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Formerly Moody’s RMS

This is the second blog in a series of four blogs examining three potential “protection gaps” and the importance of “protection gap analytics”. To read the first blog post in this series, click here.

Year-by-year, we can check to see if the gap between insured and economic disaster losses in emerging economies is starting to shrink. The gap remains resolutely stuck in the range 80 to 100 percent uninsured. Even a 90 percent average flatters the proportion, as coverage is concentrated in high value hotels, factories and central business districts whereas almost all ordinary houses are without insurance.

We should not be surprised how the emerging markets gap stays so wide.

See what happened in Japan. Unregulated mass rebuilding after the war led to a rising toll of flood disasters. In one single year in the 1950s, more than a million properties were flooded. Then in 1959 there was Typhoon Vera and the Ise Bay storm surge flood catastrophe in which more than 5,000 died. In 1960 the Government declared the level of risk to be intolerable and directed that seven to eight percent of government expenditure should be invested in funding disaster risk reduction. The annual investment proved successful and by the 1980s the annual number of houses flooded had reduced to only three percent of its 1950s level.

For any emerging economy the question can be asked: when did the nation reach the equivalent of Japan in 1960 and start to invest in disaster risk reduction. China passed the point of “intolerable disaster risk” towards the end of the 1990s, while India is undergoing that transition today. This is not just investment in physical disaster risk reduction, but also good risk governance and education.

Insurance is a product of this disaster risk management culture.

There are five preconditions for getting property insurance to thrive:

i) The government needs to have a strategic focus on actions around improved risk management.

ii) Property owners need to understand how insurance works, that in a typical year there will be no recovery from paying the annual premium.

Iii) Insurers should have information on the “landscape of risk”, as determined by the underlying hazards and previous loss experience.

iv) The insureds need to supply data on the specific exposure being insured so that insurance can be properly priced.

v) The home, or business, owner needs sufficient disposable income to afford the premiums.

An additional factor concerns the potential for high mortality disasters. Unless the insurance coverage is more or less universal, after a disaster with many deaths, governments will not be prepared to stand back and allow restitution to be handled by an impersonal insurance mechanism.

The death toll in the 2008 Wenchuan earthquake in China was 69,000 while less than one percent of the 845 billion Yuan (US$130 billion) economic loss was repaid by insurance. For this reason, high disaster mortality is likely to correlate with wide protection gaps. In the 2016 central Italy earthquakes, out of US$43 billion in economic losses only US$9 billion was recovered from insurance; a protection gap of 79 percent, but closer to 98 percent for residential properties. More than 100,000 people have died in earthquakes in Italy since 1900. Perhaps it is the potential for high mortality disasters, which sustains a wide protection gap?

There have been many experiments to try to seed insurance in emerging markets. We should learn from the results. No disaster microinsurance scheme for the poor has been self-sustaining once the initial funding has been withdrawn.

In meso-insurance schemes, index-based insurance is provided to cooperatives or associations that represent their members. This transfers responsibility for exposure definition and claims management. However, one meso-insurance scheme failed because the cooperative leadership was unwilling to differentiate payments to members according to which of them had actually suffered a loss.

There has been better experience of sovereign insurance schemes, as with the Caribbean Catastrophe Risk Insurance Facility (CCRIF) system. According to a parametric index trigger, after a disaster the CCRIF provides governments financial assistance with their immediate funding needs, making payment within fourteen days. With Hurricane Matthew in 2016, the CCRIF paid out US$29 million or about one percent of the total economic losses in the Caribbean excluding Bahamas. It would have paid out a similar amount again if Bahamas had renewed their premium for the year — when CCRIF would have refunded 1.4 percent of the total loss in these islands.

In the 2017 Hurricanes Irma and Maria, the CCRIF paid out US$31 million for Irma losses in Barbuda, Anguilla and the Turks and Caicos, representing around three percent of their US$950 million economic losses. For Maria, the CCRIF paid out US$19 million to Dominica, 1.3 percent of the total economic loss estimated as U$1.4 billion.

Irma DFiD
An upturned car lies crushed underneath a shipping container outside a house, on the British Virgin Island of Tortola, 11 September 2017. The island was badly damaged by Hurricane Irma on 6 September. Image: Flickr/DfID

Based on the recent hurricanes, the CCRIF reduces the protection gap by one to three percent. Insurance varies significantly across the Caribbean from being almost completely absent in Haiti to the situation in the Bahamas after Hurricane Matthew in 2016, when around 40 percent of the US$1 billion economic loss was repaid by insurance. The lowest protection gap is found in French territories where the national “Cat Nat” system extends to the Caribbean departments. Anyone with property insurance is covered. In 2017 the scheme repaid some 1.2 billion Euros (US$1.39 billion) of the Irma damages, principally in St. Martin.

In 2018, the CCRIF announced some moderate growth with two new territories (British Virgin Islands and Montserrat) joining the scheme while other members raised their contribution by at least ten percent. However more rapid expansion seems unlikely — the current level of premium contributions is within the available expenditure of these islands. There is also an ever-present potential for basis risk, when an extreme event causes a significant loss, but the parametric formula does not trigger a repayment. Such situations are politically more tolerable when the payouts are limited.

One potential impediment to greater purchase of insurance could be any expectation that outside aid will arrive after a major loss. However, the total aid given by United Nations agencies and international bodies to Dominica in the six months after Hurricane Maria, amounted to less than US$30 million. U.S. aid to all the Caribbean islands worst impacted by the 2017 hurricanes was US$22.5 million, while the U.K. gave £15 million (US$19.3 million).

Emerging markets insurance schemes are likely to depend on subsidies, as in crop insurance programs designed to direct income support to poor farmers. For the Africa Risk Capacity sovereign drought insurance pool, it takes US$1.50 of investment to provide a US$1 of cover. The threshold for a drought payout has also been deliberately reduced so that participating countries can expect a recovery at least once in every five years, a lower threshold than is appropriate for efficient insurance.

How do we evaluate investment in risk reduction alongside risk transfer? At the end of a decade of risk transfer you have to continue the risk transfer. At the end of a decade of risk reduction, you have reduced the need or cost of ongoing risk transfer. Pointing to the protection gap, insurers will only champion an insurance solution. Yet it may be even more effective, as they discovered in Japan, to shrink the gap by reducing the risk, before sponsoring insurance.  Who has the role of selling a holistic solution?

Click here to read the third blog in this four-part series, entitled “The Intangibles Protection Gap“.

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Robert Muir-Wood
Robert Muir-Wood
Chief Research Officer, Moody's RMS

Robert Muir-Wood works to enhance approaches to natural catastrophe modeling, identify models for new areas of risk, and explore expanded applications for catastrophe modeling. Robert has more than 25 years of experience developing probabilistic catastrophe models. He was lead author for the 2007 IPCC Fourth Assessment Report and 2011 IPCC Special Report on Extremes, and is Chair of the OECD panel on the Financial Consequences of Large Scale Catastrophes.

He is the author of seven books, most recently: ‘The Cure for Catastrophe: How we can Stop Manufacturing Natural Disasters’. He has also written numerous research papers and articles in scientific and industry publications as well as frequent blogs. He holds a degree in natural sciences and a PhD both from Cambridge University and is a Visiting Professor at the Institute for Risk and Disaster Reduction at University College London.

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