to one in 39 during 2004-2013.
This is partly explained by population growth, but the
numbers affected have also declined in absolute terms.
Death rates, on the other hand, increased over the same
period, reaching an average of more than 99,700 deaths per
year between 2004 and 2013. This partly reflects the huge
loss of life in three megadisasters (the 2004 Asian tsunami,
Cyclone Nargis in 2008 and the 2010 Haitian earthquake).
However, the trend remains upward even when these three
events are excluded from the statistics.
Analysis of EM-DAT data also shows how income levels impact
on disaster death tolls. On average, more than three times as
many people died per disaster in low-income countries (332
deaths) than in high-income nations (105 deaths). A similar
pattern is evident when low- and lower-middle-income
countries are grouped together and compared to high- and
upper-middle-income countries. Taken together, higher-income
countries experienced 56% of disasters but lost 32% of lives,
while lower-income countries experienced 44% of disasters
but suffered 68% of deaths. This demonstrates that levels of
economic development, rather than exposure to hazards per
se, are major determinants of mortality.
Between 1994 and 2013, EM-DAT recorded 6,873 natural disasters worldwide,
which claimed 1.35 million lives or almost 68,000 lives on average each year. In
addition, 218 million people were affected by natural disasters on average per
annum during this 20-year period.
Executive
Summary
08 | The Human cost of Natural Disasters
In CRED’s view, EM-DAT data presented in this report point to
several major conclusions:
•Rising death rates at a time when the numbers of people
affected are falling highlights the continued vulnerability of
communities to natural hazards. Given the accuracy of today’s
weather forecasting and developments in early warnings, our
data raise questions about the effectiveness of global disaster
mitigation efforts. We believe more work must be done to
evaluate the real outcomes of disaster risk reduction (DRR)
interventions on human lives and livelihoods.
•In view of the disproportionate burden of natural hazards in
lower-income countries, including the huge disparity in
death rates in richer and poorer countries, mitigation
measures in less developed countries require significant
improvement.
•Better flood control for poorer communities at high risk of
recurrent flooding would be an important step in the right
direction. Effective, low-cost solutions exist, including
afforestation, floodplain zoning, building embankments,
better warnings and restoration of wetlands. Such actions
would bring development benefits too, since EM-DAT data
show that flooding is the main cause of disaster damage to
schools, hospitals and clinics etc. in lower-income countries.
•In light of predictions that climate change will increase the
frequency of storms and other extreme weather events,
better management, mitigation and deployment of storm
warnings could save more lives in future.
•Reducing the size of drought-vulnerable populations should
be a global priority over the next decade, given the
effectiveness of early warnings and the vast numbers of
people affected, particularly in Africa.
•Better research into how and why households and
communities are affected by disasters is urgently needed
so that responses are based on evidence, rather than
assumptions. Without such micro-level research, future DRR
and disaster prevention will not be effective.
The Human cost of Natural Disasters | 09
Chapter 1
Global
natural
disasters:
patterns
& trends
Introduction
Between 1994 and 2013, an average of 218 million people was
affected by natural disasters every year, according to the EM-DAT
database. Over this period, EM-DAT recorded 6,873 disasters1,
which claimed a total of 1.35 million lives, an average of almost
68,000 deaths per year.
In order to be recorded as a natural disaster in EM-DAT, an event
must meet at least one of the following criteria:
•Ten or more people reported killed
•100 or more people reported affected
•Declaration of a state of emergency or
•Call for international assistance.
While EM-DAT is the most comprehensive disaster database
available worldwide, and every effort is made to collect and validate
information from our sources, we are aware that certain regions,
including Africa, tend to under-report events.
ACTION POINT
More comprehensive data collection, following standardized
protocols, would aid research into natural disasters, which in
turn would improve the focus and quality of advice available
to policy-makers and help target disaster preparedness and
response measures more effectively.
1Excluding biological disasters
BOX 1
Denitions used
in this report
Total deaths
Persons confirmed as dead and
persons missing and presumed dead,
excluding indirect deaths from
diseases, epidemics and other effects
which occur after the emergency phase
of the disaster. Thus, for example, the
majority of drought-related deaths are
beyond the scope of this report
because they are due to malnutrition,
disease and displacement after the
emergency phase of the event. In this
report, the variable “total deaths” is
used for all analyses.
Injured
People suffering physical injuries,
trauma or an illness that requires
medical treatment as a direct result
of a disaster.
Homeless
People needing immediate assistance
for shelter.
Affected
People requiring immediate assistance
during a period of emergency, including
displaced or evacuated people.
Total affected
Sum of injured, homeless and affected.
In this report, the variable “total
affected” is used for all analyses.
Displaced
People forced to leave their homes due
to the damage or destruction of
housing and other essential property &
infrastructure by a natural disaster.
Mitigation
Any procedure or action undertaken to
reduce the adverse impacts that a
disaster may have on the human and
built environment.
10 |
Global trends
and patterns
in disaster
occurrence
Natural disasters hit every continent in the world in the period 1994-
2013. Asia bore the brunt of them in terms of frequency and the total
numbers of people killed and affected (Figure 1 & Figure 2). This is due
mainly to Asia’s large and varied landmass - with multiple river basins,
flood plains, mountains, active seismic and volcanic zones etc. at
high risk from natural hazards - plus high population densities in
disaster-prone regions.
In total, Asia was hit by 2,778 disasters over the 20-year period, with
3.8 billion people affected in addition to nearly 841,000 deaths.
Within Asia, the Southern, Eastern and South-Eastern regions were hit
most frequently by natural disasters, recording 2,481 events or 36%
of all disasters recorded worldwide between 1994 and 2013.
In 2014, 48% of disasters occurred in Asia. Over 85% of those killed
and 86% those affected globally were also in Asia.
In terms of countries, the USA and China reported the highest
numbers of natural disasters during this period (Figure 2). Again, this
can be attributed to their large and heterogeneous landmasses and
densely populated regions. These factors tend to push all big, heavily-
populated states up the ranking of disaster-prone countries simply by
virtue of their size and because so many people are in harm’s way.
Natural disasters increased in frequency substantially in the 1990s
before stabilizing, then declining from a peak in 2005. Overall,
however, the number of disasters reported annually was significantly
higher at the end of the period 1994-2013 than it was at the start.
The preliminary results for 20142show that the number of disasters
(246) was much lower than the annual average during the previous
ten years (369 disasters in the period 2004-2013).
This increase in disaster frequency was largely due to a sustained
rise in the number of climate-related disasters such as storms and
floods (Figure 3). EM-DAT recorded nearly 240 climate-related disasters
per year before 2000, compared to 341 per year after that date, a
44% increase. While occurrences of climate-related disasters have
declined from their peak in the last three years, they remain at more
than double the levels recorded in 1980-1989 (an average of 140
climate-related disasters per year) and 50% higher than in 1994.
Meanwhile, the numbers of geophysical disasters (mainly earthquakes,
tsunamis and volcanic eruptions) have remained more or less stable
throughout the past 20 years.
22014 preliminary data exposed here are the one at the date of the
16th December 2014.
The Human cost of Natural Disasters | 11
Figure 2
Number of disasters reported per country (1994-2013)
Number of
natural disasters
■1-28
■29-87
■88-242
■243-509
Figure 1
Number of disasters worldwide and by continent
over the period 1994-2013
1994
0
0
50
100
150
200
250
300
350
400
450
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
World
Africa
Americas
Asia
Europe
Oceania
12 | The Human cost of Natural Disasters
Changes in climate patterns (whether cyclic or human-induced)
are among the major causes of more frequent climate-related
events. From a DRR policy point of view, however, the weather
is less important than population growth and patterns of
economic development. Today, not only are more people in
harm’s way than there were 50 years ago, but development in
earthquakes zones, flood plains and other high-risk areas has
increased the likelihood that a routine hazard will become a
major catastrophe.
BOX 2
Hazards versus
disasters
In this report, the term hazard refers to
a severe or extreme event such as a
flood, storm, earthquake, volcanic
eruption etc. which occurs naturally in
any part of the world. Natural hazards
only become natural disasters when
human lives are lost and livelihoods
damaged or destroyed. EM-DAT does
not record hazards that occur in
unpopulated regions so this report,
too, is exclusively concerned with
natural events that impact on human
populations.
1994
0
0
50
100
150
200
250
300
350
400
450
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
■Geophysical
■Climatological, Hydrological, Meteorological
Figure 3
Number of disasters by major category
(climate-related & geophysical) (1994-2013)
ACTION POINT
While climate change is likely to maintain the upward
global trend in natural disasters for the foreseeable future,
the impacts on human populations can be mitigated.
Better flood control is one “low-hanging fruit” in policy
terms since affordable and effective technologies already
exist such as dams, dykes, mobile dykes and better early
warning systems.
| 13
BOX 4
Megadisasters
A megadisaster is an event that kills
more than 100,000 people.
