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 21