Abstract
Risk assessment is an exercise in estimating the risks and potential losses caused by an
adverse event or combination of several adverse events. Losses can be tangible or
intangible, can also be characterized as being immediate/direct (during the event), and
short-term as well as long-term (indirect) losses. Some types of risks are relatively
straightforward to estimate, while others can only be estimated approximately. In addition,
data limitations must often be taken into account. A further complication, and an important
analytical step, is the presence of risk aversion and extremely large risks. It is important to
combine and compare risks from low probability, high consequence events with risks from
more frequent, but low to medium consequence events.
This report investigates these issues, based on a risk analysis chain perspective. It
concludes that different measures of risk as well as different modelling approaches are
needed for each of them. Furthermore, for the case of extreme risk, traditional risk
management strategies, successfully applied for frequent risks, are likely to fail. However,
while there are fundamental differences between frequent and extreme risks in various
dimensions, from a risk management point of view, they can be assessed together, given
the right measures and decision support approaches.
Regarding the tangible and intangible loss assessment component, it is argued that
from a methodological perspective, it is beneficial to distinguish between dependent risks,
i.e. risks which, if realized, are changing the likelihood of other risks, and independent
ones. Additionally, based on a reflexive modernity point of view, e.g. seeing the decision
making as an participatory rather than hierarchical process, different risk bearers exposed
to the same risks, but over different scales, have to be separated. This is important as one
cannot assume that the weights (or importance) of various dimensions stay constant over
different scales, especially when intangibles are also considered.
Finally, as an example, we select a country level top down approach and discuss
possible policy objectives, relevant dimensions and quantifiable indicators. Especially
within Europe, a need to include contingent liabilities within fiscal planning processes
appears to be necessary.
Keywords: Loss assessment, tangibles, intangibles, frequent risk, extreme risk, processbased approach, integrated methodology.
1
Acknowledgments
The research leading to these results has received funding from the European
Community’s Seventh Framework Programme [FP7/2007-2013] under grant agreement n°
265138.
2
Table of contents
1
Introduction ................................................................................................................. 6
2
Developing the Risk Analysis Chain ......................................................................... 7
3
Assessment of Frequent and Infrequent Risk ........................................................ 12
4
Assessing Short and Long Term Effects ................................................................ 15
5
Accounting for Intangible Losses............................................................................ 19
6
Frameworks for Assessing Multi-risks.................................................................... 23
7
8
6.1
Measuring across dimensions and governance scales ......................................... 23
6.2
Identifying and defining the decision problem ....................................................... 24
Example of Proposed Decision Support Approach: Multi-Criteria Analysis ....... 27
7.1
Human and Social capital ..................................................................................... 29
7.2
Natural Capital ...................................................................................................... 33
7.3
Financial and Economic capital ............................................................................ 34
7.4
Governance Objective .......................................................................................... 39
Summary and Conclusions ...................................................................................... 42
3
List of Figures
Fig. 1 Public vs. expert based perceptions of risks ............................................................. 8
Fig. 2 Catastrophe modelling appoach . ............................................................................ 10
Fig. 3 Random sampling from standard normal distribution. .............................................. 12
Fig. 4 The layering approach for risk reduction and risk financing. .................................... 13
Fig. 5 Natural disaster risk and categories of potential disaster impacts. .......................... 15
Fig. 6 Long-term impacts due to repeated events ............................................................. 17
Fig. 7 Planning for disaster risks. ...................................................................................... 18
Fig. 8 Components of Total Economic Value ..................................................................... 20
Fig. 9 Economic Valuation Methods. ................................................................................. 21
Fig. 10 Approach for multi-risk assessment of tangible and intangible losses ................... 25
Fig. 11 Fatalities per event according to country income group ......................................... 30
Fig. 12 Cross-country sample of financing modalities ....................................................... 33
Fig. 13 Direct losses for country groups in absolute terms and with respect to GDP ........ 35
Fig. 14 Observed GDP in Honduras with events vs. projected growth without events ...... 36
Fig. 15 Loss of income after drought and flood events in Uttar Pradesh ......................... 37
Fig. 16 Share of Japan's import trade by port 1994–1997. ............................................... 37
Fig. 17 Risk management index (RMI).............................................................................. 41
4
List of Tables
Table 1 Types of risk............................................................................................................ 7
Table 2 Overview of disaster risk management policy options .......................................... 11
Table 3 Categories and characteristics of disaster impacts ............................................... 15
Table 4 Generally used approaches to measure intellectual capital ................................ 20
Table 5 Assessment of tangible/intangible losses for coastal hazards ............................. 25
Table 6 Methods for assessing intangible direct/indirect losses ........................................ 26
Table 7 MCA framework for multiscale and multirisk assessment ..................................... 28
Table 8 Overview of criteria and indicators related to the human/social objective ............. 29
Table 9 Overview of criteria and indicators related to the environmental objective ............ 33
Table 10 Overview of criteria and indicators related to the financial/economic objective ... 34
Table 11 Government liabilities and disaster risk. .............................................................. 38
Table 12 Overview of criteria and indicators related to the governance objective ............. 40
5
1 INTRODUCTION
Risk assessment is essentially an exercise in estimating the risk and potential losses
caused by a hazard (single risk) or combination of several hazards (multi-risk). The losses
can be tangible (e.g., loss of property or destruction of infrastructure) or intangible (e.g.,
deterioration of the “quality of life” in a community, region or country following a disaster
event or loss of reputation of the owner of an industrial complex affected by an adverse
event). The losses can also be characterized as immediate/direct (during the event), and
short-term as well as long-term (indirect) (e.g., economic consequence of the disruption of
a motorway). Some types of risks are relatively straightforward to estimate, while others
can only be estimated approximately. In addition, data limitations must often be taken into
account. A further complication, and an important analytical step, is the presence of risk
aversion to average and extremely large risks (e.g., a non-linear utility function in decision
theory). It is important to combine and compare risks from low-probability, highconsequence events with risks from more frequent, but low- to medium-consequence
events.
The aim of this task and deliverable (within the MATRIX project, WP 5.3) while focusing
mostly on extreme event risks (disasters, technological events and other large scale
disruptions), is to discuss and develop a consistent framework and approach for the
estimation of tangible and intangible losses, both for the short-term and the long-term, as
well as for low-probability, high-consequence events in addition to more frequent, lowconsequence events. Models for consistent assessment of single risk, i.e., to determine
the hazard and consequences of a particular hazard occurring in a particular geographical
area during a given period of time (WP2), multi-hazards, i.e., to determine the whole risk
from several hazards (WP3) and multi-vulnerabilities, i.e., physical or socio-economic
vulnerabilities (WP4) will be developed in other work packages. The main task in work
package 5 is to provide the framework and methodology for using the multi-hazard, multivulnerability models to assess the risks (e.g., expected losses). Here, we discuss key
analytical steps for assessing multi-risks, and develop an empirically-based multi-risk and
multi-criteria framework that provides decision support for assessing policy options within
the context of large scale multi-risks.
We start in section 2 by discussing key ingredients for assessing and modelling risks from
a risk analysis chain perspective. In addition, we show that different types of
measurements (risk values) have to be used to characterize the different sorts of risk
being considered. In section 3, we discuss why different risk instruments need to be used
for the different types of risk. Section 4 discusses the different short- and long-term effects
if risk realizes, i.e., a specific disaster event occur. In section 5 the assessment of
intangibles is introduced and put into a process based approach within section 6. Section 7
gives an example for the assessment of the economic efficiency of policy options based on
a top down (country level) approach and section 8 ends with a summary and discussion.
6
2 DEVELOPING THE RISK ANALYSIS CHAIN
There are different rationales behind why one should differentiate between frequent (high
probability/low consequence) and infrequent (low probability/high consequence) risks1/
natural disastrous events. The “Risk Analysis Chain” (Pflug and Römisch, 2007) which
consists of the three M’s (measuring, modelling, and managing risk) is the starting basis
for our discussion. Therefore, the topics that are directly associated with this and
discussed in this report in some detail are:
 Risk measurement, including risk perception.
 Risk modelling, including risk preference.
 Risk management.
Risk typology
Risk is often used to convey information about the adverse outcome of measures. The ISO
Standard 31010 defines risk as the effect of uncertainty on objectives. In a more narrow
and rigorous sense, risk can be distinguished from uncertainty. Here, risk can be
considered as a quantified uncertainty, i.e., both the consequences and their probabilities
are known and quantified. Yet, there is greater complexity involved (see for example
Rescher 1983; National Research Council, 1997; Oberkampf et al. 2004; Lindell, Michael
and Perry, 2004); still very prominent in the literature is the work by Knight (1921) who
suggested the following fourfold distinction to distinguish complete uncertainty
(probabilities and consequences not quantifiable), from subjective risks (probabilities
known, but consequences largely subject to discussion), objective risk (probabilities and
consequences quantifiable), and certainty (probability equals 1), where we are sure of the
outcomes (Table 1).
Table 1Types of risk (based on Knight, 1921)
Type
Explanation
Complete uncertainty Consequences uncertain,
(ignorance)
probabilities uncertain
Subjective risk
Consequences quantifiable, but
subject to discussion, probabilities
quantifiable
Objective risk
Consequences quantifiable,
probabilities quantifiable
Certainty
Probability is 1 (100%)
Example
Discovery of a new, renewable
energy source with huge potential
Nuclear accidents
Natural disasters, Health risks
Laws of mechanics
Of the many uncertainties to be considered, key ones relevant for this discussion are
epistemic (e.g., uncertainties due to a lack of data), model (e.g., uncertainties about how
accurately the model reflects the true system), parametric (uncertainties regarding the
values of the parameters within the model) and aleatoric (inherent randomness). While the
first three types are relevant for both mitigation and adaptation assessments, the aleatoric
(chance) type is specific to extreme events. A standard concept for the representation of
natural disaster risk is the loss exceedance curve, which indicates the probability of loss
caused by an event exceeding a certain loss level. The inverse of this probability, the
recurrence or return period, is a standard concept allowing the calculation of events and
their consequences in a probabilistic manner. It is clear that sometimes the uncertainties
associated with a given event are too great to be of help to the given event decision
1
From now on referred to as frequent and extreme risks.
7
making process, more so for the case of extreme risks due to the scarcity of the data.
However, they still can be seen as being important for assessing their relative importance
in general terms (Handmer, 2003).
Perception of Risk
Different people perceive risk differently (Slovic, 2000). It can be differentiated between
subjective vs. expert/objective risk perception. Particularly when dealing with risk, public
and expert opinion may often diverge owing to, on the one hand, the complexities of
understanding risk, and on the other, to variations in risk perceptions (such as individuals
generally being risk averse, while the public sector normally is considered risk neutral,
Arrow and Lind, 1970). As shown in Figure 1, people’s perceptions (and related “public
outrage”) may not overlap with expert-based assessments (“actual hazards”). As decisions
of individuals are linked to their perception of hazards and risks, it seems necessary to
accept those more subjective risk perceptions, although they may be based on limited
information and sometimes be in opposition to expert opinions.
Fig. 1 Judgment of perceived risk (frequency of deaths per year) for experts (top) and
laypeople (bottom) compared to annual fatality rates (Slovic, Fischhoff and Lichtenstein,
2000).
This is even more problematic for extreme risks, since they are either ignored or soon
forgotten (Slovic, 2008). Kunreuther (2009) calls this problem the “natural disaster
syndrome” and discusses methods to circumvent this problem.