Megadisasters impact significantly on
EM-DAT total figures and therefore
have to be taken into account when
interpreting our data. Three
megadisasters occurred in the period
1994-2013. The impact of the 2004
Asian tsunami, which killed 226,400
people in 12 countries, can be seen in
Figure 4. The 2008 peak reflects the
138,000 lives lost in Cyclone Nargis in
Myanmar, and 2010 peaked due to the
222,600 deaths in Haiti earthquake.
To put these totals into context, high-
income countries together lost 181,900
people to natural disasters throughout
the period 1994-2013.
BOX 3
Points to bear
in mind when
interpreting
historical trends
in disaster
occurrence
The volume and quality of data about
natural disasters increased enormously
after 1960 when the US’s OFDA actively
began to collect information about
these events. The arrival of CRED in
1973 further improved data recording,
while the development of global
telecommunications and the media,
plus increased humanitarian funding
and reinforced international
cooperation, also contributed to better
reporting of disasters. Thus part of the
apparent increase in the frequency of
disasters in the past half-century is,
no doubt, due to improved recording.
Of greater significance, however, is the
rising number of people on the planet.
Since disasters only occur when a
natural hazard impacts on human
beings, the total number of people in
harm’s way increases the chances of a
disaster occurring. The growth of cities
and other high-density communities
accentuates this risk.
ACTION POINT
More focused studies to understand exactly how
residents interpret disaster warnings (and why they did
not evacuate in time) will help reorient communication
strategies for early warnings.
Global trends in
human impacts
of natural
disasters
The proportion of the global population affected by natural disasters
has declined over the past two decades (Figure 4) from an average
of one in every 23 people on the planet in the period 1994-2003 to
one in 39 during 2004-2013. The affected total peaked in 2002, due
mainly to drought in India, which hit 300 million people, and a
sandstorm in China which affected 100 million of people.
While this overall decline is partly explained by a rising global
population, the absolute numbers of people affected by natural
disasters have also fallen, from an annual average of 260 million in
1994-2003 to 175 million per year in the period 2004-2013.
Preliminary EM-DAT data for 2014 show that even fewer than average
people were affected by disasters worldwide last year (102 million in
total), extending the declining trend in the numbers of people affected.
Average death rates, on the other hand, increased during the same
20-year period. The annual toll from natural disasters averaged more
than 99,700 deaths per year between 2004 and 2013, compared to
68,000 deaths on average for the full 20-year period (1994-2013).
This increase was due largely to the huge numbers of lives lost during
three megadisasters: the 2004 Asian tsunami, Cyclone Nargis in 2008
and the 2010 Haitian earthquake. But even excluding these mega-
disasters, death rates still increased over the past two decades, with
an average of 41,000 people killed each year in the period 2004-2013,
up from 35,000 in 1994-2003.
Preliminary data for 2014 show there were fewer than 7,500 deaths from
disasters last year, well below the annual average for the previous ten
years (99,700) and also the lowest total for any year since 1986.
While last year’s lower mortality figures are good news, the overall
rise in average death tolls at a time when the total number of people
affected is falling demonstrates the continued vulnerability of com-
munities to natural hazards. It also implies that DRR programmes
need to increase their effectiveness.
Given the development of early warning systems, and the accuracy of
modern weather forecasting, these data suggest that more work
should be undertaken to evaluate the efficiency and the real outcomes
on human lives and livelihoods of DRR interventions.
14 | The Human cost of Natural Disasters
Figure 4
Numbers of people affected &
killed annually by natural disasters
worldwide (1994-2013)
1994
0
0
100
200
300
400
500
600
700
0
50
100
150
200
250
300
350
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Affected
Exponential smoothing trend - Affected
Deaths
Exponential smoothing trend - Deaths
BOX 5
The HFA Decade: A United Nation’s Ofce
for Disaster Risk Reduction perspective
Within weeks of the Indian Ocean tsunami in 2004, representatives of 168 UN member states met in Kobe,
Japan, at the 2005 World Conference on DRR to adopt the Hyogo Framework for Action (2005-2015): Building
the Resilience of Nations and Communities to Disasters (HFA). Though voluntary and non-binding, the HFA has
been embraced by central and local governments, the private sector and civil society groups.
A clear sign of how the HFA has improved the global culture of disaster risk management lies in the fact that
121 countries have undertaken legislative and policy changes in line with its recommendations. There are now
HFA focal points in 191 countries and 85 platforms for DRR, while 141 countries have carried out at least one
review of their efforts to implement this framework for action through advances in risk governance, stronger
institutions, education and science, as well as addressing underlying drivers of risk and strengthening
preparedness and response mechanisms.
The HFA’s five priorities have also spurred the growth of the urban resilience movement through UNISDR’s
four-year-old Making Cities Resilient Campaign. More than 2,400 towns and cities now participate in this
campaign worldwide, including 73 capital cities. Political and social commitment has grown around the HFA’s
goals of reducing mortality and economic losses.
The experience of the HFA’s implementation since it was unanimously endorsed by the UN General Assembly
in 2005 has fuelled consultations on its successor - the post-2015 framework for DRR. The main challenges
during these consultations have repeatedly been identified as the need to address the underlying drivers of
risk such as poverty, climate change, poor governance, eco-system decline, rapid urbanization and population
growth. All of these factors highlight the cross-cutting nature of DRR and underline the importance for the
post-2015 global development agenda of a strong outcome document from the Third UN World Conference on
Disaster Risk Reduction in Sendai, Japan, in March 2015.
267
228
13
83
Number of people
affected (Millions)
Number of deaths
(Thousands)
The Human cost of Natural Disasters | 15
Classifying
natural
disasters
EM-DAT classifies disasters according to the type of hazard that
provokes them (cfr. technical notes). Thus, for example, tsunamis are
geophysical disasters because their root cause is seismic activity
(earthquake). This report focuses on four major categories: geo-
physical, hydrological, meteorological and climatological disasters3.
Taken together, hydrological, meteorological and climatological events
are also referred to as climate-related disasters.
Overall, climate-related events account for the overwhelming majority
(91%) of natural disasters that occurred worldwide between 1994 and
2013. Floods and storms alone account for 71% of the global total
(Figure 5). Climate-related events also outnumbered geophysical
disasters in the ten most disaster-affected countries in the world
(Figure 6).
The trend was confirmed in 2014, with 87% of disasters climate-related.
Geophysical
Earthquake
Mass
Movement
(dry)
Volcanic
activity
Hydrological
Flood
Landslide
Wave
action
Meteorological
Storm
Extreme
temperature
Fog
Climatological
Drought
Glacial lake
outburst
Wildre
Biological
Animal
accident
Epidemic
Insect
infestation
Extra-terrestrial
Impact
Space
weather
BOX 6
Modelling
cascading
disasters
Some hazards trigger other natural
and/or technological disasters,
thereby creating a cascading chain of
events. For example, floods can cause
mudslides which cut roads and power
lines, while extreme heat may spark
wildfires, heavy pollution and
outbreaks of bronchial disease.
A notorious example of a cascading
disaster was the March 2011
earthquake off the coast of Japan, the
seismic waves and tsunami from which
damaged the Fukushima nuclear power
plant.
A better understanding of the ways in
which such events escalate will, in
future, help the disaster response
community to anticipate consequences
of natural hazards and either break an
expected chain of events or respond
more effectively to likely impacts.
Databases such as EM-DAT are
valuable tools in such studies. CRED,
for example, is part of the EU-funded
Snowball Project which seeks to
model, simulate and predict chain
reactions within cascading disasters.
3Biological disasters are excluded from this report as they have
distinct characteristics and DRR strategies. Disasters with an extra-
terrestrial origin are also excluded because only one event was
recorded in this category since 1900 (the Chelyabinsk meteor in
Russia in February 2013).
16 | The Human cost of Natural Disasters
Figure 5
Share of occurrence of natural disasters by disaster type (1994-2013)
Figure 6
Climate-related vs. geophysical disasters: number of events by sub-group :
10 most disaster-affected countries (1994-2013)
China Peop. Rep.
United States
Philippines
India
Indonesia
Bangladesh
Mexico
Russia
Vietnam
Japan
0 100 200 300 400 500
■Geophysical
■Climate-related disasters
(Climatological, Hydrological
& Meteorological)
43%
2,937
1,942
552 381 369 322 255
105
28% 8% 6% 5% 5% 4% 1%
■Flood
■Storm
■Earthquake
■Extreme temperature
■Landslide
■Drought
■Wildre
■Volcanic activity
The Human cost of Natural Disasters | 17
Chapter 2
Human
impacts
of natural
disasters
Introduction
The impact of natural disasters on human beings depends on
multiple, inter-locking factors, including the type of hazard, its
location and duration, and the size and vulnerability of the
population in harm’s way. This report employs two main
yardsticks to measure the severity of impacts:
•The absolute and relative numbers of people affected and killed
in the emergency phase of a disaster;
•The economic costs, including assets and infrastructure damaged
and destroyed (Chapter 3).
