The global property protection gap in natural catastrophe risk has widened steadily over the past 40 years. In historical terms, we find that most underinsurance of extreme events is for climate-related events such as flood and windstorm, but in expected terms, earthquakes comprise the largest share of underinsurance. Using a framework to define the protection gap in historical and expected terms, this paper breaks down the gap by geography and risk type and presents an empirical analysis of the key drivers of the gap. First, uninsured expected Cat losses are estimated using models that combine geophysical vulnerability maps, economic exposure data and insurance market information. Second, each country’s expected (or optimal) property insurance penetration is modelled and compared to actual penetration to derive a measure of property underinsurance. Third, we explore the factors that affect property insurance demand, applying regression analysis to an unbalanced panel data set that includes 53 countries observed over a 15-year period. Several significant economic, financial market, sociodemographic, cultural and institutional variables are identified. The results lead to a taxonomy of the root causes of underinsurance and a set of proposed measures to narrow the protection gap.
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1977 to 2016 in 2016 USD terms; Source: Swiss Re database of natural catastrophes.
Esho et al. (2004).
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See Cummins and Weiss (2016) for a discussion on the stability of the U.S. property and casualty industry capitalisation over time.
Kousky and Cooke (2012).
Terrorism risks are examples of dependencies between individual risk scenarios due to their man-made character which limits their insurability. This is a key difference to Nat Cat risks which are mostly independent.
See Cummins and Barrieu (2013).
Park and Lemaire (2011).
Browne et al. (2000).
Kunreuther and Pauly (2004).
Lazo et al. (2014).
Tversky and Kahneman (1973).
Dillon et al. (2014).
Meyer et al. (2014).
Cameron and Shah (2012).
Browne and Hoyt (2000).
Aseervatham et al. (2013).
Kousky et al. (2013).
Old Dominion University Social Science Research Center (2016).
Cai et al. (2009).
Ernst and Young (2014).
Our model does not address how the underlying risk expectation changes due to long-term climate change scenario models, economic development or changes in insurance penetration. This is beyond the scope of this paper and we leave this for future research.
For example, earthquake losses are under-represented in the historical data. They account for 33 per cent of historical losses (1977-2016) but for 51 per cent of modelled exposures.
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Source for these data is Swiss Re's global Cat loss database. This view allocates all losses of one catastrophe to the dominant peril since the miscellaneous data sources do not allow a split-out. For example, flood losses from Hurricane Katrina are allocated to the wind category.
Our analysis shows that the relationship between economic development and insurance penetration is more significant if development is measured by consumption rather than GDP per capita.
The groups are defined for 2014 CPC < 10,000 USD; 10,000–25,000 USD; and CSP > 25,000 USD. See Table 4 in the Appendix.
We applied the average protection gap for each of the three income groups to multiply with the missing GDP for the countries not in groups.
See also Swiss Re (2015).
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For a review of cost–benefit analyses on disaster mitigation measures see Shreve and Kelman (2014).
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See Cole et al. (2013) regarding rainfall insurance in India.
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†This paper has been granted the 2017 Shin Research Excellence Award—a partnership between The Geneva Association and the International Insurance Society—for its academic quality and relevance by the decision of a panel of judges comprising both business and academic insurance specialists.
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Holzheu, T., Turner, G. The Natural Catastrophe Protection Gap: Measurement, Root Causes and Ways of Addressing Underinsurance for Extreme Events† . Geneva Pap Risk Insur Issues Pract 43, 37–71 (2018). https://doi.org/10.1057/s41288-017-0075-y