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The Natural Catastrophe Protection Gap: Measurement, Root Causes and Ways of Addressing Underinsurance for Extreme Events


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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  1. 1977 to 2016 in 2016 USD terms; Source: Swiss Re database of natural catastrophes.

  2. Outreville (1990, 2011).

  3. Esho et al. (2004).

  4. Treerattanapun (2011).

  5. Mossin (1968).

  6. Eling et al. (2014).

  7. Cole et al. (2013).

  8. Demirguc-Kunt et al. (2008).

  9. Kousky and Kunreuther (2014).

  10. Born and Klein (2016).

  11. See Berliner (1985), Gollier (1997), Courbage and Liedtke (2003), and Kunreuther and Michel-Kerjan (2007).

  12. Kunreuther et al. (1995) or Courbage and Liedtke (2003)

  13. See Cummins and Weiss (2016) for a discussion on the stability of the U.S. property and casualty industry capitalisation over time.

  14. Kousky and Cooke (2012).

  15. 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.

  16. Ibragimov and Walden (2007); Ibragimov et al. (2009).

  17. See Cummins and Barrieu (2013).

  18. Outreville (2011); Feyen et al. (2011).

  19. Park and Lemaire (2011).

  20. Milliman (2017) and Swiss Re (2016a).

  21. Gilbert (2001).

  22. Browne et al. (2000).

  23. Kunreuther and Pauly (2004).

  24. Lazo et al. (2014).

  25. Tversky and Kahneman (1973).

  26. Kunreuther (2015).

  27. Dillon et al. (2014).

  28. Meyer et al. (2014).

  29. Cameron and Shah (2012).

  30. Gallagher (2014).

  31. Browne and Hoyt (2000).

  32. Aseervatham et al. (2013).

  33. Kousky et al. (2013).

  34. Cole et al. (2013); Eling et al. (2014).

  35. Old Dominion University Social Science Research Center (2016).

  36. FEMA (2014–2016).

  37. Cai et al. (2009).

  38. Ernst and Young (2014).

  39. 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.

  40. 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.

  41. Esho et al. (2004); Park and Lemaire (2011).

  42. Browne et al. (2000); Treerattanapun (2011).

  43. Ehrlich and Becker (1972).

  44. Swiss Re (2016b).

  45. See annual Swiss Re sigma report on World Insurance:

  46. Source: World Bank,

  47. World Bank (2014).

  48. World Economic Forum (2015).

  49. World Bank (2015).

  50. Source: Heritage Foundation,

  51. Source: Wikipedia,

  52. Swiss Re (2015).

  53. 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.

  54. It has been standard in the literature to use insurance penetration as an indicator of insurance demand, for example Outreville (2011), Park and Lemaire (2011), and Treerattanapun (2011).

  55. 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.

  56. See, for example, Enz (2000), Zheng et al. (2009) and Swiss Re (2015). For an application of the concept on city level see McKinsey (2014).

  57. 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.

  58. 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.

  59. See, for example, Millo (2016a, b).

  60. See also Swiss Re (2015).

  61. Schanz and Sommerrock (2016).

  62. Schanz and Wang (2014).

  63. Deryugina (2013).

  64. For a review of cost–benefit analyses on disaster mitigation measures see Shreve and Kelman (2014).

  65. Dumm et al. (2011).

  66. Jaffee et al. (2010), Kunreuther and Michel-Kerjan (2010) and Kleindorfer et al. (2012).

  67. Bin and Landry (2013).

  68. Thaler and Sunstein (2008).

  69. Kunreuther (2015) and Kunreuther and Lyster (2016).

  70. See Cole (2015).

  71. See Cole et al. (2013) regarding rainfall insurance in India.

  72. See Giné et al. (2008).

  73. For example, see Barnett et al. (2008).

  74. See Clarke (2016).

  75. World Bank (2011).

  76. See, for example, Kousky and Kunreuther (2014) and Kunreuther (2015).

  77. See Cummins (2006).

  78. See Jaffee and Russell (1997) for description of the case of market disruption after the Northridge Earthquake in 1994.

  79. See, for example, Michel-Kerjan et al. (2015).

  80. Von Peter et al. (2012).


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Correspondence to Thomas Holzheu.

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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.



See Tables 3, 4 and 5.

Table 3 Descriptive statistics of variables
Table 4 List of countries in the samples
Table 5 Model output by country

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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).

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