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Assessing the Financial Vulnerability to Climate-related Natural Hazards

Working paper by Mechler, Reinhard; Hochrainer, Stefan; Pflug, Georg; Lotsch, Alexander; Williges, Keith/World Bank, 2010

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National governments are key actors in managing the impacts of extreme weather events, yet many highly exposed developing countries - faced with exhausted tax bases, high levels of indebtedness, and limited donor assistance - have been unable to raise sufficient and timely capital to replace or repair damaged infrastructure and restore livelihoods after major disasters. Such financial vulnerability hampers development and exacerbates poverty. Based on the record of the past 30 years, this paper finds many developing countries, in particular small island states, to be highly financially vulnerable, and experiencing a resource gap (net disaster losses exceed all available financing sources) for events that occur with a probability of 2 percent or higher. This has three main implications. First, efforts to reduce risk need to be ramped-up to lessen the serious human and financial burdens. Second, contrary to the well-known Arrow-Lind theorem, there is a case for country risk aversion implying that disaster risks faced by some governments cannot be absorbed without major difficulty. Risk aversion entails the ex ante financing of losses and relief expenditure through calamity funds, regional insurance pools, or contingent credit arrangements. Third, financially vulnerable (and generally poor) countries are unlikely to be able to implement pre-disaster risk financing instruments themselves, and thus require technical and financial assistance from the donor community. The cost estimates of financial vulnerability - based on today's climate - inform the design of"climate insurance funds"to absorb high levels of sovereign risk and are found to be in the lower billions of dollars annually, which represents a baseline for the incremental costs arising from future climate change.

Policy Research Working Paper 5232


Assessing the Financial Vulnerability
to Climate-Related Natural Hazards


Reinhard Mechler
Stefan Hochrainer


Georg Pflug
Alexander Lotsch
Keith Williges


The World Bank
Development Economics
Office of the Senior Vice President and Chief Economist
March 2010


Background Paper to the 2010 World Development Report


WPS5232




Produced by the Research Support Team


Abstract


The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.


Policy Research Working Paper 5232


National governments are key actors in managing the
impacts of extreme weather events, yet many highly
exposed developing countries—faced with exhausted
tax bases, high levels of indebtedness, and limited donor
assistance—have been unable to raise sufficient and
timely capital to replace or repair damaged infrastructure
and restore livelihoods after major disasters. Such
financial vulnerability hampers development and
exacerbates poverty. Based on the record of the past 30
years, this paper finds many developing countries, in
particular small island states, to be highly financially
vulnerable, and experiencing a resource gap (net disaster
losses exceed all available financing sources) for events
that occur with a probability of 2 percent or higher.
This has three main implications. First, efforts to reduce
risk need to be ramped-up to lessen the serious human
and financial burdens. Second, contrary to the well-


This paper—prepared as a background paper to the World Bank’s World Development Report 2010: Development in a
Changing Climate—is a product of the Development Economics Vice Presidency. The views expressed in this paper are
those of the authors and do not reflect the views of the World Bank or its affiliated organizations. Policy Research Working
Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at alotsch@worldbank.org.


known Arrow-Lind theorem, there is a case for country
risk aversion implying that disaster risks faced by
some governments cannot be absorbed without major
difficulty. Risk aversion entails the ex ante financing
of losses and relief expenditure through calamity
funds, regional insurance pools, or contingent credit
arrangements. Third, financially vulnerable (and generally
poor) countries are unlikely to be able to implement
pre-disaster risk financing instruments themselves, and
thus require technical and financial assistance from
the donor community. The cost estimates of financial
vulnerability—based on today’s climate—inform the
design of “climate insurance funds” to absorb high levels
of sovereign risk and are found to be in the lower billions
of dollars annually, which represents a baseline for the
incremental costs arising from future climate change.





Assessing the Financial Vulnerability to Climate-Related


Natural Hazards



Background paper for the World Development Report 2010


“Development and Climate Change”


Reinhard Mechler*, Stefan Hochrainer*, Georg Pflug*, Alexander Lotsch# with Keith


Williges*


* International Institute for Applied Systems Analysis, Laxenburg, Austria


# The World Bank Group, Development Economics, Washington DC







2




1 Introduction
Given increasing empirical evidence on climate change altering intensities and frequencies of
natural hazards, the management of extreme event risk has been receiving increasing attention in
international climate change policy. In Bali at the 13th Conference of the parties to the UNFCCC,
agreement was found regarding the creation of a Climate Change Adaptation Fund, which is to
sponsor concrete adaptation projects in vulnerable countries. We suggest that one key
impediment for finally releasing support from this fund has been a lack of operational
methodologies for assessing vulnerability to natural hazards and climate change, which are
integrated with the cost implications of supporting efforts to reduce vulnerability. A host of
estimates for the adaptation costs, often put together using “hand-waving“ methodologies, have
been suggested, and the robustness and underlying basis of such estimates has often been
difficult to verify.


For weather extremes, which form a subset of the adaptation challenge and are supposed to
increase in intensity and frequency with a changing climate, we conduct an assessment of the
costs of managing and financing today’s public sector risks. National governments have become
key actors in managing climate variability and change. Yet, many highly exposed developing
countries are faced with exhausted tax bases and high levels of indebtedness and cannot raise
sufficient and timely capital to replace or repair damaged assets and restore livelihoods following
major disasters, leading to an exacerbation of disaster impacts on poverty and development
(Mechler, 2004; Bayer et al., 2005; Hochrainer, 2006; Cummins and Mahul, 2007). Exposed
countries often have to rely on donors to “bail” them out after devastating events. However, the
evidence regarding ex–post assistance shows that only partial relief and reconstruction funding is
usually being made available; furthermore, this support is often associated with substantial time
lags (of at a minimum one year); on top of this, such post disaster aid has often not reached those
in need effectively.


Over the last few years, there has been a paradigm shift in national and international
responses to this problem towards more proactive efforts and upgrading the role of pre disaster
risk management (Linnerooth-Bayer, Mechler and Pflug, 2005). Countries, donors and the
international community have been working together to devise and implement risk management
systems for reducing, pooling and sharing risk. One focus has been on risk financing
mechanisms for transferring private and public sector risks from local and national levels to a
global scale. Important precedents have been implemented over the last few years as private-
public partnerships with donors and governments providing technical assistance, subsidizing or
paying premiums, such as the Caribbean Catastrophe Risk Insurance Facility (CCRIF).


This paper focuses on countries’ ability to absorb risks within its own limits, or vice versa the
need for transferring risks more globally by implementing novel risk sharing mechanisms. Based
on an estimate of country-wide risk for the 70+ countries most exposed to weather extremes, we
assess countries’ current financial vulnerability to climate extremes, which we operationalize as
the public sector’s ability to pay for relief to the affected population and support the
reconstruction of affected public sector assets such as infrastructure. We find a number of
countries already highly financially vulnerable for smaller to medium sized events, and suggest
that efforts to reduce risk need to be seriously stepped up. In such cases of obvious risk aversion,
where disaster risks faced by governments cannot be absorbed without major difficulty, there is
also a case for considering to prefinance risks, as the benefits of financing risks can often






3


outweigh their costs. Almost without exception, financially vulnerable countries are highly
unlikely to be able to implement pre-disaster risk financing instruments themselves. Thus, there
is a need for technical and financial assistance from the donor community. Our estimates may
inform decisions pertaining to a “climate insurance fund” absorbing extreme event country risk,
which exceeds the ability of any given country to absorb the losses in a given event. We find the
costs of such a high risk layer backup fund to be in the lower billions of dollars annually. Our
assessment relates to today’s climate, yet we suggest that estimates of today’s climate variability
and related risks, although also associated with substantial uncertainty, can be interpreted as a
baseline for vey uncertain future projections.