Other costs, including repairs, rehabilitation and rebuilding
expenditure, plus lost productivity and increased poverty, are
harder to quantify but nevertheless must be taken into account
when analyzing the overall economic burden of natural disasters.
18 | The Human cost of Natural Disasters
Human impacts
of disasters
by type
Figure 7
Number of people affected by disaster type (1994-2013)
(NB: deaths are excluded from the total affected)
■Flood
■Drought
■Storm
■Earthquake
■Extreme temperature
■Other (Mass movement,
volcanoes, wildres)
25%
1.1
billion
55%
2.4
billion 15%
660
million 3%
121
million
2%
93
million
13
million
The impact of disasters is different according to their type
(Figure 7). Flooding impacted on more people than any other
type of disaster, accounting for 55% of the total people
affected (nearly 2.5 billion people) in the period 1994-2013.
Preliminary 2014 data show that hydrological disasters (floods
and landslides) were responsible for 71% of deaths last year
and 36% of the total number of people affected.
Earthquakes (including tsunamis) are typically far more deadly
than any other type of disaster, accounting for 55% of the
disaster deaths over the 20-year period, claiming nearly
750,000 lives (Figure 8).
The Human cost of Natural Disasters | 19
18%
250,000
12%
160,000
12%
160,000
2%
22,000
1%
20,000
Figure 8
Number of deaths by disaster type (1994-2013)
■Earthquake
■Storm
■Flood
■Extreme temperature
■Drought
■Other (Mass movement,
volcanoes, wildres)
Floods
Floods were the most frequent type of disaster in 1994-2013,
accounting for 43% of all events (Figures 5). They also affected
more people than all other types of natural disaster put
together, i.e. 55% of the global total in the past 20 years
(Figure 7). Floods also became increasing frequent, rising from
123 per year on average between 1994 and 2003 to an annual
average of 171 in the period 2004-2013.
Asia and Africa were hit by floods more than other continents,
but these were also an increasing danger elsewhere. In South
America, for example, 500,000 people were affected by flood
on average between 1994 and 2003. By the following decade
(2004-2013) that number had risen to two million people, a
four-fold increase.
Floods were less deadly than earthquakes or storms in terms
of the numbers of lives lost as a direct result of the event.
However, the nature of disastrous floods has changed in recent
years. Flash floods have become more frequent, as have acute
riverine and coastal flooding. Urbanization has also significantly
increased flood run-offs. Together, these factors have pushed
up the average death toll from floods in many parts of the
world. In Asia, for example, flooding affected very large areas
of agricultural land as well as human habitations on alluvial
flood plains. Since 2004, when the numbers of people killed
per flood reach a significant low (around 50 deaths per event),
mortality has again crept up (Figure 9) due mainly to high
tolls in three particular years. In 2007, floods killed 3,200
people in India and Bangladesh alone. In 2010, flooding killed
2,200 people in Pakistan and another 1,900 in China, while in
2013, an exceptionally high number of 6,500 people died due
to floods in India.
55%
750,000
20 | The Human cost of Natural Disasters
Studies have shown that recurrent flooding has severe and
long-term impacts on livelihoods and health, particularly in poor
rural regions dependent on farming. In rural India, for example,
children in households exposed to recurrent flooding have
been found to be more stunted and underweight than those
living in non-flooded villages. Children exposed to floods in
their first year of life also suffered the highest levels of chronic
malnutrition due to lost agricultural production and interrupted
food supplies4.
Many of these impacts are preventable, since flooding - unlike
most types of natural disaster - lends itself to primary prevention
through affordable technologies such as dams and dykes.
Other measures such as practical education of mothers have
also been shown to be effective in protecting children from
flood-related malnutrition.
ACTION POINT
In view of the serious health and socio-economic impacts
of flooding, CRED believes that flood control should be
regarded as a development issue, rather than largely a
humanitarian concern. Priority should be given to cost-
effective mitigation measures in poor regions at high risk
of recurrent flooding, together with malnutrition
prevention programmes.
200
100
1995 2000 2005 2010
0
Figure 9
Number of deaths per ood (1994-2013) in Asia
Observed number
of deaths/ood
Expected trend with
local regression
4Rodriguez-Llanes, J.M., Ranjan-Dash, S., Degomme, O.,
Mukhopadhyay, A., Guha-Sapir, D. (2011). “Child malnutrition
and recurrent ooding in rural eastern India: a community-
based survey”. BMJ Open 2001;1: e000109.
The Human cost of Natural Disasters | 21
Figure 10
Occurrence (a) and death tolls (b) of storms
broken down by national income bracket (1994-2013)
Storms
EM-DAT recorded 1,942 storms over the period 1994-2013,
making this type of disaster the second most frequent after
floods. Storms, including cyclones, killed more than 244,000
people in this 20-year period and cost US$936 billion in
recorded damage. This makes storms the most expensive type
of natural disaster during the past two decades, and the
second most costly in terms of lives lost.
Cyclones typically cut through wide swathes of densely
populated regions, while frequent storms tend to affect large
populations. Storms also affect higher-income countries more
often than lower-income nations (Figure 10a), hence their high
cost in terms of recorded lost assets.
In terms of lives lost, low-income countries bore the brunt of
storms, suffering 63% of all deaths from this type of disaster,
even though they experienced just 12% of global total of such
events (Figure 10b). If income groups are bracketed together,
the division between rich and poor is even more stark, with
low- and lower-middle-income countries accounting for 91% of
all storm deaths, against just 9% for high-income and upper-
middle-income countries.
Regionally, the impacts of storms have increased in Asia,
especially in the South and South-East. The average death
rate per event on that continent rose until 1997, decreased for
the next seven years, then returned to a rising trend from
2004 (Figure 11). Cyclone Nargis was the most deadly storm in
this period, claiming 138,000 lives in Myanmar in 2008, the
highest toll since Cyclone Gorky in Bangladesh in 1991.
Scientific evidence suggests that climate change will increase
the upward trend in the numbers of floods and storms
worldwide, while the population requiring protection can, at
best, be expected to increase by same rate of population
growth in the affected regions. On the positive side, weather
forecasting has made extraordinary progress in recent years,
with predictions now highly reliable within a 48-hour period.
Arguably, better forecasting may be one reason behind the
global decrease in deaths from storms identified by EM-DAT
over the past 20 years.
■High-income
■Upper-Middle-income
■Lower-Middle-income
■Low-income
40%
26%
63%
4% 5%
28%
22%
12%
155,066 deaths
8,840 deaths 12,768 deaths
67,836 deaths
425 disasters
222 disasters
783 disasters
512 disasters
(a)
Occurence
(b)
Death tolls
22 | The Human cost of Natural Disasters
However, given that EM-DAT data also show a rising number of
people affected by storms (Figure 11), plus an increasing death
toll in Asia and huge financial losses, we conclude that storm
preparedness needs strengthening. Of course, the occurrence of
storms cannot be controlled, but better management and
mitigation could reduce deaths tolls and other losses from
these predictable hazards.
200
300
100
1995 2000 2005 2010
0
Figure 11
Number of deaths per storm in Asia (1994-2013)
Observed number
of deaths/storm
Expected trend with
local regression
ACTION POINT
Storm early warning systems need to be deployed more
effectively, particularly in poor rural communities at
higher risk. Proven preventive measures, such as cyclone
shelters and wind-resistant buildings, are also options
which (according to resources available) could help
protect vulnerable populations.
The Human cost of Natural Disasters | 23
Drought
More than one billion people were affected by droughts in the
period 1994-2013, a larger number than for any other type of
disaster apart from flooding (Figure 7). Africa suffered droughts
more frequently than any other continent, with 131 droughts,
of which 75 occurred in East Africa (Figure 12).
Droughts are associated with agricultural failures, loss of
livestock, drinking water supply shortages and outbreaks of
epidemic diseases. Since droughts can last for several years,
some have extensive, long-term economic impacts and result
in the displacement of large sections of the affected population.
Consecutive failures of seasonal rains in Eastern Africa in 2005,
for example, led to food insecurity for at least 11 million people.
The figure of just 2% of disaster deaths worldwide being due
to drought (Figure 8) is rather misleading since it excludes the
vast majority of indirect deaths caused by drought-related
malnutrition, disease and displacement. These indirect deaths
are often poorly documented or not counted at all.
Furthermore, while droughts represented a relatively small
proportion of the global total of occurrences (5% of all disas-
ters between 1994 and 2013), the drought-affected population
accounted for a disproportionately high 25% of the worldwide
total (Figure 7). This is despite the effectiveness of weather
warnings and early warning systems.
The available data point to two broad conclusions:
•The fact that 41% of all drought disasters were recorded in
Africa suggests that drought overwhelms the ability of
poorer African countries to cope far more frequently than
it does in richer countries which also suffer from this
extreme form of climate variability.
•While indirect drought-related deaths are extremely difficult
to quantify (given poor record-keeping) there is little doubt
that drought-disaster mortality, plus the associated economic
costs, have taken a high toll in terms of increased hunger,
poverty and the perpetuation of under-development.
