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The Insurance Gap & Post-Catastrophe Recovery

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The Insurance Gap & Post-Catastrophe Recovery Cambridge Judge Business School Cambridge Centre for Risk Studies 2017 Risk Summit THE INSURANCE GAP & POST-CATASTROPHE RECOVERY Jessica Tsang, Research Assistant Centre for Risk Studies Learning From Post-Catastrophe Recovery Project Pandora – current multi-threat risk framework Historical: Kobe Earthquake impacts Project Pandora: GDP@Risk 70 100 65 60 GDP@Risk 55 Crisis GDP Trajectory 0 50 1992 1994 1996 1998 2000 2002 2004 2006 2012201520132016201420172015 20182016 20192017 20202018 20212019 20222020 Project Pandora – Calibration using case studies Research proposal: Taking steps toward: . Impact from natural disasters . Impact from multi-threat disasters . The role of insurance in recovery . Calibrate Pandora resilience factors Economic Damage to Economic Loss . Economic damage . Economic loss • Stock loss such as damage to • Flow loss such as GDP property, infrastructure • Measured post-disaster • Mostly instantaneous • Difficult to measure, difficult to isolate • Well-documented increasing the cause economic damage in recent years • May not necessarily be a loss Total economic and insured catastrophe damage/loss (2014 prices) Source: Swiss Re Economic Research and Consulting and Cat Perils Source: Hsiang and Jina, 2014 What factors affect this function? What is the role of insurance? Insurance, GDP, and Economic Damage 1990-2015 Non-Life Insurance Penetration vs GDP per capita (log-log scale) – Flood & Storm Events 1990-2015 : Circle Size = Econ. Damage 10.0% Other Candidate Countries USA, Storm 2005 Bahamas, Storm 2004 Jamaica, Storm 2004 USA, Storm 2012 Cambodia, Flood 2011 Germany, Flood 2013 China, Flood 2010 China, Flood 1998 UK, Flood 2007 India, Flood 2005 1.0% Japan, Storm 2004 Thailand, Flood 2011 Life Insurance Penetration (%) - Philippines, Storm 2013 Non Vietnam, Storm 2006 Bangladesh, Flood 2004 0.1% 100 1,000 10,000 100,000 GDP per capita ($bn) Size Legend . Insurance penetration is positively correlated with GDP/capita (non-linear) . Significant economic damages occur at all income and insurance levels $1bn $4bn $18bn $160bn Disaster Type and Severity Drought Flood Storm EQ Impact varies by disaster type, even in direction . Storms/earthquakes impact capital; floods/droughts impact productivity Output Loss What sectors are affected? Output . Floods positively impact agricultural output, Gain Severity which can lead to industrial growth What is the impact to behaviour? . Floods and storms can often be forecasted –> Source: Based on findings from Skidmore & Toya, 2002 preparation for known risk . Mitigation preferences vary by income level Impact varies by disaster severity, and only the largest seem to matter . Non-linear relationship between disaster intensity and growth Source: Felbermayr & Moderate severity impacts can be good Groschl, 2014 . Moderate flood GDP impact +1%; Severe storm Reduction GDP perin in capita% GDP impact -1.1% Disaster Index, weighted percentiles Very severe disasters can cause other ‘disasters’ . E.g. political revolutions Creative Destruction or Always Negative? Negative Impact Positive Impact . Destruction of productive capital, . Replacement of least productive infrastructure, environment capital . Deaths, outward migration . Introduction of new technology Supply . Reduction in consumption . Increase in re-construction and investment activity . Outflow of population . In-flow of population . Fiscal imbalances Demand . Instability Level, quality and timing of re-construction Disaster type and Quality of institutions Fiscal severity resilience The Role of Insurance: Fiscal Capacity to Rebuild Risk (Annualized Expected Loss) Meeting immediate needs Reality . Liquidity gap Investment Regulation Retain RT GAP . Ex-post disaster financing can be Ideal unreliable and slow to materialize Investment Regulation Retain Transfer Meeting future needs . Inefficient diversion of funds . Increased debt Risk