The report proceeds as follows. In section 2, we discuss the developmental challenges
imposed by disaster risk and the case for prefinancing disaster risk. In section 3, we present our
methodology for assessing country-wide financial vulnerability to extreme events based on the
CATSIM model. Section 4 finally presents salient findings and a short discussion of key
implications.




2 Disasters and development: The financial dimension
Climate-related disaster losses1 have escalated in recent decades. Although largely driven by
socioeconomic change, the increase in monetary losses by an order of magnitude within the last
four decades cannot entirely be explained by population or economic growth only (Mills, 2005).
The 4th assessment report of the IPCC found increased impacts of extremes such as cyclones and
flooding due to altered intensities and frequencies of natural hazards (Parry et al., 2007), many of
which are expected to increase in frequency or severity in various places in a future warmer
climate (Solomon et al., 2007). Disaster impacts can be devastating, particularly in heavily
exposed low- and middle-income countries, and especially the vulnerable within these countries
suffer the most. During the 25 year period from 1979 to 2004 over 95% of natural disaster deaths
occurred in developing countries and direct economic losses averaged US$54 billion per annum.
Not only are there considerable differences in the human and economic burden, but also in
insurance cover. In the richest countries average total losses during this period amounted to 3.7%
as measured in gross national income with about 30% of the losses insured. In low-income
countries, total losses amounted to 12.9% with 99% of these losses uninsured (Munich Re,
2005).2 It should be emphasized that these disaster statistics do not (for the most part) reflect
medium to long-term indirect losses, which can be very significant, particularly in countries with
little capacity to cope with extremes, yet are generally very difficult to parcel out from other
effects.


There are many ways for absorbing the financial burdens of disasters, with market-based
insurance being one, albeit prominent, option. Households often use informal mechanisms
relying on family and relatives abroad; governments may simply rely on their tax base or
international assistance. Yet, it is a fact that in the face of large and covariate risks, such ad hoc
mechanisms often break down and a severe shortfall, a resource gap, remains (see Linnerooth-
Bayer and Mechler, 2007).



1 The literature does often not clearly distinguish between losses and damages. We refer to damages as the physical


impacts, and losses as the monetized values (direct losses) or the economic follow on effects (indirect losses).
2 These losses are mostly direct losses of productive assets and property (stocks); only to a minor extent are indirect
losses of value added (flows), such as business interruption losses, accounted for and insured.






4


Financing the resource gap
The seriousness of the post-disaster resource gap, as well as the emergence of novel insurance
instruments for pricing and transferring catastrophe risks to the global financial markets, has
motivated developing country governments, as well as development institutions, NGOs and other
donor organizations, to consider pre-disaster financial instruments as an important component of
disaster risk management (Linnerooth-Bayer et al., 2005). Donor-supported pilot insurance
programs are already demonstrating their potential to pool economic losses and smooth incomes
of the poor facing weather variability, climate extremes and geophysical disasters. These
schemes provide insurance to farmers, property owners and small businesses, as well as transfer
the risks facing governments to the global capital markets. Particularly, the latter risk
management options, where donors or governments cede risk, are particularly interesting. A few
examples shown in box 1 may serve to illustrate the issues.




Box 1: Novel mechanisms for sharing extreme event risks in developing countries




 The Ethiopian weather derivative
To supplement and partly replace their traditional food-aid response to famine, the World Food
Programme (WFP) designed an index-based insurance system to provide extra capital in the case of
extreme drought, the amount being based on contractually specified catastrophic shortfalls in precipitation
measured in terms of the Ethiopia Drought Index (EDI). Rainfall data is taken from 26 weather stations
representing the various agricultural areas of Ethiopia. In 2006, WFP successfully obtained an insurance
contract based on the EDI through AXA Re, a Paris-based reinsurer (Wiseman and Hess, 2007).


 The Mexican catastrophe bond
In 2006, the Mexican government chose to insure its catastrophe reserve fund, FONDEN, against major
earthquakes with a mix of reinsurance and a catastrophe bond. The resulting contract is linked to a
parametric trigger in terms of magnitude and depth of seismicity for the three-year period 2007-09. The
catastrophe bond provides cover of US$160 million out of a total cover of $450 million for a
premium/interest totalling $26 million. The major reinsurance company, Swiss Re, issued the bond,
which pays an interest of 230 basis points if payment is not triggered. Mexico received substantial
technical assistance from the World Bank and Inter American Development Bank over the years, but ,as a
middle-income developing country and member of the OECD, it financed the transaction out of its own
means. (Cardenas, et al., 2007)


 The Caribbean Catastrophe Risk Insurance Facility (CCRIF)
The Caribbean Island States in 2007 formed the world’s first multi-country catastrophe insurance pool,
reinsured in the capital markets, to provide governments with immediate liquidity in the aftermath of
hurricanes or earthquakes. 16 Caribbean countries contribute resources ranging from US$200 thousand to
US$ 4 million depending on the exposure of their specific country to earthquakes and hurricanes. This
better-diversified portfolio is expected to result in a substantial reduction in premium cost of about 45 –
50% for the participating countries. The fund covers up to 20% of the estimated loss, and claims will be
paid depending on an index for hurricanes (wind speed) and earthquakes (ground shaking). Initial funding
by donor organizations provided support for start-up costs and helped capitalize the pool. The facility
transfers the risks it cannot retain to the international financial markets through reinsurance or through
other financial instruments (for example, catastrophe bonds). In addition, donors are adding to the
reserves. The governments of Bermuda, Canada, France, the United Kingdom, as well as the Caribbean
Development Bank, the World Bank and the EU recently pledged in excess of US $50 million to the
CCRIF (Ghesquiere, et al., 2006; World Bank, 2007)






5


Since many of these and other recent insurance programs are still in the pilot stage, and none
have experienced a major and widespread catastrophic event, it is too early to fully assess their
effectiveness in reducing economic insecurity. Yet, the need for careful examination of their
effectiveness and sustainability, even if based on a short operating history, is underscored by




Box 2: The MCII proposal


The Munich Climate Insurance Initiative (MCII) insurance proposal suggests a risk management module
as part of an international adaptation strategy. As shown on the Figure below, this module includes two
pillars, prevention and insurance, which would act together to reduce the human and economic burdens
on developing countries. The pillars would be fully financed by a post-Copenhagen adaptation fund. The
MCII endorses the growing consensus that this fund be financed in accordance with the Convention’s
principles of common but differentiated responsibilities and respective capabilities of countries
(UNFCCC, Art. 3), and that it be disbursed to those who suffer most from climate change.


The prevention pillar


Insurance activities must be viewed as part of a risk management strategy that includes, first and
foremost, activities that prevent human and economic losses from climate variability and extremes. The
first pillar of the MCII proposal thus calls for comprehensive risk management across vulnerable
countries building on detailed risk assessments. Risk assessments can uncover otherwise unforeseen
possibilities for risk reduction, and help lay the groundwork for risk transfer systems. The Prevention
Pillar would not require developing countries to fully internalize the price of increased climate-related
risk; however, qualification for participation in the Insurance Pillar might include progress on a credible
risk management strategy with a specific focus on most vulnerable communities and sectors.