ACTION POINT
Reducing the size of drought-vulnerable populations
should be a global priority over the next decade. Better
accounting systems for indirect deaths should also be put
in place, and linked to early warning systems and response
mechanisms, in order to monitor the impacts of drought
more comprehensively.
Figure 12
Occurrence of droughts by continent and by region
within Africa (1994-2013)
24%
25%
41%
7% 4%
Middle Africa 3%
Northern Africa 2%
Southern Africa 4%
West Africa 9%
East Africa 23%
Droughts
■Africa
■Asia
■Americas
■Europe
■Oceania
22 droughts
131 droughts
80 droughts
77 droughts
12 droughts
24 | The Human cost of Natural Disasters
Earthquakes, including
tsunamis
Earthquakes and tsunamis are rarer phenomena than storms
and floods, but tend to cause very high numbers of casualties
in extremely short periods of time. The tsunami that hit South-
East Asia in 2004 resulted in 226,000 direct deaths in 12
countries, while the Haiti earthquake of 2010 killed 223,000
in the emergency phase.
Overall, earthquakes and tsunamis were the deadliest types
of disaster during the past 20 years, accounting for nearly
750,000 direct deaths, which is more than all other direct
deaths from disasters put together (Figure 8).
Urbanization within highly seismic zones has increased
significantly in the last few years, exacerbating the deadliness
of these events. Slums and squatter dwellings frequently
expand on the highest risk areas, such as slopes and
embankments. As a result, ground movements and landslides
(even after small earthquakes) dislodge friable land, killing
almost all the local population. This was the case in the San
Miguel/Santa Tecla earthquake in El Salvador in January 2001
where the majority of the 944 deaths were caused by a large
landslide in Santa Tecla and Comasugua.
Protecting people from tsunamis is in many ways an even
greater challenge than reducing the impacts of ground
movements. People’s livelihoods depend on the coast (in
tourism and fishing, for example) so they are unable or
unwilling to move inland. Also, the return periods between
tsunamis are so long that it is difficult to keep safety or early
warning messages alive in people’s mind. While very strict
building regulations would, in theory, be an effective option
to reduce tsunami deaths, in practice such laws would be very
hard to implement in resource-poor settings.
One major advance, following the 2004 Asian tsunami, was
the establishment of the Indian Ocean Tsunami Early Warning
System which now provides alerts through three regional
watch centres in India, Indonesia and Australia, and a network
of 26 national tsunami information centres. It is an efficient
system which disseminated early warnings within eight
minutes of the Banda Aceh earthquake in 2012.
Table 1 shows that ground movements accounted for 500,000
deaths between 1994 and 2013, which is equivalent to 66%
of total mortality for geophysical disasters. Ground movements
also affected a cumulative total of more than 100 million
people, almost 96% of all people affected by earthquakes
worldwide in this period.
However, Table 1 also illustrates that tsunamis were almost
20 times more deadly than ground movements in terms of
the proportion of victims killed. (“Victim” in this context means
the numbers killed and the numbers affected combined.) On
average, for every 1,000 tsunami victims in this 20-year
period, 79 were killed, compared to just 4 deaths per 1,000
for ground movements.
Table 1
Occurrence, mortality and people affected
by geophysical disasters and their relative %
of the global total (1994-2013)
Ash fall 105 721 2,099,075 16.0 0.1 1.7
Ground movement 525 497,097 118,328,863 80.2 66.5 95.9
Tsunami 25 250,125 2,898,178 3.8 33.4 2.4
TOTAL 655 747,943 123,326,116 100 100 100
Occurrence Total
Deaths Total
Affected %
Occurrence % Total
deaths % Total
affected
The Human cost of Natural Disasters | 25
Impacts of
natural disasters
by country and
income grouping5
Introduction
The relative burden of natural disasters on individual
countries depends upon the dataset studied. Gross or
absolute gures give a sense of the scale of natural disasters,
while data standardized by population is a better indicator of
the relative impact. For instance, the deaths of 20,000 people
is a tragedy wherever it occurs, but the social and economic
consequences will differ radically if these deaths occur in
the USA or China, rather than a less developed country or
small island nation. In poorer countries lost assets not only
cause immediate nancial hardship, particularly in poor
households, but disaster deaths also have wide-reaching
consequences for future family wellbeing as well as economic
development.
This sub-chapter gives an overview of countries most affected by
natural disasters in relative and absolute terms. Countries are then
grouped by income level to demonstrate the greater burden
experienced by lower-income countries. By analyzing EM-DAT data in
this way, CRED is contributing to current knowledge about which
populations are most vulnerable to disasters and the relative
resilience of different national economies.
Impacts of disasters
by country
In absolute terms, the population giants China and India top the
league tables in terms of the cumulative number of people affected
by natural disasters between 1994 and 2013 (Figure 13). They account
for more than 3.3 billion people affected over this period or 76% of
the global total of 4.3 billion people.
The picture is radically different, however, when the data is standardized
to reflect the numbers of people affected by natural disasters per
100,000 head of population. In this case, only three of the most-
affected countries are in Asia (China still appears in seventh position),
while six are in Africa. Moldova is the only European country on either
list, ranking ninth in the standardized league table (Figure 13).
5From this sub-chapter and onwards, smaller states are excluded
from EM-DAT data. Chapter 5 focuses on small states.
26 | The Human cost of Natural Disasters
Figure 13
Top 10 countries by population affected by natural disasters vs.
countries most affected per 100,000 inhabitants (1994-2013)
■Top 10 countries with highest proportion of affected people over the total population
(per 100,000 inhabitants)
■Top 10 countries with the highest absolute number of affected people (in million)
Eritrea
31,000
Ethiopia
41m
Niger
7,800
Somalia
10,000
Lesotho
15,000
Kenya
46m
Kenya
8,200
Cambodia
12,000
India
819m
Bangladesh
127m
Philippines
124m
Viet Nam
44m
Mongolia
21,000
Zimbabwe
14,000
Moldova R.
8,000
Thailand
75m
Pakistan
58m
Iran Isl. R.
40m
China P.R.
10,000
China P.R.
2,506m
The Human cost of Natural Disasters | 27
Ranking by death tolls also varies significantly when mortality
is standardized by population size. Figure 14, for example,
shows that India, China and Indonesia experienced low death
rates as a proportion of their overall population, despite very
high absolute numbers.
Tragically, Haiti suffered the largest numbers killed both in
absolute and relative terms (Figure 14) due largely to the terrible
toll of the 2010 earthquake and its immediate aftermath. Haiti
experienced more than six times the number of fatalities per
population compared to Somalia (where 20,000 people died
from a drought in 2010) and more than seven times the
number of fatalities per head of population compared with
the third ranked country, Myanmar (where, as noted previously,
Cyclone Nargis claimed 138,000 lives in 2008).
Figure 14
Number of deaths and relative mortality (deaths/million inhabitants)
for 8 selected countries (1994-2013)
■Number of Deaths
■Nb of deaths per million inhabitants
India
China Peop. Rep.
Indonesia
Venezuela
Honduras
Myanmar
Somalia
Haiti
0.00
50,000
100,000
150,000
200,000
250,000
200
0.0
400
600
800
1,000
1,200
1,400
598,660
5
41
115
169
170
194
1,301
230,798
23,136
15,190
30,363
98,660
127,481
139,409
28 | The Human cost of Natural Disasters
Impact of natural
disasters by national
income level
As noted previously, the impact of a disaster partly reflects its
magnitude and intensity. National preparedness and the
efficiency of responses to disasters are also major determinants
of death tolls. Thus the vulnerability of affected populations
has a direct and significant bearing on the numbers killed.
It is noticeable that India, China, Indonesia and Myanmar have
made major commitments to reduce disaster losses by acting
on the priorities of the Hyogo Framework for Action. In the
case of Indonesia, DRR became a pillar of national development
immediately after the catastrophe of the Indian Ocean tsunami.
Similarly, for India a key trigger was the 1999 cyclone which
claimed around 10,000 lives in Odisha State; casualties in two
recent major cyclones were minimal. China has also reported
that it has succeeded in keeping economic losses within a
target of 1.5% of GDP.
EM-DAT data show that when countries are grouped by
income (Annex A) they experienced broadly similar disaster
events regardless of their income (Figure 15). In fact, the
highest number of disasters occurred in upper-middle-income
countries (1,992 events or 30% of the global total) while low-
income countries experienced 17% of global disasters (1,119
events).
Figure 15
Number of disasters per income group (1994-2013)
■High-income
■Upper-Middle-income
■Lower-Middle-income
■Low-income
30%
27%
26%
17%
1,751 disasters
1,119 disasters
1,700 disasters
1,992 disasters
Disparity of deaths between
lower- and higher- income countries
Unlike disaster occurrences, the pattern for death tolls is very
different when the income level of each country is taken into
account. Taken together, high- and upper-middle-income
countries experienced 56% of disasters (Figure 15) but lost
32% of lives (Figure 16) in the past 20 years. In the same
period, low- and lower-middle-income countries experienced
44% of disasters but suffered a disproportionately high 68%
of global mortality.
