## **Box 5.  Probabilistic catastrophe risk modeling**

<mark>A government’s financial exposure can be developed using input from probabilistic catastrophe risk models. This technique was originally developed by the insurance industry to assess the risk on a portfolio of assets and is increasingly used by governments to assess their exposure to adverse natural events. A typical risk model is made</mark> of the following modules:

**_<mark>Hazard module</mark>_** <mark>:  This module defines the frequency and severity of potential perils (e.g. earthquake, tropical cyclone) at specific locations within the region of interest.  This is done by analyzing historical frequencies and reviewing scientific studies performed on the severity and frequencies in the region of interest.  This module then</mark> generates thousands of stochastic events based on historical data and experts’ opinions.

**_<mark>Exposure module</mark>_** <mark>:  This is a geo‐referenced database of assets at risk, assigning a list of attributes (e.g., exact location, contraction type, number of stories) for each asset.  This information is used to determine the area’s vulnerability, captured though vulnerability functions.  At a larger scale, for example when analyzing an entire</mark> country, proxies are used to define the vulnerability of entire neighborhoods or even cities.

**_<mark>Loss module:</mark>_** <mark>This module combines the hazard module and the exposure module to calculate different risk metrics, such as the</mark> _<mark>annual expected loss</mark>_ <mark>, which is an expression of the long‐term (for example, 1,000 years) average annual loss, and the probable maximum loss for a given return period, which represents the expected loss</mark> severity based on likely occurrence, such as the 1‐in‐50‐year loss or the 1‐in‐100‐year loss.

<mark>Risk matrices generated by probabilistic risk models can be used to complement historical analysis and are particularly useful to policy makers in assessing the probability of losses and the maximum loss that could be</mark> <u><mark>generated by major events (e.g. an earthquake affecting a major city or a cyclone affecting a major port).</mark></u>

# **Funding Gap Analysis**

Once a risk profile has been developed, potential funding needs can be contrasted with available resources. As discussed in previous sections, the time dimension must also be considered. Relief and recovery operations will require resources that can be mobilized immediately after a disaster, while reconstruction needs will generally require major resources in the medium term to the longer term (one year or more), respectively. Contrasting potential resource requirements with resource availability will help the identification and the quantification of potential **_disaster funding gaps_** during the relief, recovery and reconstruction phase. The Inter‐American Development Bank (IADB) Disaster Deficit Index (see Box 6) was a first attempt at developing a standard methodology to identify potential funding gap among Latin American and Caribbean countries (IADB, 2000).<sup>4</sup> A dynamic disaster funding gap analysis was recently conducted in Vietnam (World Bank 2010). The recovery funding surplus (gap) is defined as the difference between the resources available during the fiscal year and the post‐disaster emergency assistance and recovery expenditures, if positive (negative). The reconstruction surplus (gap) is defined as the difference between the reconstruction resources made available for investment in infrastructure rehabilitation (usually through reallocation of capital expenditures) and the post‐disaster reconstruction needs. In the case of Vietnam, it showed that while no recovery funding gap was observed over 2000‐09, major disasters would create a substantial recovery funding gap. It also showed that reconstruction funding gaps have been recurrent over the last

> 4 This index does not account for the time dimension: immediate post‐disaster resources (e.g., emergency assistance) are mixed with longer term resources (e.g., reconstruction).

15