8
Risk preference and aversion
Not everybody has to be risk averse (i.e. a decision maker who dislikes risk) and there is a
large literature dealing with how to measure/model risk aversion for different decision
makers. For example, governments may be risk neutral in their investment decisions for all
kinds of risks, except extremes, as they have the ability to efficiently spread and pool risk
over the population (see Mechler, 2004; Hochrainer and Pflug, 2009). Usually, frequent,
low-consequence financial risks are adapted to by autonomous adaptation or via simple
after-the-fact-instruments, e.g., taking credits or using part of some savings, and therefore
one is not risk averse against such events. The way in which particular entities decide their
risk management strategies is often a function of their perception of the risk they are
exposed to (Loefstedt and Frewer, 1998; Slovic 2000). Especially for low probability
events, people are often not worried about the consequences. Kunreuther (1996, 2009)
called the effect of limited interest in protection (e.g., insurance or structural mitigation
measures) prior to a disaster and the resulting high costs to insurers and federal
governments following a catastrophic event as the Natural Disaster Syndrome. Risk
aversion is usually modelled with utility functions, however, several new approaches, such
as “prospect theory” (Kahneman and Tvarsky, 1970) and “experimental economics” try to
analyse risk behaviour in a different manner.
Measuring and Modelling Risks
The objective in measuring risk is to assign values to the risk. There is a plethora of
possible risk measures in the literature (see for a comprehensive list Das, 2008). Standard
statistical risk parameters include location parameters such as the mean, the median,
quantiles or linear combinations of it as well as dispersion parameters, most prominent of
which are the variance and standard deviation, but also the mean absolute deviation, Gini
measure, lower and upper semi variance, as well as Value at Risk (VaR), to name but a
few. Location parameters and the mentioned dispersion parameters are very useful for
measuring frequent risks. However, while such measures are appropriate for determining
what happens on average, they cannot answer the question as to how wrong something
could be if an extreme event occurs. As an example, VaR has become a standard risk
measure in many industries. Unfortunately, existing methods to calculate VaR assume
normality of the data, an assumption which is often strongly violated. One way out of this
problem is to use appropriate risk measures for the tail, such as the Conditional Value at
Risk, Expected Shortfall and other models and risk measures (see for a discussion McNeil,
Frey and Embrechts, 2005). Hence, different risk measures must be used, depending
upon the case at hand.
Risk modelling is about how to arrive at a risk estimate. While for frequent events,
methods and models to assess risk are well established in the respective fields (see Das,
2006; Pflug and und Römisch, 2007), extreme risk modelling is still an emerging field
(Woo, 1999, 2011). The standard approach for estimating natural disaster risk and
potential impacts is to understand natural disaster risk as a function of hazard, exposure
and vulnerability (see e.g., UNISDR, 2005). Hazard analysis involves determining the type
of hazards affecting a certain area with a specific intensity and recurrency. Assessing
exposure involves analyzing the relevant elements (population, assets) exposed to
relevant hazards in a given area. Vulnerability is a multidimensional concept
encompassing a large number of factors that can be grouped into physical, economical,
social and environmental factors (see Hochrainer, 2006; Wolf, 2011). For example, in
catastrophe modelling, usually only physical vulnerability is looked at (Figure 2).
9
Fig. 2 Catastrophe modelling approach (Grossi, Kunreuther and Windeler, 2005).
In a catastrophe model, the hazard module characterizes the hazard in a probabilistic
manner. Often, the full suite of events which can impact the exposure at risk is described –
by magnitude and associated annual probability, among other characteristics. The
exposure module or inventory describes individual single structures or a collection of
structures that may be damaged. The physical vulnerability module estimates the damage
to the exposed elements at risk, given the magnitude of the hazard. Physical vulnerability
is typically characterized as a mean estimate of damage (average percentage of houses
destroyed given a magnitude) and associated uncertainty given a hazard level. Finally, a
financial loss module estimates losses to the various stakeholders who must manage the
risk (see for a general discussion Kozlowski and Mathewson 1997, a detailed application
for flash floods can be found in Muir-Wood et al. 2005, or more recently Hsu et al., 2011
and Woo, 2011).
Managing Risk
Policy options to ultimately reduce the various impacts of extreme events and to manage
risk must be related to assessing risk, reducing risk (prevention and preparedness),
preparing for impacts, spreading risk over a larger basis (risk transfer), and finally,
responding to an event and the subsequent reconstruction and rehabilitation (Table 2).
While ex post efforts are important and today still dominate policy, there is a need for risk
management to enhance deliberate ex ante efforts. In light of climate change adding a
longer term focus, we will focus on the ex ante options, risk assessment, risk prevention,
preparedness and risk financing undertaken by government and international institutions.
While prevention and preparedness options reduce the losses, insurance and other risk
financing instruments lessen the variability of losses by spreading and pooling risks.2 By
providing indemnification in exchange for a premium payment, insured victims benefit from
the contributions of the many others who are not affected, and thus in the case of a
disaster, they receive a contribution greater than their premium payment. However, over
the long run, insured persons or governments can expect to pay significantly more than
their expected losses. This is due to the costs of insurance transactions and the capital
reserved by (re-) insurance companies for potential losses, as well as the financial return
required for absorbing the risks (Froot 2001). But, when the real opportunity costs (such as
those associated with business or personal post disaster bankruptcy) are considered, in
many instances, transferring risk still pays off. Response and reconstruction/rehabilitation
efforts on the other hand do not focus on risk (and probability), but on reacting to impacts.
However, taking into account indirect impacts, the response strategy might have an
2
Insurance and other risk financing mechanisms are based on the Law of large numbers, which states that with an
increasing number of observations, the probability distribution can be estimated more precisely and the variance around
the mean decreases.
10
important effect on the overall consequences for an economy and therefore risk, and has
to be included within an integrated risk management decision making process.
Table 2 Overview of disaster risk management policy options
Type
Ex ante risk management
Risk
assessment
Effect
Assessing risk
Key
options
Hazard
assessment
(frequency,
magnitude and
location)
Vulnerability
assessment
(population and
assets
exposed)
Risk
assessment as
a function of
hazard,
exposure and
vulnerability
Hazard
monitoring and
forecasting
(GIS, mapping,
and scenario
building)
Ex post disaster management
Prevention
Preparedness
Risk financing
Response
Reconstruction and
rehabilitation
Reduces risk
addressing
underlying
factors
Physical and
structural risk
reduction
works (e.g.
irrigation,
embankments
)
Reduces risk
in the onset of
an event
Transfers risk
(reduces variability
and longer term
consequences)
Risk transfer (by
means of (re-)
insurance) for public
infra-structure and
private assets,
microinsurance
Responding to an
event
Rebuilding and
rehabilitating during
the post event period
Humanitarian
assistance
Rehabilitation/
reconstruction of
damaged
infrastructure
Land-use
planning and
building codes
Contingency
planning,
networks for
emergency
response
Networks of
emergency
responders
(local/national)
Alternative risk
transfer
Clean-up,
temporary repairs
and restoration of
services
Revitalization for
affected sectors
(exports, tourism,
agriculture, etc.)
National and local
reserve funds
Damage
assessment
Macroeconomic and
budget management
(stabilization,
protection of social
expenditures)
Shelter
facilities and
evacuation
plans
Calamity Funds
(national or local
level)
Mobilization of
recovery
resources (public/
multilateral/insura
nce)
Incorporation of
disaster mitigation
components in
reconstruction
activities
Economic
incentives for
proactive risk
management
Education,
training and
awareness
raising about
risks and
prevention
Early warning
systems,
communication
systems
After introducing the key ingredients of the risk chain, we will now examine whether and
how to adapt it to accommodate (i) frequent and extreme risks; (ii) short and long term risk
and (iii) tangible and intangible risks.
11
3 ASSESSMENT OF FREQUENT AND INFREQUENT RISK
We suggest that low probability/high consequence events must be treated in a different
methodological manner than frequent/low consequence events. In fact, low probability
events need a theory of their own, namely extreme value theory (EVT) (Embrechts,
Klüppelberg, and Mikosch, 2002). One reason for this is that standard estimation
techniques only serve well where there is greatest density of data, however, these
methods can be severely biased in estimating tail behavior (Coles, 2001). Furthermore,
most data is (naturally) concentrated toward the center of the distribution (Figure 3) and
so, by definition, extreme data are scarce and therefore any estimation associated with
large uncertainties which sometimes (dependent up on the data and information at hand)
makes it difficult to come to “objective risk estimates” in the sense of Knight (1921)
discussed above.
Fig. 3 Random sampling from a standard normal distribution.
Most importantly, the fundamental question raised for extreme risks is how to model the
rare phenomena that lie outside the range of any available observation. The methods used
in EVT dealing with this issue are quite different from the usual assessment of frequent
risks (see for a comprehensive discussion Embrechts et al., 1997; Reiss and Thomas,
2006). For example, much real world data follows a normal distribution (or similar ones)
and the estimation of distributional parameters can be done based on such assumptions
(which are discussed in any statistical textbook). However, for extremes, the tails are much
fatter than classical distributions predict. Fisher and Tippett (1928) have shown (based on
an asymptotic argument) that the distribution of the maxima (if not degenerate) can only be
one of three types (Gumbel, Weibull or Frechet). Afterwards, a rigorous foundation on EVT
was presented by Gnedenko in 1943. Gumbel (and Jenkinson) in the 1950’s studied and
formalized statistical applications. The classic limit laws were generalized in the 1970’s by
Pickands. Here, the classical modelling approaches for estimating the extreme value
parameters are the “Block Maxima” and the “Peaks over Threshold” methods (Embrechts
et al. 1997). In recent times, point process approaches have been more often used (Reiss
and Thomas, 2007). Therefore, from the assessment side, frequent and extreme risks
(probability of events and consequences) must be assessed with different methods
12
because fundamentally different approaches are required (see Malervergne and Sornette,
2005).
Risk Management of Frequent vs. Extreme Risks
Options to deal with frequent and extreme risks differ greatly. Strategies at the household
level can be separated into informal arrangements (on the household level or groupbased) as well as formal ones (market based or publicly provided) (see Skoufias 2003 for
a set of possible coping strategies). Governments also can intervene in the private sector
in case of disastrous events (see Blomquist et al. 2002) and can also be seen as risk
bearers (Hochrainer, 2006)
However, when it comes to assessing different options, a risk-based analysis is of key
importance in order to answer questions such as: How much should be invested in the
prevention of disaster losses? How much in risk financing including insurance? And where
are limits in terms of taking action? These are complex questions, which ultimately
depends on the wider costs and benefits of both types of activities, on their interaction
(financial instruments, through incentives, influence prevention activities) as well as
acceptability. Cost and benefits, in turn, depend on the nature of the hazard and losses
(e.g., the occurrence probability and exposure). One way to think about prevention and
insurance is illustrated by the layering approach shown in Figure 4 (see also Mechler et al.
2009). For the low- to medium loss events that happen relatively frequently, prevention is
likely to be more cost effective (in the sense of having a cost-benefit ratio higher than 1,
see selected case studies in Hochrainer et al, 2011) in reducing burdens (MMC, 2005)
than insurance. The reason is that usually (not always) the costs of risk reduction often
increase disproportionately with the severity of the consequences (Rescher, 1983).
Moreover, individuals and governments are generally better able to finance lower
consequence events (disasters) from their own means, for instance, savings, taking
credits, or via calamity reserve funds.
Fig. 4 The layering approach for risk reduction and risk financing (Mechler et al., 2008)
The opposite is generally the case for costly risk-financing instruments, including
insurance, catastrophe bonds and contingent credit arrangements. Catastrophe insurance
13
premiums fluctuate widely and are often substantially higher than the pure risk premium
(average expected loss), mainly because the insurer’s cost of back-up capital is reflected
in the premium. For this reason, it may be advisable to use those instruments mainly for
lower probability hazards that have debilitating consequences (catastrophes). Finally, as
shown in the uppermost layer of Figure 4, most individuals and governments find it too
costly to insure against very extreme risks occurring less frequently than, say, every 500
years. This can be seen as residual risk which cannot be controlled for.
Quantitative (optimization) approaches use different objective functions and restrictions
when considering extreme and frequent risks within the decision making process. For
example, one may want to decrease the average risk as much as possible (via the
objective function), but also want to make sure that extreme risks (measured via CVaR)
are below some given level. It can also be the other way round, i.e., to decrease extreme
risk as much as possible, while keeping the average risk below a predetermined level.