The insurance pillar


MCII’s proposed insurance pillar has two tiers that reflect the different layers of risk that need to be
addressed for effective climate adaptation: (1) “high level” risk that exceeds the ability of any given
country to pay in the case of an extreme event, and (2) “middle level” risk that is within the ability of any
given country to cope if the proper facilitating framework were in place. “Low level” risks can often be
more cost effectively addressed with prevention measures, and this risk layer is therefore not addressed in
the MCII proposal. As pictured below, the first tier would provide insurance cover to vulnerable countries
for a pre-defined high layer of risk (e.g., this might be defined for events that are expected to occur only
every 100 or 500 years), and the premiums would be fully paid from an adaptation fund. The second tier
would enable risk-pooling and -transfer mechanisms that provide cover for medium-loss events (e.g., this
might be defined as events expected to occur less frequently than every 10 years but more frequently than
every 100 years). Both tiers would be fully financed by a post-Copenhagen adaptation fund (and thus
presumably by Annex 1 countries).



A two-tiered insurance pillar as part of an adaptation fund. Source: MCIII (2008)






6


recent experience with disaster insurance systems in developed countries, especially the
widespread inefficiencies of agricultural insurance systems and the insurance controversies
following Hurricane Katrina’s devastation to poor communities in New Orleans. The question
arises whether developing countries should follow the path of the developed world in insuring
against catastrophic events, and which insurance instruments and modifications may be
appropriate for better tackling the developmental dimensions of natural disasters.


One recent proposal for supporting vulnerable countries with coping with climate-related
events was put forward by the Munich Climate Insurance Initiative (MCII) in the context of the
UNFCCC negotiations. The proposal suggests a two-pillar (prevention and insurance)
international risk-management strategy as part of an adaptation regime (see box 2).




How much risk reduction and how much insurance?
How much to invest in risk reduction and how much to use insurance (in a wider sense
comprising market-based and other ways of risk financing) is a complex question, depending on
the occurrence probability of hazards, the associated size of impacts, the costs, and benefits of
both types of activities, as well as on their interaction (keeping in mind that financial
instruments, through incentives, influence prevention activities). In figure 1, we illustrate the
linkages between effectiveness of instruments and occurrence probability. Since the benefits of
measures that reduce risk become less for very low probability, but high consequence events, it
is generally the case that prevention is more cost effective for the higher probability events with
low to medium-sized losses. For those frequent events (e.g., with return period of 10 years, or
annual probability of 10%) prevention and response measures (for instance, constructing levees
against floods or retrofitting homes against seismic risks) are likely to have higher benefit/cost
ratios than if applied to the less frequent events (meaning non-linear losses as probabilities
decrease). Similarly, individuals and governments are generally better able to finance lower
consequence events from their own means, for instance, savings or calamity reserve funds, and
including international assistance.





Fig. 1: Efficiency of risk management instruments and occurrence probability







7


The opposite is generally true for costly risk-financing instruments, like insurance, catastrophe
bonds and contingent credit arrangements As catastrophe insurance premiums and the cost of
other instruments 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, it is generally advisable to use those instruments for lower probability hazards, which
may have debilitating consequences. To put it differently, risk financing is generally used to
smooth the variability of losses, while risk reduction reduces (expected) risk. It would not be
economically rational to implement insurance for frequent, but low loss events, which may be
covered domestically or be reduced easily. Finally, most individuals and governments find it too
costly to insure against very extreme risks occurring less frequently than say every 500 years.




Financial vulnerability and the resource gap: The rationale for financing disaster risk
According to an early theorem by Arrow and Lind (1970) governments should not insure if they
are not averse to risks, i.e. if financial risks faced by the government can be absorbed without
major difficulty. According to Arrow and Lind a government may


 Pool risks as it possesses a large number of independent assets and infrastructure so that
aggregate risk is negligible, or


 Spread risk over the population base, so that per-capita risk to risk-averse households is
negligible.


In theory, thus, governments are not advised to incur the extra costs of transferring their disaster
risks if they carry a large portfolio of independent assets and/or they can spread the losses of the
disaster over a large population. Because of their ability to spread and diversify risks, Priest
(1996) refers to governments as "the most effective insurance instrument of society."
Furthermore, the extra costs of insurance can be significant; for example Froot (2001) reports
cost up to seven times greater than the expected loss, due to high transaction costs, uncertainties
inherent in risk assessment, the limited size of risk transfer markets and the large volatility of
losses. Consequently, according to Arrow and Lind governments should behave risk-neutrally
and evaluate their investments only through the expected net present (social) value. The Arrow
and Lind theorem serves as the basis for government strategies for dealing with risk. In practice,
most governments neglect catastrophic risks in decision making, thus implicitly or explicitly they
behave risk-neutrally (Guy Carpenter, 2000). The case for sovereign self-insurance, however,
may not hold for highly exposed developing country governments, especially those that are not
sufficiently diversified or cannot spread losses over the tax-paying public. Already in 1991, the
Organization of American States' primer on natural disasters states that the risk neutral
proposition is valid only up to certain point and that the reality in developing countries suggests
that some governments cannot afford to be risk-neutral:


The reality of developing countries suggests otherwise. Government decisions
should be based on the opportunity costs to society of the resources invested in
the project and on the loss of economic assets, functions and products. In view of
the responsibility vested in the public sector for the administration of scarce
resources, and considering issues such as fiscal debt, trade balances, income
distribution, and a wide range of other economic and social, and political
concerns, governments should not act risk-neutral (OAS, 1991).







8


In these cases, governments may justifiably act as risk-averse agents. This means that the Arrow-
Lind theorem may not apply to governments of countries that have (see Mechler, 2004;
Hochrainer and Pflug, 2009):


 high natural hazard exposure;
 economic activity clustered in a limited number of areas with key public infrastructure


exposed to natural hazards; and


 constraints on tax revenue and domestic savings, shallow financial markets, and high
indebtedness with little access to external finance.




These conditions are fundamental to determining the financial vulnerability of a country (i.e., the
central government), which we will discuss in the following.




3 Methodology for determining financial vulnerability
Risk and vulnerability are concepts with multiple and ambiguous meanings. As an analytical
term, vulnerability has been used in a complex array of disciplinary contexts, including
geography, risk and hazard, anthropology, engineering and ecology. Vulnerability as commonly
defined in the context of climate change (e.g. IPCC, 2001) as a function of both potential impacts
and society’s capacity to adapt to these impacts (see figure 2).







Fig. 2: IPCC’s definition of vulnerability to climate change







9


A narrower definition that focuses only on the affected system is common in the risk/hazards and
vulnerability communities. Turner et al. (2003) define vulnerability as the degree to which a
system or subsystem is likely to experience harm due to exposure to a hazard, either as a
perturbation or stressor. In this framework, multiple hazards can be caused or aggravated by
global-change phenomena, where vulnerability is a function of the exposure, sensitivity and
resilience of the system in question. Risk, alternatively, is a function of the hazard (likelihood
and severity) and its potential consequences (exposure, sensitivity), but usually stops short of
considering the coping capacity and resilience of the exposed system.


We consider governments financially vulnerable to disasters if they cannot access sufficient
funding after a disaster to cover their liabilities in terms of reconstructing public infrastructure
and providing assistance to households and businesses in need. We operationalize this concept
by the term resource gap, which we define the net loss associated with a disaster event after
exhausting all possible ex-pots and ex ante financing sources. Such a resource gap is a useful
measure of sovereign financial vulnerability. The repercussions of a resource gap can be
substantial. The inability of a government to repair infrastructure in a timely manner and provide
adequate support to low-income households can result in adverse long-term socio-economic
impacts. As a case in point Honduras experienced extreme difficulties in repairing public
infrastructure and assisting the recovery of the private sector following Hurricane Mitch in 1998.
Five years after Mitch’s devastation the GDP of Honduras was 6% below pre-disaster
projections.3 When considering whether Honduras and other highly exposed countries should
protect themselves against resource gaps and associated long-term negative consequences, it is
important to keep in mind that risk management measures have associated opportunity costs,
which means that they can reduce GDP by diverting financial resources from other public sector
objectives, such as investments into social or infrastructure projects.