This disparity between rich and poor is further illustrated by
average death rates per disaster. On average, more than three
times the number of people died per disaster in low-income
countries than in high-income countries, with 332 deaths for
the poorest countries against 105 deaths on average in the
richest countries in 1994-2013 (Figure 17).
The Human cost of Natural Disasters | 29
Figure 16
Number of deaths per income group (1994-2013)
Figure 17
Total numbers of deaths compared to the average number
of deaths per disaster by income group (1994-2013)
■High-income
■Upper-Middle-income
■Lower-Middle-income
■Low-income
35%
33% 19%
13%
441,000 deaths
182,000 deaths
474,000 deaths
252,000 deaths
Upper-
Middle-
income
Lower-
Middle-
income
Low-
income
High-
income
500 350
300
250
200
150
100
50
0
450
400
350
300
250
200
150
100
50
0
■Number of Deaths
(thousand)
■Number of Deaths
per event
30 | The Human cost of Natural Disasters
Furthermore, low-income countries account for 43 deaths per
one million inhabitants, while in high-income countries the
death rate is only nine per million (Figure 18).
These data demonstrate that the developmental level of a
country is a major determinant of the death rate, rather than
a country’s exposure to natural disasters per se. National
resilience to natural disasters, and its ability to recover from
such events, also depend largely on the developmental level
of the country or region in question.
ACTION POINT
Disaster mitigation measures need significant
improvement in lower-income countries to reduce the
burden of disasters on them.
Figure 18
Number of deaths per one million inhabitants
by income group (1994-2013)
Upper-
Middle-
income
Lower-
Middle-
income
Low-
income
High-
income
6
12
43
9
45
40
35
30
20
20
15
10
5
0
The Human cost of Natural Disasters | 31
Chapter 3
Disaster
damage to
housing &
infrastructure6
Introduction
Poor and unregulated construction is another key determinant of mortality
during natural disasters, particularly in lower-income countries, when
badly-built houses, ofces, schools, work-places and health facilities
collapse and kill people. For example, thousands of schoolchildren have
died in major earthquakes during the past 20 years. Disaster damage and
destruction also generate signicant rebuilding costs as well as a host of
longer-term social and economic consequences when people are forced to
ee their homes.
6Damaged includes partially damaged and fully destroyed in this report. EM-DAT
records about the physical impacts of disasters are partial: 129 events (of 6,500
events) report health facilities damaged and 248 report education infrastructures
damaged.
32 | The Human cost of Natural Disasters
Damage to health facilities also reduces a country’s ability to
respond to disasters in the first instance, increasing the
likelihood that an international humanitarian operation will
be required. This was the case after the Bam earthquake in
2003 in Iran when all of the city’s key health facilities were
damaged, and patients and staff were killed.
In the aftermath of disasters, international reconstruction
efforts are needed when major events cause more damage to
vital infrastructure than the national economy can afford to
repair. After Cyclone Nargis hit Myanmar in May 2008, for
example, the IFRC took three years to rebuild more than
16,000 houses, 25 schools and 20 rural health centres.
In the long-term, the loss of education and health infrastructure
can in some cases slow economic and social development for
generations. Many studies have shown that the health and
nutritional status of children in particular are especially
vulnerable to disasters, both in the emergency phase and also
due to malnutrition and under-nutrition in the aftermath7.
These effects need to be quantified to improve the effectiveness
of DRR interventions.
Meanwhile, UNISDR is actively campaigning for safe schools and
hospitals in earthquake and flood zones to reduce fatalities,
injuries and physical damage. Turkey, for example, has given
a commitment to make all schools and hospitals earthquake-
proof by 2017.
Disparities between
disaster types and
between income groups
Flooding damaged more housing worldwide (Figure 19) and
more schools and hospitals (Figure 20) than any other type of
disaster. In total, EM-DAT recorded more than 185,000 health
and education facilities either damaged or destroyed worldwide
over the 20-year period (Figure 20).
Once again, EM-DAT data highlight disparities between upper-
and lower-income countries, with lower-income countries
experiencing greater damage to all three types of property
recorded in the database: housing, health facilities and edu-
cation infrastructure. For example, higher building standards
in high-income countries are reflected in the fact that just 3%
of damaged housing occurred in these countries (Figure 21),
even though they suffered a broadly similar number of disasters
as other income groups between 1994 and 2013.
In total, more than 116 million homes were damaged by
disasters worldwide in the period 1994-2013, with the majority
(60%) in upper-middle-income countries. This total largely
reflects events in China where 64 million homes were damaged
(i.e. 93% of the total for upper-middle-income countries).
Excluding China, lower-middle income countries ranked highest
in terms of damaged housing.
Figure 19
Houses damaged per disaster type (1994-2013)
■Flood
■Earthquake
■Storm
22%
25m
21%
24m
57%
66m
7Rodriguez-Llanes, J.M., Ranjan-Dash, S., Degomme, O., Mukhopadhyay, A., Guha-Sapir, D. (2011). “Child malnutrition and
recurrent flooding in rural eastern India: a community-based survey”. BMJ Open 2001;1: e000109.
The Human cost of Natural Disasters | 33
Figure 21
Houses damaged per income group (1994-2013)
■High-income
■Upper-Middle-income
■Lower-Middle-income
■Low-income
12%
25% 60%
3%
29 million
15 million
69 million
3.4 million
Figure 20
Health and education facilities damaged
by disaster type (1994-2013)
38%
32% 30%
33%
43%
24%
20%
67%
13%
Damaged &
destroyed
Damaged
Destroyed
49,000
6,700
37,000
65,000
23,000
4,300
Damage to health and educational infrastructure
occurred overwhelmingly (85%) in low- and lower-
middle income countries (Figure 22). In lower-income
countries, for example, Cyclone Sidr destroyed more
than 4,000 schools in Bangladesh in 2007. Peru lost
600 health facilities in one cyclone in 1997, an
earthquake in Pakistan destroyed more than 600
health facilities in 2005, while a tropical cyclone in
1999 devastated 11,000 schools in India.
As other studies have shown, preparedness and
response planning would be aided by a better
understanding of the relative impact of poor building
practices on disaster mortality figures. DRR managers
could then compare quantified data with the
effectiveness of evacuation procedures, for example,
and identify where it would be best to target new
measures. Meanwhile, given the vulnerability of
health and education infrastructure in lower-income
countries, initiatives such as the UNISDR Safe
Schools campaign should be fully supported.
BOX 7
Nepal vs Chile:
how national
income level affects
earthquake impacts
Impacts will, of course, depend on the magnitude of the
disaster, its location, the local population density, the
warning systems available, the strength of local buildings
and policies in place to strengthen “non-resistant-to-
earthquakes” infrastructure. Some of these characteristics
can be altered, but the geographical location and the
magnitude of the disaster cannot.
Tab le 2 illustrates how much harder low-income countries
are hit by natural disasters than high- income countries
experiencing a similar event. In this case, two earthquakes:
one in low-income Nepal, the other in high-income Chile.
In this example, the total number of deaths was slightly
higher in Chile than Nepal (the magnitude of the earth-
quake was also higher in Chile than Nepal) but all other
indicators show that Nepal suffered a greater impact, with
168,000 people affected and more than 33,000 houses
damaged or destroyed, compared with fewer than 28,000
people affected in Chile and the damage and destruction of
9,300 houses.
Disno 2005-321 2011-351
Magnitude
(Richter scale) 7.9 6.9
Deaths 11 7
Affected 27,645 167,949
Houses
damaged 8,800 25,272
Houses
destroyed 550 8,300
Commercial
damaged 0 204
Commercial
destroyed 0 63
Health
damaged 0 38
Health
destroyed 1 26
Education
damaged 0 387
Education
destroyed 0 111
Chile Nepal
Table 2
ACTION POINT
In view of the importance of health and
education to development, a high priority
should be given to national and international
efforts to protect health and education
infrastructure from disaster damage and
destruction in lower-income countries.
The Human cost of Natural Disasters | 35
ACTION POINT
Given the importance of data for effective
planning, better disaster data collection about
damage to buildings would improve global and
local impact analyses, aid monitoring of
mitigation efforts and help decision-makers
to target new measures more effectively.
■High-income
■Upper-Middle-income
■Lower-Middle-income
■Low-income
12%
37%
48%
3%
4,600
69,000
89,000
23,000
BOX 8
Homelessness,
displacement and
need for better
data for planning
Improvements in preparedness for natural disasters in
recent years, including effective early warning systems
and evacuation procedures, mean that more people now
survive these events than in the past. Without homes,
however, many survivors become displaced.
Based on EM-DAT data, the IDMC last year estimated that
22 million people in at least 119 countries were displaced
by natural disasters in 2013, about three times as many
as were newly displaced by conflict and violence8.