Mimizing both types of risk is also an alternative. However, the necessary computational
requirements and the complexity of the problem increase greatly and the later alternative
is therefore very difficult to use in real world settings (Pflug and Römisch, 2007).
In summary, while there are fundamental differences between the estimation of frequent
and extreme risks (classical estimation procedures versus extreme value statistics) as well
as feasible risk management strategies (mitigation options versus risk financing strategies
for extremes, such as re-insurance), they can still be assessed together, given the right
risk measures (averages versus tail measures) and decision approaches are used.
14
4 ASSESSING SHORT AND LONG TERM EFFECTS
We focus now on assessing natural disaster risk. Disaster risk is commonly defined as the
probability of potential impacts affecting people, assets or the environment (see Mechler
2004; Matrix deliverable D3.2: “Dictionary of the terminology adopted”). If risk becomes
manifest in an event, they may cause a variety of consequences that are commonly
classified into social, economic, environmental, and political categories (other
classifications are also possible, for a critical literature review see Przyluski and Hallegatte,
2011). They may also be classified according to whether they are triggered directly by the
event or occur over time as indirect or macroeconomic effects (GTZ, 2004; UNISDR, 2009;
Matrix deliverable D3.2). Figure 5 describes a possible first-order separation of these
effects.
Fig. 5 Natural disaster risk and categories of potential disaster impacts (Mechler, 2004).
As stated, a list of impacts can be structured around three broad categories: social,
economic and environmental. However, the effects may also be considered as being of a
direct or indirect nature, and whether they can be indicated in monetary or non-monetary
terms (Table 3).3 The discussion on intangible effects is treated in more detail in a
separate chapter.
Table 3 Categories and characteristics of disaster impacts (based on Mechler, 2004)
Categories of impacts
Characteristics
Direct
Due to direct contact with disaster, immediate effect
Indirect
Occur as a result of the direct impacts, medium-long term
effect
Monetary
Impacts that have a market value and can be measured in
monetary terms
Non-monetary
Non-market impacts, such as health impacts
3
Disasters could also exert pressures on the political-institutional setup in affected countries, such as was experienced
following the massive 2010 earthquake in Haiti, when the ruling government basically stopped functioning due to the
utter devastation and ensuing chaos. Yet, these effects are probably limited to extremely large-scale events, and
cannot realistically be planned for.
15
Social consequences may affect individuals or have a general bearing at the societal
level. The most relevant direct effects are



The loss of life.
People injured and affected.
Damage to cultural and heritage sites (in addition to the monetary loss).
The main indirect social effects would include






Increase in inequities, such as levels of relative poverty.
Increase in stress symptoms or increased incidence of depression.
Disruption in school attendance.
Disruptions to the social fabric.
Disruption of living environments.
Loss of social contacts and relationships.
Environmental impacts generally fall into two categories: impacts on the environment as
a provider of assets that can be used (use values), e.g., water for consumption or irrigation
purposes, soil for agricultural production. These impacts are or should be taken into
account when validating economic impacts. The second category relates to the
environment as creating non-use or amenity values. Effects on biodiversity and natural
habitats fall into this category where there is not a direct, measurable benefit, but ethical or
other reasons exist for protecting these assets and services.
Economic impacts are usually grouped into three categories: direct, indirect, and
macroeconomic effects (ECLAC, 2003). These effects fall into stock and flow effects:
direct economic damages are mostly the immediate damages or destruction to assets or
“stocks,” due to the event per se. A smaller portion of these losses results from the loss of
already produced goods. These damages can result from the disaster itself, or from
consequential physical events, such as fires in the aftermath of an earthquake. Effects can
be divided into those related to the private, public and economic sectors. In the private
sector, this includes the loss of and damage to houses, apartments and building contents
(for example, furniture, and computers). In the public sector, damage to education facilities
such as schools, health facilities (hospitals) and so-called lifeline infrastructure such as
transport (roads, bridges) and irrigation, drinking water and sewage installations as well as
electricity potentially cause serious consequences. In the economic sectors, this also
includes damage to buildings, but most important is the loss of machinery and other
productive capital. Another category of direct damages are the extra outlays of the public
sector in terms of emergency spending for relief and recovery.
Direct stock damages have indirect impacts on the “flow” of goods and services. Indirect
economic effects occur as a consequence of physical destruction affecting households
and firms. The most important indirect economic impacts are




Diminished production/service due to the interruption of economic activity.
Increased prices due to the interruption of economic activity leading to a reduction in
household income.
Increased costs as a consequence of destroyed roads, e.g., due to detours for
distributing goods or going to work.
Loss or reduction of wages due to business interruption.
16
Assessing the macroeconomic impacts involves taking a different perspective and
estimating the aggregate impacts on economic variables like gross domestic product
(GDP), consumption and inflation due to the effects of disasters, including the reallocation
of government resources to relief and reconstruction efforts. As the macroeconomic effects
reflect indirect effects as well as the relief and restoration efforts, these effects cannot
simply be added to the direct and indirect effects without causing duplication (ECLAC,
2003). It should be kept in mind that the social and environmental consequences also
have economic repercussions. The reverse is also true, since the loss of business and
livelihoods can affect human health and well-being.
While long-term effects are most likely for more extreme events, it should be noted that
long-term effects may result from repeated smaller loss events (frequent risks), as
illustrated in Figure 6.
Fig. 6 Long-term impacts due to repeated events (Hochrainer, 2006)
While the first event in Figure 6 caused higher losses than the second one, the second
event caused more drastic long term consequences than the first one. Hence, as shown in
Figure 7, plans for disaster risk management must be reviewed on a regular basis within
an integrated manner, to be sustainable in the long run.
17
Fig. 7 Planning for disaster risks (Bettencourt et al., 2006).
We will see in the next chapter that a similar approach is used for the assessment and
management of both tangible and intangible risks. The discussion above will then be
embedded into the general approach.
18
5 ACCOUNTING FOR INTANGIBLE LOSSES
The purpose of assessing losses is to provide end users with information that is useful for
their decision making. Hence, any losses - tangible and intangible - that will affect the risk
bearers’ current and future performance should be assessed and monitored on a
continuous basis. Intangibles have been analyzed extensively in the business literature,
most often within the economics of innovation, but also recently in risk analysis (see the
FP 7 project ConHaz, http://conhaz.org/) . Comprehensive literature reviews for accounting
for intangibles for businesses can be found in Cohen and Levine (1989) and Canibano et
al. (2000) who criticized the lack of unifying frameworks. That situation is now different, as
nowadays, numerous frameworks can be found in the literature on how one could assess
tangibles as well as intangibles at various scales, both at the macro and micro levels.
Creating new frameworks seems less of an issue now than coming up with a standardized
version and production of practical guidelines (Ricardis, 2006). This is less the case in
treating intangibles within risk-based methodologies.
Literature Review on the Assessment of Intangibles
Most frameworks dealing with the assessment of intangibles can be found within the
private sector. Frameworks and models that connect different measures of intangibles at
the company level include the ‘Balanced Scorecard, the Danish Intellectual Capital
Statement, the Skandia Intellectual Capital Navigator, the Intellectual Assets Monitor, the
PriceWaterhouseCoopers Value Reporting, the KPMG Value Explorer, Value Dynamics
and the Value Creation Index, to name but a few (Jarboe, 2007) . One of the reasons for
the different frameworks proposed is the fact that different intangibles have to be treated
differently, depending on the circumstances. Furthermore, there is also the unit-of-analysis
issue (see the review given by Jarboe, 2007). The most recent report on methods for
measuring intangibles is by Sveiby (2010), which lists the methods for measuring
intangibles in a chronological order, and more importantly for MATRIX, Markantonis and
Meyer (2011) who focus on natural hazards. In particular, the assessment of intellectual
capital as an important intangible asset has gained importance (Dumay, 2008). For
example, the approaches used to measure intellectual capital can be grouped into the
categories listed in Table 4.
19
Table 4 Generally used approaches to measure intellectual capital (Dumay, 2008)
Approach
Description
Direct Intellectual
Estimate the $-value of intangible assets by identifying
Capital methods (DIC) their various components. Once these components are
identified, they can be directly evaluated, either
individually or as an aggregated coefficient.
Market Capitalization
methods (MCM)
Return on Assets
methods (ROA)
Scorecard methods
(SC)
Visualisation
methods (VIS)
Calculate the difference between a company’s market
capitalization and its stockholders’ equity as the value of its
intellectual capital or intangible assets.
Average pre-tax earnings of a company for a period of time are
divided by the average tangible assets of the company. The
result is a company ROA that is then compared with its industry
average. The difference is multiplied by the company's average
tangible assets to calculate an average annual earning from the
intangibles. Dividing the above average earnings by the
company’s average cost of capital or an interest rate, one can
derive an estimate of the value of its intangible assets or
intellectual capital.
The various components of intangible assets or intellectual
capital are identified and indicators and indices are generated
and reported in scorecards or as graphs. SC methods are
similar to DIC methods, except that no estimate is made of the
$-value of the intangible assets. A composite index may or may
not be produced.
Provide visual representations of the way that intangibles and /
or IC interact to create value so that informed interventions into
IC development can be made.
As indicated above, the mentioned approaches can be separated into direct and indirect
valuation approaches and this is now done similarly within the natural hazards community.
Markantonis and Meyer (2011) recently did a literature review and evaluation of cost
assessment methods for intangible effects due to natural hazards based on welfare and
environmental economics (including different hazards, see the ConHaz project,
http://conhaz.org/). The concept of Total Economic Value (Pearce and Turner 1990)
differentiates between use values as well as non-use values (see Figure 8).
Fig. 8 Components of Total Economic Value (based on Pearce and Turner, 1990).
Not all of the use and non-use values can be assessed with the same approach. In
principle, one can distinguish between direct (stated preferences) and indirect (revealed
20
preferences) methods. While the later estimates the value from actual market behavior,
the former creates hypothetical or contingent markets to analyze choices. Furthermore,
both can be seen as behavioral methods while there also exist non-behavioral methods
which are used, for example, to estimate the value of life or life satisfaction. Figure 9
shows the methods and subsequent assessment possibilities.
Fig. 9 Economic Valuation Methods (Pearce and Turner, 1990; Dassanayake et al., 2010;
Markantonis and Meyer, 2011).
Naturally, each of the valuation methods has its advantages as well as disadvantages.
Again, Markantonis and Meyer (2011) compared the various methods with regard to their
applicability for natural hazards intangible cost assessment, using criteria such as scope,
spatial scale, time scale, data availability, required effort, precision, skills required, ability
to deal with the dynamics of risk and implementation (Appendix 1 shows a summary of the
comparison they made).
Some recent examples of the economic assessment of intangible effects includes
- Loss in “work productivity”, which is possible to be measured in much detail. In a
study (Pato, 2011) about the intangible costs due to epilepsy, it was found that the
greatest percentage of costs is due to work productivity loss by the persons
involved. The mean direct cost per persons was around 1055.2 Euro, while the
mean indirect financial costs came up to 1528.8 Euro per person.
- Estimating the value of the loss of life is a very controversial topic, but in principle
also possible to be defined or measured (Hochrainer-Stigler et al., 2011). For
example, the value of (statistical) life would be for India between 200,000$ and
7,000,000$ per life.
- Frequency versus fatality (F-N) curves are also possible to be used for developing
societal acceptability and tolerability levels.
21
- Costs of major trauma can also be seen as intangible, but measured in monetary
terms. A study by Haeusler et al. (2006) found that such costs are usually
underestimated and the direct medical costs makes up only a small part of the total
costs.
- An analysis by McDaniels and Trousdale (2005) looked at possibilities of including
non-market value losses, such as adverse impacts on land resources, and
proposed a multi-attribute value assessment that includes deeply held, complex,
intangible values.