In the following, we outline a methodology based on the CATSIM model (Hochrainer and
Mechler, 2009) to calculate this resource gap, and derive a global country-level estimate for the
most hazard-exposed countries. Figure 3 shows schematically how we combine risk estimates
with financial resilience to lead to an estimate of financial vulnerability.





3 Own calculations.






10





Fig. 3: Illustration for calculating financial vulnerability




The standard approach for estimating natural disaster risk (the probability of potential impacts) is
to understand natural disaster risk as a function of hazard, exposure and (physical) vulnerability.
We focus on risk to assets, economic and financial vulnerability, with financial vulnerability as a
subset of economic vulnerability. Our methodology follows the following steps.


3.1 Step 1: Assessment of public sector liabilities
Disaster risk emanates from explicit and implicit contingent public sector liabilities, classified in
table 2. 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 (see table 1).


We calculate direct risk (potential losses and their probabilities) accruing to a national
government’s liabilities for hydrometeorological events. The calculation is done as a function of
hazard, exposure (assets) and the physical vulnerability of assets. We only focus on assets
(produced capital), and do not account for the risk to human and environmental capital. We
calculate loss distributions in terms of 50, 100, 250 and 500 year events.















11


Table 1: Government liabilities and disaster risk


Liabilities Direct: obligation in any event Contingent: obligation if a particular
event occurs


Explicit
Government liability
recognized by law or
contract


Foreign and domestic sovereign
borrowing, Expenditures by
budget law and budget
expenditures


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


Source: Modified After Schick and Polackova Brixi, 2004




In this first CATSIM step the risk of direct losses is assessed in terms of the probability of asset
losses in the relevant country or region. Consistent with general practice, risk is modeled as a
function of hazard (frequency and intensity), the elements exposed to those hazards and their
physical vulnerability (Burby, 1991; Swiss Re, 2000). 4 In more detail,




1. Natural hazards, such as hurricanes, or floods, are described by their intensity (e.g. peak
flows for floods) and recurrency (such as a 1 in 100 year events, i.e. with a probability of
1%). We focus on sudden-onset climate-related events only such as tropical cyclones,
floods and winterstorms. Generally, for the sudden-onset events analysts generally equate
given loss and risk data with asset losses.5




Our estimates, i.e. probability of given events and corresponding losses on the country level, are
based on available data as reported in Cummins and Mahul (2009) as well as own estimates
based on past losses and using extreme value theory. While Cummins and Mahul (2009) present
risks based on catastrophe model approaches, for countries where such data were not available,
we used reported loss data from CRED (2009) and Munich Re (2009) to estimate a Generalized
Pareto distribution using either Maximum likelihood optimization methods if more data points
existed (i.e., usually more than 10 observations) or Minimum-Distance methods if only a few data
points were available. Based on the estimated parameters the selected loss return periods were
calculated. The historically observed cumulated yearly relative losses (losses in percent of GDP)
were taken as the basic data. A Generalized Pareto distribution was fitted to the tails (i.e. to the
data exceeding the 80%-quantile) by minimizing the integrated square distance between the
empirical distribution and the estimated distribution. For countries with more than 25 data points,
the maximum likelihood estimates of the parameters were calculated alternatively. The higher
quantiles (90%, 95%, 98%, 99%, 99.6%, 99.8%) of the fitted distribution as well as the



4 In the hazards and risk community, “sensitivity” is referred to as “vulnerability”, and often exposure is included in
the sensitivity component; thus, risk is defined by hazard and vulnerability. In catastrophe models carried out for
insurance purposes, the contract specifications of the underwritten and exposed portfolios are added as a fourth
component (eg. Swiss Re, 2000).
5 An indication that this assumption can be maintained is the fact that loss data are usually relatively quickly
available after a catastrophe, which indicates that flow-indirect impacts emanating over months to years, are usually
not considered.






12


probability of first loss (obtained from the empirical distribution) were taken as input to the
CATSIM model.




2. Exposure of elements at risk: From an economic perspective, governments are exposed to
natural disaster risk and potential losses due to three functions: (i) the allocation of goods
and services (security, education, clean environment, (ii) the provision of support to
private households and business in the case of market failure, (iii) and the distribution of
income as shown on Figure 4 (see Musgrave, 1959).





Fig. 4: Sources of government disaster risk




Total capital stock for each country is taken from Sanderson and Striessnig (2009). These
estimates are based on a perpetual inventory method using Penn World tables with data on
investments starting in 1900 and assuming annual growth and depreciation of 4 percent. To
compute public sector liabilities, due to a lack of globally comparable data, we take the following
assumptions: (i) Based on World Bank (1994) we use an estimate of 20% of total capital stock as
the infrastructure component (category 1 in the chart), and then add another 30% for relief and
reconstruction to affected households and business (categories 2 and 3 in the chart). From a
normative view, this share a government should be prepared to refinance, can be broadly justified
by examining the very limited empirical evidence on actual spending in events (see figure 5).







13


2%
8%


0%


18%


40%


48%


13%


51%
58%


44%


87%


31%


0%


10%


20%


30%


40%


50%


60%


70%


80%


90%


100%


Northridge EQ 94 Umbria earthquake,
97


Poland Floods 97 Sudan floods, 98


Insured loss Government reconstruction and relief Private sector loss financing and net loss


Fig. 5: Insurance and government assistance for selected disasters as a percentage of direct
losses (Source: Linnerooth-Bayer and Mechler, 2007)




3. Physical vulnerability describes the degree of damage to the capital stock due to a natural
hazard event. The method commonly used here are fragility curves setting the degree of
damage in relation to the intensity of a hazard.




Based on data on the return period and losses in percent of capital stock, CATSIM generates loss
frequency distributions describing the probability of specified losses occurring, such as a 100-year
event causing a loss of 200 million USD, a 50-year event causing a 40 million USD loss, and so
on.6 It should be kept in mind that top-down estimates at this broad scale are necessarily rough.
Since most disasters are rare events, there is often little in terms of historical data; furthermore it
is difficult to include dynamic changes in the system, for example, population and capital
movements and climate change. To improve the data information, bottom-up assessments can be
undertaken that involve a detailed analysis of the occurrence of hazards in certain areas, the
exposed elements and vulnerabilities of structures on a more microscale level.


As already indicated risk and potential losses are summarized by means of loss-frequency
distributions, which relate probabilities of loss to assets destroyed. For example, figure 6 shows a
cumulative loss-frequency distribution for flood risk in a hypothetical country. The horizontal
axis shows the fraction of capital stock destroyed by a disaster, and the vertical axis represents the
probability that losses will not exceed a given level. For example, with a probability of 0.9 (90%)
flood losses will not exceed 250 million LCU; inversely, there is a 0.1 (10% chance) that such a
loss and larger will occur.