In CRED’s view, more systematic data collection about
damage to housing and infrastructure, using standardized
protocols, would be a valuable step towards better disaster
analysis at the global level. One useful move in this
direction is the creation of 72 loss accounting databases
at national levels by UNISDR/UNDP/LaRed.
They can be consulted on:
http://www.desinventar.net/index
Figure 22
Health and education facilities
damaged by income group
(1994-2013)
8IDMC 2014: Global Estimates 2014:
People displaced by disasters
BOX 9
Combining severity & frequency data:
a guide to resilience
As economies develop, industrialization and urbanization increase population densities. When this
development occurs in areas at high risk of natural hazards, the frequency of disasters increases because more
people are in harm’s way.
The number of deaths per event is one important measure of the severity of a disaster. The combination of the
total of disaster deaths with the frequency over the period 1994-2013 (Figure 23) illustrates relative exposure
to natural disasters, in this case for countries grouped by income level. The graph is divided into quadrants to
aid comprehension and also to demonstrate its usefulness as a policy tool.
When applied at the country level, its principal use is to review the relative experience of disasters within a
region, balancing the frequency of disasters and the magnitude of their impact. It can thus serve as a guide to
identify which population’s risk factors require closer analysis and which countries have been able to control
disaster impacts more effectively. If there are lessons to be learnt from positive local experiences, these could
potentially be translated into policies in neighbouring countries.
This graph could also help to monitor the evolution of mitigation efforts over time. While the frequency of
hazards cannot be altered, deaths can be reduced (as indicated by the downward arrow on the right). If Country
A moved from the “bad” upper-left quadrant to the lower-left quadrant, that would show disaster deaths were
falling, suggesting successful mitigation efforts. But if Country B rose from the “good” lower-right quadrant to
the upper- right quadrant, that might flag up a need to re-evaluate preparedness and response measures since
death tolls would be rising. Of course, during any such monitoring, the intensity of disasters which hit these
countries would also have to be taken into account.
Figure 23
Severity and frequency of natural disasters
by income group (1994-2013)
500,000
450,000
400,000
350,000
300,000
250,000
200,000
150,000
1,000 1,200 1,400 1,600 1,800 2,000 2.200
Low-income
Lower-Middle-income
High-income
Upper-Middle-income
Total number of deaths
Total number of disasters
The Human cost of Natural Disasters | 37
Chapter 4
Counting
the
economic
costs
of disasters
Introduction
Natural disasters cause major economic losses around the globe,
with the recorded costs of climate-related disasters far out-
weighing the direct economic impacts of earthquakes and other
geophysical events. In sum, EM-DAT recorded total losses of US$
2,600 billion9over the period 1994-2013. Some commentators
suggest that this total may be underestimated by as much as 50%.
However, in the absence of standard methodology, the degree of
bias is guesswork.
9All economic losses and GDP are adjusted at 2013 US$ value
38 | The Human cost of Natural Disasters
EM-DAT data show that storms are the most expensive type
of disaster in terms of recorded lost assets (US$ 936 billion),
followed by earthquakes (US$ 787 billion) and floods (US$
636 billion) (Figure 24). In terms of absolute values, losses in
Asia accounted for 50% of the total, followed by the Americas
at 35% (Figure 25). Insured losses were much greater in
higher- income countries, dwindling to almost nothing in low-
income ones where insurance is beyond the reach of most
people (Figure 26).
As previously noted, while the occurrence of disasters was
broadly similar in all income groups, low- and lower-middle-
income countries reported more than two-thirds of the deaths
(see chapter 2) but just 10% of economic losses. High- and
upper-middle-income countries reported one third of the
deaths but 90% of losses in absolute values (Figure 27).
Figure 24
Breakdown of recorded economic damage (US$)
by disaster type (1994-2013)
Figure 25
Absolute losses by continent
(1994-2013)
36%
25%
31%
4%
4%
■Storm
■Drought
■Flood
■Climate related - others
■Geophysical
35%
12%
50%
2%
1%
■Asia
■Americas
■Europe
■Oceania
■Africa
912 billion
1,285 billion
307 billion
62 billion
17 billion
787 billion
109 billion
636 billion
936 billion
114 billion
The Human cost of Natural Disasters | 39
64%
7%
26%
3%
■High-income
■Upper-Middle-income
■Lower-Middle-income
■Low-income
Figure 26
Economic losses and insured losses (US$)
per income group (1994-2013)
Figure 27
Income group analysis of economic damage (US$)
(1994-2013)
Low
income
Lower Middle
income
Upper Middle
income
High
income
0 500 1000 1500 2000 billions
■Insured losses
■Economic losses
678 billion
173 billion 71 billion
1,660 billion
40 |
Economic losses:
absolute values
vs. percentage
of GDP
The global pattern of losses as a percentage of Gross Domestic
Product (GDP) varies starkly from the pattern of global losses in
absolute terms, reflecting the much smaller economies of low- and
lower-income countries and the relatively greater economic impact of
disasters on them. Figure 28 illustrates how high asset values in high-
income countries pushed up their recorded losses to US$ 1,660 billion
dollars over the period 1994-2013. This compares to just US$ 71
billion recorded in low-income countries. As a proportion of GDP10,
however, the situation is reversed, with recorded losses amounting to
just 0.3% of GDP in high-income countries, against 5.1% in low-
income countries.
These GDP figures understate the real disparity between rich and poor,
since under-reporting of losses is greater in low-income countries than
high-income ones, a phenomenon discussed in more detail below.
The impact of natural disasters on national economies also varies
greatly at the country level, depending on yardstick used (Figures 29
& 30). In absolute values, the USA lost more than any other country
between 1994 and 2013, following by Japan and then China. In terms
of GDP, however, the losses were greatest in the Democratic People’s
Republic of Korea, followed by Mongolia and then Haiti.
BOX 10
Economic losses
due to one
major disaster
in most-affected
countries
In the most disaster-affected countries
in terms of GDP, just one type of
disaster is the cause of the
overwhelming majority of the recorded
economic damage. For example, in DPR
of Korea, 76% of recorded losses were
due to floods between 1994 and 2013
(Figure 30). These losses correspond to
33% of the country’s GDP on average.
Similar percentages can be seen in
Mongolia, Haiti, Yemen and Honduras.
These data suggest that mitigation
policies need to focus on the single
most costly type of disaster.
Upper
Middle
income
Lower
Middle
income
Low
income
High
income
1800 6
5
4
3
2
1
0
1600
1400
1200
1000
800
600
400
200
0
■Economic losses ■Economic losses as % of GDP
Figure 28
Economic losses in absolute values
and compared to GDP
1659
0.3 0.6
0.2
5.1
678
173
71
10 See technical notes for methodology
(billion US$) %
The Human cost of Natural Disasters | 41
Japan China
Peop. Rep.
Italy Germany Thailand India Mexico France TurkeyUnited
States
800
700
600
500
400
300
200
100
0
billions
Figure 29
Top 10 countries reporting economic losses
from natural disasters in absolute values (US$) 1994-2013
739
482 453
66 56 46 46 39 39 35
Mongolia Haiti Yemen HondurasKorea Dem.
Peop. Rep.
40
35
30
25
20
15
10
5
0
Economic losses as % of GDP
Figure 30
Top ve countries ranked by losses as a percentage of GDP
showing the impact of one disaster type (1994-2013)
38.9
33.9
14.9
11.1
6
42 | The Human cost of Natural Disasters
Under-reporting
of economic losses
Information on the economic damage caused by natural
disasters is only available for 36% of disasters reported from
1994 to 2013. Records are particularly partial from Africa, where
losses were reported from just 13% of events. Reporting levels
vary according to the continent (Table 3a), type of disaster
(Table 3b) and the national income (Table 3c). Such gaps in
our knowledge should be of international concern at a time
of limited financial resources and competing priorities.
With climate-related disasters set to increase, it is essential in
our view to help lower-income countries to estimate their
losses effectively in order for them (and the international
community) to better understand which types of disaster
cause the greatest losses. Choices about DRR action would
then be more evidenced-based and therefore more likely to be
effective. A review of case studies using different methodologies
to calculate losses would be a major step forward in establishing
a common and tested approach to estimating economic losses
worldwide.
ACTION POINT
Reporting of economic losses should be
improved, particularly for lower-income
countries. Priority should also be given
to a review of existing methodologies to
estimate losses and the development of
realistic, standard operational methods.