- A paper by Luechinger and Raschky (2009) tried to monetarize life satisfaction and
the loss of it due to flood disasters.
- Zhai and Ikeda (2006) estimated the economic value of evacuation by using a
contingent valuation approach.
- Wegner and Pascual (2011) performed a cost benefit analysis within the context of
ecosystem services for human well being.
- Choi et al. (2010) evaluated the economic value of cultural heritage sites by
performing a choice modelling technique.
The mentioned studies already indicate that there is currently a great deal of effort going
into tackling the issue of intangibles, including the incorporation of intangibles within a risk
management analysis.
The assessment of intangibles on the business level is not new and has been measured
for a long time, while for natural hazards it is only recently been established more formally
in terms of possible assessment methods. Generally speaking, intangibles are difficult to
assess and are also generally very expensive to measure. Furthermore, considerable
uncertainty within the results can be expected, sometimes making the use of intangibles’
estimates very difficult/impossible. This also indicates that no one method will be able to
fulfill all purposes for the decision making process, and approaches must be selected
dependent upon the goals and audience.
22
6 FRAMEWORKS FOR ASSESSING MULTI-RISKS
Based on the previous chapters, we now suggest elements leading up to a process-based
framework for assessing multi-risks.
6.1 Measuring across dimensions and governance scales
We assume at the beginning that, given the right definitions, everything can be measured
if it can be observed. The term measurement is used here in a very broad sense, so that
everything is said to be a measurement if it tells the decision maker more than they knew
before (see Hubbard, 2010). The following clarification chain outlines this concept (implicit
in every definition dealing with uncertain decisions):
(i) If it matters to a risk bearer, it is observable.
(ii) If it is observable, it can be described as an value.
(iii) If the value can be defined as a range of possible amounts, it can be
measured (Hubbard, 2010).
It is easy to see that some measurements will not be done, simply because the additional
information cannot outweigh the costs. Furthermore, some measurements have so much
uncertainty in it that their usefulness within the decision making process is very limited
(however, these more indicative results can still provide valuable information, see for
example Pate-Cornell, 1996). Here, we define the different classification criterias that
could be used, while the multi-criteria approach adopted will be discussed in the next
section.
To account for risk scales, one can differentiate between different risk bearers:
- Local level, e.g., Individuals, households, businesses, municipalities.
- Regional level, e.g., Districts.
- National level, e.g., Government.
- Supranational level, e.g., European Union, World Bank.
The classification of impacts can be done in multiple ways, as discussed above, and we
list them separately in the following order:
- Tangible: market costs.
- Intangible: non-market costs, e.g., cultural heritage, health, psychological
consequences.
- Direct: Due to direct contact with disaster, immediate effect.
- Indirect: Occur as a result of the direct impacts, medium to long term effects.
- Monetary: Impacts that have a market value or can be reasonably measured in
monetary terms.
- Non-monetary: Non-market impacts.
For some systems studied, possible intangible losses have to be first defined, which could
be based on some pre-studies or definitions. For example, for the classification of
intangibles within ecosystem services (e.g., provision of products obtained from
ecosystems or non material benefits obtained from them), one could use the Millennium
Ecosystem Assessment (2005) report in the initial stage. Some additional dimensions
could come into play afterwards. It should be noted that dependent on the hazard and the
risk bearer, different dimensions may play an important role, e.g., for droughts, flow
indicators (e.g., crop failure due to lack of precipitation) may serve to better represent the
23
risk while for floods, stock indicators (e.g., houses/assets destroyed) may be more
appropriate (see the various work packages of the ConHaz project for more information,
http://conhaz.org/).
6.2
Identifying and defining the decision problem
The existence of different levels and scales at which a system can be analyzed implies the
unavoidable existence of non-equivalent descriptions of it (Giampietro, 2003). In addition,
the fact that there exists multiple social values at different dimensions and scales (Munda,
2006) shows that there cannot be one overall approach that works for all tangible and
intangible loss assessments at all stages (especially in the decision making process with
multiple actors, see for example Arrow’s impossibility theorem, Arrow, 1951). However, the
selection and quantification of the most important additional information has to be done at
each stage or process. Therefore, we will use the “applied information economics”
framework (see Hubbard, 2010) to set up the next stage. This consists of five steps:
(1) Define a decision problem and the relevant uncertainties.
(2) Determine what is the current knowledge.
(3) Compute the value of the additional information needed (what to measure and how
to measure it) for the decision problem to be analyzed and the corresponding costs.
(4) Apply the measurement most relevant to the problem (i.e., has a high information
value) under the given budget constraint.
(5) Make a decision and apply it. Monitoring: Return to step 1 and repeat.
We will discuss these steps now in more detail. First, the decision problem and the
relevant uncertainties, both for intangible and tangible losses, have to be defined. Already
here, different perspectives for different risk bearers can be expected and should therefore
be incorporated at this step. The second step is very important, as usually there are
already some methods and approaches in the literature for such or similar problems. In
step 3 the value of additional information should be assessed, how much would it cost to
evaluate and how much more information would one obtain and be used within the
decision making process. If different dimensions and measurements are detected, one
should choose the ones that would give the most efficient (e.g., given the most)
information to the decision problem under given budget constraints. Given these steps, the
last action would be to make the decision. Afterwards, monitoring is needed to update
risks and eventually alter the risk management decisions (see Figure 7).
Now, tangible and intangible loss assessment could be done just as explained above (see
the ConHaz project which compiled and assessed the methods discussed in chapter 5 for
different natural hazards), but the question remains as to how such an assessment could
be achieved under a multi-risk setting and if there are any differences.
As a first step, it seems beneficial, from a methodological point of view, to distinguish
between dependent risks and independent risks. If the multi-risks are independent then the
total tangible and intangible losses could be simply summed up (e.g., taking the sum of
average losses or convoluting the corresponding single loss distributions). However, if the
risks are dependent or if they can be related either due to escalation (multiplier effects) of
both losses separately or due to the combination of losses, such additional effects have to
be taken into account. Equally important, different risk bearers exposed to the same risks,
but on different scales have to be separated, as they eventually weight some (use and
non-use) dimensions differently. Accordingly, we suggest a process-based approach to the
quantification of tangible and intangible losses within the context of multi-risk bearers at
different scales, as well as multi-risk situations. The approach is separated into three
phases (Figure 10).
24
Fig. 10 Approach for multi-risk assessment of tangible and intangible losses
First Phase: Single risk assessment of tangible and intangible losses;
At the very beginning of the assessment, the (i) relevant risk bearer (all levels that are
important, i.e., who would be affected if the risk realized, left hand side of Figure 10) and
(ii) all single risks for all possible dimensions (First Phase column in Figure 10) should be
determined based on the classification criteria given above. For example, for each risk, a
table such as that shown in Table 5 could be produced.
Table 5 Assessment of tangible/intangible losses for coastal hazards (based and
expanded from Lequeux and Ciavola, 2011)
Dimensions
Tangible Direct Tangible
Intangible direct Intangible
losses
Indirect losses
losses
indirect losses
Human Capital
Loss of life,
Trauma effects.
Health effects
Social Capital
Decrease
in Loss of social
security
cohesion
Financial and Physical
Loss
of
Increased
economic
damage
to industrial
vulnerability of
capital
assets
production
survivors
Natural Capital Loss of crops
Temporary loss Long-term
of ecosystem environmental
services
degradation
25
Afterwards, (iii) we suggest the supposed dimensions and tangible/intangible (direct and
indirect) losses are combined with established methods to measure them, qualitatively or
quantitatively. For example, for human capital, it could look like Table 6 below:
Table 6 Methods for assessing intangible direct/indirect losses
Quantitative
assessment Qualitative assessment
methods
methods
Human Capital
Working hours lost
Tangible Direct losses
Human Capital
Reduction
in
work
Tangible indirect losses
productivity
Human Capital
Loss of life:
Loss of life:
Intangible direct losses
Statistical value of life Threshold methods
approach
Human Capital
Reduction in life satisfaction Life satisfaction
Intangible indirect direct
Trauma: medical help
Furthermore, (iv) how much it would cost to apply these methods needs to be evaluated,
as well as what the uncertainty would be (using the “applied information economics”
framework for example). Additionally, (v) the possible interplay of the capital dimensions
have to be determined, including multiplier effects from lower to upper scales (e.g., local to
regional levels) as well as time-dependency.
Second Phase: Multi-risk assessment of tangible and intangible losses;
In the second phase, the additional losses (to avoid double counting) in a multi-risk setting
have to be determined (Second Phase column in Figure 10). If the risks are independent,
then these losses would be zero. However, if they are dependent, then the additional
multiplier effects (of the losses, not the dimensions) given the multi-risk situation must be
determined (including cascading effects, see the recent tragic example in Japan). Again,
this could be performed and structured around the tables already given above, e.g., losses
for all possible dimensions, selection of possible methods to assess them, evaluation of
the costs and remaining uncertainty. The interplay of the dimensions will again need to be
considered and time-dependencies incorporated.
Third Phase: Combining the single and multi-risks.
Given the importance of the different risks analyzed, an ordering of the importance of the
tangible and intangible losses should be performed. This will depend on the risk bearer’s
risk aversion over different scales as different (also social) values can be placed on each
dimension (see the discussion in chapter 2). Either bottom-up or top-down approaches can
be used here. In the simplest setting, the risk bearers do not have to interact because we
may assume that they are not affected by the decision made at each level. However, this
will most likely not be the case, and interaction assessment among the different levels
must be performed in an iterative process, starting with phase one, i.e., refining the
decision problem sketched in Figure 10.
26
7 EXAMPLE
ANALYSIS
OF
PROPOSED DECISION SUPPORT APPROACH: MULTI-CRITERIA
In this section, we now look at risk bearers on the national level and present a multi-criteria
analysis based on the discussion given in the previous chapters. One of the key inputs
often demanded by policy advisors at this level regarding disaster risk management (DRM)
and climate adaptation (CA) (both of which have a high priority in the European Union)
concerns the economic efficiency of policy options. Despite potentially high economic
returns (MMC, 2005; Mechler, 2005; Benson et al. 2007), disaster mitigation is sorely
under-funded. In the US, several studies show that only about 10% of earthquake- and
flood-prone households have adopted loss-reduction measures (Kunreuther, 2006).
Kunreuther attributes this mainly to myopia, which appears hard to influence with public
policies. Even with extensive public awareness campaigns such as in earthquake-prone
California, there has been little change in risk perception. Policy makers, faced with
myopic voters, also appear reluctant to allocate public resources to reducing disaster risks.
In the absence of concrete information on net economic and social benefits and faced
with limited budgetary resources, many policy makers have been reluctant to commit
significant funds for risk reduction, although happy to continue pumping considerable
funds into high profile, post-disaster response (Benson and Twigg, 2004).
This is especially the case for development and donor organizations. According to some
estimates, bilateral and multilateral donors currently allocate 98% of their disaster
management funds for relief and reconstruction and only 2% for pro-active disaster risk
management (Mechler, 2005). Accordingly, while additional information on the economic
case for risk reduction and adaptation to extremes may help in leveraging additional funds,
a more comprehensive approach to defining impact categories and policy objectives
seems desirable.
Additionally, there are fundamental challenges when calculating the costs and benefits of
options, such as lack of data, large uncertainties around any estimates, as well as the
difficulties in pricing the priceless, i.e., intangibles (see also Gowdy, 2005). Accordingly,
some authors (e.g., Moench et al., 2009) have suggested that for DRM and CA, methods
for assessing economic efficiency may be most usefully employed as heuristic tools and in
a context of iterative stakeholder decision-making processes (as suggested in Figure 10).