6 It is standard practice to refer to 20-, 50-, 100-, 500- and 1000-year events.






14


Probability of losses not exceeding a certain level


0.90


0.92


0.94


0.96


0.98


1.00


0 250 500 750 1000 1250 1500
Government liabilities (million LCU)



Fig. 6: Risk of losses as measured by a cumulative loss-frequency distribution




An important summary measure of this distribution is the annual expected losses, or the losses to
be expected on average every year. The annual expected loss is the sum of all losses weighted by
the probability of occurrence. Graphically, the expected losses are represented by the area above
the cumulative distribution curve. However, it has to be kept in mind that disasters are not
average events, rather they are extreme events occurring very rarely. Over a specified time
period, like 100 years, catastrophes may occur, and the losses suffered over this period will be
close to the sum of annual expected losses over these years.Based on available information,
potential losses due to earthquake events in terms of percent of capital stock lost can be
established for a country, state or region.


3.2 Step 2: Estimation of the public sector’s financial resilience
Given limited resources to reduce human and economic losses, governments need to be
financially resilient, or be able to provide sufficient funds to finance reconstruction of public
capital, provide relief to households and support business in their recovery efforts. Sources of
funding for reconstruction include aid, budget diversion as well as multilateral and international
lending. However, these are not infinite and come at a cost.


Based on the information on direct risks to the government portfolio, financial resilience can
be evaluated by assessing the government’s ability to finance its obligations for the specified
disaster scenarios. Financial resilience is directly affected by the general conditions prevailing in
an economy, i.e., changes in tax revenue have important implications on a country’s financial
capacity to deal with disaster losses. The specific question underlying the CATSIM tool is
whether a government is financially prepared to repair damaged infrastructure and provide
adequate relief and support to the private sector for the estimated damages of 10- 50- 100- and
200-year events? For this assessment, it is necessary to examine the government’s sources,
including sources that will be relied on (probably in an ad hoc manner) after the disaster and
sources put into place before the disaster (ex ante financing). These sources are described below
(based on Mechler, 2004 and Hochrainer, 2006).






15



Ex post financing sources
The government can raise funds after a disaster by accessing international assistance, diverting
funds from other budget items, imposing or raising taxes, taking a credit from the Central Bank
(which either prints money or depletes its foreign currency reserves), borrowing by issuing
domestic bonds, borrowing from the IFIs and issuing bonds on the international market (Benson,
1997 a,b,c; Fisher and Easterly, 1990). Each of these financing sources can be characterized by
costs to the government as well as factors that constrain its availability, which are assessed by this
CATSIM module (see table 2).


Aid inflows from abroad after a catastrophe include private and public donations from private
institutions, government agencies and inter-governmental agencies in the form of relief, technical
assistance, grants, commodities and money (Albala-Bertrand 1993). The amount of aid is as
much dependent on the event as on the will of the donors to grant assistance. Thus there is
considerable uncertainty as regards the amount of aid obtained post-catastrophe necessitating a
case by case examination. As discussed, a value of 10.4% of direct losses for this parameter was
estimated. It is assumed that all aid inflows will be divided up between the public and the private
sector in relation to their share of infrastructure (government) and non-infrastructure (private
sector) in total capital stock. As there is uncertainty whether aid will in fact be made available,
the availability of aid is assumed to be constrained in three scenarios: 0, 50, 100% made
available, i.e. 0% of losses are financed by aid, 5.2% and 10.4%. These scenarios will be looked
at in combination with the scenarios on the availability of foreign borrowing as is explained
below.



Table 2: Ex Post and ex-ante financing sources for relief and reconstruction


Type Source Considered in model


Ex-post sources


Decreasing government
expenditures Diversion from budget x


Raising government revenues Taxation -


Deficit financing
Domestic


Central Bank credit -


Foreign reserves -


Domestic bonds and credit X
Deficit financing
External


Multilateral borrowing X
International borrowing X
Aid X


Ex-ante sources


Reserve funds X
Insurance X
Contingent Credit X




Budget diversion means using funds that were earmarked for other purposes and thus implies
foregoing the returns and benefits of these projects. As well, there is often high political cost to
diversion when money is taken from ministries. It is assumed that the government is able to






16


divert some funds from government spending to reconstruction activities. In recent research
maximum diversion post-disaster for the four Latin American countries Bolivia, Colombia,
Dominican Republic and El Salvador was estimated at 5-10% of current expenditure
(government spending) (Freeman et al. 2002b: 35). For this report, we use an average value of
7.5% for both Honduras and Argentina.


Establishing additional taxes after a catastrophe will decrease private savings when
consumption is to stay constant and exert additional depressionary effects on the economy.
Furthermore, disaster taxes are expensive to administer. For this reason, no additional tax
revenue is assumed.


Given a budget deficit, deficit financing options are accessing credit from the Central Bank
or the private sector (commercial banks and private households), tapping into the foreign
reserves of the central bank, obtaining loans from IFIs or selling bonds abroad (Benson 1997c).


Central bank credit is usually granted by selling government bonds to the Central Bank
resulting in money creation which is potentially inflationary if money growth is not held in
proportion to real GDP growth (Fischer and Easterley, 1990). Using foreign exchange reserves
of the central bank creates the potential for a balance-of-payment crisis due to the lack of needed
reserves for imports. The sources reserves and central bank credit are generally considered to be
particularly problematic, e.g. an assessment of a World Bank and IMF team on reconstruction
financing options in El Salvador after the earthquakes in 2001 stated:


Under any monetary system, a country needs to maintain a strong underlying fiscal
position and a sound credit policy, with an adequate cushion of net international reserves,
to preserve macroeconomic stability. Expanding the money supply or reducing the central
bank’s net international reserves are never optional sources of financing for
reconstruction costs. (IMF and World Bank, 2001: 5).


Central Bank credit and tapping into reserves are used in practice as deficit financing sources,
but from a normative planning point of view, they should not be considered in the case study
countries in Latin America where inflation and external debt issues are important policy issues
(Ferranti, 2000: 61). For these reasons, these two sources will not be considered as viable sources
for ex-post catastrophe finance in this report.


Borrowing domestically also incurs costs: domestically, credit may be compressed
particularly so in shallow credit markets resulting in a rise of the interest rate and a crowding-out
of domestic investment. Borrowing from the private sector via issuing domestic government
bonds is another option. However, it is a common characteristic that in developing countries
domestic bond or financial markets are rather shallow (Ferranti 2000). We assume 10%
additional government borrowing from the private sector, which seems an optimistic assumption
given the post disaster crunch and shallow domestic financial markets in most of the disaster
vulnerable countries studied.


A major source of a country’s ex-post disaster funding is foreign borrowing. The importance
of (foreign) borrowing for reconstruction is demonstrated by the following statement that also
came from the post-earthquake IMF and World Bank mission to El Salvador.


From the standpoint of macroeconomic policy, the key question is how much and how
rapidly can the government afford to borrow to finance the reconstruction costs, while
keeping fiscal policy on a sustainable path (IMF and World Bank, 2001).






17


We consider borrowing to be constrained by the existing country debt. CATSIM assumes that the
sum of all loans cannot exceed the so-called credit buffer for the country. In the Highly Indebted
Poor Countries Initiative (HIPC) the credit buffer is defined as 150% of the typical export value
of this country minus the present value of existing loans (HIPC, 2002).



