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Optimising disaster recovery


By Jonathan Gale, Chief Underwriting Officer, Reinsurance, AXA XL

Natural catastrophes have played a prominent role across recorded history, and every disaster has something to teach us.

For instance, we’ve learned to use dams and levees to control flooding and how buildings can be built to better withstand earthquakes, fires, storms and flooding. However, engineering solutions can only go so far, and natural disasters continue to exact a fearsome and escalating toll. In fact, annual losses from catastrophes rose from an average of USD 27 billion in 1970-1980 to nearly USD 200 billion in 2010-2019, driven by global economic development and the increasing value of assets in hazardous areas. (Source)

While there is an ever-growing body of knowledge and data about preparing for and responding to various natural disasters, what we have learned about the recovery phase following them is mostly anecdotal. That includes the relationship between insurance penetration and how quickly and capably an affected region recovers from a disaster, an issue of keen importance for policymakers, investors and the re/insurance industry.

That was the background for the extensive research conducted over the past three years by the Centre for Risk Studies at Cambridge Judge Business School (CCRS) into the socioeconomic impacts of natural disasters, including the relative effects of different factors on disaster recovery. Done in collaboration with AXA XL, the research sought to better understand how insurance influences the pace and quality of recovery and how it has the potential to build resilience. The findings are contained in a comprehensive new report entitled Optimizing Disaster Recovery: The Role of Insurance Capital in Improving Economic Resilience.

Profiling recovery efforts

Assessments of natural disasters inevitably focus on the relevant severity: for instance, a Category 5 hurricane (Harvey, Irma and Maria, U.S., 2017), a 7.0 earthquake (Haiti, 2010), and flood levels reaching 12.85m (Passau, Germany, 2013). This emphasis on severity metrics is understandable as it helps people grasp the relative gravity of disastrous events.

However, severity is only one aspect of a disaster. Individual natural disasters can better be understood as a mosaic that includes the scope, severity and duration of the event; the impacts on property and the natural environment in the affected area; and, importantly, the political, economic and social context within the country/region.

The CCRS team analysed 18 natural disasters—floods, storms, earthquakes and tsunamis—considering these and other dimensions. From the report:

“A range of natural disaster case studies was chosen to include major events in recent decades across the globe, and ensure coverage of a global geographic distribution, a variety of economy types and income levels, and ranging levels of insurance penetration and market maturity. The focus is on documenting the narratives throughout the recovery process to understand the key events and timelines of recovery, how the recovery was managed and financed, what decisions were made and when, and what were the eventual outcomes in terms of the speed and quality of recovery”.

CCRS has published detailed case studies on eight of these events and has also investigated the recovery phase following major earthquakes in ten countries. These 18 analyses form the foundation of this comprehensive report, providing contrasting chronicles about the recovery process.

Testing the hypotheses

In a second component of the research, CCRS collected extensive data on 103 disaster recovery events, including the 18 profiled previously. These were selected “to include an even distribution of cases across disaster types, income groups and global geography”. The dataset “captures numerous explanatory variables … and offers a way to understand and measure socioeconomic recovery with quantitative and statistical methods to further support the findings of the detailed case studies”.

Armed with this rich dataset, CCRS used regression analyses to test 12 hypotheses concerning the relative importance of different independent variables in controlling the speed of the recovery process; these were developed “based on anecdotal evidence and previously made conclusions about recovery”. The team found statistically significant evidence supporting eight of the hypotheses.

There are no major surprises here as the results generally confirm our intuitive understanding of how different factors can enable—or hinder—a fast recovery. For example, they established that “better prepared countries” and “places with better disaster risk management decision-making” recover faster. In this context, the findings demonstrate how pre-event planning encompassing risk mitigation and catastrophe action planning, and backed by financing secured in advance, has a significantly positive impact on recovery outcomes.

Interestingly, however, the analyses also found that “countries with authoritarian governments” recover faster, but the hypothesis that “places with extensive community participation recover faster” couldn’t be accepted. As the authors note:

“Well-governed societies with clearly defined roles, often via a specific disaster management agency, and robust plans and processes for when a disaster hits are able to respond and mobilise resources quickly and effectively. In terms of governance and decision making, authoritarian governments are generally found to have faster rates of recovery than democratic nations, on account of a faster decision making and actioning process. On a related note, extensive community participation in the decision-making process after an event is found to slow recovery since it often takes time to satisfy all stakeholders with proposed rebuilding plans and actions”.

The role of insurance

Other organizations including the Insurance Development Forum (AXA XL is a founding member), Geneva Association and the World Bank have conducted research and published papers on the positive economic impacts that can be achieved by reducing the “insurance protection gap”, i.e., the difference between economic losses caused by disasters and the portion of those losses covered by insurance coverage. Their investigations also found a positive relationship between insurance penetration and the pace and quality of the disaster response.

With a dataset covering 103 recovery events, CCRS was able to examine this issue in even greater detail. Specifically, one of the 12 hypotheses CCRS tested was “places with higher insurance penetration recover faster”. The statistical analysis confirmed this to be the case; the key findings include:

  • Each percentage point increase in insurance penetration (non-life premiums divided by a country’s GDP) reduces recovery times by almost 12 months.
  • In countries with high insurance penetration (3% – 4%, including in Western Europe, Japan, Australia and South Korea), recovery rates average less than 12 months. In contrast, in countries with very low insurance penetration (Bangladesh, Haiti, Nepal and the Philippines), they extend for more than four years.
  • The U.S. is anomalous. It has very high insurance penetration (>4%), but the fragmented nature of the coverages (particularly flood), disaster response and scale of loss have resulted in recovery rates averaging just over three years: for example, Hurricanes Andrew (1992), Katrina (2005) and Sandy (2012); and the Great Mississippi and Missouri River Floods (1993).

A vital resource

The perspectives offered in this report, along with the catalogue of data on recovery events, constitute an immensely valuable resource that governments, aid organizations, and the private sector—including re/insurers—can use to guide decisions about how to shorten recovery periods and enhance outcomes.

CCRS’s research findings should also reinforce ongoing efforts to close the protection gap, especially in emerging market countries where the benefits are most pronounced. Here, there are a range of options, including:

  • supporting the development of more stable and robust local insurance markets
  • creating public-private partnerships where a government joins forces with the global private re/insurance sector
  • forming regional government-sponsored risk transfer mechanisms, commonly called “pools”, in which several countries establish a collective vehicle for mitigating disaster risk (notable examples here include the Caribbean Catastrophe Risk Insurance Facility and the African Risk Capacity).

While the global re/insurance sector has an important part to play in closing the protection gap, the private market alone can go only so far in providing the necessary coverages. The entities capable of making meaningful differences are governments and organizations like the World Bank, working in conjunction with public-private partnerships such as the Insurance Development Forum. There are several initiatives underway in different parts of the world where these and other groups are working to close the protection gap. The CCRS research results only strengthen the case for these efforts and should provide further stimulus for other governments looking to increase insurance penetration in their country’s/regions.

In the next phase of AXA XL’s project with CCRS there are plans to develop an online database, accessible worldwide, of the research to date, and, over time, to expand the database with additional case studies and related information.

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