Africa 12
Americas 39
Asia 41
Europe 40
Oceania 52
Drought 30
Earthquake 41
Extreme temperature 11
Flood 33
Landslide 11
Mass movement (dry) 10
Storm 53
Volcanic activity 11
Wildlife 33
High-income 51
Low-income 14
Lower-Middle-income 34
Upper-Middle-income 37
a) %
b)
c)
Table 3
Percentage of disasters (a) per
continent, (b) by disaster type
and (c) per income group
for which economic losses
were reported (1994-2013)
The Human cost of Natural Disasters | 43
Chapter 5
Spotlight
on Small
States11
Introduction
Small states are dened by the World Bank as those with
populations of 1.5 million or less. Many of them face economic
challenges which larger countries do not, due, for example, to
their small domestic markets, narrow resource bases, limited
diversication in terms of produce and exports, and diseconomies
of scale. Some small states are isolated, which increases transport
costs; others have open economies, making them vulnerable to
external shocks from global markets.
11 See technical notes
44 | The Human cost of Natural Disasters
Many small states are also
highly vulnerable to natural disasters.
Hurricanes, cyclones, droughts, volcanic eruptions and tsunamis
typically affect the entire population of small nations, while
some small island states and archipelagos also have to contend
with storms, storm surges, landslides and flooding.
Small population sizes increase these vulnerabilities. If a high
percentage of the national population is affected or killed, a
natural disaster can have devastating
long-term impacts
on
development and overall economic activity in these countries.
These problems are particularly severe for Small Island
Developing States
(SIDS)
, many of which depend on agriculture,
fishing (both subsistence and commercial) and tourism. SIDS
with a high proportion of productive capital located on the coast
are particularly at risk. Indeed, climate change and rising sea
levels due to climate change threaten to destroy some low-lying
SIDS altogether.
For others, the cost of recovering from natural disasters can
overwhelm public finances, curtailing their options for future
development. This can be true even if they are classified as
high-income countries
(Figure 31)
. In high-income Grenada, for
example, tropical cyclone Ivan caused economic losses
equivalent to almost three times annual GDP in 2004. In view
of the extreme vulnerability of small island states to natural
disasters, some 40 of the 60 small states analyzed in this
chapter are part of the SIDS network.
Figure 31
Number of small states in this report sample
by income group (1994-2013)
45%
20%
33%
2%
■High-income
■Upper-Middle-income
■Lower-Middle-income
■Low-income
20 states
12 states
1state
27 states
The Human cost of Natural Disasters | 45
Disaster occurrence
and impacts
on small states
Storms are by far the most frequent natural hazard to affect
small states, accounting for 54% of all recorded disasters in
these nations between 1994 and 2013 (Figure 32).
Predictions of increasing numbers of storms due to climate
change is of particular concern to SIDS, not only because of
the likely impacts on local people but also because storms
can alter vegetation and water salinity, affecting agriculture
and fisheries in the longer-term.
Figure 32
Disaster occurrence by type in small states (1994-2013)
167
58
26 25
16 19
19% 8% 8% 5% 6%
■Storm
■Flood
■Drought
■Earthquake
■Volcanic activity
■Other (landslide, extreme
temperature, wildre)
54%
46 | The Human cost of Natural Disasters
Figure 33
Top ve small states with populations most affected by natural disasters
(1994-2013)
Swaziland
Djibouti
Guyana
Fiji
St Lucia
0 0.2 0.4 0.6 0.8
0.35
1.05
1.15
1.66
11.2 1.4 1.6 1.8
Figure 33 shows that the African nations of Swaziland and
Djibouti experienced the highest number of people affected by
natural disasters in the period 1994-2013, followed by three
SIDS: Guyana, Fiji and St Lucia. Disaster death rates were
highest in Bhutan, Djibouti, Luxembourg, Samoa and Vanuatu
(Figure 34).
In terms of the percentage of the total population affected by
disasters, four out of the top five small states were SIDS:
Kiribati, St Lucia, Monserrat and Niue (Figure 35). Figure 36
shows that economic impacts were severe for small states. In
the Caribbean, natural disasters caused losses equivalent to
79.4% of the GDP in St Kitts and St Nevis, and 74.8% in
Grenada. Dominica, the Cayman Islands and the Maldives
suffered broadly similar (and very high) disaster-related losses
as a percentage of GDP. These figures highlight the extreme
economic vulnerability of SIDS and other small island states
to natural disasters.
0.39
Millions
Figure 34
Top ve small states ranked by natural disaster death tolls
(1994-2013)
Bhutan
Djibouti
Luxembourg
Samoa
Vanuatu
0 50 100
148
170
196
263
150 200 250 300
165
The Human cost of Natural Disasters | 47
Figure 35
Top ve small states per proportion of total population affected
by natural disasters (1994-2013).
Kiribati
French Guiana
St Lucia
Montserrat
Niue
0
35
39
49
52
37
%
%
Figure 36
Top ve small states ranked by disaster-related economic losses
as percentage of GDP (1994-2013)
Bhutan
Djibouti
Luxembourg
Samoa
Vanuatu
010
20 30
43.7
45.9
74.8
79.4
50
40 60 70 80 90
10 20 30 40 50 60
43.9
48 | The Human cost of Natural Disasters
Chapter 6
The power
of maps:
georeferencing
EM-DAT data
Introduction
Since 2014, CRED has been taking the rst steps towards
integrating EM-DAT data into a Geographic Information System
(GIS). Termed “georeferencing”, the aim of this project is to
identify and map the impacts of disasters as precisely as possible.
By presenting this data in map form, the project also will make
the “footprint” of disasters more visual and therefore more
accessible to disaster planners and policy-makers, as well as the
general public.
Georeferencing will also aid disaster research since it will allow
EM-DAT data to be merged with other standardized or
georeferenced datasets (about population or income within
disaster-affected areas, for example). This will move studies into
risk and vulnerability from the macro- to the micro-level, which
in turn will help planners to rene disaster mitigation and
response measures.
Existing EM-DAT data is already being georeferenced; future data
will be incorporated into the new recording system. Increasingly,
therefore, information about disasters will be recorded in terms of
administrative units where natural hazards have affected people
and/or damaged infrastructure, rather than listing events.
For this project, CRED is using the standardized FAO Global
Administrative Unit Layers (GAUL) dataset combined with the
reported affected administrative units present in EM-DAT. Each
GAUL administrative unit is labelled with a unique identier and
is already geocoded.
Each EM-DAT disaster record will contain the unique GAUL codes
of the administrative units affected, together with the coordinates
of their centroids (or geometric centres) (Table 4) plus a map
representing the area affected (Figure 37).
The Human cost of Natural Disasters | 49
Table 4
Example of the results of geocoding two disasters in Angola:
codes of the affected administrative units (Level 1 or Level 2)
and coordinates of their centroids
Figure 37
Map of two georeferenced earthquakes (El Salvador 2001)
2000 2 Angola
Dombre-Grande (Baia 4214/4291 -13.1/-9.5 13.1/14.5
Farta-BenguelaProvince),
Massangano (Cambambe-
Kwanza Norte Province)
2001 146 Angola Namibie city, Macala, Lucira 408/398 4339/4337 -14.7/-14.6 12.4/13.1
(Namibie), Bibala, Camacuio 4338/4214 -14.0/-13.1 13.0/13.1
(Namibie province), Luacho, 4284/4287 -14.9/-15.0 13.6/14.5
Senje, Muhaningo, Seco, 4276/4261 -14.0/-17.0 14.5/16.6
Dombre-Grande, Canto (Baia 4262/4258 -16.7/-16.4 14.9/16.3
Farta-Benguela province), Lubango, -/- -8.9/-8.9 13.3/13.9
Quipongo, Caluquembe (Huila
province), Namacunde, Xangongo
(Ombadja), Onjiva (Cuanhama)
(Cunene province), Luanda city,
Bengo province
Country
name
LocationYear Seq Adm1 code Adm2 code Lat
centroid Long
centroid
■Earthquake of the 01/13/2001 (844 deaths)
■Earthquake of the 02/13/2001 (315 deaths)
■District not affected
Honduras
Guatemala
50 | The Human cost of Natural Disasters
Uses for georeferenced
EM-DAT data
The potential uses of georeferenced EM-DAT data are multiple.
When crossed with other georeferenced datasets, researchers
will be able to study the relative effectiveness of different
mitigation measures, or the relationship between certain events:
whether a famine is occurring in the same place as a drought,
for example. Mapping the footprint of a disaster will also help
to answer questions about the full extent of its impacts, and
the relationships between its extent and its consequences.
Spatial analyses also could highlight the different impacts of
natural disasters on urban and rural regions, for instance.
Then, by presenting data in the form of a map, georeferencing
can help identify information gaps more readily than, say,
tabulated figures. For instance, if all the surrounding districts
have recorded disaster impacts, but one district in the middle
has not, it is reasonable to assume that this is due to a lack
of communications from that one district, rather than a genuine
absence of impacts there.