Ex ante financing sources
In addition to accessing ex post sources, a government can arrange for financing before a disaster
occurs. Ex ante financing options include reserve funds, traditional insurance instruments (public
or private), alternative insurance instruments, such as catastrophe bonds, or arranging a contingent
credit. The government can create a reserve fund, which accumulates in years without
catastrophes. In the case of an event, the accumulated funds can be used to finance reconstruction
and relief. A catastrophe bond (cat bond) is an instrument whereby the investor receives an above-
market return when a specific catastrophe does not occur, but shares the insurer’s or government’s
losses by sacrificing interest or principal following the event. Contingent credit arrangements call
for the payment of a fee for the option of securing a loan with pre-arranged conditions after a
disaster. Insurance and other risk-transfer arrangements provide indemnification against losses in
exchange for a premium payment. Risk is transferred from an individual to a (large) pool of risks.
These ex-ante options can involve substantial annual payments and opportunity costs; statistically
the purchasing government will pay more with a hedging instrument than if it absorbs the loss
directly. While a number of countries have reserve funds implemented (albeit generally with low
nominal amounts), insurance and contingent credit options are only currently being considered
with prime examples being Mexico, Colombia and the countries participating the in the Caribbean
pool. Table 3 graphically shows the ex post and ex ante instruments that can be accessed to
finance post-disaster needs. Another critical point suggested in this chart is the time dimension,
which generally is in favour of ex ante instruments releasing financing rather quickly.



































18


Table 3: Ex-post vs. ex ante financing instruments.


Traditional RTParametric
RT,
Contingent
debt


Reserve fundEx-ante financing


Donor assistance
Tax increase


Domestic/
external credit


Relief
Budget
reallocation


Budget
contingencies


Ex-post financing


Financing tools


Reconstruction


Recovery


Relief


Financial needs for
post disaster
operations


Long term
Over 9 months


Medium term
3-9 months


Short term
1-3 months


Immediate
hours/days



Source: Cummins and Mahul, 2009.


3.3 Step 3: Assessment of financial vulnerability and the “resource gap”
Using the information on direct risks to the government portfolio and financial resilience,
financial vulnerability can be evaluated. We define financial vulnerability as the (probabilistic)
availability of government finances for paying for relief and reconstruction. The resource gap is
the difference between the cost of a disaster (step 1), and the funds available to the government
to rebuild and provide relief and assistance with recovery efforts.7 Figure 7 illustrates the
calculation of this metric for a hypothetical case.


Given losses due to a certain event, such as the 100 year event (public sector loss of 4,000
local currency units (LCU)), the algorithm evaluates the sources for funding these losses. An
implicit ordering of these sources is assumed according to the availability and marginal
opportunity costs of the sources: grants from donors and international financial institutions (IFIs)
would have the least costs associated as these are donations; thus they would be used first.
Second, diversions from the budget could be used, then domestic credit, followed by borrowing
from IFIs and the international markets (bonds). While in this illustration a 100 year event could
be financed, for a 200 year (public sector loss of 10,000 LCU), there would be lack of (ex-post)
sources and consequently a resource gap. Ghesquiere and Mahul (2007) added another important
dimension related to the timing of resource flows.



7 The term resource or financing/resource gap has been heavily used in the economic growth modeling literature as


the difference between required investments in an economy and the actual available resources. In this report, this
tradition is followed and the financing gap is understood as the lack of financial resources to restore assets lost
due to natural disasters and continue with development as planned.






19


Financing sources: financing supply


0


2


4


6


8


10


12


Amount available


M
ar


gi
na


l c
os


t


Diversion


International bonds


Domestic bonds
and credit


Borrowing
from IFIs


Grants


Loss function: financing needs


200 year event
100 year event


10 year event


0


0.02


0.04


0.06


0.08


0.1


0 2,000 4,000 6,000 8,000 10,000 12,000
Losses in LCU


Pr
ob


ab
ili


ty


resource
gap



Fig. 7: Illustration for calculating the disaster resource gap




As shown in figure 7, while enough funding may be available over time, yet there may be a
sporadic resource gap, as generally in the aftermath of a disaster event, urgent expenditure needs
are high and immediately available financial resources often very limited. As an example how
the resource gap is calculated here we refer to box 3.


Due to the focus of this work in step 3, we do not go into more detail on steps 4 and 5 in this
report but rather present a small overview for the sake of completeness of the CATSIM
methodology.


3.4 Step 4: Illustrating the developmental consequences of a resource gap
Financial vulnerability can have serious repercussions on the national or regional economy and
the population. If the government cannot replace or repair damaged infrastructure, for example,
roads and hospitals, or provide assistance to those in need after a disaster, this will have long-
term consequences. The consequences on long-term economic development can be illustrated by
the CATSIM tool. Generally, economic welfare will be higher on average if the government does
not allocate its resources to catastrophe insurance or other risk management, but the economy
has fewer extremes and is more stable with public sector insurance. Investing in the risk
financing instruments can thus be viewed as a trade-off between economic growth and stability.
Budgetary resources allocated to catastrophe reserve funds, insurance and contingent credit (as
well as to preventive loss-reduction measures) reduce the potential resource gap, and thus can
ensure a more stable development path. On the other hand, ex ante financing and prevention
measures come at a price in terms of other investments foregone and will inevitably have an
adverse impact on the growth path of an economy.







20


Box 3: Calculating the resource gap for Grenada




We show the approach for the case of Grenada. The Figure below shows a user interface of the IIASA
CATSIM model for assessing the financial vulnerability of Grenada, a small-island Caribbean country, to
cyclone risk (see also Hochrainer and Mechler, 2009).







The year events and corresponding losses can be seen on the x-axis (hazard) and y-axis (losses) of the
graph. For each year event, some losses for the government occur, e.g. a 20 year event would cause losses
for the government of approximately 120 million USD. The money to finance the losses can be separated
into outside assistance, diversion from the budget as well as credit arrangements. However, as the
probabilities of disaster events go lower and losses get higher, e.g. the higher the return period, more
money is needed till the maximum capacity is reached. In the case of Grenada a 67 year event would
cause for the first time a resource gap in the graph. Assuming more restricted credit and diversion
possibilities this resource gap would go done to even a 20 year event (the mathematical formulation of the
problem can be found in the Appendix A).




3.5 Step 5: Reducing financial vulnerability and building resilience
Vulnerability and resilience must be understood as dynamic and can be tackled. There are two
types of policy interventions: those that reduce the risks of disasters by reducing exposure and
sensitivity and those that build financial resilience of the responding agencies. Based on an






21


assessment of the resource gap and potential economic consequences, CATSIM illustrates the
pros and cons of strategies for building financial resilience using ex-ante financial instruments.
Below, we suggest how risk financing instruments may be supported. Due to the scope of this
report, we here do not go into more detail on steps 4 and 5, which would merit separate
discussions (see Mechler et al., 2006, Hochrainer, 2006).




Uncertainty
Uncertainty is inherent in every estimation procedure, some of these uncertainties can be
quantified some of them not. Also in this report, large uncertainties around the estimates still
remain, mostly due to lack of additional data for recalibration and back-testing. This is a quite
usual thing for low frequency but high consequence events today and will only get better in the
future if loss reports of disaster events are based on a holistic methodological approach which
can be applied for in all areas of the world for as much hazard types as possible. In this report,
the losses and corresponding probabilities are based on models or past data; in the latter case due
to the usually small number of data points, the parameter estimates have large confidence
bounds. In this report it was decided to use the mean estimates and neglect the confidence
bounds because of violation of some assumptions like minimum amount of data points necessary
for variance calculations and so on, which would lead to biases in the uncertainty bounds. Also
for the exposure estimates, data were used that were based on simplified versions of capital stock
estimation techniques, such as relying on the Penn World tables and selecting a specific
depreciation rate. However, for such a large number of countries, and the necessity to have
capital stock estimates for all countries, only such approaches can be used. The government
liabilities are also taken to be the same in percentage for all countries, an assumption which is
not valid if only a country specific approach is taken. However, here it serves well, also because
such assumptions can be found empirically in developed as well as developing countries after
catastrophe events. Furthermore, the estimation of the resilience of the government is also very
difficult, either because data is not there or it is difficult to estimate the different financing
sources in each case. Also here, average results are used were appropriate or estimated via
econometric methods using databases such as the World Bank. Last but not least, because the
loss financing situation for each country is also time dependent, e.g. a disaster within a strong
growth period might be less disastrous then in a recession, the loss estimates are only valid for a
certain time period. All in all, the numbers presented should be regarded as ballpark numbers and
not taken for granted forever or exact without mentioning the uncertainties in it. They numbers
however should serve as a starting point at which scale we have a problem and how it could be
possibly handled within a generic risk management approach.