A limited number of studies have used alternative tools such as multi-criteria analysis
(MCA), which seems well-suited to supporting the stakeholder-based adaptation decisionmaking (FLOODsite, 2007; Kienberger et al. 2009; Meyer et al. 2009; Cardona et al.,
2003; Hochrainer and Mechler, 2009). We adopt an MCA framework by identifying
objectives, criteria and indicators (this is not an exhaustive list, other indicators are also
possible, see for example, Scolobig, Castn Broto and Zabala, 2008; or Meyer, Scheur and
Haase, 2009) leading to a multi-criteria system for assessing disasters risk management
policies (Table 7).4
4
Some of this work described here is the outcome of work done for the UNEP project Multi-Criteria Analysis for
Climate Change (MCA4C) project.
27
Table 7 MCA framework for multiscale and multirisk assessment.
Domain of
impacts
Objective
Type of impacts
Indicators
Human capital
Fatalities
Improved health
Reduced direct disaster
health risk and impact
Preserve cultural
heritage
Decreased indirect healthrelated risk and impact
Reduced impact on cultural
heritage
Protect biodiversity
Biodiversity impacts reduced
Number of affected
Reduced incidence of disease
outbreak post-disaster
Reduced losses of cultural assets
Governance
capital
Social capital
Financial and economic capital
Natural capital
Species mortality
Support ecosystem
services
Improved economic
performance
Reduced impact on
environment as provider of
useful services
Wetlands impact
Water supply for consumption or
irrigation purposes
Soil for agricultural production
Mangrove forests
Reduction in direct risks
Asset losses
Reduction in indirect,
economic risks
Generate
employment
Employment generated pre
and post-disaster
Contribute towards
fiscal sustainability
Stabilization and reduction of
post disaster fiscal
expenditure
Reduce poverty
Reduce inequity
Improve governance
Reduced additional
incidence of post disaster
poverty
Microeconomic losses (lost turnover
and profits)
Macroeconomic losses (GDP,
sectoral value added)
Direct employment generated during
relief and reconstruction phase
Avoided employment losses with
disaster prevention
Financial vulnerability indices
Resource gap indicator
Reduction in additional number of
people under national poverty lines
post disaster
Reduced number of people without
access to basic services: health,
energy, education, transport
Reduced incidence of
systemic pre disaster
poverty
Disaster relief and
reconstruction spent
according to loss burdens
Reduction in the number of exposed
people in poverty
Effectiveness of DRM
related policies
Index of DRM effectiveness
Balance between private and public
sector loss burden
We focus on national governments as clients of this information and the role these may
play in fulfilling the overall goal of the project, identified as helping to enable national
governments to carry out a multi-criteria analysis of disaster risk management policy
(including dynamics such as climate change) in their own countries, thereby supporting the
28
drafting of sound strategies plans, whilst also contributing towards achieving national
development goals.
In the following we list different criteria for objectives related to the economy, social
systems, environment and governance related to disaster risk management on the country
level from a top-down perspective. We call these Level 2 criteria (criteria 1 would be the
risk bearer) according to the dimension of risk based on capital framework approaches
often used in multi-dimensional risk settings (see for example the sustainable livelihood
approaches from DFID, 1999). The level 3 and subsequent lower-order criteria examine
the different tangible and intangible dimensions in increasing detail, e.g., while level 3
criteria would be improved economic performance, the level 4 criteria separates this into
the reduction in direct risk as well as the reduction in indirect risk, including indicators to
measure them.
7.1 Human and Social capital
For the human and social objective, we suggest focusing on the reduction of social
impacts such as loss of life, disaster-induced poverty and equity (see Table 8).
Table 8 Overview of criteria and indicators related to the human/social capital
Level 2
HUMAN/SOCIAL
criterion
Level 3
Human health
Poverty reduced
Employment Reduced
criteria
outcomes improved
generated
inequities
Level 4
criteria
Reduced
disaster
health
risks
Indicators Fatalities
Number of
affected
Reduced
incidence
of
disaster
related
disease
Reduced
additional
incidence
of post
disaster
poverty
Reduction
in
additional
number of
people
below
poverty
lines post
disaster
Reduced
number of
people
without
access to
basic
services
Reduced
incidence
of
systemic
pre
disaster
poverty
Stabilized
employment
postdisaster
Disaster relief
and
reconstruction
spent
according to
loss burdens
Balance
between
private and
public sector
loss burden
In the following, we explain the different criterias and indicators used in more detail. To
avoid confusion, each of the level 3 criteria suggested above will have its own heading and
we subsequently discuss lower level criteria and its related indicators afterwards (boxes
will summarize indicators and data availability).
29
LEVEL 3 CRITERION: HUMAN HEALTH OUTCOMES IMPROVED
Two level 4 criteria were selected here: Reduced disaster health risks and reduced
incidence of disaster related disease. We start with reduced disaster health risks.
Level 4 criterion: Reduced disaster health risks
Indicator 1: Number of fatalities reduced
Indicator 2: Number of affected reduced
Data sources: EMDAT, Munich Re and other databases
Resolution: Country level
Models: Priest et al, 2007; Jonkman and Vrijling, 2008; Thouret and Martelli 2010
Comment: The number of fatalities is a hard indicator, the number of affected a rather
soft, as there are various definitions.
Disasters first and foremost kill people and destroy houses and livelihoods, rendering the
affected population homeless and vulnerable. Data for these impact categories are
regularly reported on a country basis, for example in the EMDAT (CRED, 2011) or Munich
Re databases. As one example, Figure 11, for a sample of large natural disasters over the
period 1980-2004, shows that fatalities per event were higher by orders of magnitude in
low- and middle-income countries compared with high-income countries. Within each
country, comparisons over time may also be made, while similar statistics are maintained
for the number of affected.
Fig. 11 Fatalities per event according to country income groups. Data source: Munich Re,
2005.
Fatalities per event
Fatalities/event
250
200
150
100
50
0
Low income Middle income High income
Per capita income country groups
Another criterion may relate to the outbreak of disease post disasters, such as
communicable (Diarrhoea, Hepatitis A and E) and vector-borne (Malaria) types in the
aftermath of heavy rain. Yet, as Watson et al. (2007) show, the relationship between
natural disasters and disease outbreak is often misunderstood. The risk is less directly
related to the general chaos following disasters and epidemics caused by “dead bodies”,
but rather it can be linked to population displacement and its implications on the availability
of safe drinking water, sanitation facilities, crowded shelters, the underlying health status
of the population, as well as available healthcare services.
30
Level 4 criterion and indicator: Reduced incidence of disease outbreak postdisaster
Data sources: Health statistics
Resolution: Country level
Models: Fraser et al. 2004; Rega et al. 2010; Woolhouse, 2011;
Comment: Needs to be compared to a counterfactual situation (health incidence in normal
times), for which often data exist.
LEVEL 3 CRITERION: POVERTY REDUCED
Disasters may increase existing in-equities by increasing the number of those in poverty or
malnourished, as demonstrated by a limited number of surveys, e.g. surveys conducted in
Honduras and Ethiopia (see Carter et al., 2007). Poverty and disasters are interlinked as
discussed above in the economic section. Two approaches may help with reducing
poverty. One is to target post-disaster poverty and provide relief support to those most in
need (e.g., by providing employment through public works), which may also be done via
relief works providing temporary employment. The other route is to approach poverty as
affected by systemic risk, locking people into low levels of asset accumulation and income
(poverty traps). One interesting line of suggestions has focused on developing novel risk
financing measures, such as index-based microinsurance, where the index is tied to a
hazard rather than the loss, and often combined with loan provision. The idea here is to
transfer systemic risk (such as to farmers due to droughts) out of the affected region or
country, and thus allow agents to engage in higher profit activities.
Level 4 criterion: Reduced post-disaster poverty
Indicator 1: Reduction in the incidence of post-disaster livelihood poverty in disaster
affected regions
Indicator 2: Reduced loss of access to basic services: health, energy, education,
transport
Data sources: National statistics, surveys, modelling studies
Models: Carter et al. 2006; Bowles et al. 2006; Hochrainer et al. 2009;
Comment: need to compare to a counterfactual situation (poverty incidence in normal
times); data and surveys often not available.
Overall, few data exist, and ex-ante and ex-post surveys would generally be necessary to
gauge the poverty impact. Alternatively, the effects of disasters on poverty may be
modelled based on making key assumptions on poverty reduction policies.
Level 4 criterion: Reduced systemic pre disaster poverty
Indicator: Reduction in the incidence of pre-disaster livelihood poverty in disaster exposed
regions
Data sources: Surveys, modelling studies (e.g., Van et al., 2010)
Comment: Modelling and empirical studies necessary to tease out the disaster effect,
which is very difficult.
LEVEL 3 CRITERION: EMPLOYMENT GENERATED
Stabilizing post-disaster employment has generally been recognized as a key route for
providing income to those in gravest need. Public works programmes focusing on the
31
reconstruction of infrastructure and public buildings are often used to rebuild essential
infrastructure and other assets while providing a basic income to the poor.
Level 4 Criterion and indicator: Stabilized employment post-disaster during relief
and reconstruction phases, e.g., using public works programmes
Data sources: National statistics, surveys, (see Alderman, 2008; Ninno et al. 2009)
Comment: Data often available by way of public works programmes.
LEVEL 3 CRITERION: INEQUITIES REDUCED
Not all losses can be reduced and markets may not efficiently function to spread losses,
and losses often end up with households and governments. In OECD countries, social
solidarity is often invoked, and private households and business get compensated to a
large extent by taxpayer, public money. In non-OECD, due to scarce government funds,
this is not the case, and the international community must help, yet more public support is
clearly necessary. A better balance between private and public sector loss burdens seems
desirable in many instances.
Level 4 criterion: Disaster relief and reconstruction spent according to loss burdens
Indicator: Shares of losses absorbed by households business, governments, donors and
international institutions
Data sources: National statistics, surveys, assessments (e.g., Linnerooth-Bayer et al.
2005)
Comment: As for indicator on private investment, data more difficult to get, Insurance
data are normally proprietary, but increasingly statistics are kept, particularly by donors
and international institutions.
For coping with the residual impacts, there are many funding modalities helping to share
and finance disaster losses, such as private and public (tax revenue) savings, insurance,
as well as international assistance. As a cross-country sample of major disasters shows
(Figure 12), in addition to insurance markets, governments as “insurers of last resort” have
an important role in supporting infrastructure reconstruction and relief support for
households and businesses. For example, looking at large events in Europe, government
spending as a share of direct losses has ranged from 11% (drought in Portugal) to 48%
(flooding in Poland), whereas insurance has ranged from virtually zero to 21% 5 (Mechler et
al., 2010, see also Schwarze et al. 2011).
5
Data on private sector spending are not available, and thus are lumped together with the net loss.
32
Fig. 12 Cross-country sample of financing modalities of disaster losses by insurance,
government assistance, and the private sector and net loss (as a percentage of direct
losses).
Private sector and net loss
Government
Insurance
100%
90%
80%
44%
48%
58%
70%
62%
79%
60%
50%
40%
32%
48%
30%
17%
41%
20%
11%
20%
21%
Austria Flooding
2002
Spain drought 2005
10%
1%
8%
Umbria EQ 1997
Poland Floods 1997
0%
10%
Portugal drought
2005
Better balancing the sharing of loss across sectors and agents (also considering the risks
they have been exposed to) is one means to a more equitable distribution of disaster
impacts.
7.2 Natural Capital
As discussed, environmental impacts and indicators may be broken down into those
pertaining to use values (water supply) and non use (provision of amenity) values (see
Table 9). Generally, as mentioned above, the data situation pertaining to environmental
impact indicators relevant to disaster risk is difficult.