Figure 38
Proportion of population affected to Population Potentially Exposed (PPE)
to four oods in France
■Countries
■1.3 % - April 2001
■0,6 % - December 2003
■0,2 % - November 2005
■0,1 % - July 2000
Belgium
France
Spain
Montpellier
Marseille
Lyon
Poitiers
Nantes
Brest Paris
English Channel
Atlantic Ocean
Mediterranean Sea
Lille
Strasbourg
Germany
Switzerland
Italy
Hitherto, impact modelling has carried a high level of
uncertainty and is often based on assumptions, rather than
hard data. Observed impacts are more reliable indicators of
the likely consequences of future events. Thus by incorporating
past EM-DAT records into this new recording system, geo-
referencing will help researchers to identify the most disaster-
prone locations and populations at highest risk. Georefe-
rencing can also be applied to economic damage, relating
impacts to GDP or other economic indices such as mean
income, for example, or household consumption. EM-DAT data
can therefore be used to localize areas where natural hazards
pose significant risks to livelihoods as well as to lives.
Georeferencing will also allow maps to be built up that show
disaster deaths (or the number of people affected) as a
proportion of the total Population Potentially Exposed (PPE).
PPE is the sum of all people situated in the disaster-affected
area. Figure 38, for example, maps the proportion of the
affected population to the PPE during four floods in France
between 2000 and 2005. Studies into the vulnerability of a
population could then be conducted by comparing the PPE to
actual deaths or numbers of people affected during past
events, while future vulnerability could be simulated by
merging population growth forecasts with georeferenced EM-
DAT data. Clearly, analyses based on PPE will be more
accurate than ones based on national population, since not
all of a country’s population is exposed to a disaster. With
the help of georeferencing, it will be possible to refine PPE,
going from macro- to micro-level analyses.
The Human cost of Natural Disasters | 51
Technical
Notes
Classication used for Figure 2
The classification was made with the Jenks natural breaks method. For more information: “Additional technical notes” on
www.emdat.be
EM-DAT classication of natural disasters
EM-DAT’s classification of natural disasters aligns with the IRDR’s “Peril Classification and Hazard Glossary” which in turn
is based on pre-existing work by Munich RE and CRED.
The IRDR’s classification is available online: http://www.irdrinternational.org/2014/03/28/irdr-peril-classication-and-
hazard-glossary/
The natural disaster category is divided into 6 sub-groups, which in turn cover 17 disaster types and more than 30 sub-
types. The EM-DAT classification is available on this page : http://www.emdat.be/new-classication
Chapter 1
Calculation of population affected and number of deaths related to the population
To calculate this proportion, only population data for the year and country where a value for number of affected/deaths
was available were taken into account. Thus if for certain years the number of people affected/deaths are nil or unknown,
population for this country is not taken into account.
The percentage calculated is equal to the sum of number of people affected/deaths for a year ‘j’ multiply by 100,000 for
the rate related to affected and by 1 million for the rate related to deaths, and divided by the population for the year ‘j’
for each country. The final rate for the country for the period 1994-2013 will be the average of the previous calculated rates.
Number of people aected per 100,000 inhabitants for i = average
Number of deaths per 1million inhabitants for i = average
X = population aected ( x > 0)
i = country
j = year (from 1994 to 2013)
GNI vs. GDP
In this report, GNI was used to differentiate the countries into the 4 different categories, from high to low-income countries.
The GDP was used to assess the burden of disaster on the development of the country while the economic losses were
related to it.
GDP gives information about the value produced within a country’s borders. Whereas the GNI reveals the value produced
by all the citizens within the country and overseas. In other words the GDP shows the strength of a country’s local income.
The GNI focusses on the economic strength of the citizens of a country.
Chapter 2
xij *100,000
Population ij
()
xij *1,0 00,000
Population ij
()
52 | The Human cost of Natural Disasters
Chapter 4
Calculation of economic losses related to GDP
To calculate this proportion, only GDP data for the year and country where a value for economic losses was available
were taken into account. Thus if for certain years the economic losses are nil or unknown, GDP for this country is not
taken into account.
The percentage calculated is equal to the sum of economic losses for a year ‘j’, multiply by 100, and divided by the GDP
for the same year ‘j’ for each country. The final percentage for the country for the period 1994-2013 is the average of the
previous calculated percentages.
Economic losses as % of GDP for i = average
X = economic losses ( x > 0)
i = country
j = year (from 1994 to 2013)
Chapter 5
List of small states
The small states in this report are 60 countries where the population is less than 1.5 million. 40 of those are SIDS. For
more information: “Additional technical notes” on www.emdat.be
xij *10 0
GDP ij
()
The Human cost of Natural Disasters | 53
Annexes
Annex A: List of country per income group (World Bank, 2014)
For this report, CRED has adopted the World Bank (http://data.worldbank.org/ ) revised classification of the world’s
economies based on estimates of GNI per capita for 2013:
•Low-income: $1,045 or less
•Lower-middle-income: $1,046 to $4,125
•Upper-middle-income: $4,126 to $12,745
•High-income: $12,746 or more
High income Upper Middle income Lower Middle income Low income
Australia Albania Armenia Afghanistan
Austria Algeria Bolivia Bangladesh
Belgium Angola Cameroon Benin
Canada Argentina Congo Rep. Burkina Faso
Canary Is. Azerbaijan Cote d’Ivoire Burundi
Chile Belarus Egypt Cambodia
Croatia Bosnia-Herzegovina El Salvador Central African Rep.
Czech Rep. Botswana Georgia Chad
Denmark Brazil Ghana Eritrea
Finland Bulgaria Guatemala Ethiopia
France China Peop. Rep. Honduras Gambia
Germany Colombia India Guinea
Greece Costa Rica Indonesia Guinea Bissau
Hong Kong (China) Cuba Kyrgyzstan Haiti
Ireland Dominican Rep. Lao Peop. Dem. Rep. Kenya
Israel Ecuador Lesotho Korea Dem. Peop. Rep.
Italy Gabon Mauritania Liberia
Japan Hungary Moldova Rep. Madagascar
Korea Rep. Iran Islam Rep. Mongolia Malawi
Kuwait Iraq Morocco Mali
Latvia Jamaica Nicaragua Mozambique
Lithuania Jordan Nigeria Myanmar
Netherlands Kazakhstan Pakistan Nepal
New Zeland Lebanon Palestine (West Bank) Niger
Norway Libyan Arab Jamah Papua New Guinea Rwanda
Oman Macedonia FRY Paraguay Sierra Leone
Poland Malaysia Philippines Somalia
Portugal Mexico Senegal Tajikistan
Puerto Rico Montenegro Rep. South Sudan Tanzania Uni. Rep.
Russia Namibia Sri Lanka Togo
Saudi Arabia Panama Sudan Uganda
Slovakia Peru Syrian Arab Rep. Zaire / Congo Dem. Rep.
Slovenia Romania Ukraine Zimbabwe
Spain Serbia Uzbekistan
Sweden South Africa Vietnam
Switzerland Taiwan (China) Yemen
United Kingdom Thailand Zambia
United States Tunisia
Uruguay Turkey
Turkmenistan
Venezuela
54 | The Human cost of Natural Disasters
Annex B: list of acronyms
CRED: Centre for Research on the Epidemiology of Disasters
DRR: Disaster Risk Reduction
EM-DAT: Emergency Events Database, the International Disaster Database
FAO: Food and Agriculture Organization of the United Nations
GAUL: Global Administrative Unit Layers
GDP: Gross Domestic Product
GNI: Gross National Income
HFA: Hyogo Framework for Action
IDMC: The International Displacement Monitoring Centre
IFRC: International Federation of Red Cross and Red Crescent Societies
IRDR: Integrated Research on the Disaster Risk
OFDA: Office of US Foreign Disaster Assistance
PPE: Population Potentially Exposed
SIDS: Small Island Developing States
UN: United Nations
UNISDR: United Nations International Strategy for Disaster Reduction
WHO: World Health Organization
The Human cost of Natural Disasters | 55
Acknowledgements
This report was made possible by the collaborative effort of many members of
the CRED team. Pascaline Wallemacq managed the production with Claudia
Heike Herden, with a unfailing sense of timing, discreet pushiness and
understanding of the issues. Tefera Delbiso, Philippe Hoyois, Regina Below
contributed material from their past work and ideas. Denis McClean and Sarah
Landelle helped refine the text and push the process through between their
many pressing tasks.
The text was rewritten and edited by Rowena House in record time for which
we are grateful.
Mardi did the layout and infographics with their usual flair.
None of this could be possible without the support of Université Catholique de
Louvain and the Institute of Health and Society (IRSS) who support the natural
disaster research programme for over 35 years.
•www.cred.be
•www.emdat.be
•www.cedat.be
•www.unisdr.org
Contact
CRED
•Mail:
Pascaline Wallemacq :
pascaline.wallemacq@uclouvain.be
Regina Below:
regina.below@uclouvain.be
•Phone:
+32 2 764 3327
•Postal Address:
School of Public Health
Université catholique de Louvain
Clos Chapelle-aux-Champs, Bte B1.30.15
1200 Brussels, BELGIUM
UNISDR
•Mail:
isdr@un.org
•Phone:
+41 229178907-8
•Postal Address:
9-11 Rue de Varembé
CH 1202, Geneva
SWITZERLAND
design : www.mardi.be