3.6 The data sample
We calculate financial vulnerability for the 73 most weather prone, vulnerable countries that
have had at least one large sudden-onset natural disaster event over the last 30 years but also
selected some countries where it was feasible to estimate risks based on observed losses. A large
event is defined by a threshold of a ratio of (asset) losses expressed per GDP of larger than 1%.
For those countries we define risk, loss distributions and financial exposure. As a next step, we
assess financial vulnerability. Financial vulnerability is indicated in terms of resource gap
recurrence, i.e. it indicates an event and recurrence of this event, for which government financial






22


funds would be insufficient to cover government disaster liabilities caused by the event. Table 1
lists data sources employed for our estimation.



Table 4: Data used in the assessment


Variable Data source


Disaster losses (USD) EMDAT, Munich Re databases


Risk estimates 1. Cummins and Mahul (2009) based on various sources.


2. IIASA estimates


Capital stock data Sanderson and Striessnig (2009)


Socioeconomic data World Bank, 2009




The disaster loss sample is based on information from two databases and was compiled by
Okuyama (2009) with the threshold for a large event defined arbitrarily by a loss exceeding 1
percent of GDP.8 One database is the open-source EMDAT disaster database (CRED, 2009)
maintained by the Centre for Research on the Epidemiology of Disasters at the Université
Catholique de Louvain. The other database is the proprietary Munich Re NatCat Service
database, which mainly serves to inform insurance and reinsurance pricing. Probabilistic risk
estimates are based on two sources: (i) a recent report by Cummins and Mahul (2009), who
report such estimates from various sources for a number of countries, (ii) IIASA estimates of risk
using extreme value statistics. Capital stock data were used from a new global database also
generated IIASA by Sanderson and Striessnig (2009). Finally, other socioeconomic data were
taken from the World Development Indicators (World Bank, 2009).





















8 In order to define the “event set” the threshold of stock losses is set as a share (1%) of flow effects (GDP). It would


have been more systematic to define an asset based threshold, yet we responded to the larger intuitive appeal of
using GDP as a denominator.






23


4 Findings and discussion
We find that a number of countries are highly financially vulnerable. As shown in Figure 8, the
majority of the disaster prone countries in our sample experience a disaster financing problem, a
resource gap, below a 50 year event.

























Fig. 8: Number of countries experiencing a resource gap for some return periods




Figure 9 shows how risk as measured in expected losses translates into an estimate of financial
vulnerability based on assessments of financial and economic resilience. No clear relationship is
discernible between the losses as percent of GDP and the resource gap (in terms of a
probabilistic return period) and the resource gap for a 100 year event, which shows that the
translation is a more complex one and GDP as a mere indicator of vulnerability may not suffice.




-


100


200


300


400


500


0% 2% 4% 6% 8% 10%
Expected losses as % GDP


Fi
na


nc
e


ga
p


ye
ar


0%


50%


100%


150%


200%


250%


0% 2% 4% 6%
Expected losses as % GDP


10
0


ye
ar


fi
na


nc
e


ga
p



Fig. 9: Risk vs. financial vulnerability




Count rie s Experienc ing Init ia l Resourc e Gap Betw een
Spec if ied Return Periods


0


5


10


15


20


25


30


35


40


45


50 Y ears o r
Less


51 t o 100
Y ears


101 t o 250
Y ears


No Gap W it hin
Periods


Return Periods


C
o


u
n


tr
ie






24


In figure 10 the countries’ resource gap year events are shown. We find the following countries
to be particularly financially vulnerable


 Small Island Development States (SIDS), such as the Caribbean and Pacific Islands.
 Highly indebted and hazard prone countries, such as in Central America (Honduras,


Nicaragua, El Salvador), Africa (Madagascar, Mozambique) and Asia (Nepal).





Fig. 10: Global map exhibiting calculations of the resource gap year




What does this mean in terms of implications for financing losses and risks? To answer this
question, we calculate losses and gaps for the 50, 100, and 250-year events (see figures 11, 12
and 13), and discuss implications of these estimates in the following.







25



Fig. 11: 50 year resource gap estimates





Fig. 12: 100 year resource gap estimates







26



Fig. 13: 250 year resource gap estimates




Implications for supporting the management of climate variability and change in exposed
countries
For financially vulnerable countries, we see three main implications.


1. In such countries, efforts to reduce risk need to be seriously stepped up in order to reduce the
serious human and financial burdens to the affected population, business and fiscal stance.


2. The second implication is that in case of high financial vulnerability, contrary to the Arrow
Lind Theorem (1970), there is a case for country risk aversion; this means, that financial
disaster risks faced by the government cannot be absorbed without major difficulty. Risk
aversion calls for deliberating to prefinance losses and relief expenditure by way of risk
financing instruments, such as calamity funds, regional insurance pools or contingent credit
arrangements. In fact, some of the countries found financially vulnerable are exactly doing
this already and the Caribbean Catastrophe Insurance Pool is probably the best known
example.


3. The third implication is that, without exception, all financially vulnerable countries due to
their development status are very unlikely to be able to implement pre-disaster risk financing
instruments themselves in order to reduce their financial vulnerability out of their own means
and require technical and financial assistance from the donor community. There are
important precedents such as


 The World Food Programme (WFP) with USAID funding sponsored an index-based
drought insurance scheme for government relief expenditure in Ethiopia.


 In the Caribbean case mentioned above, where island states have recently formed the
world’s first multi-country and index-based catastrophe insurance pool for providing






27


governments with immediate liquidity in the aftermath of hurricanes or earthquakes,
donors and IFIs have provided significant capital to the extent of 50 million USD. This
funding helps to back up the pool in its early years when accumulated country
contributions are insufficient to render this scheme robust to withstand major events such
as hurricanes.



How much money would a pool require to fill the funding gap post-disaster for all disaster
prone countries?
Our estimates may also inform decisions pertaining to a “climate insurance fund,” which would
fund “high level” country losses that exceed the ability of any given country to pay in the case of
an extreme event. Figure 14 shows the funding requirement for different layers of disaster event
recurrence. If for example, funding would be set aside to cover resource gaps for more frequent
events with a return period of 50 to the 100 year events, than about 1.4 billion USD would be
required annually. Covering more infrequent losses as well (such as up to a 250 year event),
would mean that more funding would be required, up to 2.6 billion USD. Also, other layers, such
as 100-250 year resource gap layers, could be considered to be covered with associated funding
requirements. Bundling many risks in a portfolio leads to a diversification effect and thus lower
funding requirements. We took the simplifying assumption that risks would be independent.
Thus the estimates of the funding requirements have to be considered a lower bound.
Furthermore, we only looked at average costs. However, it would be important to consider the
whole range of possible costs and associated probabilities, i.e. a global cost curve would be
needed. Hence, one research direction for the future is to come up with such a cost curve to
incorporate the variability in more detail and accordingly to introduce risk functionals (Pflug and
Römisch, 2007).