Table 9 Overview of criteria and indicators related to the natural capital
Level 2 criterion
ENVIRONMENT
Level 3 criteria
Preserved ecosystem
components
Preserved ecosystem habitat
Level 4 criteria
Reduced impact on environment
as provider of useful services
Biodiversity impacts reduced
Indicators
Water supply for consumption or
irrigation purposes
Soil for agricultural production
Animal mortality
Wetlands impact
33
LEVEL 3 CRITERION: PRESERVING ECOSYSTEM COMPONENTS
Level 4 criterion: Reduced impact on environment as provider of useful services
Indicator 1: Water supply post-disaster for consumption or irrigation purposes sufficiently
available and in good quality (drinking water in m3)
Indicator 2: Avoided soil erosion (in m3)
Data sources: Case by case basis, modelling studies
Spatial resolution: Case by case
Comment: Water supply and soil erosion difficult to measure, few monitoring systems
exist.
LEVEL 3 CRITERION: PRESERVATION OF ECOSYSTEM HABITAT
Level 4 criterion: Biodiversity impacts reduced
Indicator 1: Animal mortality
Indicator 2: Wetlands impact
Data sources: Case by case basis, modelling studies
Resolution: Case by case
Comment: Very little is known on disasters impacting biodiversity and wetlands
Indicators may be the mortality of animals when hit by flooding or windstorms, and
damage to wetlands. For example, coastal wetlands may be affected by storm surgeinduced salt-water intrusion, creating severe hydrological impacts and changes, which
ultimately may bear upon the species living in these habitats.
7.3 Financial and Economic capital
Three Level 3 criteria are suggested here: improved economic performance, contribution
towards fiscal sustainability, and triggering private investments (Table 10).
Table 10 Overview of criteria and indicators related to the financial/economic capital
Level 2
FINANCIAL/ECONOMIC
criterion
Level 3
Improved
Contribute
Trigger private
criteria
economic
towards fiscal
investments
performance
sustainability
Level 4
criteria
Reduction in
direct risks
Reduction in
indirect,
economic risks
Indicators
Asset losses
Microeconomic
losses (lost
turnover and
profits)
Macroeconomic
losses (GDP,
sectoral value
added)
Stabilization and
reduction of post
disaster fiscal
expenditure
Better
involvement of
private sector in
risk sharing
Balance between
private and public
sector loss burden
34
We will now discuss these objectives in more detail.
LEVEL 3 CRITERION: IMPROVED ECONOMIC PERFORMANCE
Improved economic performance here is interpreted as avoided direct (asset) and indirect
(flow) impacts triggered by disaster (level 4 criterion). The reduction in direct losses is one
possible and standard criterion, against which efforts to manage and reduce risks can be
judged.
Level 4 criterion: Reduction in direct risks
Indicator: Observed and modelled losses of public and private assets: machinery,
building, infrastructure (in LCU, as percent of capital stock or GDP)
Data sources: EMDAT, MuRe, Swiss Re databases,
Models: open source modelling efforts (Global earthquake model), UN disaster
assessment missions, case by case basis, modelling studies (Mechler, 2004; Altay et al.
2005.
Resolution: mostly country level
To illustrate this, Figure 13 shows, broken down according to country income groups, total
disaster losses to be substantially higher in richer countries with a higher accumulation of
assets. Yet, as a percentage of GDP those impacts are substantially larger in the lower
income country group indicating those assets are more vulnerable to hazards.
Fig. 13 Direct losses for country groups in absolute terms and when expressed in terms of
compare to GDP (MunichNatCatService, 2005)
Direct losses as % GDP
Direct losses in billion 2005 USD
0.8%
900
0.7%
800
0.6%
700
0.5%
600
Losses/GDP
Billion USD
1,000
500
400
300
200
0.4%
0.3%
0.2%
0.1%
100
0
0.0%
Low income
Middle income
High income
Per capita income country groups
Low income
Middle income
High income
Per capita income country groups
Compared to the direct, mostly asset-based losses, economic impacts and risks frame the
impacts of disaster in a welfare context and better inform policy makers about the real
opportunity costs imposed by disasters.
35
Level 4 criterion: Reduction of indirect economic losses/risks
Indicator 1: Microeconomic losses: lost turnover and profits (in LCU or percent valued
added)
Indicator 2: Macroeconomic losses: GDP, sectoral value added, change in GDP (in LCU
or percent valued added) (see also for a comprehensive discussion Przyluski and
Hallegatte, 2011)
Data sources: UN disaster assessment missions, case by case basis, modelling studies
(Freeman et al. 2002; Hochrainer, 2006, Hallegate 2008)
Resolution: Country and subnational level
Comment: The flow impacts are more relevant to estimate the economic burden and
opportunity costs associated with disasters, yet there is less information available publicly;
a number of efforts are ongoing to estimate economic risks in open source initiatives
As one example of macroeconomic impact following a disaster is the case of Honduras,
heavily hit by Hurricane Mitch at the end of 1998, may be illuminating. In order to identify
the net macroeconomic effects of disasters and thus the net benefits of measures
implemented to stabilize the economy, the counterfactual ex-post (GDP business as usual
without a disaster) is compared to the observed state of the system ex-post (actual GDP).
The analysis requires projecting economic development into a future without an event,
done here using a model-based approach (ECLAC, 2003) and a statistical approach (own
calculations), presented in Figure 14.
Fig. 14 Observed GDP in Honduras with events vs. projected growth without events.
Source: Zapata, 2008; own calculations.
GDP in Honduras
Million constant 2000 USD
8,000
7,500
7,000
6,500
6,000
Projected w/o event-ECLAC
Projected w/o event-IIASA
Observed
5,500
2004
2003
2002
2001
2000
1999
1998
1997
1996
5,000
Figure 14 shows GDP growth becoming negative in the year after Hurricane Mitch, then
later rebounding. However, overall the net effect would be negative, where for example, in
2004, about 6 years after the event, this gap can be considered to have, ceteris paribus,
amounted to about 6% of potential GDP given statistical estimates based on pre-disaster
GDP, and up to 8.6% based on the Zapata (2008) projections.
As one example of microeconomic impacts, disasters can consume a substantial portion of
a households income. For example, in a recent survey, subsistence farmers in the state of
Uttar Pradesh in Northeastern India, heavily exposed to drought and flood and living near
subsistence levels, report substantial livelihood losses to the extent of more than 50% of
36
their annual income (see Figure 15), again demonstrating how poorest of a region suffer
proportionally greater from the impacts of disasters.
Fig. 15 Loss in percentage of annual household income after drought and flood events in
Uttar Pradesh for (i) all households and (ii) households living below the poverty line
(Hochrainer et al., 2009).
In a world of global competition, disasters may also affect the competitiveness of
industries. Yet, there is relatively little evidence for this in the literature, as it is not
straightforward to isolate the impact of disasters from other underlying effects. For
example, Chang (2000) shows that the 1995 Great Hanshin earthquake affecting Kobe
and its port, essentially led to a shut-down of the port for two years. As a result, the port
suffered a major reduction in competitiveness, dropping from a ranking of 6th worldwide
among container ports to rank 17 by 1997. Figure 16 presents the (on a monthly basis)
share of imports of five major Japanese before and after the earthquake in 1995, showing
how Kobe lost a large market share after the event and failed to recover, leading
eventually to a 30% drop in its market share in Japan.
Fig. 16 Share of Japan's import trade by port 1994–1997.
One key factor had been foreign transhipment cargo, which during the post-disaster period
moved to other Asian ports; yet, there are other non-disaster related drivers, such as the
rise of air cargo transport and the fact that other regional ports had been gaining volume
already before the disaster.
37
Yet, data are often not readily at hand, and it is generally difficult to single out the micro
and aggregate costs imposed by a disaster. One complication also concerns accounting
methodologies: disasters destroy assets, which are not measured in national accounting;
however, rebuilding the asset base and revitalizing the economy positively shows up in
national accounts.
LEVEL 3 CRITERION: CONTRIBUTE TOWARDS FISCAL SUSTAINABILITY
Level 4 criterion and indicator: Stabilization and reduction of fiscal expenditure
post disaster (in LCU or percent GDP)
Source: Fiscal accounts, IMF databases, modelling studies
Resolution: country level
Comment: Disaster losses and impacts often end as hidden deficits in government
budgets, and thus represent true opportunity costs when identified properly.
Disasters may exert significant costs to national governments due to the role they assume
in dealing with disaster losses and risks. Generally, governments assume responsibility for
replacing damaged infrastructure, providing relief post-event and ensuring rapid recovery
of the economy overall. The associated planning problem in this subset of the economics
of disaster risk management and adaptation is one of contingency liability planning with
fiscal disaster risk emanating from explicit and implicit contingent public sector liabilities as
classified in Table 11. The explicit liability consists of rebuilding damaged or lost
infrastructure, which is due to the public sector’s allocative role in providing public goods.
Implicit liabilities are related to the commitment of providing relief due to the distributive
function in reallocating wealth and providing support to the needy.
Table 11 Government liabilities and disaster risk. Source: Modified after Schick and
Polackova Brixi, 2004
Explicit
Government liability
recognized by law or
contract
Foreign and domestic sovereign
borrowing, Expenditures by
budget law and budget
expenditures
Contingent: obligation if a particular
event occurs
State guarantees for nonsovereign
borrowing and public and private
sector entities, reconstruction of
public infrastructure
Implicit
A "moral" obligation
of the government
Future recurrent costs of public
investment projects, pension and
health care expenditure
Default of subnational government and
public or private entities, disaster
relief
Liabilities
Direct: obligation in any event
There are two problems to be noted: One is that it is standard practice for (central)
governments to plan and take appropriate measures for direct liabilities, but little is
generally done to systematically tackle contingent liabilities. Also, in reality, governments
recognize their normative roles to varying degrees due to an implicit or explicit assumption
of risk neutrality, i.e., the ability to pool and share disaster losses after they have occurred.
In the case of an event, governments of developing countries typically finance their postdisaster expenses by diverting from their budgets or from already disbursed development
loans, as well as by relying on new loans and donations from the international community
(Mechler, 2004). In the past, these post-disaster sources of finance have often proven
woefully inadequate to assure timely relief and reconstruction in developing countries.
What more, post-disaster assistance is not only often inadequate, but it can discourage
governments and individuals from taking advantage of the high returns of preventive
actions (Gurenko, 2004).
38
Yet, over the last few years, some countries such as Mexico, Colombia and the majority of
the Caribbean states have begun to account for contingent liabilities in their budgets. An
interesting case in point is Mexico, which started budgeting for disaster emergency
expenditure in the aftermath of the 1985 earthquake. After experiencing high volatility of
budgeted and spent funds, Mexico started a sort of self insurance mechanism via a
reserve fund, then later formally insured their post-disaster expenditure in the reinsurance
and capital markets for hurricane and earthquake risk.
In Europe for example, the EU Solidarity Fund was established in 2002 “ to show practical
solidarity with Member States and candidate countries by granting exceptional financial aid
if these were the victims of disasters of such unusual proportions [...] that their own
capacity to face up to them reaches to their limits” (Commission Report, 2004, p. 25). The
limits are determined by a disaster loss threshold that triggers eligibility for assistance from
the Fund, which can be called upon to cover non-insurable damages, such as public
expenses for restoring public infrastructure, providing services for relief and clean up, and
protecting cultural heritage. However, the EUSF seems underfunded and similar
transactions may also be necessary in the future (Hochrainer et al. 2010).
LEVEL 3 CRITERION: TRIGGERING PRIVATE INVESTMENTS
There is some discussion concerning getting the private sector involved DRM and CA, yet
little concrete evidence on whether possibilities and incentives exists. One concrete area is
risk financing, which is at the heart of the insurance and reinsurance industry, and a better
balance between losses shared out between the private and public sectors would indicate
a better involvement of the private sector. For adaptation to extremes, the question has to
be framed around identifying a proper balance between efficiency (have the exposed pay
for insurance premiums or the losses/impacts in line with the risk they accepted by living in
an exposed area) and equity (have the public sector absorb fat-tailed events that
overburden people’s awareness and ability to pay).
