Table 1: Government liabilities and disaster risk

|**Liabilities**|**Direct:**obligation in any event|**Contingent:**obligation if a particular<br>event occurs|
|---|---|---|
|**Explicit**<br>Government liability<br>recognized by law or<br>contract|<br>Foreign and domestic sovereign<br>borrowing, Expenditures by<br>budget law and budget<br>expenditures|State guarantees for nonsovereign<br>borrowing and public and private<br>sector entities,**reconstruction of**<br>**public infrastructure**|
|**Implicit**<br>A "moral" obligation<br>of the government|Future recurrent costs of public<br>investment projects, pension and<br>health care expenditure|Default of subnational government and<br>public or private entities,**disaster**<br>**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).<sup>4</sup> 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.<sup>5</sup>

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.

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