0 0.5 1 1.5 2 2.5 3


Global annual cost (billions 2008 USD)


50 - 100


50 - 250


100 - 250


100 - 500


250 - 500


R
et


ur
n


Pe
rio


d
in


y
ea


rs



Fig. 14: Funding requirements to cover resource gaps for different layers of return periods


Our estimates can be used to gauge the support needed by vulnerable countries to buffer against
extremes and the scale of funding necessary when implementing a global fund for this purpose.






28


Our findings may also be used for identifying the scope of the problem of managing climate
variability today and in the future, and may thus be interpreted as a sort of baseline estimate for
what may be to come, if we do not manage emission reductions and adaptation to climatic risks
better. Comparing our estimates of the costs of financing high loss layers of sudden onset events
with the at least as uncertain numbers currently available regarding the costs of adaptation to
both slow and sudden onset climate variability and change, we arrive at the lower end of the
estimates of the prominent UNFCCC (2007) report. For example, for adapting infrastructure
alone to climate change impacts (including extreme events), the UNFCCC calculates a broad
range of 8 –130 billion USD necessary in 2030 (in 2030 values) (UNFCCC, 2007). Others have
even put their estimates a magnitude higher, yet essentially these, as well as our numbers are
associated with uncertainty, and remain imprecise. However, as societies have found many ways
for coping with uncertainty, such quantitative estimates may be sufficiently precise to at least
start a process for thinking about appropriate options and courses of action for absorbing current
and future extreme event risks.







29


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31


Appendix A: Calculating the resource gap (Hochrainer, 2006):


Assume that there is a strict order between financing instruments, represented lexicographically


by the vector  ),...,1( pkxpxpx , so that the first entry (instrument) would be preferred until
depletion of the other instruments. Let  ),...,1( kxxpb



be the maximal amount for each


instrument for a given event. Then the loss financing scheme  ),...,1( pkxpxpx for a given
event with return period y/1 is the solution of:







where y/1 is the critical return period if pbpx
  . Since pb



is nonlinearly dependent on the


return period, the relative composition of px
 is different for every event size.







32


Appendix B: Additional tables


Table B1: Resource gaps
Resource gap (in bn 2008 USD)


Country
Resource
gap year
event


20 year
event


50 year
event


100 year
event


250 year
event


500 year
event


Algeria 144 0.000 0.000 0.000 0.423 0.705
Anguilla 122 0.000 0.000 0.000 0.029 0.043
Antigua and Barbuda 19 0.004 0.035 0.220 0.447 0.522
Argentina 153 0.000 0.000 0.000 0.208 0.367
Armenia 50 0.000 0.001 0.256 3.263 4.265
Australia 447 0.000 0.000 0.000 0.000 0.260
Austria 550 0.000 0.000 0.000 0.000 0.000
Bahamas, The 51 0.000 0.000 1.192 2.399 2.802
Bangladesh 14 2.635 4.209 5.189 6.864 7.422
Barbados 124 0.000 0.000 0.000 0.181 0.269
Belize 16 0.044 0.082 0.246 0.453 0.522
Bermuda 93 0.000 0.000 0.017 0.348 0.458
Bolivia 19 0.006 0.056 0.257 4.192 5.504
Brazil 358 0.000 0.000 0.000 0.000 2.467
Cambodia 53 0.000 0.000 0.045 0.236 0.300
Canada 417 0.000 0.000 0.000 0.000 1.056
Cayman Islands 23 0.000 0.050 0.906 2.095 2.492
China 368 0.000 0.000 0.000 0.000 5.476
Comoros 15 0.008 0.143 0.622 0.623 0.624
Costa Rica 36 0.000 0.072 1.438 12.505 16.194
Czech Republic 115 0.000 0.000 0.000 2.800 3.973
Denmark 550 0.000 0.000 0.000 0.000 0.000
Dominica 85 0.000 0.000 0.015 0.128 0.166
Dominican Republic 18 0.131 0.386 21.512 24.786 25.877
Ecuador 106 0.000 0.000 0.000 6.299 8.550
Fiji 22 0.000 0.043 0.093 0.229 0.275
Grenada 60 0.000 0.000 0.080 0.292 0.363
Grenadines 19 0.001 0.017 0.162 0.372 0.442
Guatemala 48 0.000 0.015 0.701 6.318 8.191
Guyana 25 0.000 0.059 0.192 0.911 1.151
Haiti 81 0.000 0.000 0.023 0.162 0.208
Honduras 16 0.056 0.633 1.813 4.049 4.794
Hungary 113 0.000 0.000 0.000 1.570 2.206
India 116 0.000 0.000 0.000 20.370 29.131
Indonesia 305 0.000 0.000 0.000 0.000 0.836
Iran, Islamic Rep. 550 0.000 0.000 0.000 0.000 0.000
Jamaica 13 1.027 1.386 4.075 7.390 8.495
Japan 550 0.000 0.000 0.000 0.000 0.000
Kazakhstan 544 0.000 0.000 0.000 0.000 0.000
Lao PDR 60 0.000 0.000 0.084 1.953 2.576
Latvia 48 0.000 0.003 0.185 0.311 0.353
Madagascar 36 0.000 0.005 0.227 0.548 0.654
Malaysia 550 0.000 0.000 0.000 0.000 0.000
Maldives 61 0.000 0.000 0.027 0.235 0.304
Mauritania 13 0.041 0.682 0.682 0.687 0.689
Mauritius 25 0.000 0.706 5.818 7.344 7.853






33


Mexico 135 0.000 0.000 0.000 16.909 26.657
Moldova 141 0.000 0.000 0.000 0.052 0.084
Montserrat 22 0.000 0.002 0.016 0.038 0.045
Morocco 127 0.000 0.000 0.000 0.321 0.481
Mozambique 30 0.000 0.049 0.530 3.916 5.044
Nepal 108 0.000 0.000 0.000 0.867 1.190
Nicaragua 52 0.000 0.000 2.394 6.150 7.402
Nigeria 39 0.000 0.003 0.032 0.183 0.234
Pakistan 52 0.000 0.000 19.953 20.332 20.458
Peru 84 0.000 0.000 0.054 0.058 0.060
Philippines 123 0.000 0.000 0.000 2.286 3.374
Poland 179 0.000 0.000 0.000 1.924 4.223
Russia 550 0.000 0.000 0.000 0.000 0.000
Samoa 11 0.083 0.211 0.367 0.631 0.719
Senegal 71 0.000 0.000 0.004 0.048 0.063
South Africa 133 0.000 0.000 0.000 11.077 17.323
Spain 550 0.000 0.000 0.000 0.000 0.000
Sri Lanka 62 0.000 0.000 0.823 24.558 32.469
St. Kitts and Nevis 63 0.000 0.000 0.097 0.328 0.406
St. Lucia 117 0.000 0.000 0.000 0.055 0.078
Sudan 22 0.000 0.351 0.801 2.250 2.733
Tajikistan 14 0.061 0.269 0.575 2.162 2.690
Tonga 13 0.005 0.020 0.043 0.158 0.196
Turks and Caicos
Islands


68 0.000 0.000 0.053 0.226 0.283


United States 550 0.000 0.000 0.000 0.000 0.000
Vanuatu 13 0.011 0.064 0.182 0.000 0.182
Venezuela, RB 40 0.000 0.541 19.528 36.708 42.435
Zimbabwe 19 0.004 0.279 0.868 6.450 8.310



Table B2: Funding requirements to cover resource gaps for different layers of return periods


Layer from event year to event
year


Annual cost (bn 2008 USD)


50 - 100 1.4


50 - 250 2.6


100 - 250 2.4


100 - 500 2.6


250 - 500 1.4





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