Level 4 criterion: Better involvement of private sector in risk sharing
Indicator: Better balance between private and public sector loss burden (in LCU or
percent GDP)
Source: Loss data, insurance data
Resolution: Different levels
Comment: Balance to be struck between efficiency: having exposed agents absorb the
risks themselves involving the private sector such as insurance, and equity- public sector
assistance for those that cannot easily absorb risks and losses. Insurance data are
normally proprietary.
7.4 Governance Objective
Related to Figure 7, of the three suggested governance criterias in Table 12, namely policy
effectiveness, political security, and maintaining and improving energy security, policy
effectiveness seems to be the most relevant within the extreme risk context. Energy
security with reference to disasters can be grouped under reducing losses and economic
impacts.
39
Table 12 Overview of criteria and indicators related to the governance objective
Level 2 criterion
GOVERNANCE
Level 3 criteria
Improved policy
effectiveness
Level 4 criteria
Effectiveness of
DRM related
policies
Index of DRM
effectiveness
Indicators
Maintain or
improve
political
security
na
Maintain or
improve energy
security
na
Na
Na
LEVEL 3 CRITERION: IMPROVED POLICY EFFECTIVENESS
Level 4 criterion: Effectiveness of institutions dealing with DRM
Indicator: Effectiveness of risk management as measured by an index
Data sources: Anecdotal evidence, index by Cardona et al. 2003
Resolution: Country level
Comment: A number of things are aggregated and it is hard to tell which elements are
driving the index involving a certain level of subjectivity. Index is monitored over time.
Cardona et al. (2003) developed a risk management index for Latin America, which
measures the degree of preparedness of key institutions dealing with DRM in the four
areas of risk assessment identification, risk reduction, post-disaster management, and risk
governance and financial protection, based on surveys and the study of relevant reports.
Effectiveness implies that the analyzed institutions have appropriate abilities to manage
and organize DRM activities. Based on the study of policy documents and their own
calculations, Cardona et al. (2003) calculated the index for countries in Latin America and
the Caribbean (Dominican Republic, Ecuador, Argentina, El Salvador, Guatemala, Peru,
Colombia, Mexico, Jamaica, Costa Rica and Chile) over the time period 1985 to 2000 in
five time steps (Figure 17).
40
Fig. 17 Calculating the risk management index (RMI) for a number of Latin American and
the Caribbean countries. (Cardona et al., 2003)
According to this index, in many countries, the institutional capacity to manage risk and
disaster impacts has increased.
41
8 SUMMARY AND CONCLUSIONS
This report investigated, based on the risk analysis chain perspective, e.g., measurement,
modelling, and managing risk, the issue of how frequent/low impact risks differ from low
probability/high impact ones. It was concluded that different measures of risk as well as
different modelling approaches are needed for each of them. Furthermore, in the case of
extreme risk, traditional risk management strategies, successfully applied for frequent
risks, are likely to fail. However, while there are fundamental differences between frequent
and extreme risks in various dimensions, from a risk management point of view, they can
be assessed together, given the right measures and decision approaches are used.
In a next step the question how to account for intangibles was addressed. Based on the
differences between frequent and infrequent risk and current work within other EU
projects, namely ConHaz, different dimensions and categorizations of tangibles and
intangibles were given and embedded within a process based approach. It was argued,
that from a methodological perspective, it is beneficial do distinguish between dependent
risks, i.e., risks that, if realized, are changing the likelihood of other risks, and independent
ones. Furthermore, possible cascading events have to be incorporated and timedependencies looked at. Additionally, especially from a reflexive modernity point of view,
different risk bearers exposed to the same risks but on different scales are separated. This
is important as one cannot assume that the weights (or importance) of various dimensions
stay constant over different scales, e.g., from the household to the regional level
(especially when intangibles are considered ).
Finally, as an example we selected the country level and discussed possible policy
objectives, relevant dimensions and quantifiable indicators. Especially within Europe, a
need to include contingent liabilities within fiscal planning processes seems to be
necessary.
42
References
Alderman, H. (2008). Managing Risk to Increase Efficiency andReduce Poverty.
Background paper for World Development Report 2008, Washington DC.
Arrow, K.J. (1951). Social Choice and Individual Values. Yale University Press.
Arrow, K.J. and Lind, R.C. (1970). Uncertainty and the Evaluation of Public Investment
Decisions’, The American Economic Review 60: 364-78.
Benson, C. and Twigg, J. (2004). Measuring mitigation: Methodologies for Assessing
Natural Hazard Risks and the Net benefits of Mitigation - A Scoping Study.
International Federation of the Red Cross and Red Crescent Societies, ProVention
Consortium, Geneva.
Benson, C., Twigg, J., with Rossetto, T. (2007), Tools for Mainstreaming Disaster Risk
Reduction: Guidance Notes for Development Organizations, ProVention Consortium,
Geneva.
Bettencourt, S., Croad, R., Freeman, P., Hay, J., Jones, R., King, P., Lal, P., Mearns, A.,
Miller, G., Pswarayi- Riddihough, I., Simpson, A., Teuatabo, N., Trotz, U., Van Aalst, M.
(2006). Not if but when – Adapting to natural hazards in the Pacific Islands Region: A
policy note. East Asia and Pacific Region, Pacific Islands Country Management Unit;
The World Bank, Washington D.C.
Blomquist, J., J. P. Cordoba, M. Verhoeven, P. Moser and C. Bouillon (2002). Social
Safety Nets in Response to Crises: Lessons and Guidelines from Asia and Latin
America. In OECD (eds.). Towards Asia’s Sustainable Development: The Role of
Social Protection. Paris: 297-332.
Bowels, Samual, Steven Durlauf and Karla Hoff (eds.) (2006). Poverty Traps. Princeton
University Press, Princeton.
Cañibano, L., García-Ayuso, M. and Sánchez, P., (2000). Accounting for Intangibles: A
Literature review. Journal of Accounting Literature 19:102-130.
Cardona, O.D., Hurtado, J.E., Duque, G., Moreno, A., Chardon, A.C., Velásquez, L.S. and
S.D. Prieto. (2003). Indicators for Risk Measurement: Methodological fundamentals
IDB/IDEA Program of Indicators for Disaster Risk Management, National University of
Colombia, Manizales.
Carter, Michael R. and Christopher B. Barrett. (2006). The Economics of Poverty Traps
and Persistent Poverty: An Asset-Based Approach. Journal of Development Studies
42: 178-199.
Carter, M., Little,P, Mogues, T., and W. Negatu (2007). Poverty Traps and Natural
Disasters in Ethiopia and Honduras. World Dvelopment. Volume 35, Issue 5, May
2007, Pages 835-856
Chang, S. (2000). Disasters and transport systems: loss, recovery and competition at the
Port of Kobe after the 1995 earthquake. Journal of Transport Geography, 8 (1): 53-65
Choi, Andy S. et al. (2010). ‘Economic valuation of cultural heritage sites: a choice
modelling approach,’ Tourism Management, 31(2): 213-220.
Cohen, W. and R. Levin (1989). Empirical Studies of Innovation and Market Structure. In
Schmalensee R; and R. Willig (Eds.) Handbook of Industrial Organisation, NorthHolland, chapter 18, p 1060-1107.
Coles,S. (2001). An Introduction to Statistical Modelling of Extreme Values. Springer
Series in Statistics, Springer, New York.
Commission Report (2004). European Union Solidarity Fund Annual report 2002-2003 and
Report on the experience gained after one year of applying the new instrument. Com
(2004) 397 final, Brussels, 26.5.2004.
CRED (2011). EM-DAT: International Disaster Database. Brussels, Belgium, Centre for
Research on the Epidemiology of Disasters, Université Catholique de Louvain.
Das, S. (2006) Risk Management. John Wiley and Sons, Singapore.
43
Dassanayake, D.R. Kortenhaus, A. And Oumeraci, H. (2010). Evaluation of Intangible
Losses due to Coastal Floods. Storm surges congress, Hamburg.
del Ninno, Carlo, K. Subbarao, and Milazzo, A. (2009). How to Make Public Works Work: A
Review of the Experiences. SP Discussion Paper. No. 0905. Washington, DC: The
World Bank.
Department for International Development (DFID) (1999) Sustainable Livelihood Guidance
Sheets. London: DFID.
Dumay, J.C. (2008) Intellectual Capital in Action. Faculty of Economics and Business. The
University of Sydney.
ECLAC (2003). Handbook for Estimating the Socio-Economic Effects of Natural Disasters.
Mexico City, United Nations Economic Commission for Latin America and the
Caribbean.
Embrechts, P., Klüppelberg, C., and Mikosch, T. (1997). Modelling extremal events for
insurance and finance. Springer, Berlin.
FLOODsite (2007). GIS-based Multicriteria Analysis as Decision Support in Flood Risk
Management. Report T10-07-07.
http://www.ufz.de/data/meyer_FLOODsiteReport_MCA_V1_4_P44_fin_small951 1.pdf.
Accessed 1 December, 2011
Fraser C, Riley S, Anderson RM, Ferguson NM. (2004) Factors that make an infectious
disease outbreak controllable. Proc Natl Acad Sci 101:6146–6151
Froot, KA. (2001). The market for catastrophe risk: a clinical examination. Journal of
Financial Economics, Elsevier 60(2-3): 529-571
Fisher, R.A., and L. H. C. Tippett (1928). Limiting forms of the frequency distribution of the
largest and smallest member of a sample. Proc. Cambridge Phil. Soc., 24: 180–190.
Grossi, P., H. Kunreuther, and D. Windeler (2005). An Introduction to Catastrophe Models
and Insurance. In P. Grossi and H. Kunreuther (eds.). Catastrophe Modeling:A New
Approach to Managing Risk. New York, Springer.
GTZ (2004). Risk analysis - a basis for disaster risk management. Guidelines. GTZ,
Eschborn.
Gurenko, E. (2004). Introduction. in E. Gurenko (Ed.). Catastrophe Risk and Reinsurance:
A Country Risk Management Perspective, Risk Books, London, 3-16.
Handmer, J. (2003) The chimera of precision: Inherent uncertainties in disaster loss
assessment. The Australien Journal of Emergency Management, 18(2): 88-97.
Häusler JM, Zimmermann H, Tobler B, Arnet B,
Hüsler J., (2001). Die
volkswirtschaftlichen Kosten von Polytrauma. Luzern/Bern
Hochrainer, S. (2006). Macroeconomic Risk Management against Natural Disasters.
German University Press (DUV), Wiesbaden, Germany.
Hochrainer, S. and Pflug, G. (2009). Natural Disaster Risk Bearing Ability of Governments:
Consequences of Kinked Utility. Journal of Natural Disaster Science, 31(1): 11-21.
Hochrainer, S., and Mechler, R. (2009). Assessing Financial and Economic Vulnerability to
Natural Hazards: Bridging the Gap between Scientific Assessment and the
Implementation of Disaster Risk Management with the CatSim Model. In: Patt, A.,
Schröter, D., Klein, R., and de la Vega-Leinert, A. (eds.): Assessing Vulnerability to
Global Environmental Change. London, Earthscan: 173-194.
Hochrainer, S., Mechler, R., Kull, D., Singh, P, Chopde, S., and Wajih, S. (2009).
Combining Innovative Strategies for Effective Drought Risk Management: Costs and
Benefits of Insurance and Irrigation in Uttar Pradesh, India. In: Moench, M., Fajber, E.,
Dixit, A., Caspari, E., and Anil Pokhrel (eds.): Catalyzing Climate and Disaster
Resilience. Kathmandu, ISET: 223-250.
Hochrainer, S., Linnerooth-Bayer, J. and Mechler, R. (2010). The European Union
Solidarity Fund: Its Legitimacy, Viability and Efficiency. Mitig. Adapt. Strat. Glob.
Change, 15(7): 797-810.
44
Hochrainer-Stigler, S., Kunreuther, H., Linnerooth-Bayer, Mechler, R., Michel-Kerjan, E.,
Muir-Wood, R., Ranger, N., Vaziri, P., and Young, M., (2011). The Costs and Benefits
of Reducing Risk from Natural Hazards to Residential Structures in Developing
Countries. Wharton Risk Center Working Paper Series, WP2011-01, Wharton School,
Pennsylvania.