transfer: . Increased taxes Low probability, high severity . Inflation events Retention: Price of stability Higher likelihood, . Existence of insurance necessary lower cost events for a stable investment environment Probability Insurance is not the only factor Expected Loss ($) . Quality of institutions Source: Derived from UNISDR, 2015 . Strong financial sector & regulation Proposed Case Studies Category: Asia – Monsoons & Typhoons Category: High income countries Southeast Asia – High occurrence of typhoons United States – high income economy with . Vietnam – 2006 (Typhoon Xangsane and large and frequent disasters Typhoon Chanchu) . US - storm 2005 (Hurricane Katrina), 2012 . Philippines – 2013 (Typhoon Haiyan) (Hurricane Sandy) Indian Sub-continent – Monsoon Riverine Europe – high income economies with flooding moderate disasters . Bangladesh – floods 2004 . Germany - storm 2013 . India – floods 2005 . UK - flood 2007 Southeast Asia - Monsoon Riverine Flooding Japan – large economy with high frequency of . Cambodia - 2011 disasters . Japan – storm 2004 . Thailand - 2011 Caribbean – middle income economies with China – large economy with high frequency of large and frequent disasters disasters . Bahamas, Jamaica - storm 2004 (Hurricane . China - flood 1998, 2010 Frances, Jean, Ivan) Top 15 countries - annual economic damage (total) . US and China incur largest average annual loss due to flood and storms; other large economies appear here as well with a smaller share of GDP lost . Smaller economies also incur significant total damage such as Thailand Annual Average Economic Damage from Floods 2015 GDP ($trn) and Storms ($bn) -2525 -2020 -1515 -1010 -55 0 5 10 15 20 25 30 UnitedUnited States States of of AmericaAmerica China China Japan Japan India India Germany Germany Thailand Thailand Mexico Mexico France France United KingdomUnited Kingdom Australia Australia Italy Italy Pakistan Pakistan Philippines Philippines Korea, RepublicKorea, Republic of of Bangladesh Bangladesh Top 15 countries - annual economic damage (% of GDP) . Not only small economies suffer large losses as a percentage of their economy due to natural disasters, e.g. China, Thailand Annual Average Economic Damage 2015 GDP ($trn) from Floods and Storms (% of GDP) -6%6 -5%5 -4%4 -3%3 -2% 2 -1%1 0% 1% 2% 3% BahamasBahamas MyanmarMyanmar YemenYemen BangladeshBangladesh Cuba Cuba VietnamVietnam NicaraguaNicaragua $11trn China China JamaicaJamaica ThailandThailand CambodiaCambodia El ElSalvador Salvador Haiti Haiti PakistanPakistan Oman Oman Event Analysis: Insurance Penetration Range Event Year Country Non-Life Ins. Penetration vs Economic Loss (%GDP); Circle Size = Total Econ. Damage 6.0% USA, Storm 2005, $158.2bn 5.0% USA, Storm 2012, $77.5bn 4.0% Bahamas, Germany, Flood Storm 2004, 2013, $12.9bn $1.6bn 3.0% UK, Flood 2007, Jamaica, Storm $8.4bn 2004, $0.9bn Japan, Storm 2004, $15.1bn 2.0% Thailand, Flood 2011, China, China, Flood Cambodia, $40.3bn 2010, Flood 1998, Flood 2011, Life Insurance Penetration (Event Year, %) Year, (Event Penetration Insurance Life $18.2bn $31.7bn - $0.5bn 1.0% Vietnam, Storm 2006, Non $1.1bn Philippines, India, Flood Storm 2013, Bangladesh, 2005, $6.2bn $10.1bn 0.0% Flood 2004, 0.1% 1.0% $2.2bn 10.0% Economic Damage (Event Year, % of GDP) Summary of Upcoming Year’s Research Overall Objective: Determine the impact of insurance as a factor of resilience Over upcoming year: Case Study Comparisons . Comparison of variety of income levels (and insurance penetration): - Bangladesh riverine flooding vs Germany riverine flooding - US hurricane season vs. South-east Asia typhoon season . Comparison between events in different years and regions - US hurricane: Hurricane Sandy 2012 vs Hurricane Katrina 2004 - Bangladesh: 1998 floods vs. 2004 floods . Analyse local level sector data and resultant impacts to macro-economy . Timing of insurance payments compared to timing of recovery . Impact of alternative financing mechanisms Future Goal: Calibrate resilience factor within Pandora multi-threat framework .
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