## Box 5. Developing domestic catastrophe risk insurance markets in Fiji

TC Winston highlighted a major protection gap: 94 percent of houses are uninsured against tropical cyclones. The building stock in Fiji comprises approximately 240,000 residential homes (Pacific Risk Information System – PacRIS). With only 15,000 homes (or 6 percent of the housing stock) purchase coverage against tropical cyclone, a significant gap in insurance coverage emerges. The supply of TC coverage within the domestic insurance industry is limited because insurers are unable to secure reinsurance capacity. Insurance companies can only supply property catastrophe insurance for houses of a high construction standard and a current engineer’s certificate which is required by the international reinsurers to provide reinsurance.

Most low‐income households do not meet the standard required to purchase an engineer’s certificate which demonstrates that there is market failure creating a major contingent liability for the GoF. Uninsured households create an implicit liability or social obligation for the GoF to provide financial assistance in the aftermath of a disaster, as was seen in the case of TC Winston where GoF has spent FJ$80 million to support the reconstruction of housing.

The World Bank and the IFC have been developing two pilot catastrophe risk insurance schemes to test the viability of market‐based insurance solutions to provide household coverage against tropical cyclones. The first product is property insurance for houses of low‐build quality that are still deemed insurable with some basic reinforcement, such as roof strapping. Such houses are estimated to represent half of the housing stock, or approximately 121,000 households, in Fiji. The second product is a parametric‐based livelihoods (protection) insurance product for households with non‐insurable assets, which account for approximately 68,000 households.

# Empirical Assessment of Linkages

This section reviews countries’ exposure to macro‐financial and climate related risks. We start with a discussion of physical and transition risks and then seek to identify countries that are subject to “double jeopardy” due to the simultaneous presence of both risks. The data and analysis presented in this section are subject to a range of caveats and should thus be considered as an initial illustration of macro‐financial – climate risk linkages. The chosen climate risk indicators provide a broad indication of countries’ vulnerability. However, there is ongoing work on some of these risks (e.g., transition risks) to develop better indicators. Second, country coverage is limited for some of the indicators. The listing of countries that are subject to both elevated macro‐financial and climate related risks can thus not claim to capture all countries that may face high twin risks. Another caveat is that the classification of countries according to climate related risks is very simple, dividing countries for each indicator into two groups, depending on whether they are above or below the median of the indicator. Additional work will be required to identify more appropriate thresholds for risk categories. Further analysis will be required to deepen the understanding of macro‐financial – climate risk linkages and to address some of these caveats.

### _Measuring climate‐related and macro‐financial risks_

This section presents information on four sets of indicators used to measure climate change‐related physical (linked to extreme weather events and gradual global warming) and transition risks (linked to high carbon intensity or high fossil fuel exports). For the measurement of macro‐financial risks, we use macro‐financial risk indicators generated by the Economist Intelligence Unit (EIU).

### _Extreme weather events_

We use data from the Emergency Events Database (EM‐DAT) compiled by the Centre for Research on the Epidemiology of Disasters (CRED) as the measure of countries’ vulnerability to extreme weather events.

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