Hsu WK, Huang PC, Chen CW, Chang CC, Hung DM, Chiang WL (2011). An integrated
flood risk assessment model for property insurance industry in Taiwan. Natural
Hazards 58: 1295-1309
Hubbard, D.W. (2010). How to measure anything. 2nd edition. Wiley, New Jersey
Jarboe, K.P. (2007). Measuring Intangibles. A Summary of Recent Activity. Working paper
#02. Athena Alliance. http://www.athenaalliance.org/pdf/ MeasuringIntangibles.pdf.
Jonkman S.N., and Vrijling J.K. (2008). Loss of life due to floods. Journal of Flood Risk
Management 1: 43-56
Kahneman, D. and Tversky, A. (1979). Prospect theory: An analysis of decisions under
risk. Econometrica 47: 313–327.
Kienberger S, Land S, and Zeil P (2009) Spatial vulnerability units—expert-based spatial
modelling of socioeconomic vulnerability in the Salzach catchment, Austria. Nat
Hazards Earth Syt Sci 9: 767–778
Knight, F.H. (1921). Risk, Uncertainty, and Profit. Hart, Schaffner, and Marx, New York.
Kozlowski, R. T. and Mathewson, S.B. (1997). A Primer on Catastrophe Modeling. Journal
of Insurance Regulation 15, 322-341.
Kunreuther, H. and Mark Pauly (2006).“Rules rather than discretion: Lessons from
Hurricane Katrina,” Journal of Risk and Uncertainty, Vol. 33, Numbers 1-2, September.
Kunreuther, H. And Michel-Kerjan, E.O. (2009). At War with the Weather. MIT Press,
Cambridge.
Lequeux, Q, and Ciavola, P. (2011). Methods for Estimating the Costs of Storms and
Coastal Hazards. WP7 report, Conhaz project.
Lindell, M. K. and Perry, R.W. (2004). Communicating Environmental Risk in Multiethnic
Communities. Thousand Oaks CA, Sage
Linneroth-Bayer, J., R. Mechler, and Pflug, G. (2005). Refocusing Disaster Aid. Science
309 (5737): 1044-1046
Loefstedt, R. and L. Frewer (1998). Risk & Modern Society. London, Earthscan
Publications.
Luechinger, Simon and Paul A. Raschky (2009). Valuing Flood Disasters Using the Life
Satisfaction Approach. Journal of Public Economics 93(3-4): 620-633
Malevergne, Y. and Sornette, D. (2005). Extreme Financial Risks : From Dependence to
Risk Management. Springer, New York, ISBN: 978-3-540-27264-9.
Markantonis, V. and and Meyer, C. (2011). Valuating the intangible efffects of natural
hazards: A review and evaluation of the cost-assessment methods. Paper presented at
the ESEE 2011 Conference, Istanbul 14-17 June 2011.
McNeil, A., Frey,F. and Embrechts., P.(2005). Quantitative Risk Management, Princeton,
Series in Finance
McDaniels, T. and W. Trousdale, 2005, “Evaluating Losses of Traditional Native Values
With Multi-attribute Value Assessment” Ecological Economics, 55, 2, pp. 173-186.
Mechler, R. (2004). Natural Disaster Risk Management and Financing Disaster Losses in
Developing Countries. Karlsruhe, Verlag Versicherungswirtschaft GmbH
Mechler, R. (2005). Cost-benefit analysis of natural disaster risk management in
developing countries. Working paper. Deutsche Gesellschaft fuer Technische
Zusammenarbeit (GTZ), Eschborn.
Mechler, R., Hochrainer, S., Pflug, G., Lotsch, A., Williges, K. (2009). Assessing the
Financial Vulnerability to Climate-Related Natural Hazards. Policy Research Working
Paper. 5232. Washington DC: World Bank.
45
Mechler, R., Hochrainer, S., Aaheim, A., Kundzewicz, Z., Lugeri, N., Moriondo, M., Salen,
H., Bindi, M., Banaszak, I., Chorynski, A., Genovese, E., Kalirai, H., Linnerooth-Bayer,
J., Lavalle, C., McEvoy, D., Matczak, P., Radziejewski, M., Rübbelke, D., Schelhaas,
M.-J., Szwed, M., Wreford, A. (2010). A risk management approach for assessing
adaptation to changing flood and drought risks in Europe. In: M. Hulme, H. Neufeldt
(eds.). Making Climate Change Work for Us: European Perspectives on Adaptation and
Mitigation Strategies. Cambridge University: 200-229
Meyer V., Scheuer, S., Haase, D. (2009). A multicriteria approach for flood risk mapping
exemplified at the Mulde river, Germany. Nat Hazards 48:17–39
Millennium Exosystem Assessment (2005). Ecosystems and Human Well-Being. World
Resources Institute, Washington, DC.
Munda, G. (2006) Contemporary Economic Evaluation: A concise discussion. Report EUR
22463 EN, JRC, Ispra, Italy.
Moench, M., Hochrainer, S., Mechler, R., Kull, D., Linnerooth-Bayer, J., Patnaik, U.,
Mustafa, D., Opitz-Stapleton, S., Khan, F., Rehman, A., Dixit, A., Pokhrel, A., Upadhya,
M., Chopde, S., Singh, P., Tiwari, S., Borgoyary, M., Bose, S., Wajih, S.A., Srivastav,
A., and Singh, G. (2009). Rethinking the Costs and Benefits of Disaster Risk Reduction
under Changing Climate Conditions. In: Moench, M., Fajber, E., Dixit, A., Caspari, E.,
and Anil Pokhrel (eds.): Catalyzing Climate and Disaster Resilience. Kathmandu, ISET:
1-34.
Muir-Wood, R., Drayton, M., Berger, A., Burgess, P., and Wright,T.(2005). Catastrophe
loss modelling of storm-surge flood risk in eastern England, Phil. Trans. R. Soc. A. 363:
1407–1422
Multihazard Mitigation Council (MMC) (2005). Natural Hazard Mitigation Saves: An
Independent Study to Assess the Future Savings from Mitigation Activities: Volume 2Study Documentation, Multihazard Mitigation Council, Washington DC.
Munich Re (2005). Natural Catastrophes in 2004, Topics, Munich Re Group, Munich.
National Research Council (1997). Recommendations for Probabilistic Seismic Hazard
Analysis: Guidance on Uncertainty and the Use of Experts. National Academy Press,
Washington DC.
Oberkampf, W.L., J.C. Helton, C.A. Joslyn, S.F. Wojtkiewicz, and S. Ferson (2004).
Challenge problems: uncertainty in system response given uncertain parameters.
Reliability Engineering and System Safety 85(1-3): 11-19.
Pato A, Cebrián Pérez E, Cimas Hernando I, Lorenzo González JR, Rodríguez Constenla
I, Gude Sampedro F. (2011) Analysis of direct, indirect, and intangible costs of
epilepsy. Neurologia. 26(1):32-38.
Pearce, DW,, Turner, K. (1990). Economics of natural resources and the Environment.
Harvester Wheatsheaf, New York
Penning-Rowsell, E., C. Green, P. Thompson, A. Coker, S. Tunstall, C. Richards, D.
Parker (1992). The Economics of Coastal Management - A Manual of Benefit
Assessment Techniques (The Yellow Manual), London.
Pflug, G. and Römisch, W. (2007). Modelling, Measuring and Managing Risk. World
Scientific, Singapore.
Priest, S.J., Wilson, T., Tapsell, S., Penning-Rowsell, E., Viavattene, C. and FernandezBilbao, A. (2007). Building a model to estimate Risk to Life for European flood events.
Milestone report T10-07-10 for the Integrated Project FLOOD-site. Enfield: Flood
Hazard Research Centre.
Przyluski, V. and Hallegatte, S. (2011). Indirect Costs of Natural Hazards. ConHaz: Costs
of Natural Hazards. WP2 Final Report, September 2011, http://conhaz.org/.
Rega P, Bork C, Burkholder-Allen K, Bisesi M, Gold J. (2010) The Transitional Medical
Model: An Innovative Methodology for a Community’s Disease Outbreak and Pandemic
Preparedness and Response Plan. Am J Disaster Med. 5(2):69-81.
46
Reiss , R.D. and Thomas, M. (2007). Statistical Analysis of Extreme Values. Third Edition.
Birkhaeuser, Berlin
Reporting Intellectual Capital to Augment Research, Development and Innovation in SMEs
(RICARDIS (2006): Encourage Corporate Measuring and Reporting on Research and
Other Forms of Intellectual Capital. Report to the Commission of the High Level Expert
Group on RICARDIS. European Commission, Directorate-General for Research.
Rescher, N. (1983). Risk: A Philosophical Introduction to the Theory of Risk Evaluation
and Management. University Press of America, London.
Schick, A. and Polackova Brixi, H. (eds.) (2004). Government at Risk. World Bank and
Oxford University Press, Washington DC.
Schwarze, R., Schwindt, M., Weck-Hannemann, H., Raschky, P., Zahn, F. and Wagner, G.
G. (2011). Natural hazard insurance in Europe: tailored responses to climate change
are needed. Environmental Policy and Governance 21: 14–30.
Scolobig, A. and Castn Broto, V. and Zabala, A. (2008) Integrating multiple perspectives in
socialmulticriteria evaluation of flood-mitigation alternatives : the case of MalborghettoValbruna. Environment and planning C : government and policy: 26 (6): 1143-1161.
Skoufias, E. (2003). Economic Crises and Natural Disasters: Coping Strategies and Policy
Implications. World Development 31(7): 1087-1102.
Slovic, P. (2000). Informing and educating the public about risk. In: Slovic, P. The
Perception of Risk. London: Earthscan Publications
Sveiby, K.E. (2010) Methods for measuring Intangible Assets.
http://www.sveiby.com/articles/IntangibleMethods.htm
Thouret, JC., and Martelli, KM (2010). Volcanic hazards and risk in cities, with emphasis
on lahars: A review. Urban Habitat Constructions under Catastrophic Events
(Proceedings) – Mazzolani (Ed). Taylor & Francis Group, London, ISBN 978-0-41560685-1
UNISDR (United Nations International Strategy for Disaster Reduction) (2005). Hyogo
Declaration. World Conference on Disaster Reduction, Kobe, Hyogo, Japan.
UNISDR (2009): UN international strategy for disaster reduction, terminology section.
http:/unisdr.org/we/inform/terminology
Le Van, C., Schubert, K., and Nguyen, TA. (2010). With exhaustible resources, can a
developing country escape the poverty trap? Journal of Economic Theory 145: 24352447
Watson, JT, Gayer, M, Connolly, MA (2007). Epidemics after natural disasters. Journal of
Emergency and Infectious Disease. 13(1):1-5
Wegner, G., & Pascual, U. (2011). Cost-benefit analysis in the context of ecosystem
services for human well-being: A multidisciplinary critique. Global Environmental
Change.
Wolf, S. (2011). Vulnerability and risk: comparing assessment approaches. Natural
Hazards, online first, 8 September 2011, DOI: 10.1007/s11069-011-9968-4
Woo, G. (1999). Mathematics of Natural Catastrophes. Imperial College Press, U.K.
Woo, G. (2011). Calculating Catastrophe. Imperial College Press, U.K.
Woolhouse, M. (2011) How to make predictions about future infectious disease risks Phil
Trans R Soc B 366: 2045-2054
Zapata, R. (2008). Quantification of disaster impacts. Seminar presentation.
Zhai, G. and Ikeda, S. (2006). Flood Risk Acceptability and Economic Value of Evacuation.
Risk Analysis, 26: 683–694.
47
Appendix
Fig. A1. Analysis of methods for intangible cost assessments. (Markantonis and Meyer,
2011)
48